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hf-stack-v1

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repo_id stringlengths 15 89	file_path stringlengths 27 180	content stringlengths 1 2.23M
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/results-imagenetv2-matched-frequency.csv	model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff eva_giant_patch14_336.clip_ft_in1k,82.200,17.800,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.266,-2.536,+6 eva02_large_patch14_448.mim_in22k_ft_in1k,82.130,17.870,96.260,3.740,305.08,448,1.000,bicubic,-7.492,-2.690,+2 eva02_large_patch14_448.mim_m38m_ft_in1k,82.130,17.870,96.160,3.840,305.08,448,1.000,bicubic,-7.444,-2.764,+2 eva_giant_patch14_560.m30m_ft_in22k_in1k,82.040,17.960,96.440,3.560,"1,014.45",560,1.000,bicubic,-7.746,-2.552,-1 eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,81.900,18.100,96.150,3.850,305.08,448,1.000,bicubic,-8.070,-2.862,-3 eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,81.890,18.110,96.370,3.630,305.08,448,1.000,bicubic,-8.162,-2.678,-5 eva_giant_patch14_336.m30m_ft_in22k_in1k,81.820,18.180,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.746,-2.662,-1 eva_giant_patch14_224.clip_ft_in1k,81.750,18.250,96.080,3.920,"1,012.56",224,0.900,bicubic,-7.130,-2.600,+1 eva_large_patch14_336.in22k_ft_in1k,81.190,18.810,95.880,4.120,304.53,336,1.000,bicubic,-7.480,-2.842,+4 eva_large_patch14_336.in22k_ft_in22k_in1k,80.930,19.070,96.010,3.990,304.53,336,1.000,bicubic,-8.276,-2.844,-2 vit_large_patch14_clip_336.openai_ft_in12k_in1k,80.520,19.480,95.500,4.500,304.53,336,1.000,bicubic,-7.748,-3.026,+13 regnety_1280.swag_ft_in1k,80.480,19.520,96.180,3.820,644.81,384,1.000,bicubic,-7.750,-2.506,+16 tf_efficientnet_l2.ns_jft_in1k_475,80.470,19.530,95.730,4.270,480.31,475,0.936,bicubic,-7.764,-2.816,+14 convnext_xxlarge.clip_laion2b_soup_ft_in1k,80.450,19.550,95.780,4.220,846.47,256,1.000,bicubic,-8.154,-2.928,0 beitv2_large_patch16_224.in1k_ft_in22k_in1k,80.270,19.730,95.150,4.850,304.43,224,0.950,bicubic,-8.124,-3.448,+5 tf_efficientnet_l2.ns_jft_in1k,80.250,19.750,95.860,4.140,480.31,800,0.960,bicubic,-8.102,-2.788,+5 eva_large_patch14_196.in22k_ft_in22k_in1k,80.170,19.830,95.380,4.620,304.14,196,1.000,bicubic,-8.404,-3.278,0 maxvit_base_tf_512.in21k_ft_in1k,80.150,19.850,95.480,4.520,119.88,512,1.000,bicubic,-8.070,-3.050,+11 eva_large_patch14_196.in22k_ft_in1k,80.150,19.850,95.450,4.550,304.14,196,1.000,bicubic,-7.782,-3.048,+19 maxvit_xlarge_tf_512.in21k_ft_in1k,80.100,19.900,95.480,4.520,475.77,512,1.000,bicubic,-8.438,-3.164,-2 convnextv2_huge.fcmae_ft_in22k_in1k_512,79.990,20.010,95.900,4.100,660.29,512,1.000,bicubic,-8.868,-2.848,-11 maxvit_large_tf_512.in21k_ft_in1k,79.980,20.020,95.160,4.840,212.33,512,1.000,bicubic,-8.244,-3.438,+7 beit_large_patch16_512.in22k_ft_in22k_in1k,79.950,20.050,95.350,4.650,305.67,512,1.000,bicubic,-8.646,-3.306,-8 convnextv2_huge.fcmae_ft_in22k_in1k_384,79.940,20.060,95.690,4.310,660.29,384,1.000,bicubic,-8.730,-3.048,-12 maxvit_xlarge_tf_384.in21k_ft_in1k,79.710,20.290,95.160,4.840,475.32,384,1.000,bicubic,-8.604,-3.384,-3 vit_large_patch14_clip_224.openai_ft_in1k,79.620,20.380,95.000,5.000,304.20,224,1.000,bicubic,-8.234,-3.426,+15 maxvit_large_tf_384.in21k_ft_in1k,79.590,20.410,95.070,4.930,212.03,384,1.000,bicubic,-8.396,-3.498,+8 eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,79.520,20.480,95.200,4.800,87.12,448,1.000,bicubic,-9.170,-3.524,-17 vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,79.520,20.480,95.000,5.000,632.46,336,1.000,bicubic,-9.072,-3.662,-13 beit_large_patch16_384.in22k_ft_in22k_in1k,79.500,20.500,95.180,4.820,305.00,384,1.000,bicubic,-8.902,-3.428,-11 vit_large_patch14_clip_224.openai_ft_in12k_in1k,79.400,20.600,95.070,4.930,304.20,224,1.000,bicubic,-8.774,-3.476,+2 vit_huge_patch14_clip_224.laion2b_ft_in1k,79.370,20.630,94.920,5.080,632.05,224,1.000,bicubic,-8.218,-3.298,+16 maxvit_base_tf_384.in21k_ft_in1k,79.340,20.660,95.080,4.920,119.65,384,1.000,bicubic,-8.582,-3.464,+5 vit_large_patch14_clip_336.laion2b_ft_in1k,79.240,20.760,95.000,5.000,304.53,336,1.000,bicubic,-8.616,-3.368,+6 vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,79.200,20.800,95.120,4.880,632.05,224,1.000,bicubic,-9.056,-3.432,-10 deit3_huge_patch14_224.fb_in22k_ft_in1k,79.190,20.810,94.870,5.130,632.13,224,1.000,bicubic,-7.996,-3.390,+27 eva02_base_patch14_448.mim_in22k_ft_in1k,79.150,20.850,95.110,4.890,87.12,448,1.000,bicubic,-9.102,-3.454,-11 deit3_large_patch16_384.fb_in22k_ft_in1k,79.080,20.920,94.870,5.130,304.76,384,1.000,bicubic,-8.640,-3.642,+7 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,79.040,20.960,95.010,4.990,200.13,384,1.000,bicubic,-9.266,-3.572,-17 caformer_b36.sail_in22k_ft_in1k_384,79.040,20.960,94.920,5.080,98.75,384,1.000,bicubic,-9.018,-3.662,-5 vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.980,21.020,94.890,5.110,304.53,336,1.000,bicubic,-9.200,-3.682,-9 maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,78.900,21.100,94.580,5.420,116.14,384,1.000,bicubic,-8.928,-3.792,+1 beit_large_patch16_224.in22k_ft_in22k_in1k,78.830,21.170,94.610,5.390,304.43,224,0.900,bicubic,-8.648,-3.694,+7 convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,78.750,21.250,94.950,5.050,200.13,384,1.000,bicubic,-9.098,-3.496,-2 beitv2_large_patch16_224.in1k_ft_in1k,78.730,21.270,94.230,5.770,304.43,224,0.950,bicubic,-8.682,-4.004,+11 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,78.650,21.350,94.660,5.340,200.13,320,1.000,bicubic,-9.308,-3.816,-10 deit3_large_patch16_224.fb_in22k_ft_in1k,78.630,21.370,94.720,5.280,304.37,224,1.000,bicubic,-8.352,-3.516,+26 convnextv2_large.fcmae_ft_in22k_in1k_384,78.570,21.430,94.840,5.160,197.96,384,1.000,bicubic,-9.628,-3.688,-17 tf_efficientnet_b7.ns_jft_in1k,78.530,21.470,94.370,5.630,66.35,600,0.949,bicubic,-8.310,-3.722,+30 vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.490,21.510,94.660,5.340,304.20,224,1.000,bicubic,-9.404,-3.748,-11 vit_large_patch14_clip_224.laion2b_ft_in1k,78.440,21.560,94.580,5.420,304.20,224,1.000,bicubic,-8.846,-3.664,+10 regnety_320.swag_ft_in1k,78.380,21.620,95.200,4.800,145.05,384,1.000,bicubic,-8.454,-3.162,+28 coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,78.240,21.760,94.350,5.650,73.88,384,1.000,bicubic,-9.142,-3.962,+4 caformer_m36.sail_in22k_ft_in1k_384,78.170,21.830,94.180,5.820,56.20,384,1.000,bicubic,-9.276,-4.128,0 caformer_b36.sail_in22k_ft_in1k,78.110,21.890,94.400,5.600,98.75,224,1.000,bicubic,-9.310,-3.928,0 convnextv2_huge.fcmae_ft_in1k,78.060,21.940,94.060,5.940,660.29,288,1.000,bicubic,-8.520,-3.912,+36 convformer_b36.sail_in22k_ft_in1k_384,78.040,21.960,94.170,5.830,99.88,384,1.000,bicubic,-9.562,-4.264,-10 convnext_xlarge.fb_in22k_ft_in1k_384,77.990,22.010,94.480,5.520,350.20,384,1.000,bicubic,-9.762,-4.076,-14 volo_d5_512.sail_in1k,77.970,22.030,94.160,5.840,296.09,512,1.150,bicubic,-9.088,-3.810,+9 vit_large_patch16_384.augreg_in21k_ft_in1k,77.940,22.060,94.460,5.540,304.72,384,1.000,bicubic,-9.144,-3.842,+6 convnext_large_mlp.clip_laion2b_augreg_ft_in1k,77.940,22.060,94.390,5.610,200.13,256,1.000,bicubic,-9.396,-3.828,-2 convnextv2_large.fcmae_ft_in22k_in1k,77.900,22.100,94.390,5.610,197.96,288,1.000,bicubic,-9.584,-3.966,-13 deit3_base_patch16_384.fb_in22k_ft_in1k,77.870,22.130,94.030,5.970,86.88,384,1.000,bicubic,-8.870,-4.086,+23 vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,77.770,22.230,94.160,5.840,86.86,384,1.000,bicubic,-9.436,-3.874,-2 volo_d5_448.sail_in1k,77.750,22.250,94.050,5.950,295.91,448,1.150,bicubic,-9.202,-3.888,+10 volo_d4_448.sail_in1k,77.740,22.260,93.940,6.060,193.41,448,1.150,bicubic,-9.052,-3.944,+18 maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,77.710,22.290,94.330,5.670,116.09,384,1.000,bicubic,-9.754,-4.044,-15 maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,77.690,22.310,94.070,5.930,116.14,224,0.950,bicubic,-9.204,-3.944,+8 tf_efficientnetv2_xl.in21k_ft_in1k,77.630,22.370,93.960,6.040,208.12,512,1.000,bicubic,-9.118,-4.054,+16 tf_efficientnetv2_l.in21k_ft_in1k,77.580,22.420,94.280,5.720,118.52,480,1.000,bicubic,-9.222,-3.856,+11 caformer_s36.sail_in22k_ft_in1k_384,77.540,22.460,94.070,5.930,39.30,384,1.000,bicubic,-9.318,-4.142,+7 convnextv2_base.fcmae_ft_in22k_in1k_384,77.490,22.510,94.410,5.590,88.72,384,1.000,bicubic,-10.154,-4.006,-27 caformer_b36.sail_in1k_384,77.490,22.510,93.530,6.470,98.75,384,1.000,bicubic,-8.918,-4.284,+32 convnext_base.clip_laiona_augreg_ft_in1k_384,77.480,22.520,93.960,6.040,88.59,384,1.000,bicubic,-9.022,-4.008,+22 maxvit_base_tf_512.in1k,77.440,22.560,93.970,6.030,119.88,512,1.000,bicubic,-9.162,-3.948,+14 caformer_m36.sail_in22k_ft_in1k,77.440,22.560,93.590,6.410,56.20,224,1.000,bicubic,-9.154,-4.434,+16 convnext_large.fb_in22k_ft_in1k_384,77.420,22.580,94.200,5.800,197.77,384,1.000,bicubic,-10.052,-4.186,-26 seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,77.420,22.580,93.990,6.010,149.39,384,1.000,bicubic,-9.868,-4.344,-18 regnety_1280.swag_lc_in1k,77.380,22.620,94.500,5.500,644.81,224,0.965,bicubic,-8.602,-3.350,+56 swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,77.340,22.660,93.910,6.090,196.74,384,1.000,bicubic,-10.124,-4.340,-27 vit_base_patch16_clip_384.laion2b_ft_in1k,77.320,22.680,93.860,6.140,86.86,384,1.000,bicubic,-9.298,-4.148,+8 maxvit_large_tf_512.in1k,77.300,22.700,93.790,6.210,212.33,512,1.000,bicubic,-9.226,-4.090,+12 convnextv2_base.fcmae_ft_in22k_in1k,77.290,22.710,94.110,5.890,88.72,288,1.000,bicubic,-9.708,-4.058,-11 seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,77.290,22.710,94.050,5.950,93.59,320,1.000,bicubic,-9.434,-4.126,+3 tf_efficientnet_b6.ns_jft_in1k,77.290,22.710,93.890,6.110,43.04,528,0.942,bicubic,-9.168,-4.000,+17 convformer_b36.sail_in22k_ft_in1k,77.270,22.730,93.970,6.030,99.88,224,1.000,bicubic,-9.728,-4.202,-15 swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,77.240,22.760,94.250,5.750,87.92,384,1.000,bicubic,-9.856,-3.984,-21 regnety_160.swag_ft_in1k,77.230,22.770,94.600,5.400,83.59,384,1.000,bicubic,-8.790,-3.452,+41 convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,77.180,22.820,94.170,5.830,88.59,384,1.000,bicubic,-9.954,-4.052,-25 caformer_m36.sail_in1k_384,77.160,22.840,93.630,6.370,56.20,384,1.000,bicubic,-9.006,-4.190,+28 maxvit_large_tf_384.in1k,77.130,22.870,93.460,6.540,212.03,384,1.000,bicubic,-9.100,-4.228,+21 beitv2_base_patch16_224.in1k_ft_in22k_in1k,77.120,22.880,94.020,5.980,86.53,224,0.900,bicubic,-9.354,-4.032,+8 volo_d3_448.sail_in1k,77.090,22.910,94.110,5.890,86.63,448,1.000,bicubic,-9.412,-3.600,+4 swin_large_patch4_window12_384.ms_in22k_ft_in1k,77.070,22.930,93.760,6.240,196.74,384,1.000,bicubic,-10.062,-4.474,-29 vit_large_r50_s32_384.augreg_in21k_ft_in1k,77.070,22.930,93.710,6.290,329.09,384,1.000,bicubic,-9.112,-4.212,+21 coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,77.010,22.990,93.320,6.680,73.88,224,0.950,bicubic,-9.494,-4.574,-1 swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,76.970,23.030,93.520,6.480,196.74,256,0.900,bicubic,-9.982,-4.586,-23 vit_base_patch16_clip_384.openai_ft_in1k,76.960,23.040,93.750,6.250,86.86,384,1.000,bicubic,-9.246,-4.126,+15 convformer_m36.sail_in22k_ft_in1k_384,76.960,23.040,93.690,6.310,57.05,384,1.000,bicubic,-9.932,-4.426,-21 beit_base_patch16_384.in22k_ft_in22k_in1k,76.930,23.070,93.910,6.090,86.74,384,1.000,bicubic,-9.870,-4.226,-18 seresnextaa101d_32x8d.sw_in12k_ft_in1k,76.900,23.100,93.850,6.150,93.59,288,1.000,bicubic,-9.584,-4.180,-2 tf_efficientnetv2_m.in21k_ft_in1k,76.890,23.110,93.640,6.360,54.14,480,1.000,bicubic,-9.102,-4.304,+30 vit_base_patch16_clip_384.openai_ft_in12k_in1k,76.870,23.130,93.780,6.220,86.86,384,0.950,bicubic,-10.156,-4.402,-34 cait_m48_448.fb_dist_in1k,76.870,23.130,93.380,6.620,356.46,448,1.000,bicubic,-9.622,-4.372,-5 tf_efficientnet_b5.ns_jft_in1k,76.830,23.170,93.580,6.420,30.39,456,0.934,bicubic,-9.258,-4.176,+19 resnext101_32x32d.fb_wsl_ig1b_ft_in1k,76.810,23.190,93.210,6.790,468.53,224,0.875,bilinear,-8.288,-4.228,+92 maxvit_base_tf_384.in1k,76.790,23.210,93.440,6.560,119.65,384,1.000,bicubic,-9.512,-4.358,+2 convnext_large.fb_in22k_ft_in1k,76.750,23.250,93.710,6.290,197.77,288,1.000,bicubic,-10.276,-4.494,-39 tiny_vit_21m_512.dist_in22k_ft_in1k,76.710,23.290,93.470,6.530,21.27,512,1.000,bicubic,-9.748,-4.414,-8 deit3_large_patch16_384.fb_in1k,76.690,23.310,93.350,6.650,304.76,384,1.000,bicubic,-9.122,-4.248,+32 convnextv2_large.fcmae_ft_in1k,76.660,23.340,93.570,6.430,197.96,288,1.000,bicubic,-9.458,-4.252,+10 convnext_base.fb_in22k_ft_in1k_384,76.650,23.350,93.700,6.300,88.59,384,1.000,bicubic,-10.146,-4.564,-29 convnext_xlarge.fb_in22k_ft_in1k,76.630,23.370,93.860,6.140,350.20,288,1.000,bicubic,-10.700,-4.468,-55 beitv2_base_patch16_224.in1k_ft_in1k,76.630,23.370,92.930,7.070,86.53,224,0.900,bicubic,-8.964,-4.576,+43 coatnet_2_rw_224.sw_in12k_ft_in1k,76.620,23.380,93.380,6.620,73.87,224,0.950,bicubic,-9.944,-4.516,-22 xcit_large_24_p8_384.fb_dist_in1k,76.620,23.380,93.090,6.910,188.93,384,1.000,bicubic,-9.376,-4.600,+14 regnety_160.sw_in12k_ft_in1k,76.610,23.390,93.560,6.440,83.59,288,1.000,bicubic,-9.376,-4.274,+17 vit_base_patch8_224.augreg2_in21k_ft_in1k,76.590,23.410,93.340,6.660,86.58,224,0.900,bicubic,-9.628,-4.492,-5 caformer_s36.sail_in22k_ft_in1k,76.580,23.420,93.620,6.380,39.30,224,1.000,bicubic,-9.210,-4.206,+25 caformer_s36.sail_in1k_384,76.550,23.450,93.450,6.550,39.30,384,1.000,bicubic,-9.192,-4.222,+28 volo_d5_224.sail_in1k,76.550,23.450,93.330,6.670,295.46,224,0.960,bicubic,-9.520,-4.246,+5 deit3_base_patch16_224.fb_in22k_ft_in1k,76.530,23.470,93.570,6.430,86.59,224,1.000,bicubic,-9.170,-4.176,+29 maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,76.530,23.470,93.370,6.630,116.09,224,0.950,bicubic,-10.112,-4.650,-35 dm_nfnet_f6.dm_in1k,76.510,23.490,93.340,6.660,438.36,576,0.956,bicubic,-9.852,-4.556,-17 maxvit_small_tf_512.in1k,76.500,23.500,93.360,6.640,69.13,512,1.000,bicubic,-9.584,-4.404,0 vit_base_patch16_384.augreg_in21k_ft_in1k,76.490,23.510,93.760,6.240,86.86,384,1.000,bicubic,-9.504,-4.242,+5 tiny_vit_21m_384.dist_in22k_ft_in1k,76.470,23.530,93.130,6.870,21.23,384,1.000,bicubic,-9.638,-4.580,-5 swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,76.450,23.550,93.700,6.300,87.92,256,0.900,bicubic,-9.818,-4.182,-17 convformer_m36.sail_in1k_384,76.370,23.630,93.100,6.900,57.05,384,1.000,bicubic,-9.210,-4.442,+28 convformer_s36.sail_in22k_ft_in1k_384,76.360,23.640,93.530,6.470,40.01,384,1.000,bicubic,-10.018,-4.454,-26 convformer_m36.sail_in22k_ft_in1k,76.360,23.640,93.450,6.550,57.05,224,1.000,bicubic,-9.788,-4.400,-10 convnext_small.in12k_ft_in1k_384,76.340,23.660,93.420,6.580,50.22,384,1.000,bicubic,-9.842,-4.502,-17 cait_m36_384.fb_dist_in1k,76.330,23.670,93.060,6.940,271.22,384,1.000,bicubic,-9.728,-4.670,-6 swin_large_patch4_window7_224.ms_in22k_ft_in1k,76.310,23.690,93.390,6.610,196.53,224,0.900,bicubic,-10.002,-4.512,-26 vit_large_patch16_224.augreg_in21k_ft_in1k,76.300,23.700,93.610,6.390,304.33,224,0.900,bicubic,-9.536,-4.054,+4 convnext_base.clip_laion2b_augreg_ft_in12k_in1k,76.290,23.710,93.810,6.190,88.59,256,1.000,bicubic,-10.080,-4.174,-30 swin_base_patch4_window12_384.ms_in22k_ft_in1k,76.250,23.750,93.300,6.700,87.90,384,1.000,bicubic,-10.188,-4.766,-34 cait_s36_384.fb_dist_in1k,76.220,23.780,92.960,7.040,68.37,384,1.000,bicubic,-9.234,-4.518,+27 regnety_160.lion_in12k_ft_in1k,76.190,23.810,93.670,6.330,83.59,288,1.000,bicubic,-9.798,-4.164,-6 dm_nfnet_f4.dm_in1k,76.150,23.850,93.050,6.950,316.07,512,0.951,bicubic,-9.686,-4.768,0 xcit_medium_24_p8_384.fb_dist_in1k,76.130,23.870,92.980,7.020,84.32,384,1.000,bicubic,-9.686,-4.612,0 maxvit_small_tf_384.in1k,76.120,23.880,92.610,7.390,69.02,384,1.000,bicubic,-9.420,-4.852,+18 flexivit_large.1200ep_in1k,76.100,23.900,93.010,6.990,304.36,240,0.950,bicubic,-9.544,-4.530,+12 volo_d2_384.sail_in1k,76.090,23.910,93.130,6.870,58.87,384,1.000,bicubic,-9.952,-4.444,-17 tf_efficientnet_b8.ap_in1k,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.274,-4.562,+32 tf_efficientnet_b7.ap_in1k,76.080,23.920,92.970,7.030,66.35,600,0.949,bicubic,-9.044,-4.282,+48 convformer_b36.sail_in1k_384,76.070,23.930,92.720,7.280,99.88,384,1.000,bicubic,-9.670,-4.804,+2 maxvit_tiny_tf_512.in1k,76.060,23.940,93.160,6.840,31.05,512,1.000,bicubic,-9.604,-4.424,+5 flexivit_large.600ep_in1k,76.050,23.950,92.960,7.040,304.36,240,0.950,bicubic,-9.490,-4.528,+10 volo_d4_224.sail_in1k,76.020,23.980,92.980,7.020,192.96,224,0.960,bicubic,-9.852,-4.492,-12 tf_efficientnetv2_l.in1k,76.000,24.000,93.070,6.930,118.52,480,1.000,bicubic,-9.664,-4.404,+3 vit_base_patch8_224.augreg_in21k_ft_in1k,75.970,24.030,93.370,6.630,86.58,224,0.900,bicubic,-9.828,-4.420,-9 xcit_large_24_p8_224.fb_dist_in1k,75.970,24.030,92.710,7.290,188.93,224,1.000,bicubic,-9.432,-4.692,+18 flexivit_large.300ep_in1k,75.940,24.060,92.650,7.350,304.36,240,0.950,bicubic,-9.348,-4.750,+25 convnext_base.fb_in22k_ft_in1k,75.930,24.070,93.580,6.420,88.59,288,1.000,bicubic,-10.344,-4.512,-45 convnext_base.clip_laion2b_augreg_ft_in1k,75.840,24.160,93.210,6.790,88.59,256,1.000,bicubic,-10.318,-4.470,-37 xcit_large_24_p16_384.fb_dist_in1k,75.820,24.180,92.740,7.260,189.10,384,1.000,bicubic,-9.934,-4.798,-10 dm_nfnet_f3.dm_in1k,75.790,24.210,92.910,7.090,254.92,416,0.940,bicubic,-9.896,-4.660,-6 eva02_small_patch14_336.mim_in22k_ft_in1k,75.790,24.210,92.820,7.180,22.13,336,1.000,bicubic,-9.928,-4.814,-9 convformer_s36.sail_in1k_384,75.780,24.220,93.090,6.910,40.01,384,1.000,bicubic,-9.598,-4.386,+13 deit3_huge_patch14_224.fb_in1k,75.780,24.220,92.740,7.260,632.13,224,0.900,bicubic,-9.444,-4.620,+24 xcit_small_24_p8_384.fb_dist_in1k,75.770,24.230,92.970,7.030,47.63,384,1.000,bicubic,-9.784,-4.600,-4 caformer_b36.sail_in1k,75.740,24.260,92.690,7.310,98.75,224,1.000,bicubic,-9.764,-4.620,-1 resnext101_32x16d.fb_wsl_ig1b_ft_in1k,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.446,-4.288,+119 vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,75.710,24.290,92.770,7.230,86.57,224,0.950,bicubic,-10.460,-4.986,-48 tf_efficientnet_b4.ns_jft_in1k,75.690,24.310,93.050,6.950,19.34,380,0.922,bicubic,-9.470,-4.418,+26 caformer_s18.sail_in22k_ft_in1k_384,75.680,24.320,93.510,6.490,26.34,384,1.000,bicubic,-9.734,-4.192,+1 efficientnet_b5.sw_in12k_ft_in1k,75.680,24.320,93.040,6.960,30.39,448,1.000,bicubic,-10.216,-4.696,-31 hrnet_w48_ssld.paddle_in1k,75.670,24.330,92.830,7.170,77.47,288,1.000,bilinear,-8.810,-4.404,+79 vit_medium_patch16_gap_384.sw_in12k_ft_in1k,75.660,24.340,92.980,7.020,39.03,384,0.950,bicubic,-9.870,-4.656,-9 volo_d1_384.sail_in1k,75.650,24.350,93.060,6.940,26.78,384,1.000,bicubic,-9.594,-4.134,+11 volo_d3_224.sail_in1k,75.620,24.380,92.980,7.020,86.33,224,0.960,bicubic,-9.794,-4.296,-3 convnextv2_base.fcmae_ft_in1k,75.620,24.380,92.850,7.150,88.72,288,1.000,bicubic,-9.854,-4.534,-9 dm_nfnet_f5.dm_in1k,75.610,24.390,92.780,7.220,377.21,544,0.954,bicubic,-10.490,-4.908,-51 caformer_m36.sail_in1k,75.600,24.400,92.400,7.600,56.20,224,1.000,bicubic,-9.632,-4.800,+9 vit_base_patch16_clip_224.openai_ft_in1k,75.590,24.410,92.960,7.040,86.57,224,0.900,bicubic,-9.702,-4.476,+2 vit_base_r50_s16_384.orig_in21k_ft_in1k,75.570,24.430,92.790,7.210,98.95,384,1.000,bicubic,-9.406,-4.500,+33 deit_base_distilled_patch16_384.fb_in1k,75.550,24.450,92.500,7.500,87.63,384,1.000,bicubic,-9.874,-4.906,-11 inception_next_base.sail_in1k_384,75.530,24.470,92.560,7.440,86.67,384,1.000,bicubic,-9.672,-4.854,+10 regnetz_e8.ra3_in1k,75.510,24.490,92.690,7.310,57.70,320,1.000,bicubic,-9.524,-4.582,+26 cait_s24_384.fb_dist_in1k,75.500,24.500,92.610,7.390,47.06,384,1.000,bicubic,-9.548,-4.736,+24 convformer_s36.sail_in22k_ft_in1k,75.480,24.520,93.190,6.810,40.01,224,1.000,bicubic,-9.934,-4.378,-13 xcit_medium_24_p8_224.fb_dist_in1k,75.470,24.530,92.890,7.110,84.32,224,1.000,bicubic,-9.604,-4.360,+19 resnext101_32x8d.fb_swsl_ig1b_ft_in1k,75.470,24.530,92.730,7.270,88.79,224,0.875,bilinear,-8.832,-4.446,+86 vit_base_patch16_clip_224.openai_ft_in12k_in1k,75.460,24.540,92.750,7.250,86.57,224,0.950,bicubic,-10.482,-4.978,-49 regnety_2560.seer_ft_in1k,75.440,24.560,92.830,7.170,"1,282.60",384,1.000,bicubic,-9.710,-4.608,+7 vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,75.420,24.580,92.710,7.290,88.34,448,1.000,bicubic,-10.360,-4.928,-42 regnety_320.swag_lc_in1k,75.400,24.600,93.680,6.320,145.05,224,0.965,bicubic,-9.148,-3.762,+50 beit_base_patch16_224.in22k_ft_in22k_in1k,75.390,24.610,93.020,6.980,86.53,224,0.900,bicubic,-9.822,-4.638,-2 tf_efficientnetv2_m.in1k,75.380,24.620,92.760,7.240,54.14,480,1.000,bicubic,-9.824,-4.604,-3 tf_efficientnet_b6.ap_in1k,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.408,-4.698,+34 vit_base_patch16_224.augreg2_in21k_ft_in1k,75.360,24.640,93.230,6.770,86.57,224,0.900,bicubic,-9.734,-4.300,+7 regnety_120.sw_in12k_ft_in1k,75.330,24.670,92.970,7.030,51.82,288,1.000,bicubic,-10.070,-4.612,-21 volo_d2_224.sail_in1k,75.310,24.690,92.520,7.480,58.68,224,0.960,bicubic,-9.892,-4.670,-4 mvitv2_large.fb_in1k,75.270,24.730,92.360,7.640,217.99,224,0.900,bicubic,-9.974,-4.854,-13 coat_lite_medium_384.in1k,75.270,24.730,92.230,7.770,44.57,384,1.000,bicubic,-9.608,-5.142,+22 dm_nfnet_f2.dm_in1k,75.260,24.740,92.450,7.550,193.78,352,0.920,bicubic,-9.932,-4.896,-6 caformer_s18.sail_in1k_384,75.210,24.790,92.700,7.300,26.34,384,1.000,bicubic,-9.816,-4.658,+9 convnext_small.fb_in22k_ft_in1k_384,75.160,24.840,93.060,6.940,50.22,384,1.000,bicubic,-10.618,-4.830,-53 efficientnetv2_rw_m.agc_in1k,75.150,24.850,92.570,7.430,53.24,416,1.000,bicubic,-9.660,-4.582,+23 ecaresnet269d.ra2_in1k,75.130,24.870,92.830,7.170,102.09,352,1.000,bicubic,-9.838,-4.392,+10 vit_base_patch16_clip_224.laion2b_ft_in1k,75.120,24.880,92.700,7.300,86.57,224,1.000,bicubic,-10.350,-4.876,-39 deit3_large_patch16_224.fb_in1k,75.120,24.880,92.280,7.720,304.37,224,0.900,bicubic,-9.654,-4.756,+23 deit3_small_patch16_384.fb_in22k_ft_in1k,75.090,24.910,92.810,7.190,22.21,384,1.000,bicubic,-9.734,-4.676,+17 vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,75.090,24.910,92.580,7.420,88.30,384,1.000,bicubic,-10.276,-5.080,-30 xcit_medium_24_p16_384.fb_dist_in1k,75.090,24.910,92.440,7.560,84.40,384,1.000,bicubic,-10.334,-4.890,-40 tiny_vit_21m_224.dist_in22k_ft_in1k,75.070,24.930,92.590,7.410,21.20,224,0.950,bicubic,-10.016,-4.776,-6 maxvit_tiny_tf_384.in1k,75.020,24.980,92.450,7.550,30.98,384,1.000,bicubic,-10.080,-4.928,-11 convformer_b36.sail_in1k,74.980,25.020,91.600,8.400,99.88,224,1.000,bicubic,-9.838,-5.346,+13 xcit_small_24_p8_224.fb_dist_in1k,74.970,25.030,92.320,7.680,47.63,224,1.000,bicubic,-9.898,-4.870,+8 convnext_small.in12k_ft_in1k,74.960,25.040,92.520,7.480,50.22,288,1.000,bicubic,-10.370,-5.026,-34 tf_efficientnet_b8.ra_in1k,74.920,25.080,92.330,7.670,87.41,672,0.954,bicubic,-10.448,-5.064,-38 xcit_small_12_p8_384.fb_dist_in1k,74.850,25.150,92.450,7.550,26.21,384,1.000,bicubic,-10.228,-4.832,-11 eca_nfnet_l2.ra3_in1k,74.820,25.180,92.650,7.350,56.72,384,1.000,bicubic,-9.880,-4.616,+15 deit3_base_patch16_384.fb_in1k,74.800,25.200,92.240,7.760,86.88,384,1.000,bicubic,-10.274,-5.034,-11 convnext_tiny.in12k_ft_in1k_384,74.730,25.270,92.810,7.190,28.59,384,1.000,bicubic,-10.392,-4.796,-21 tf_efficientnet_b7.ra_in1k,74.730,25.270,92.220,7.780,66.35,600,0.949,bicubic,-10.202,-4.988,-4 deit3_medium_patch16_224.fb_in22k_ft_in1k,74.690,25.310,92.470,7.530,38.85,224,1.000,bicubic,-9.860,-4.718,+19 convnext_large.fb_in1k,74.650,25.350,91.970,8.030,197.77,288,1.000,bicubic,-10.196,-5.244,+1 xcit_large_24_p16_224.fb_dist_in1k,74.650,25.350,91.860,8.140,189.10,224,1.000,bicubic,-10.266,-5.268,-5 convformer_s18.sail_in1k_384,74.640,25.360,92.480,7.520,26.77,384,1.000,bicubic,-9.762,-4.632,+42 caformer_s36.sail_in1k,74.640,25.360,92.080,7.920,39.30,224,1.000,bicubic,-9.866,-4.916,+23 xcit_small_24_p16_384.fb_dist_in1k,74.590,25.410,92.460,7.540,47.67,384,1.000,bicubic,-10.500,-4.852,-24 tf_efficientnet_b5.ap_in1k,74.590,25.410,91.990,8.010,30.39,456,0.934,bicubic,-9.668,-4.984,+50 maxvit_large_tf_224.in1k,74.580,25.420,91.700,8.300,211.79,224,0.950,bicubic,-10.362,-5.270,-13 vit_medium_patch16_gap_256.sw_in12k_ft_in1k,74.570,25.430,91.960,8.040,38.86,256,0.950,bicubic,-9.876,-5.250,+26 swin_base_patch4_window7_224.ms_in22k_ft_in1k,74.560,25.440,92.540,7.460,87.77,224,0.900,bicubic,-10.712,-5.024,-48 dm_nfnet_f1.dm_in1k,74.560,25.440,92.170,7.830,132.63,320,0.910,bicubic,-10.142,-5.012,+1 convformer_s18.sail_in22k_ft_in1k_384,74.550,25.450,92.760,7.240,26.77,384,1.000,bicubic,-10.448,-4.810,-20 maxxvit_rmlp_small_rw_256.sw_in1k,74.520,25.480,92.000,8.000,66.01,256,0.950,bicubic,-10.104,-5.068,+2 regnetz_040.ra3_in1k,74.510,25.490,91.880,8.120,27.12,320,1.000,bicubic,-9.730,-5.052,+45 seresnet152d.ra2_in1k,74.500,25.500,92.090,7.910,66.84,320,1.000,bicubic,-9.860,-4.950,+35 convnext_small.fb_in22k_ft_in1k,74.490,25.510,92.700,7.300,50.22,288,1.000,bicubic,-10.772,-4.982,-52 davit_base.msft_in1k,74.490,25.510,91.750,8.250,87.95,224,0.950,bicubic,-10.152,-5.270,-3 fastvit_ma36.apple_dist_in1k,74.480,25.520,91.960,8.040,44.07,256,0.950,bicubic,-10.118,-5.042,0 gcvit_base.in1k,74.480,25.520,91.770,8.230,90.32,224,0.875,bicubic,-9.964,-5.312,+18 regnetz_040_h.ra3_in1k,74.460,25.540,92.250,7.750,28.94,320,1.000,bicubic,-10.032,-4.508,+8 resnest200e.in1k,74.460,25.540,91.880,8.120,70.20,320,0.909,bicubic,-9.384,-5.004,+74 coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,74.460,25.540,91.640,8.360,41.72,224,0.950,bicubic,-10.450,-5.318,-21 tf_efficientnetv2_s.in21k_ft_in1k,74.440,25.560,92.500,7.500,21.46,384,1.000,bicubic,-9.846,-4.752,+30 maxvit_rmlp_small_rw_224.sw_in1k,74.400,25.600,91.390,8.610,64.90,224,0.900,bicubic,-10.092,-5.620,+6 caformer_s18.sail_in22k_ft_in1k,74.380,25.620,92.500,7.500,26.34,224,1.000,bicubic,-9.694,-4.698,+49 convformer_m36.sail_in1k,74.380,25.620,91.480,8.520,57.05,224,1.000,bicubic,-10.114,-5.386,+2 flexivit_base.1200ep_in1k,74.350,25.650,91.800,8.200,86.59,240,0.950,bicubic,-10.326,-5.194,-14 resnetrs200.tf_in1k,74.340,25.660,91.940,8.060,93.21,320,1.000,bicubic,-10.104,-4.902,+8 seresnextaa101d_32x8d.ah_in1k,74.320,25.680,91.720,8.280,93.59,288,1.000,bicubic,-10.246,-5.356,-10 maxvit_base_tf_224.in1k,74.290,25.710,91.760,8.240,119.47,224,0.950,bicubic,-10.570,-5.228,-28 regnety_160.swag_lc_in1k,74.230,25.770,92.870,7.130,83.59,224,0.965,bicubic,-9.552,-4.410,+76 convnextv2_tiny.fcmae_ft_in22k_in1k_384,74.220,25.780,92.470,7.530,28.64,384,1.000,bicubic,-10.886,-5.158,-53 mvitv2_base.fb_in1k,74.220,25.780,91.650,8.350,51.47,224,0.900,bicubic,-10.230,-5.208,+1 resnest269e.in1k,74.210,25.790,91.960,8.040,110.93,416,0.928,bicubic,-10.298,-5.030,-9 convformer_s36.sail_in1k,74.210,25.790,91.230,8.770,40.01,224,1.000,bicubic,-9.850,-5.516,+42 convnext_tiny.in12k_ft_in1k,74.200,25.800,92.690,7.310,28.59,288,1.000,bicubic,-10.250,-4.650,-3 seresnext101d_32x8d.ah_in1k,74.180,25.820,91.850,8.150,93.59,288,1.000,bicubic,-10.178,-5.070,+13 coatnet_rmlp_2_rw_224.sw_in1k,74.180,25.820,91.300,8.700,73.88,224,0.950,bicubic,-10.428,-5.440,-21 cait_xs24_384.fb_dist_in1k,74.170,25.830,91.930,8.070,26.67,384,1.000,bicubic,-9.892,-4.954,+36 efficientnetv2_rw_s.ra2_in1k,74.160,25.840,91.710,8.290,23.94,384,1.000,bicubic,-9.646,-5.022,+63 resnext101_32x4d.fb_swsl_ig1b_ft_in1k,74.130,25.870,91.990,8.010,44.18,224,0.875,bilinear,-9.096,-4.770,+126 vit_base_patch16_384.orig_in21k_ft_in1k,74.120,25.880,92.360,7.640,86.86,384,1.000,bicubic,-10.080,-4.858,+20 flexivit_base.600ep_in1k,74.120,25.880,91.750,8.250,86.59,240,0.950,bicubic,-10.404,-5.186,-19 flexivit_base.300ep_in1k,74.120,25.880,91.390,8.610,86.59,240,0.950,bicubic,-10.286,-5.494,+4 vit_large_r50_s32_224.augreg_in21k_ft_in1k,74.110,25.890,92.390,7.610,328.99,224,0.900,bicubic,-10.308,-4.782,-4 xcit_small_12_p16_384.fb_dist_in1k,74.110,25.890,92.090,7.910,26.25,384,1.000,bicubic,-10.602,-5.028,-38 eca_nfnet_l1.ra2_in1k,74.110,25.890,92.070,7.930,41.41,320,1.000,bicubic,-9.902,-4.956,+38 convnext_base.fb_in1k,74.110,25.890,91.710,8.290,88.59,288,1.000,bicubic,-10.318,-5.258,-5 volo_d1_224.sail_in1k,74.100,25.900,92.030,7.970,26.63,224,0.960,bicubic,-10.062,-4.746,+18 inception_next_base.sail_in1k,74.080,25.920,91.380,8.620,86.67,224,0.950,bicubic,-10.012,-5.416,+22 vit_base_patch32_clip_384.openai_ft_in12k_in1k,74.050,25.950,92.410,7.590,88.30,384,0.950,bicubic,-11.164,-4.994,-82 vit_base_patch16_224_miil.in21k_ft_in1k,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.226,-5.104,+3 xcit_large_24_p8_224.fb_in1k,74.040,25.960,90.890,9.110,188.93,224,1.000,bicubic,-10.354,-5.774,-4 tf_efficientnet_b7.aa_in1k,74.030,25.970,91.870,8.130,66.35,600,0.949,bicubic,-10.386,-5.038,-9 vit_base_patch16_224.augreg_in21k_ft_in1k,74.010,25.990,92.470,7.530,86.57,224,0.900,bicubic,-10.522,-4.824,-33 resnetv2_152x4_bit.goog_in21k_ft_in1k,74.010,25.990,92.350,7.650,936.53,480,1.000,bilinear,-10.906,-5.088,-58 resnext101_32x16d.fb_swsl_ig1b_ft_in1k,73.990,26.010,92.180,7.820,194.03,224,0.875,bilinear,-9.346,-4.666,+101 tf_efficientnetv2_s.in1k,73.990,26.010,91.540,8.460,21.46,384,1.000,bicubic,-9.908,-5.156,+32 swinv2_base_window16_256.ms_in1k,73.980,26.020,91.750,8.250,87.92,256,0.900,bicubic,-10.620,-5.340,-42 regnetz_d32.ra3_in1k,73.960,26.040,91.950,8.050,27.58,320,0.950,bicubic,-10.062,-4.918,+22 seresnext101_32x8d.ah_in1k,73.960,26.040,91.450,8.550,93.57,288,1.000,bicubic,-10.226,-5.424,+4 resnetv2_152x2_bit.goog_in21k_ft_in1k,73.950,26.050,92.670,7.330,236.34,448,1.000,bilinear,-10.560,-4.764,-37 crossvit_18_dagger_408.in1k,73.930,26.070,91.410,8.590,44.61,408,1.000,bicubic,-10.272,-5.408,0 rexnetr_300.sw_in12k_ft_in1k,73.920,26.080,92.210,7.790,34.81,288,1.000,bicubic,-10.626,-5.046,-43 resnetrs420.tf_in1k,73.920,26.080,91.780,8.220,191.89,416,1.000,bicubic,-11.084,-5.344,-73 xcit_small_12_p8_224.fb_dist_in1k,73.920,26.080,91.720,8.280,26.21,224,1.000,bicubic,-10.316,-5.150,-6 resmlp_big_24_224.fb_in22k_ft_in1k,73.900,26.100,91.760,8.240,129.14,224,0.875,bicubic,-10.498,-5.352,-20 pit_b_distilled_224.in1k,73.900,26.100,90.780,9.220,74.79,224,0.900,bicubic,-9.866,-5.688,+42 tf_efficientnet_b6.aa_in1k,73.890,26.110,91.750,8.250,43.04,528,0.942,bicubic,-10.222,-5.134,+2 edgenext_base.usi_in1k,73.880,26.120,91.760,8.240,18.51,320,1.000,bicubic,-10.078,-5.010,+16 regnety_1280.seer_ft_in1k,73.860,26.140,91.900,8.100,644.81,384,1.000,bicubic,-10.572,-5.192,-32 deit3_small_patch16_224.fb_in22k_ft_in1k,73.850,26.150,91.970,8.030,22.06,224,1.000,bicubic,-9.226,-4.806,+113 tf_efficientnet_b3.ns_jft_in1k,73.850,26.150,91.860,8.140,12.23,300,0.904,bicubic,-10.202,-5.058,+6 maxvit_rmlp_tiny_rw_256.sw_in1k,73.830,26.170,91.440,8.560,29.15,256,0.950,bicubic,-10.394,-5.428,-12 vit_small_r26_s32_384.augreg_in21k_ft_in1k,73.790,26.210,92.300,7.700,36.47,384,1.000,bicubic,-10.258,-5.028,+5 davit_small.msft_in1k,73.780,26.220,91.560,8.440,49.75,224,0.950,bicubic,-10.472,-5.380,-19 fastvit_ma36.apple_in1k,73.780,26.220,91.490,8.510,44.07,256,0.950,bicubic,-10.102,-5.252,+16 regnety_080.ra3_in1k,73.760,26.240,91.800,8.200,39.18,288,1.000,bicubic,-10.166,-5.090,+8 maxvit_small_tf_224.in1k,73.760,26.240,91.410,8.590,68.93,224,0.950,bicubic,-10.666,-5.414,-36 regnetz_d8.ra3_in1k,73.750,26.250,92.000,8.000,23.37,320,1.000,bicubic,-10.302,-4.996,-2 focalnet_base_lrf.ms_in1k,73.740,26.260,90.990,9.010,88.75,224,0.900,bicubic,-10.098,-5.618,+17 fastvit_sa36.apple_dist_in1k,73.730,26.270,91.730,8.270,31.53,256,0.900,bicubic,-10.296,-5.124,-1 resnetrs270.tf_in1k,73.730,26.270,91.580,8.420,129.86,352,1.000,bicubic,-10.698,-5.388,-42 efficientvit_b3.r288_in1k,73.720,26.280,91.450,8.550,48.65,288,1.000,bicubic,-10.434,-5.286,-16 gcvit_small.in1k,73.700,26.300,91.230,8.770,51.09,224,0.875,bicubic,-10.192,-5.428,+7 resnext101_32x8d.fb_wsl_ig1b_ft_in1k,73.670,26.330,92.140,7.860,88.79,224,0.875,bilinear,-9.028,-4.004,+130 resnet200d.ra2_in1k,73.670,26.330,91.570,8.430,64.69,320,1.000,bicubic,-10.294,-5.256,-1 resnetv2_101x3_bit.goog_in21k_ft_in1k,73.660,26.340,92.470,7.530,387.93,448,1.000,bilinear,-10.778,-4.912,-50 xcit_medium_24_p16_224.fb_dist_in1k,73.650,26.350,91.570,8.430,84.40,224,1.000,bicubic,-10.636,-5.362,-35 convnextv2_tiny.fcmae_ft_in22k_in1k,73.640,26.360,91.960,8.040,28.64,288,1.000,bicubic,-10.776,-5.300,-46 inception_next_small.sail_in1k,73.620,26.380,91.240,8.760,49.37,224,0.875,bicubic,-9.958,-5.358,+40 edgenext_base.in21k_ft_in1k,73.610,26.390,91.860,8.140,18.51,320,1.000,bicubic,-10.444,-5.336,-15 repvgg_d2se.rvgg_in1k,73.610,26.390,91.380,8.620,133.33,320,1.000,bilinear,-9.950,-5.278,+38 regnety_064.ra3_in1k,73.610,26.390,91.330,8.670,30.58,288,1.000,bicubic,-10.110,-5.392,+23 tresnet_v2_l.miil_in21k_ft_in1k,73.590,26.410,90.990,9.010,46.17,224,0.875,bilinear,-10.304,-5.500,-4 swinv2_base_window8_256.ms_in1k,73.550,26.450,91.510,8.490,87.92,256,0.900,bicubic,-10.700,-5.414,-38 tf_efficientnet_b5.ra_in1k,73.540,26.460,91.460,8.540,30.39,456,0.934,bicubic,-10.274,-5.292,+5 resnetaa101d.sw_in12k_ft_in1k,73.520,26.480,91.750,8.250,44.57,288,1.000,bicubic,-10.604,-5.356,-28 cs3se_edgenet_x.c2ns_in1k,73.510,26.490,91.480,8.520,50.72,320,1.000,bicubic,-10.036,-5.190,+34 resnet152d.ra2_in1k,73.510,26.490,91.240,8.760,60.21,320,1.000,bicubic,-10.174,-5.498,+23 regnetz_d8_evos.ch_in1k,73.490,26.510,91.710,8.290,23.46,320,1.000,bicubic,-10.636,-5.302,-33 deit3_base_patch16_224.fb_in1k,73.490,26.510,91.280,8.720,86.59,224,0.900,bicubic,-10.296,-5.306,+5 repvit_m2_3.dist_450e_in1k,73.480,26.520,91.260,8.740,23.69,224,0.950,bicubic,-10.262,-5.384,+10 regnetv_064.ra3_in1k,73.460,26.540,91.600,8.400,30.58,288,1.000,bicubic,-10.256,-5.142,+13 convnext_small.fb_in1k,73.450,26.550,91.330,8.670,50.22,288,1.000,bicubic,-10.250,-5.478,+14 coat_lite_medium.in1k,73.450,26.550,91.230,8.770,44.57,224,0.900,bicubic,-10.150,-5.498,+23 sequencer2d_l.in1k,73.450,26.550,91.090,8.910,54.30,224,0.875,bicubic,-9.944,-5.406,+43 xcit_tiny_24_p8_384.fb_dist_in1k,73.430,26.570,91.560,8.440,12.11,384,1.000,bicubic,-10.316,-4.840,+3 twins_svt_large.in1k,73.420,26.580,90.890,9.110,99.27,224,0.900,bicubic,-10.258,-5.698,+15 tiny_vit_21m_224.in1k,73.410,26.590,91.480,8.520,21.20,224,0.950,bicubic,-9.844,-5.112,+54 resnetrs350.tf_in1k,73.400,26.600,91.300,8.700,163.96,384,1.000,bicubic,-11.314,-5.692,-103 pvt_v2_b4.in1k,73.400,26.600,91.070,8.930,62.56,224,0.900,bicubic,-10.312,-5.600,+7 tf_efficientnet_b5.aa_in1k,73.390,26.610,91.210,8.790,30.39,456,0.934,bicubic,-10.298,-5.502,+9 regnety_160.deit_in1k,73.380,26.620,91.700,8.300,83.59,288,1.000,bicubic,-10.310,-5.080,+7 swin_s3_base_224.ms_in1k,73.350,26.650,91.180,8.820,71.13,224,0.900,bicubic,-10.570,-5.492,-27 convformer_s18.sail_in22k_ft_in1k,73.340,26.660,91.910,8.090,26.77,224,1.000,bicubic,-10.398,-5.138,-1 efficientnet_b4.ra2_in1k,73.340,26.660,91.270,8.730,19.34,384,1.000,bicubic,-10.074,-5.328,+30 gcvit_tiny.in1k,73.330,26.670,90.970,9.030,28.22,224,0.875,bicubic,-10.054,-5.428,+34 repvit_m2_3.dist_300e_in1k,73.320,26.680,90.890,9.110,23.69,224,0.950,bicubic,-10.184,-5.614,+16 mvitv2_small.fb_in1k,73.300,26.700,91.230,8.770,34.87,224,0.900,bicubic,-10.470,-5.346,-12 resnet152.a1h_in1k,73.300,26.700,91.180,8.820,60.19,288,1.000,bicubic,-10.150,-5.358,+21 resmlp_big_24_224.fb_distilled_in1k,73.300,26.700,91.170,8.830,129.14,224,0.875,bicubic,-10.292,-5.480,+9 swin_small_patch4_window7_224.ms_in22k_ft_in1k,73.290,26.710,92.030,7.970,49.61,224,0.900,bicubic,-10.008,-4.934,+39 vit_small_patch16_384.augreg_in21k_ft_in1k,73.290,26.710,91.990,8.010,22.20,384,1.000,bicubic,-10.514,-5.110,-20 xcit_small_24_p16_224.fb_dist_in1k,73.270,26.730,91.460,8.540,47.67,224,1.000,bicubic,-10.604,-5.276,-32 focalnet_base_srf.ms_in1k,73.270,26.730,91.260,8.740,88.15,224,0.900,bicubic,-10.550,-5.420,-24 swinv2_small_window16_256.ms_in1k,73.250,26.750,91.290,8.710,49.73,256,0.900,bicubic,-10.974,-5.488,-68 pvt_v2_b5.in1k,73.250,26.750,91.080,8.920,81.96,224,0.900,bicubic,-10.490,-5.556,-14 deit_base_distilled_patch16_224.fb_in1k,73.250,26.750,91.010,8.990,87.34,224,0.900,bicubic,-10.140,-5.478,+24 maxvit_tiny_rw_224.sw_in1k,73.230,26.770,90.770,9.230,29.06,224,0.950,bicubic,-10.274,-5.744,+6 pvt_v2_b3.in1k,73.210,26.790,91.010,8.990,45.24,224,0.900,bicubic,-9.908,-5.546,+49 resnetrs152.tf_in1k,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.502,-5.352,-13 fastvit_sa24.apple_dist_in1k,73.180,26.820,91.350,8.650,21.55,256,0.900,bicubic,-10.162,-5.202,+24 swin_base_patch4_window12_384.ms_in1k,73.160,26.840,91.130,8.870,87.90,384,1.000,bicubic,-11.316,-5.762,-102 efficientvit_b3.r256_in1k,73.150,26.850,91.110,8.890,48.65,256,1.000,bicubic,-10.652,-5.406,-30 vit_base_patch32_384.augreg_in21k_ft_in1k,73.130,26.870,91.250,8.750,88.30,384,1.000,bicubic,-10.222,-5.590,+19 nest_base_jx.goog_in1k,73.130,26.870,91.070,8.930,67.72,224,0.875,bicubic,-10.404,-5.304,-2 xcit_medium_24_p8_224.fb_in1k,73.130,26.870,90.280,9.720,84.32,224,1.000,bicubic,-10.616,-6.430,-26 regnety_640.seer_ft_in1k,73.110,26.890,91.520,8.480,281.38,384,1.000,bicubic,-10.798,-5.402,-50 swinv2_small_window8_256.ms_in1k,73.100,26.900,90.950,9.050,49.73,256,0.900,bicubic,-10.754,-5.694,-45 deit3_small_patch16_384.fb_in1k,73.090,26.910,91.220,8.780,22.21,384,1.000,bicubic,-10.338,-5.454,+4 xcit_small_24_p8_224.fb_in1k,73.090,26.910,91.160,8.840,47.63,224,1.000,bicubic,-10.744,-5.472,-42 cait_s24_224.fb_dist_in1k,73.060,26.940,91.120,8.880,46.92,224,1.000,bicubic,-10.382,-5.454,+1 efficientformerv2_l.snap_dist_in1k,73.060,26.940,90.880,9.120,26.32,224,0.950,bicubic,-10.572,-5.678,-18 focalnet_small_srf.ms_in1k,73.060,26.940,90.730,9.270,49.89,224,0.900,bicubic,-10.356,-5.708,+1 coatnet_rmlp_1_rw_224.sw_in1k,73.040,26.960,90.880,9.120,41.69,224,0.950,bicubic,-10.322,-5.570,+9 fastvit_sa36.apple_in1k,73.030,26.970,90.930,9.070,31.53,256,0.900,bicubic,-10.470,-5.700,-10 efficientvit_b3.r224_in1k,72.980,27.020,90.570,9.430,48.65,224,0.950,bicubic,-10.480,-5.760,-6 crossvit_15_dagger_408.in1k,72.970,27.030,91.090,8.910,28.50,408,1.000,bicubic,-10.870,-5.688,-52 caformer_s18.sail_in1k,72.960,27.040,91.010,8.990,26.34,224,1.000,bicubic,-10.694,-5.508,-25 regnety_320.tv2_in1k,72.940,27.060,90.760,9.240,145.05,224,0.965,bicubic,-10.222,-5.654,+21 ecaresnet101d.miil_in1k,72.930,27.070,91.180,8.820,44.57,288,0.950,bicubic,-10.054,-5.362,+43 regnetv_040.ra3_in1k,72.930,27.070,91.120,8.880,20.64,288,1.000,bicubic,-10.260,-5.538,+16 tf_efficientnet_b4.ap_in1k,72.900,27.100,90.980,9.020,19.34,380,0.922,bicubic,-10.350,-5.416,+9 maxvit_tiny_tf_224.in1k,72.900,27.100,90.830,9.170,30.92,224,0.950,bicubic,-10.502,-5.760,-7 coatnet_1_rw_224.sw_in1k,72.900,27.100,90.790,9.210,41.72,224,0.950,bicubic,-10.696,-5.592,-25 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,72.870,27.130,91.550,8.450,236.34,384,1.000,bicubic,-10.966,-5.576,-58 swinv2_cr_small_ns_224.sw_in1k,72.850,27.150,90.820,9.180,49.70,224,0.900,bicubic,-10.648,-5.664,-20 regnety_040.ra3_in1k,72.790,27.210,90.750,9.250,20.65,288,1.000,bicubic,-10.254,-5.752,+29 regnety_032.ra_in1k,72.780,27.220,90.950,9.050,19.44,288,1.000,bicubic,-9.946,-5.466,+54 convnextv2_tiny.fcmae_ft_in1k,72.770,27.230,91.180,8.820,28.64,288,1.000,bicubic,-10.694,-5.538,-20 xception65p.ra3_in1k,72.760,27.240,90.910,9.090,39.82,299,0.940,bicubic,-10.366,-5.572,+15 swin_s3_small_224.ms_in1k,72.720,27.280,90.560,9.440,49.74,224,0.900,bicubic,-11.036,-5.892,-53 resnetv2_101.a1h_in1k,72.690,27.310,90.670,9.330,44.54,288,1.000,bicubic,-10.310,-5.784,+28 efficientformer_l7.snap_dist_in1k,72.660,27.340,90.800,9.200,82.23,224,0.950,bicubic,-10.722,-5.736,-12 xcit_small_12_p8_224.fb_in1k,72.630,27.370,90.670,9.330,26.21,224,1.000,bicubic,-10.704,-5.812,-7 nfnet_l0.ra2_in1k,72.610,27.390,90.990,9.010,35.07,288,1.000,bicubic,-10.140,-5.526,+44 resnext101_64x4d.c1_in1k,72.610,27.390,90.830,9.170,83.46,288,1.000,bicubic,-10.546,-5.544,+3 focalnet_small_lrf.ms_in1k,72.610,27.390,90.720,9.280,50.34,224,0.900,bicubic,-10.884,-5.860,-28 pnasnet5large.tf_in1k,72.610,27.390,90.500,9.500,86.06,331,0.911,bicubic,-10.172,-5.540,+39 xception65.ra3_in1k,72.600,27.400,90.840,9.160,39.92,299,0.940,bicubic,-10.580,-5.752,-1 cs3sedarknet_x.c2ns_in1k,72.580,27.420,91.060,8.940,35.40,288,1.000,bicubic,-10.078,-5.290,+52 resnest101e.in1k,72.580,27.420,90.810,9.190,48.28,256,0.875,bilinear,-10.304,-5.512,+27 twins_pcpvt_large.in1k,72.580,27.420,90.700,9.300,60.99,224,0.900,bicubic,-10.550,-5.904,+2 gc_efficientnetv2_rw_t.agc_in1k,72.570,27.430,90.830,9.170,13.68,288,1.000,bicubic,-9.886,-5.466,+82 tf_efficientnet_b5.in1k,72.560,27.440,91.090,8.910,30.39,456,0.934,bicubic,-10.616,-5.446,-6 resnext50_32x4d.fb_swsl_ig1b_ft_in1k,72.560,27.440,90.840,9.160,25.03,224,0.875,bilinear,-9.612,-5.384,+119 tresnet_xl.miil_in1k_448,72.560,27.440,90.310,9.690,78.44,448,0.875,bilinear,-10.498,-5.862,+9 twins_svt_base.in1k,72.550,27.450,90.450,9.550,56.07,224,0.900,bicubic,-10.570,-5.964,-1 deit_base_patch16_384.fb_in1k,72.550,27.450,90.260,9.740,86.86,384,1.000,bicubic,-10.554,-6.108,+3 resnetv2_50x3_bit.goog_in21k_ft_in1k,72.520,27.480,91.760,8.240,217.32,448,1.000,bilinear,-11.500,-5.366,-98 xcit_small_12_p16_224.fb_dist_in1k,72.520,27.480,91.130,8.870,26.25,224,1.000,bicubic,-10.808,-5.286,-22 rexnet_300.nav_in1k,72.520,27.480,90.610,9.390,34.71,224,0.875,bicubic,-10.254,-5.628,+28 vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,72.510,27.490,90.880,9.120,88.22,224,0.900,bicubic,-10.786,-5.648,-23 deit3_medium_patch16_224.fb_in1k,72.510,27.490,90.810,9.190,38.85,224,0.900,bicubic,-10.576,-5.484,-1 convformer_s18.sail_in1k,72.510,27.490,90.510,9.490,26.77,224,1.000,bicubic,-10.476,-5.740,+10 convnext_tiny.fb_in22k_ft_in1k_384,72.470,27.530,91.540,8.460,28.59,384,1.000,bicubic,-11.618,-5.604,-114 regnety_080_tv.tv2_in1k,72.460,27.540,90.540,9.460,39.38,224,0.965,bicubic,-10.134,-5.708,+47 xcit_tiny_24_p8_224.fb_dist_in1k,72.440,27.560,90.920,9.080,12.11,224,1.000,bicubic,-10.126,-5.138,+55 sequencer2d_m.in1k,72.430,27.570,90.710,9.290,38.31,224,0.875,bicubic,-10.382,-5.564,+14 resnet101d.ra2_in1k,72.430,27.570,90.650,9.350,44.57,320,1.000,bicubic,-10.590,-5.802,0 regnetx_320.tv2_in1k,72.420,27.580,90.300,9.700,107.81,224,0.965,bicubic,-10.390,-5.908,+14 maxxvit_rmlp_nano_rw_256.sw_in1k,72.370,27.630,90.750,9.250,16.78,256,0.950,bicubic,-10.672,-5.600,-4 nest_small_jx.goog_in1k,72.350,27.650,90.690,9.310,38.35,224,0.875,bicubic,-10.774,-5.630,-17 maxvit_rmlp_nano_rw_256.sw_in1k,72.350,27.650,90.430,9.570,15.50,256,0.950,bicubic,-10.604,-5.836,+2 efficientvit_b2.r288_in1k,72.330,27.670,90.930,9.070,24.33,288,1.000,bicubic,-10.770,-5.374,-13 resnext101_32x8d.tv2_in1k,72.330,27.670,90.270,9.730,88.79,224,0.965,bilinear,-10.502,-5.962,+6 tf_efficientnet_b4.aa_in1k,72.300,27.700,90.590,9.410,19.34,380,0.922,bicubic,-10.718,-5.710,-7 tf_efficientnet_b2.ns_jft_in1k,72.280,27.720,91.110,8.890,9.11,260,0.890,bicubic,-10.098,-5.144,+65 hrnet_w18_ssld.paddle_in1k,72.270,27.730,90.810,9.190,21.30,288,1.000,bilinear,-9.778,-5.440,+108 maxvit_nano_rw_256.sw_in1k,72.270,27.730,90.570,9.430,15.45,256,0.950,bicubic,-10.658,-5.650,-3 tresnet_m.miil_in21k_ft_in1k,72.270,27.730,90.230,9.770,31.39,224,0.875,bilinear,-10.800,-5.880,-16 swin_base_patch4_window7_224.ms_in1k,72.260,27.740,90.810,9.190,87.77,224,0.900,bicubic,-11.346,-5.642,-77 resnext101_64x4d.tv_in1k,72.260,27.740,90.550,9.450,83.46,224,0.875,bilinear,-10.732,-5.694,-10 resnetv2_50x1_bit.goog_distilled_in1k,72.240,27.760,90.980,9.020,25.55,224,0.875,bicubic,-10.584,-5.538,-2 nasnetalarge.tf_in1k,72.240,27.760,90.460,9.540,88.75,331,0.911,bicubic,-10.386,-5.582,+24 efficientnetv2_rw_t.ra2_in1k,72.230,27.770,90.420,9.580,13.65,288,1.000,bicubic,-10.120,-5.772,+62 crossvit_18_240.in1k,72.230,27.770,90.270,9.730,43.27,240,0.875,bicubic,-10.170,-5.790,+54 regnetz_c16_evos.ch_in1k,72.220,27.780,91.210,8.790,13.49,320,0.950,bicubic,-10.416,-5.264,+18 cait_xxs36_384.fb_dist_in1k,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.014,-5.304,+80 twins_pcpvt_base.in1k,72.190,27.810,90.500,9.500,43.83,224,0.900,bicubic,-10.524,-5.846,+3 crossvit_18_dagger_240.in1k,72.190,27.810,90.090,9.910,44.27,240,0.875,bicubic,-10.328,-5.978,+38 regnetz_c16.ra3_in1k,72.180,27.820,91.120,8.880,13.46,320,1.000,bicubic,-10.452,-5.198,+15 regnety_320.seer_ft_in1k,72.170,27.830,90.870,9.130,145.05,384,1.000,bicubic,-11.158,-5.838,-55 maxxvitv2_nano_rw_256.sw_in1k,72.170,27.830,90.470,9.530,23.70,256,0.950,bicubic,-10.940,-5.854,-33 repvit_m1_5.dist_450e_in1k,72.160,27.840,90.370,9.630,14.64,224,0.950,bicubic,-10.352,-5.742,+34 resnet101.a1h_in1k,72.100,27.900,90.820,9.180,44.55,288,1.000,bicubic,-10.678,-5.490,-9 regnety_160.tv2_in1k,72.100,27.900,90.230,9.770,83.59,224,0.965,bicubic,-10.546,-5.984,+8 inception_next_tiny.sail_in1k,72.090,27.910,90.100,9.900,28.06,224,0.875,bicubic,-10.388,-5.922,+36 xcit_tiny_24_p16_384.fb_dist_in1k,72.070,27.930,90.580,9.420,12.12,384,1.000,bicubic,-10.500,-5.696,+23 tiny_vit_11m_224.dist_in22k_ft_in1k,72.050,27.950,91.450,8.550,11.00,224,0.950,bicubic,-11.178,-5.180,-56 cs3edgenet_x.c2_in1k,72.050,27.950,90.370,9.630,47.82,288,1.000,bicubic,-10.658,-6.000,-5 vit_relpos_medium_patch16_cls_224.sw_in1k,72.020,27.980,90.300,9.700,38.76,224,0.900,bicubic,-10.552,-5.768,+19 davit_tiny.msft_in1k,72.010,27.990,90.070,9.930,28.36,224,0.950,bicubic,-10.686,-6.204,-5 convnext_tiny_hnf.a2h_in1k,72.000,28.000,89.770,10.230,28.59,288,1.000,bicubic,-10.584,-6.238,+13 mobilevitv2_200.cvnets_in22k_ft_in1k_384,71.990,28.010,90.650,9.350,18.45,384,1.000,bicubic,-11.410,-5.932,-77 efficientformer_l3.snap_dist_in1k,71.980,28.020,90.270,9.730,31.41,224,0.950,bicubic,-10.568,-5.980,+18 convnext_tiny.fb_in1k,71.980,28.020,90.220,9.780,28.59,288,1.000,bicubic,-10.718,-6.412,-9 vit_relpos_base_patch16_clsgap_224.sw_in1k,71.970,28.030,90.250,9.750,86.43,224,0.900,bicubic,-10.790,-5.922,-18 sequencer2d_s.in1k,71.940,28.060,90.500,9.500,27.65,224,0.875,bicubic,-10.400,-5.528,+41 resnet152.a2_in1k,71.940,28.060,89.420,10.580,60.19,288,1.000,bicubic,-10.668,-6.708,+2 dm_nfnet_f0.dm_in1k,71.910,28.090,90.760,9.240,71.49,256,0.900,bicubic,-11.576,-5.808,-92 rexnetr_200.sw_in12k_ft_in1k,71.900,28.100,91.260,8.740,16.52,288,1.000,bicubic,-11.238,-5.376,-60 convnext_nano.in12k_ft_in1k,71.900,28.100,91.000,9.000,15.59,288,1.000,bicubic,-10.962,-5.556,-31 swinv2_cr_small_224.sw_in1k,71.900,28.100,90.270,9.730,49.70,224,0.900,bicubic,-11.236,-5.838,-60 convnextv2_nano.fcmae_ft_in22k_in1k,71.890,28.110,90.890,9.110,15.62,288,1.000,bicubic,-10.774,-5.630,-13 fastvit_sa24.apple_in1k,71.890,28.110,90.630,9.370,21.55,256,0.900,bicubic,-10.788,-5.642,-16 repvit_m1_5.dist_300e_in1k,71.850,28.150,90.330,9.670,14.64,224,0.950,bicubic,-10.526,-5.700,+27 resnet101.a1_in1k,71.850,28.150,89.150,10.850,44.55,288,1.000,bicubic,-10.472,-6.482,+36 eca_nfnet_l0.ra2_in1k,71.840,28.160,91.110,8.890,24.14,288,1.000,bicubic,-10.738,-5.382,0 mobilevitv2_175.cvnets_in22k_ft_in1k_384,71.840,28.160,90.770,9.230,14.25,384,1.000,bicubic,-11.098,-5.656,-44 vit_relpos_base_patch16_224.sw_in1k,71.820,28.180,90.250,9.750,86.43,224,0.900,bicubic,-10.676,-5.888,+10 regnetx_160.tv2_in1k,71.800,28.200,90.050,9.950,54.28,224,0.965,bicubic,-10.766,-6.122,+2 seresnext50_32x4d.racm_in1k,71.800,28.200,90.030,9.970,27.56,288,0.950,bicubic,-10.396,-6.118,+48 swin_small_patch4_window7_224.ms_in1k,71.770,28.230,90.260,9.740,49.61,224,0.900,bicubic,-11.438,-6.056,-77 coat_small.in1k,71.750,28.250,90.410,9.590,21.69,224,0.900,bicubic,-10.612,-5.798,+20 flexivit_small.1200ep_in1k,71.750,28.250,90.270,9.730,22.06,240,0.950,bicubic,-10.776,-5.856,0 mvitv2_tiny.fb_in1k,71.740,28.260,90.310,9.690,24.17,224,0.900,bicubic,-10.670,-5.842,+11 flexivit_small.300ep_in1k,71.730,28.270,89.980,10.020,22.06,240,0.950,bicubic,-10.448,-6.058,+45 efficientvit_b2.r256_in1k,71.720,28.280,90.350,9.650,24.33,256,1.000,bicubic,-10.970,-5.744,-30 flexivit_small.600ep_in1k,71.720,28.280,90.150,9.850,22.06,240,0.950,bicubic,-10.642,-5.934,+16 pit_b_224.in1k,71.720,28.280,89.250,10.750,73.76,224,0.900,bicubic,-10.718,-6.464,+6 xcit_large_24_p16_224.fb_in1k,71.710,28.290,89.170,10.830,189.10,224,1.000,bicubic,-11.192,-6.714,-54 pvt_v2_b2_li.in1k,71.700,28.300,90.010,9.990,22.55,224,0.900,bicubic,-10.494,-6.082,+39 resnet50.fb_swsl_ig1b_ft_in1k,71.690,28.310,90.500,9.500,25.56,224,0.875,bilinear,-9.482,-5.486,+136 ecaresnet101d_pruned.miil_in1k,71.680,28.320,90.430,9.570,24.88,288,0.950,bicubic,-10.318,-5.730,+56 coatnet_bn_0_rw_224.sw_in1k,71.670,28.330,90.380,9.620,27.44,224,0.950,bicubic,-10.730,-5.806,+3 gcvit_xtiny.in1k,71.670,28.330,90.250,9.750,19.98,224,0.875,bicubic,-10.284,-5.716,+60 tresnet_xl.miil_in1k,71.650,28.350,89.630,10.370,78.44,224,0.875,bilinear,-10.424,-6.298,+46 resnet61q.ra2_in1k,71.630,28.370,90.280,9.720,36.85,288,1.000,bicubic,-10.894,-5.850,-12 tresnet_l.miil_in1k_448,71.630,28.370,90.030,9.970,55.99,448,0.875,bilinear,-10.646,-5.948,+22 poolformerv2_m48.sail_in1k,71.620,28.380,89.800,10.200,73.35,224,1.000,bicubic,-10.998,-6.272,-32 convnextv2_nano.fcmae_ft_in22k_in1k_384,71.590,28.410,90.760,9.240,15.62,384,1.000,bicubic,-11.784,-5.984,-109 xcit_tiny_12_p8_384.fb_dist_in1k,71.590,28.410,90.700,9.300,6.71,384,1.000,bicubic,-10.798,-5.520,-2 swinv2_tiny_window16_256.ms_in1k,71.590,28.410,90.330,9.670,28.35,256,0.900,bicubic,-11.214,-5.906,-57 convit_base.fb_in1k,71.580,28.420,90.120,9.880,86.54,224,0.875,bicubic,-10.710,-5.816,+13 coatnet_0_rw_224.sw_in1k,71.570,28.430,89.400,10.600,27.44,224,0.950,bicubic,-10.820,-6.436,-5 resnetv2_50d_evos.ah_in1k,71.560,28.440,90.090,9.910,25.59,288,1.000,bicubic,-10.442,-5.810,+43 fbnetv3_g.ra2_in1k,71.530,28.470,90.370,9.630,16.62,288,0.950,bilinear,-10.510,-5.690,+40 crossvit_15_dagger_240.in1k,71.520,28.480,89.850,10.150,28.21,240,0.875,bicubic,-10.810,-6.106,+4 poolformer_m48.sail_in1k,71.520,28.480,89.770,10.230,73.47,224,0.950,bicubic,-10.962,-6.196,-17 resnet152.tv2_in1k,71.510,28.490,89.970,10.030,60.19,224,0.965,bilinear,-10.776,-6.034,+8 resnetaa50d.sw_in12k_ft_in1k,71.500,28.500,90.320,9.680,25.58,288,1.000,bicubic,-11.100,-6.178,-40 mobilevitv2_150.cvnets_in22k_ft_in1k_384,71.490,28.510,90.420,9.580,10.59,384,1.000,bicubic,-11.096,-5.894,-37 resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,71.480,28.520,90.470,9.530,88.79,224,0.875,bilinear,-10.126,-5.570,+68 wide_resnet50_2.racm_in1k,71.480,28.520,90.220,9.780,68.88,288,0.950,bicubic,-10.800,-5.844,+6 efficientnet_b3.ra2_in1k,71.470,28.530,90.060,9.940,12.23,320,1.000,bicubic,-10.776,-6.058,+7 pvt_v2_b2.in1k,71.450,28.550,90.060,9.940,25.36,224,0.900,bicubic,-10.634,-5.896,+26 resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,71.420,28.580,90.530,9.470,194.03,224,0.875,bilinear,-10.418,-5.562,+47 resnet51q.ra2_in1k,71.410,28.590,90.180,9.820,35.70,288,1.000,bilinear,-10.950,-6.006,-12 efficientvit_b2.r224_in1k,71.380,28.620,89.710,10.290,24.33,224,0.950,bicubic,-10.768,-5.996,+17 wide_resnet101_2.tv2_in1k,71.360,28.640,89.790,10.210,126.89,224,0.965,bilinear,-11.142,-6.226,-31 xcit_tiny_24_p8_224.fb_in1k,71.350,28.650,90.230,9.770,12.11,224,1.000,bicubic,-10.542,-5.740,+37 vit_relpos_medium_patch16_224.sw_in1k,71.350,28.650,89.960,10.040,38.75,224,0.900,bicubic,-11.112,-6.122,-28 resnet152.a1_in1k,71.350,28.650,89.310,10.690,60.19,288,1.000,bicubic,-11.382,-6.410,-70 tf_efficientnetv2_b3.in21k_ft_in1k,71.340,28.660,90.760,9.240,14.36,300,0.900,bicubic,-11.330,-5.866,-64 vit_base_patch16_224.orig_in21k_ft_in1k,71.320,28.680,90.480,9.520,86.57,224,0.900,bicubic,-10.470,-5.646,+43 tf_efficientnet_b4.in1k,71.320,28.680,90.110,9.890,19.34,380,0.922,bicubic,-11.288,-5.642,-56 mixer_b16_224.miil_in21k_ft_in1k,71.300,28.700,89.650,10.350,59.88,224,0.875,bilinear,-11.006,-6.070,-11 pit_s_distilled_224.in1k,71.260,28.740,89.640,10.360,24.04,224,0.900,bicubic,-10.554,-6.090,+39 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,71.250,28.750,90.480,9.520,236.34,224,0.875,bicubic,-11.626,-6.102,-90 ecaresnet50t.ra2_in1k,71.240,28.760,90.450,9.550,25.57,320,0.950,bicubic,-11.112,-5.690,-23 convmixer_1536_20.in1k,71.230,28.770,89.450,10.550,51.63,224,0.960,bicubic,-10.132,-6.164,+81 deit_base_patch16_224.fb_in1k,71.220,28.780,89.190,10.810,86.57,224,0.900,bicubic,-10.772,-6.546,+19 xcit_small_12_p16_224.fb_in1k,71.200,28.800,89.750,10.250,26.25,224,1.000,bicubic,-10.770,-6.062,+21 resnext50_32x4d.a1h_in1k,71.190,28.810,89.690,10.310,25.03,288,1.000,bicubic,-10.824,-6.244,+13 ecaresnet50t.a1_in1k,71.190,28.810,89.580,10.420,25.57,288,1.000,bicubic,-10.938,-6.062,+5 vit_relpos_medium_patch16_rpn_224.sw_in1k,71.170,28.830,90.080,9.920,38.73,224,0.900,bicubic,-11.140,-5.892,-22 crossvit_base_240.in1k,71.170,28.830,89.830,10.170,105.03,240,0.875,bicubic,-11.044,-6.004,-9 vit_base_patch32_clip_224.laion2b_ft_in1k,71.160,28.840,90.210,9.790,88.22,224,0.900,bicubic,-11.422,-5.990,-61 swin_s3_tiny_224.ms_in1k,71.150,28.850,89.710,10.290,28.33,224,0.900,bicubic,-10.994,-6.244,-2 cs3sedarknet_l.c2ns_in1k,71.110,28.890,90.350,9.650,21.91,288,0.950,bicubic,-10.674,-5.614,+30 efficientformerv2_s2.snap_dist_in1k,71.110,28.890,90.140,9.860,12.71,224,0.950,bicubic,-11.056,-5.770,-7 halo2botnet50ts_256.a1h_in1k,71.110,28.890,89.600,10.400,22.64,256,0.950,bicubic,-10.950,-6.034,+2 cs3darknet_x.c2ns_in1k,71.090,28.910,90.150,9.850,35.05,288,1.000,bicubic,-11.132,-6.080,-18 mobilevitv2_200.cvnets_in22k_ft_in1k,71.090,28.910,89.700,10.300,18.45,256,0.888,bicubic,-11.242,-6.242,-33 ecaresnet50d.miil_in1k,71.080,28.920,90.240,9.760,25.58,288,0.950,bicubic,-10.570,-5.642,+32 focalnet_tiny_lrf.ms_in1k,71.060,28.940,89.570,10.430,28.65,224,0.900,bicubic,-11.094,-6.378,-11 convnextv2_nano.fcmae_ft_in1k,71.050,28.950,90.100,9.900,15.62,288,1.000,bicubic,-11.436,-6.126,-56 focalnet_tiny_srf.ms_in1k,71.050,28.950,89.600,10.400,28.43,224,0.900,bicubic,-11.088,-6.368,-10 xcit_tiny_12_p8_224.fb_dist_in1k,71.040,28.960,89.890,10.110,6.71,224,1.000,bicubic,-10.172,-5.712,+77 xcit_small_24_p16_224.fb_in1k,71.040,28.960,89.680,10.320,47.67,224,1.000,bicubic,-11.536,-6.332,-70 tresnet_m.miil_in1k_448,71.020,28.980,88.680,11.320,31.39,448,0.875,bilinear,-10.690,-6.894,+21 resnetv2_101x1_bit.goog_in21k_ft_in1k,71.010,28.990,91.080,8.920,44.54,448,1.000,bilinear,-11.332,-5.440,-43 visformer_small.in1k,71.010,28.990,89.440,10.560,40.22,224,0.900,bicubic,-11.096,-6.438,-13 repvit_m3.dist_in1k,70.990,29.010,89.630,10.370,10.68,224,0.950,bicubic,-10.512,-5.938,+39 xcit_medium_24_p16_224.fb_in1k,70.990,29.010,89.530,10.470,84.40,224,1.000,bicubic,-11.650,-6.452,-93 resnet101.a2_in1k,70.990,29.010,89.160,10.840,44.55,288,1.000,bicubic,-11.246,-6.570,-32 lamhalobotnet50ts_256.a1h_in1k,70.990,29.010,89.040,10.960,22.57,256,0.950,bicubic,-10.562,-6.452,+32 edgenext_small.usi_in1k,70.980,29.020,89.880,10.120,5.59,320,1.000,bicubic,-10.584,-5.832,+27 resnetv2_50d_gn.ah_in1k,70.960,29.040,89.830,10.170,25.57,288,1.000,bicubic,-10.998,-6.098,-5 tnt_s_patch16_224,70.960,29.040,89.610,10.390,23.76,224,0.900,bicubic,-10.576,-6.080,+27 convnext_nano.d1h_in1k,70.960,29.040,89.430,10.570,15.59,288,1.000,bicubic,-10.522,-6.228,+38 tiny_vit_11m_224.in1k,70.940,29.060,89.840,10.160,11.00,224,0.950,bicubic,-10.552,-6.022,+31 resnest50d_4s2x40d.in1k,70.940,29.060,89.720,10.280,30.42,224,0.875,bicubic,-10.180,-5.840,+72 coatnet_nano_rw_224.sw_in1k,70.940,29.060,89.700,10.300,15.14,224,0.900,bicubic,-10.756,-5.946,+11 vit_srelpos_medium_patch16_224.sw_in1k,70.920,29.080,89.940,10.060,38.74,224,0.900,bicubic,-11.320,-6.002,-43 tf_efficientnet_b3.ap_in1k,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.900,-6.196,+1 vit_small_patch16_224.augreg_in21k_ft_in1k,70.910,29.090,90.170,9.830,22.05,224,0.900,bicubic,-10.476,-5.966,+42 coatnext_nano_rw_224.sw_in1k,70.890,29.110,90.250,9.750,14.70,224,0.900,bicubic,-11.052,-5.666,-12 coatnet_rmlp_nano_rw_224.sw_in1k,70.890,29.110,89.920,10.080,15.15,224,0.900,bicubic,-11.160,-5.958,-23 vit_base_patch16_rpn_224.sw_in1k,70.890,29.110,89.770,10.230,86.54,224,0.900,bicubic,-11.312,-6.226,-41 vit_large_patch32_384.orig_in21k_ft_in1k,70.870,29.130,90.570,9.430,306.63,384,1.000,bicubic,-10.640,-5.520,+22 nest_tiny_jx.goog_in1k,70.860,29.140,89.940,10.060,17.06,224,0.875,bicubic,-10.566,-5.678,+32 resnetrs101.tf_in1k,70.860,29.140,89.830,10.170,63.62,288,0.940,bicubic,-11.424,-6.184,-54 rexnet_200.nav_in1k,70.860,29.140,89.700,10.300,16.37,224,0.875,bicubic,-10.776,-5.966,+5 poolformerv2_m36.sail_in1k,70.850,29.150,89.330,10.670,56.08,224,1.000,bicubic,-11.366,-6.594,-49 tf_efficientnet_b1.ns_jft_in1k,70.840,29.160,90.120,9.880,7.79,240,0.882,bicubic,-10.548,-5.618,+31 regnety_032.tv2_in1k,70.840,29.160,89.850,10.150,19.44,224,0.965,bicubic,-10.916,-5.994,-5 wide_resnet50_2.tv2_in1k,70.840,29.160,89.270,10.730,68.88,224,0.965,bilinear,-10.766,-6.490,+4 tresnet_l.miil_in1k,70.830,29.170,89.600,10.400,55.99,224,0.875,bilinear,-10.650,-6.024,+16 poolformer_m36.sail_in1k,70.830,29.170,89.510,10.490,56.17,224,0.950,bicubic,-11.272,-6.188,-39 tf_efficientnetv2_b3.in1k,70.830,29.170,89.510,10.490,14.36,300,0.904,bicubic,-11.142,-6.292,-28 fastvit_sa12.apple_dist_in1k,70.830,29.170,89.240,10.760,11.58,256,0.900,bicubic,-11.024,-6.470,-16 levit_384.fb_dist_in1k,70.810,29.190,89.320,10.680,39.13,224,0.900,bicubic,-11.786,-6.698,-111 levit_conv_384.fb_dist_in1k,70.800,29.200,89.320,10.680,39.13,224,0.900,bicubic,-11.790,-6.696,-110 coat_lite_small.in1k,70.780,29.220,89.580,10.420,19.84,224,0.900,bicubic,-11.532,-6.270,-71 ecaresnetlight.miil_in1k,70.750,29.250,89.880,10.120,30.16,288,0.950,bicubic,-10.658,-5.936,+19 deit3_small_patch16_224.fb_in1k,70.750,29.250,89.450,10.550,22.06,224,0.900,bicubic,-10.620,-6.006,+25 regnetx_080.tv2_in1k,70.730,29.270,89.330,10.670,39.57,224,0.965,bicubic,-10.810,-6.212,-1 swinv2_cr_tiny_ns_224.sw_in1k,70.700,29.300,89.380,10.620,28.33,224,0.900,bicubic,-11.102,-6.438,-21 seresnet50.ra2_in1k,70.690,29.310,89.870,10.130,28.09,288,0.950,bicubic,-10.594,-5.782,+28 vit_base_patch32_clip_224.openai_ft_in1k,70.690,29.310,89.830,10.170,88.22,224,0.900,bicubic,-11.240,-6.136,-33 resnet50d.ra2_in1k,70.690,29.310,89.310,10.690,25.58,288,0.950,bicubic,-10.666,-6.428,+23 vit_relpos_small_patch16_224.sw_in1k,70.680,29.320,90.000,10.000,21.98,224,0.900,bicubic,-10.782,-5.820,+7 mobilevitv2_175.cvnets_in22k_ft_in1k,70.670,29.330,89.700,10.300,14.25,256,0.888,bicubic,-11.268,-6.090,-36 resnet101.tv2_in1k,70.630,29.370,89.390,10.610,44.55,224,0.965,bilinear,-11.258,-6.378,-34 tf_efficientnet_b3.aa_in1k,70.620,29.380,89.440,10.560,12.23,300,0.904,bicubic,-11.020,-6.282,-20 resnet50_gn.a1h_in1k,70.620,29.380,89.390,10.610,25.56,288,0.950,bicubic,-10.596,-5.994,+27 crossvit_small_240.in1k,70.620,29.380,89.360,10.640,26.86,240,0.875,bicubic,-10.398,-6.096,+51 cait_xxs24_384.fb_dist_in1k,70.600,29.400,89.730,10.270,12.03,384,1.000,bicubic,-10.372,-5.910,+51 senet154.gluon_in1k,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.626,-6.438,+22 convit_small.fb_in1k,70.580,29.420,89.590,10.410,27.78,224,0.875,bicubic,-10.840,-6.154,+4 convnext_nano_ols.d1h_in1k,70.570,29.430,89.100,10.900,15.65,288,1.000,bicubic,-11.030,-6.536,-19 twins_pcpvt_small.in1k,70.570,29.430,89.070,10.930,24.11,224,0.900,bicubic,-10.522,-6.578,+37 regnetz_b16.ra3_in1k,70.550,29.450,89.400,10.600,9.72,288,1.000,bicubic,-10.178,-6.118,+75 resnet50.c2_in1k,70.540,29.460,89.170,10.830,25.56,288,1.000,bicubic,-10.330,-6.364,+60 resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,70.530,29.470,89.770,10.230,44.18,224,0.875,bilinear,-10.394,-5.964,+50 resnetv2_50.a1h_in1k,70.530,29.470,89.110,10.890,25.55,288,1.000,bicubic,-10.868,-6.616,+1 vit_small_r26_s32_224.augreg_in21k_ft_in1k,70.520,29.480,90.110,9.890,36.43,224,0.900,bicubic,-11.344,-5.912,-46 swinv2_tiny_window8_256.ms_in1k,70.510,29.490,89.500,10.500,28.35,256,0.900,bicubic,-11.310,-6.494,-44 deit_small_distilled_patch16_224.fb_in1k,70.500,29.500,89.460,10.540,22.44,224,0.900,bicubic,-10.716,-6.164,+15 legacy_senet154.in1k,70.500,29.500,88.990,11.010,115.09,224,0.875,bilinear,-10.812,-6.570,+6 repvit_m1_1.dist_450e_in1k,70.490,29.510,89.140,10.860,8.80,224,0.950,bicubic,-10.822,-6.396,+3 halonet50ts.a1h_in1k,70.480,29.520,89.340,10.660,22.73,256,0.940,bicubic,-11.182,-6.270,-38 tf_efficientnet_lite4.in1k,70.450,29.550,89.130,10.870,13.01,380,0.920,bilinear,-11.080,-6.534,-24 resnetaa50.a1h_in1k,70.440,29.560,89.990,10.010,25.56,288,1.000,bicubic,-11.174,-5.812,-36 crossvit_15_240.in1k,70.440,29.560,89.530,10.470,27.53,240,0.875,bicubic,-11.096,-6.206,-26 poolformerv2_s36.sail_in1k,70.430,29.570,89.630,10.370,30.79,224,1.000,bicubic,-11.136,-6.060,-34 twins_svt_small.in1k,70.430,29.570,89.360,10.640,24.06,224,0.900,bicubic,-11.246,-6.298,-45 ecaresnet50t.a2_in1k,70.430,29.570,89.020,10.980,25.57,288,1.000,bicubic,-11.228,-6.530,-41 resnest50d_1s4x24d.in1k,70.420,29.580,89.220,10.780,25.68,224,0.875,bicubic,-10.568,-6.106,+28 resnest50d.in1k,70.420,29.580,88.760,11.240,27.48,224,0.875,bilinear,-10.540,-6.622,+33 seresnext101_64x4d.gluon_in1k,70.410,29.590,89.360,10.640,88.23,224,0.875,bicubic,-10.484,-5.936,+37 gernet_l.idstcv_in1k,70.410,29.590,88.980,11.020,31.08,256,0.875,bilinear,-10.944,-6.550,-8 gcresnext50ts.ch_in1k,70.400,29.600,89.420,10.580,15.67,288,1.000,bicubic,-10.830,-6.122,-3 cs3darknet_l.c2ns_in1k,70.350,29.650,89.750,10.250,21.16,288,0.950,bicubic,-10.546,-5.912,+34 resnet152s.gluon_in1k,70.310,29.690,88.870,11.130,60.32,224,0.875,bicubic,-10.698,-6.546,+22 vit_srelpos_small_patch16_224.sw_in1k,70.290,29.710,89.540,10.460,21.97,224,0.900,bicubic,-10.802,-6.030,+14 repvgg_b3.rvgg_in1k,70.230,29.770,88.740,11.260,123.09,224,0.875,bilinear,-10.276,-6.514,+69 coat_mini.in1k,70.200,29.800,89.460,10.540,10.34,224,0.900,bicubic,-11.070,-5.922,-9 xception41p.ra3_in1k,70.200,29.800,89.100,10.900,26.91,299,0.940,bicubic,-11.772,-6.684,-78 sebotnet33ts_256.a1h_in1k,70.160,29.840,88.820,11.180,13.70,256,0.940,bicubic,-11.008,-6.348,-1 efficientnet_el.ra_in1k,70.140,29.860,89.310,10.690,10.59,300,0.904,bicubic,-11.172,-6.180,-16 inception_resnet_v2.tf_in1k,70.130,29.870,88.690,11.310,55.84,299,0.897,bicubic,-10.328,-6.500,+71 resnet50.d_in1k,70.120,29.880,88.740,11.260,25.56,288,1.000,bicubic,-10.852,-6.690,+17 resnet50d.a1_in1k,70.120,29.880,88.350,11.650,25.58,288,1.000,bicubic,-11.330,-6.868,-33 poolformer_s36.sail_in1k,70.100,29.900,89.130,10.870,30.86,224,0.900,bicubic,-11.330,-6.314,-34 resmlp_36_224.fb_distilled_in1k,70.100,29.900,89.090,10.910,44.69,224,0.875,bicubic,-11.048,-6.388,-5 haloregnetz_b.ra3_in1k,70.100,29.900,88.900,11.100,11.68,224,0.940,bicubic,-10.946,-6.300,+9 ecaresnet50d_pruned.miil_in1k,70.090,29.910,89.510,10.490,19.94,288,0.950,bicubic,-10.700,-6.060,+34 resnet50.c1_in1k,70.070,29.930,89.000,11.000,25.56,288,1.000,bicubic,-10.842,-6.552,+18 gcresnet50t.ra2_in1k,70.050,29.950,89.520,10.480,25.90,288,1.000,bicubic,-11.406,-6.198,-40 sehalonet33ts.ra2_in1k,70.050,29.950,88.740,11.260,13.69,256,0.940,bicubic,-10.908,-6.532,+12 seresnext101_32x4d.gluon_in1k,70.020,29.980,88.940,11.060,48.96,224,0.875,bicubic,-10.872,-6.356,+17 regnety_320.pycls_in1k,70.020,29.980,88.900,11.100,145.05,224,0.875,bicubic,-10.790,-6.338,+28 seresnet50.a2_in1k,70.000,30.000,88.710,11.290,28.09,288,1.000,bicubic,-11.106,-6.512,-8 levit_256.fb_dist_in1k,69.970,30.030,89.240,10.760,18.89,224,0.900,bicubic,-11.554,-6.254,-55 levit_conv_256.fb_dist_in1k,69.960,30.040,89.250,10.750,18.89,224,0.900,bicubic,-11.562,-6.240,-55 resnet152d.gluon_in1k,69.940,30.060,88.500,11.500,60.21,224,0.875,bicubic,-10.536,-6.702,+53 fastvit_s12.apple_dist_in1k,69.910,30.090,88.930,11.070,9.47,256,0.900,bicubic,-11.160,-6.354,-5 maxvit_rmlp_pico_rw_256.sw_in1k,69.890,30.110,89.250,10.750,7.52,256,0.950,bicubic,-10.624,-5.964,+45 pit_s_224.in1k,69.880,30.120,88.940,11.060,23.46,224,0.900,bicubic,-11.206,-6.390,-8 swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,69.850,30.150,90.410,9.590,28.29,224,0.900,bicubic,-11.118,-5.604,0 resnext50_32x4d.ra_in1k,69.830,30.170,88.220,11.780,25.03,288,0.950,bicubic,-10.868,-7.172,+29 regnety_016.tv2_in1k,69.810,30.190,89.360,10.640,11.20,224,0.965,bicubic,-10.856,-5.970,+31 seresnet50.a1_in1k,69.810,30.190,88.550,11.450,28.09,288,1.000,bicubic,-11.292,-6.778,-16 mobilevitv2_150.cvnets_in22k_ft_in1k,69.800,30.200,89.180,10.820,10.59,256,0.888,bicubic,-11.688,-6.488,-60 resnet50.a1_in1k,69.780,30.220,88.350,11.650,25.56,288,1.000,bicubic,-11.434,-6.752,-31 mobilevitv2_200.cvnets_in1k,69.750,30.250,88.620,11.380,18.45,256,0.888,bicubic,-11.384,-6.742,-24 resnext50_32x4d.a2_in1k,69.750,30.250,88.200,11.800,25.03,288,1.000,bicubic,-11.554,-6.896,-41 resnet50.tv2_in1k,69.740,30.260,88.600,11.400,25.56,224,0.965,bilinear,-11.108,-6.834,+8 ese_vovnet39b.ra_in1k,69.730,30.270,89.550,10.450,24.57,288,0.950,bicubic,-10.620,-5.816,+53 tiny_vit_5m_224.dist_in22k_ft_in1k,69.730,30.270,89.440,10.560,5.39,224,0.950,bicubic,-11.146,-6.224,0 lambda_resnet50ts.a1h_in1k,69.730,30.270,88.830,11.170,21.54,256,0.950,bicubic,-11.428,-6.268,-30 resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,69.710,30.290,89.420,10.580,25.03,224,0.875,bilinear,-10.624,-5.980,+53 xcit_tiny_24_p16_224.fb_dist_in1k,69.710,30.290,88.720,11.280,12.12,224,1.000,bicubic,-10.744,-6.498,+42 fastvit_sa12.apple_in1k,69.700,30.300,88.950,11.050,11.58,256,0.900,bicubic,-11.144,-6.390,+3 xcit_tiny_12_p16_384.fb_dist_in1k,69.690,30.310,89.020,10.980,6.72,384,1.000,bicubic,-11.248,-6.394,-12 resmlp_24_224.fb_distilled_in1k,69.660,30.340,89.070,10.930,30.02,224,0.875,bicubic,-11.096,-6.154,+8 resnext101_64x4d.gluon_in1k,69.660,30.340,88.260,11.740,83.46,224,0.875,bicubic,-10.940,-6.732,+22 tresnet_m.miil_in1k,69.650,30.350,88.000,12.000,31.39,224,0.875,bilinear,-11.148,-6.856,+2 resnext50d_32x4d.bt_in1k,69.640,30.360,89.250,10.750,25.05,288,0.950,bicubic,-11.024,-6.170,+15 regnetx_032.tv2_in1k,69.620,30.380,89.360,10.640,15.30,224,0.965,bicubic,-11.306,-5.918,-16 efficientnet_b3_pruned.in1k,69.580,30.420,88.960,11.040,9.86,300,0.904,bicubic,-11.272,-6.284,-6 fastvit_t12.apple_dist_in1k,69.580,30.420,88.410,11.590,7.55,256,0.900,bicubic,-10.772,-6.632,+39 convnextv2_pico.fcmae_ft_in1k,69.570,30.430,89.230,10.770,9.07,288,0.950,bicubic,-11.516,-6.250,-33 eva02_tiny_patch14_336.mim_in22k_ft_in1k,69.560,30.440,89.320,10.680,5.76,336,1.000,bicubic,-11.070,-6.206,+12 repvit_m1_1.dist_300e_in1k,69.560,30.440,88.820,11.180,8.80,224,0.950,bicubic,-11.266,-6.350,-7 nf_resnet50.ra2_in1k,69.550,30.450,88.730,11.270,25.56,288,0.940,bicubic,-11.090,-6.604,+9 gernet_m.idstcv_in1k,69.540,30.460,88.690,11.310,21.14,224,0.875,bilinear,-11.196,-6.500,-1 inception_resnet_v2.tf_ens_adv_in1k,69.540,30.460,88.490,11.510,55.84,299,0.897,bicubic,-10.438,-6.458,+61 repvgg_b3g4.rvgg_in1k,69.530,30.470,88.450,11.550,83.83,224,0.875,bilinear,-10.686,-6.642,+50 gcresnet33ts.ra2_in1k,69.510,30.490,89.110,10.890,19.88,288,1.000,bicubic,-11.090,-6.212,+7 efficientnet_el_pruned.in1k,69.510,30.490,88.940,11.060,10.59,300,0.904,bicubic,-10.788,-6.282,+40 efficientnet_b2.ra_in1k,69.490,30.510,88.690,11.310,9.11,288,1.000,bicubic,-11.120,-6.624,+5 resnext50_32x4d.a1_in1k,69.490,30.510,88.340,11.660,25.03,288,1.000,bicubic,-11.976,-6.834,-86 swin_tiny_patch4_window7_224.ms_in1k,69.470,30.530,89.060,10.940,28.29,224,0.900,bicubic,-11.906,-6.484,-77 regnetx_320.pycls_in1k,69.470,30.530,88.270,11.730,107.81,224,0.875,bicubic,-10.776,-6.752,+41 vit_base_patch32_224.augreg_in21k_ft_in1k,69.450,30.550,89.440,10.560,88.22,224,0.900,bicubic,-11.266,-6.126,-9 res2net101d.in1k,69.450,30.550,88.710,11.290,45.23,224,0.875,bilinear,-11.768,-6.640,-65 gcvit_xxtiny.in1k,69.440,30.560,88.840,11.160,12.00,224,0.875,bicubic,-10.286,-6.240,+72 cspresnext50.ra_in1k,69.430,30.570,88.600,11.400,20.57,256,0.887,bilinear,-11.124,-6.726,+1 rexnet_150.nav_in1k,69.410,30.590,88.980,11.020,9.73,224,0.875,bicubic,-10.914,-6.010,+25 efficientvit_b1.r288_in1k,69.410,30.590,88.680,11.320,9.10,288,1.000,bicubic,-10.914,-6.496,+27 resnext50_32x4d.tv2_in1k,69.400,30.600,88.440,11.560,25.03,224,0.965,bilinear,-11.782,-6.900,-66 eca_resnet33ts.ra2_in1k,69.380,30.620,89.210,10.790,19.68,288,1.000,bicubic,-11.292,-6.154,-12 seresnet33ts.ra2_in1k,69.380,30.620,89.190,10.810,19.78,288,1.000,bicubic,-11.404,-6.172,-23 convmixer_768_32.in1k,69.380,30.620,88.880,11.120,21.11,224,0.960,bicubic,-10.788,-6.194,+39 inception_v4.tf_in1k,69.370,30.630,88.780,11.220,42.68,299,0.875,bicubic,-10.786,-6.190,+38 xception71.tf_in1k,69.360,30.640,88.270,11.730,42.34,299,0.903,bicubic,-10.514,-6.658,+49 darknet53.c2ns_in1k,69.350,30.650,88.780,11.220,41.61,288,1.000,bicubic,-11.182,-6.652,-6 cs3darknet_focus_l.c2ns_in1k,69.340,30.660,89.420,10.580,21.15,288,0.950,bicubic,-11.536,-6.262,-39 legacy_seresnext101_32x4d.in1k,69.340,30.660,88.050,11.950,48.96,224,0.875,bilinear,-10.892,-6.970,+28 vit_small_patch16_384.augreg_in1k,69.320,30.680,89.000,11.000,22.20,384,1.000,bicubic,-11.796,-6.574,-67 repvit_m1_0.dist_450e_in1k,69.310,30.690,88.700,11.300,7.30,224,0.950,bicubic,-11.124,-6.218,+4 mobilevitv2_175.cvnets_in1k,69.290,30.710,88.960,11.040,14.25,256,0.888,bicubic,-11.570,-6.296,-39 efficientformer_l1.snap_dist_in1k,69.280,30.720,88.560,11.440,12.29,224,0.950,bicubic,-11.218,-6.428,-8 vit_small_patch32_384.augreg_in21k_ft_in1k,69.270,30.730,89.820,10.180,22.92,384,1.000,bicubic,-11.216,-5.780,-8 convnext_pico_ols.d1_in1k,69.240,30.760,88.840,11.160,9.06,288,1.000,bicubic,-11.222,-6.412,-6 repvit_m2.dist_in1k,69.230,30.770,88.740,11.260,8.80,224,0.950,bicubic,-11.230,-6.428,-6 edgenext_small_rw.sw_in1k,69.210,30.790,88.740,11.260,7.83,320,1.000,bicubic,-11.248,-6.568,-5 vit_base_patch16_384.augreg_in1k,69.190,30.810,88.370,11.630,86.86,384,1.000,bicubic,-11.912,-6.750,-73 resnet50.b2k_in1k,69.180,30.820,88.660,11.340,25.56,288,1.000,bicubic,-11.274,-6.658,-6 resnet152c.gluon_in1k,69.140,30.860,87.890,12.110,60.21,224,0.875,bicubic,-10.772,-6.956,+33 resnet50.b1k_in1k,69.100,30.900,88.740,11.260,25.56,288,1.000,bicubic,-11.606,-6.692,-34 resnet50.a1h_in1k,69.100,30.900,88.510,11.490,25.56,224,1.000,bicubic,-11.578,-6.796,-31 tf_efficientnetv2_b2.in1k,69.100,30.900,88.230,11.770,10.10,260,0.890,bicubic,-11.096,-6.812,+16 mixnet_xl.ra_in1k,69.090,30.910,88.310,11.690,11.90,224,0.875,bicubic,-11.392,-6.626,-17 resnetblur50.bt_in1k,69.070,30.930,88.450,11.550,25.56,288,0.950,bicubic,-11.164,-6.784,+11 repvgg_b2g4.rvgg_in1k,69.010,30.990,88.360,11.640,61.76,224,0.875,bilinear,-10.372,-6.316,+69 regnety_160.pycls_in1k,69.010,30.990,88.270,11.730,83.59,224,0.875,bicubic,-11.288,-6.694,+4 resnet101d.gluon_in1k,69.010,30.990,88.080,11.920,44.57,224,0.875,bicubic,-11.416,-6.944,-12 xception65.tf_in1k,68.950,31.050,88.470,11.530,39.92,299,0.903,bicubic,-10.606,-6.188,+54 resnext101_32x4d.gluon_in1k,68.940,31.060,88.340,11.660,44.18,224,0.875,bicubic,-11.400,-6.590,-7 tf_efficientnet_b2.ap_in1k,68.930,31.070,88.330,11.670,9.11,260,0.890,bicubic,-11.380,-6.696,-5 repvit_m1_0.dist_300e_in1k,68.930,31.070,88.100,11.900,7.30,224,0.950,bicubic,-11.196,-6.644,+13 poolformerv2_s24.sail_in1k,68.920,31.080,88.670,11.330,21.34,224,1.000,bicubic,-11.828,-6.640,-50 cspdarknet53.ra_in1k,68.920,31.080,88.600,11.400,27.64,256,0.887,bilinear,-11.148,-6.478,+13 convnext_pico.d1_in1k,68.900,31.100,88.480,11.520,9.05,288,0.950,bicubic,-11.516,-6.568,-18 resnet50d.a2_in1k,68.900,31.100,87.980,12.020,25.58,288,1.000,bicubic,-12.264,-7.100,-98 mobilevitv2_150.cvnets_in1k,68.890,31.110,88.080,11.920,10.59,256,0.888,bicubic,-11.480,-6.994,-18 regnety_120.pycls_in1k,68.860,31.140,88.330,11.670,51.82,224,0.875,bicubic,-11.520,-6.796,-20 resnet152.a3_in1k,68.820,31.180,87.760,12.240,60.19,224,0.950,bicubic,-11.726,-7.240,-39 resnet152.gluon_in1k,68.820,31.180,87.700,12.300,60.19,224,0.875,bicubic,-10.876,-7.030,+32 poolformer_s24.sail_in1k,68.780,31.220,88.170,11.830,21.39,224,0.900,bicubic,-11.514,-6.904,-12 dpn107.mx_in1k,68.780,31.220,88.130,11.870,86.92,224,0.875,bicubic,-11.390,-6.812,-1 gmlp_s16_224.ra3_in1k,68.780,31.220,88.070,11.930,19.42,224,0.875,bicubic,-10.864,-6.552,+36 dpn131.mx_in1k,68.770,31.230,87.570,12.430,79.25,224,0.875,bicubic,-11.044,-7.130,+19 darknetaa53.c2ns_in1k,68.760,31.240,88.700,11.300,36.02,288,1.000,bilinear,-11.746,-6.622,-42 tf_efficientnet_b2.aa_in1k,68.760,31.240,87.950,12.050,9.11,260,0.890,bicubic,-11.324,-6.956,-1 deit_small_patch16_224.fb_in1k,68.730,31.270,88.200,11.800,22.05,224,0.900,bicubic,-11.118,-6.844,+12 regnety_080.pycls_in1k,68.710,31.290,87.970,12.030,39.18,224,0.875,bicubic,-11.158,-6.862,+8 resnet101s.gluon_in1k,68.710,31.290,87.900,12.100,44.67,224,0.875,bicubic,-11.594,-7.252,-21 seresnext50_32x4d.gluon_in1k,68.660,31.340,88.360,11.640,27.56,224,0.875,bicubic,-11.264,-6.464,+1 resnet50.ram_in1k,68.630,31.370,88.330,11.670,25.56,288,0.950,bicubic,-11.346,-6.722,-3 hrnet_w64.ms_in1k,68.630,31.370,88.070,11.930,128.06,224,0.875,bilinear,-10.846,-6.582,+38 xcit_tiny_12_p8_224.fb_in1k,68.580,31.420,88.720,11.280,6.71,224,1.000,bicubic,-11.108,-6.334,+21 resnet50.a2_in1k,68.570,31.430,88.000,12.000,25.56,288,1.000,bicubic,-12.202,-6.988,-72 tf_efficientnet_b3.in1k,68.530,31.470,88.700,11.300,12.23,300,0.904,bicubic,-12.344,-6.600,-84 dpn98.mx_in1k,68.520,31.480,87.610,12.390,61.57,224,0.875,bicubic,-11.150,-7.044,+21 regnetx_160.pycls_in1k,68.510,31.490,88.460,11.540,54.28,224,0.875,bicubic,-11.356,-6.368,0 fastvit_s12.apple_in1k,68.490,31.510,87.850,12.150,9.47,256,0.900,bicubic,-11.452,-6.944,-8 rexnet_130.nav_in1k,68.460,31.540,88.040,11.960,7.56,224,0.875,bicubic,-11.046,-6.638,+27 cspresnet50.ra_in1k,68.450,31.550,87.960,12.040,21.62,256,0.887,bilinear,-11.132,-6.750,+22 tf_efficientnet_el.in1k,68.440,31.560,88.210,11.790,10.59,300,0.904,bicubic,-11.808,-6.910,-29 regnety_064.pycls_in1k,68.440,31.560,88.060,11.940,30.58,224,0.875,bicubic,-11.276,-6.706,+10 xcit_tiny_24_p16_224.fb_in1k,68.420,31.580,88.290,11.710,12.12,224,1.000,bicubic,-11.028,-6.588,+28 cait_xxs36_224.fb_dist_in1k,68.410,31.590,88.650,11.350,17.30,224,1.000,bicubic,-11.336,-6.224,+3 skresnext50_32x4d.ra_in1k,68.400,31.600,87.570,12.430,27.48,224,0.875,bicubic,-11.764,-7.070,-23 resnet50.fb_ssl_yfcc100m_ft_in1k,68.380,31.620,88.560,11.440,25.56,224,0.875,bilinear,-10.850,-6.266,+47 efficientvit_b1.r256_in1k,68.370,31.630,87.910,12.090,9.10,256,1.000,bicubic,-11.364,-6.870,+1 dla102x2.in1k,68.350,31.650,87.890,12.110,41.28,224,0.875,bilinear,-11.096,-6.742,+24 fbnetv3_d.ra2_in1k,68.330,31.670,88.420,11.580,10.31,256,0.950,bilinear,-11.352,-6.524,+6 efficientnet_b2_pruned.in1k,68.300,31.700,88.100,11.900,8.31,260,0.890,bicubic,-11.620,-6.752,-18 res2net50d.in1k,68.300,31.700,88.100,11.900,25.72,224,0.875,bilinear,-11.954,-6.936,-39 resmlp_big_24_224.fb_in1k,68.300,31.700,87.540,12.460,129.14,224,0.875,bicubic,-12.736,-7.478,-119 vit_base_patch16_224.sam_in1k,68.290,31.710,87.730,12.270,86.57,224,0.900,bicubic,-11.948,-7.026,-39 resnext50_32x4d.gluon_in1k,68.290,31.710,87.330,12.670,25.03,224,0.875,bicubic,-11.070,-7.100,+26 ecaresnet26t.ra2_in1k,68.260,31.740,88.810,11.190,16.01,320,0.950,bicubic,-11.590,-6.280,-17 efficientformerv2_s1.snap_dist_in1k,68.240,31.760,88.330,11.670,6.19,224,0.950,bicubic,-11.452,-6.386,-3 tf_efficientnet_lite3.in1k,68.220,31.780,87.730,12.270,8.20,300,0.904,bilinear,-11.586,-7.184,-13 resnet101.a3_in1k,68.220,31.780,87.640,12.360,44.55,224,0.950,bicubic,-11.594,-6.974,-13 pit_xs_distilled_224.in1k,68.190,31.810,87.730,12.270,11.00,224,0.900,bicubic,-10.990,-6.636,+38 resnet50.ra_in1k,68.150,31.850,87.900,12.100,25.56,288,0.950,bicubic,-11.686,-7.066,-18 fbnetv3_b.ra2_in1k,68.140,31.860,87.920,12.080,8.60,256,0.950,bilinear,-11.006,-6.824,+39 tiny_vit_5m_224.in1k,68.140,31.860,87.840,12.160,5.39,224,0.950,bicubic,-11.030,-6.954,+36 regnetx_120.pycls_in1k,68.130,31.870,87.610,12.390,46.11,224,0.875,bicubic,-11.458,-7.132,-3 mobileone_s4.apple_in1k,68.130,31.870,87.110,12.890,14.95,224,0.900,bilinear,-11.296,-7.370,+11 resmlp_36_224.fb_in1k,68.090,31.910,88.180,11.820,44.69,224,0.875,bicubic,-11.682,-6.704,-19 resnet50.bt_in1k,68.070,31.930,87.810,12.190,25.56,288,0.950,bicubic,-11.570,-7.082,-8 dpn68b.ra_in1k,68.070,31.930,87.380,12.620,12.61,288,1.000,bicubic,-11.290,-7.056,+12 regnetx_016.tv2_in1k,68.050,31.950,88.230,11.770,9.19,224,0.965,bicubic,-11.386,-6.538,+5 resnetrs50.tf_in1k,68.040,31.960,87.730,12.270,35.69,224,0.910,bicubic,-11.854,-7.244,-35 dpn92.mx_in1k,67.980,32.020,87.600,12.400,37.67,224,0.875,bicubic,-12.058,-7.260,-43 nf_regnet_b1.ra2_in1k,67.970,32.030,88.220,11.780,10.22,288,0.900,bicubic,-11.338,-6.520,+12 repvit_m0_9.dist_450e_in1k,67.920,32.080,87.890,12.110,5.49,224,0.950,bicubic,-11.146,-6.490,+34 fastvit_t12.apple_in1k,67.920,32.080,87.550,12.450,7.55,256,0.900,bicubic,-11.344,-7.012,+17 resnet50d.gluon_in1k,67.910,32.090,87.120,12.880,25.58,224,0.875,bicubic,-11.168,-7.346,+30 resnetv2_50x1_bit.goog_in21k_ft_in1k,67.900,32.100,89.270,10.730,25.55,448,1.000,bilinear,-12.442,-6.412,-75 levit_192.fb_dist_in1k,67.900,32.100,87.900,12.100,10.95,224,0.900,bicubic,-11.938,-6.884,-34 levit_conv_192.fb_dist_in1k,67.900,32.100,87.890,12.110,10.95,224,0.900,bicubic,-11.938,-6.888,-36 tf_efficientnetv2_b1.in1k,67.890,32.110,87.810,12.190,8.14,240,0.882,bicubic,-11.570,-6.912,-9 regnetx_080.pycls_in1k,67.890,32.110,87.000,13.000,39.57,224,0.875,bicubic,-11.308,-7.554,+18 legacy_seresnext50_32x4d.in1k,67.860,32.140,87.620,12.380,27.56,224,0.875,bilinear,-11.216,-6.812,+25 efficientnet_em.ra2_in1k,67.850,32.150,88.130,11.870,6.90,240,0.882,bicubic,-11.394,-6.664,+11 resnext101_32x8d.tv_in1k,67.840,32.160,87.490,12.510,88.79,224,0.875,bilinear,-11.470,-7.030,0 resmlp_24_224.fb_in1k,67.820,32.180,87.610,12.390,30.02,224,0.875,bicubic,-11.554,-6.936,-6 lambda_resnet26t.c1_in1k,67.800,32.200,87.790,12.210,10.96,256,0.940,bicubic,-11.288,-6.800,+19 ecaresnet50t.a3_in1k,67.790,32.210,87.560,12.440,25.57,224,0.950,bicubic,-11.762,-7.134,-21 hrnet_w48.ms_in1k,67.760,32.240,87.410,12.590,77.47,224,0.875,bilinear,-11.546,-7.106,-3 efficientvit_b1.r224_in1k,67.760,32.240,87.340,12.660,9.10,224,0.950,bicubic,-11.492,-6.964,+5 hrnet_w44.ms_in1k,67.750,32.250,87.540,12.460,67.06,224,0.875,bilinear,-11.144,-6.824,+28 resnet33ts.ra2_in1k,67.730,32.270,88.100,11.900,19.68,288,1.000,bicubic,-11.996,-6.728,-39 coat_lite_mini.in1k,67.710,32.290,87.710,12.290,11.01,224,0.900,bicubic,-11.392,-6.898,+12 tf_efficientnet_b0.ns_jft_in1k,67.700,32.300,88.070,11.930,5.29,224,0.875,bicubic,-10.968,-6.302,+42 resnext50_32x4d.a3_in1k,67.700,32.300,86.930,13.070,25.03,224,0.950,bicubic,-11.568,-7.376,-3 legacy_xception.tf_in1k,67.690,32.310,87.560,12.440,22.86,299,0.897,bicubic,-11.350,-6.822,+15 eca_botnext26ts_256.c1_in1k,67.680,32.320,87.080,12.920,10.59,256,0.950,bicubic,-11.588,-7.526,-7 regnetx_064.pycls_in1k,67.670,32.330,87.520,12.480,26.21,224,0.875,bicubic,-11.396,-6.940,+11 resnet32ts.ra2_in1k,67.650,32.350,87.580,12.420,17.96,288,1.000,bicubic,-11.738,-7.072,-22 convnext_femto_ols.d1_in1k,67.650,32.350,87.390,12.610,5.23,288,0.950,bicubic,-11.274,-7.136,+18 halonet26t.a1h_in1k,67.620,32.380,87.250,12.750,12.48,256,0.950,bicubic,-11.486,-7.056,+3 inception_v3.gluon_in1k,67.590,32.410,87.450,12.550,23.83,299,0.875,bicubic,-11.212,-6.926,+24 hrnet_w40.ms_in1k,67.580,32.420,87.140,12.860,57.56,224,0.875,bilinear,-11.352,-7.324,+13 regnety_040.pycls_in1k,67.570,32.430,87.490,12.510,20.65,224,0.875,bicubic,-11.650,-7.166,-7 resnet101c.gluon_in1k,67.560,32.440,87.160,12.840,44.57,224,0.875,bicubic,-11.978,-7.424,-37 legacy_seresnet152.in1k,67.550,32.450,87.380,12.620,66.82,224,0.875,bilinear,-11.110,-6.990,+33 dla169.in1k,67.540,32.460,87.570,12.430,53.39,224,0.875,bilinear,-11.168,-6.774,+28 tf_efficientnet_b1.ap_in1k,67.520,32.480,87.750,12.250,7.79,240,0.882,bicubic,-11.756,-6.562,-20 efficientnet_b1.ft_in1k,67.520,32.480,87.470,12.530,7.79,256,1.000,bicubic,-11.280,-6.872,+19 mobilevitv2_125.cvnets_in1k,67.480,32.520,87.570,12.430,7.48,256,0.888,bicubic,-12.200,-7.288,-52 tf_efficientnet_cc_b1_8e.in1k,67.480,32.520,87.300,12.700,39.72,240,0.882,bicubic,-11.822,-7.074,-24 eca_halonext26ts.c1_in1k,67.480,32.520,87.240,12.760,10.76,256,0.940,bicubic,-12.006,-7.360,-40 resnet101.gluon_in1k,67.470,32.530,87.230,12.770,44.55,224,0.875,bicubic,-11.840,-7.292,-29 res2net101_26w_4s.in1k,67.460,32.540,87.010,12.990,45.21,224,0.875,bilinear,-11.740,-7.426,-16 res2net50_26w_8s.in1k,67.440,32.560,87.250,12.750,48.40,224,0.875,bilinear,-11.502,-7.044,0 repvit_m0_9.dist_300e_in1k,67.430,32.570,87.230,12.770,5.49,224,0.950,bicubic,-11.228,-6.886,+24 regnety_008_tv.tv2_in1k,67.410,32.590,88.030,11.970,6.43,224,0.965,bicubic,-11.256,-6.360,+21 resnet34d.ra2_in1k,67.400,32.600,87.960,12.040,21.82,288,0.950,bicubic,-11.036,-6.384,+36 tf_efficientnet_b2.in1k,67.400,32.600,87.580,12.420,9.11,260,0.890,bicubic,-12.208,-7.134,-57 regnety_032.pycls_in1k,67.400,32.600,87.260,12.740,19.44,224,0.875,bicubic,-11.476,-7.148,+2 convnextv2_femto.fcmae_ft_in1k,67.350,32.650,87.580,12.420,5.23,288,0.950,bicubic,-11.988,-6.980,-38 cait_xxs24_224.fb_dist_in1k,67.340,32.660,87.520,12.480,11.96,224,1.000,bicubic,-11.044,-6.796,+39 xception41.tf_in1k,67.260,32.740,87.190,12.810,26.97,299,0.903,bicubic,-11.244,-7.086,+23 coat_tiny.in1k,67.240,32.760,87.310,12.690,5.50,224,0.900,bicubic,-11.186,-6.738,+32 repghostnet_200.in1k,67.240,32.760,87.290,12.710,9.80,224,0.875,bicubic,-11.566,-7.040,-1 regnetx_032.pycls_in1k,67.240,32.760,86.990,13.010,15.30,224,0.875,bicubic,-10.928,-7.092,+50 resnest26d.gluon_in1k,67.220,32.780,87.180,12.820,17.07,224,0.875,bilinear,-11.262,-7.114,+23 convnext_femto.d1_in1k,67.190,32.810,87.480,12.520,5.22,288,0.950,bicubic,-11.526,-6.950,+5 repvgg_b2.rvgg_in1k,67.160,32.840,87.320,12.680,89.02,224,0.875,bilinear,-11.632,-7.100,-1 vit_relpos_base_patch32_plus_rpn_256.sw_in1k,67.160,32.840,86.480,13.520,119.42,256,0.900,bicubic,-12.324,-7.658,-59 botnet26t_256.c1_in1k,67.140,32.860,87.510,12.490,12.49,256,0.950,bicubic,-12.118,-7.022,-38 legacy_seresnet101.in1k,67.110,32.890,87.050,12.950,49.33,224,0.875,bilinear,-11.276,-7.212,+28 resnet50s.gluon_in1k,67.110,32.890,86.860,13.140,25.68,224,0.875,bicubic,-11.604,-7.382,+1 dla60x.in1k,67.070,32.930,87.200,12.800,17.35,224,0.875,bilinear,-11.166,-6.826,+37 visformer_tiny.in1k,67.050,32.950,87.060,12.940,10.32,224,0.900,bicubic,-11.110,-7.106,+41 dla60_res2net.in1k,67.030,32.970,87.140,12.860,20.85,224,0.875,bilinear,-11.434,-7.058,+16 resnet34.a1_in1k,67.030,32.970,86.280,13.720,21.80,288,1.000,bicubic,-10.888,-7.484,+57 xcit_tiny_12_p16_224.fb_dist_in1k,67.020,32.980,87.400,12.600,6.72,224,1.000,bicubic,-11.554,-6.798,+3 dla102x.in1k,67.000,33.000,86.790,13.210,26.31,224,0.875,bilinear,-11.512,-7.446,+6 resnet152.tv_in1k,66.990,33.010,87.560,12.440,60.19,224,0.875,bilinear,-11.332,-6.486,+25 lambda_resnet26rpt_256.c1_in1k,66.960,33.040,87.130,12.870,10.99,256,0.940,bicubic,-12.004,-7.306,-27 mixnet_l.ft_in1k,66.960,33.040,86.940,13.060,7.33,224,0.875,bicubic,-12.006,-7.242,-29 pit_xs_224.in1k,66.920,33.080,87.280,12.720,10.62,224,0.900,bicubic,-11.256,-6.882,+31 repvgg_b1.rvgg_in1k,66.920,33.080,86.780,13.220,57.42,224,0.875,bilinear,-11.448,-7.316,+18 resnet50d.a3_in1k,66.920,33.080,86.540,13.460,25.58,224,0.950,bicubic,-11.800,-7.692,-13 pvt_v2_b1.in1k,66.910,33.090,87.410,12.590,14.01,224,0.900,bicubic,-11.794,-7.092,-10 res2net50_26w_6s.in1k,66.910,33.090,86.880,13.120,37.05,224,0.875,bilinear,-11.658,-7.242,-5 tf_efficientnet_b1.aa_in1k,66.900,33.100,87.020,12.980,7.79,240,0.882,bicubic,-11.928,-7.180,-25 xcit_nano_12_p8_384.fb_dist_in1k,66.870,33.130,87.110,12.890,3.05,384,1.000,bicubic,-10.950,-6.930,+51 efficientnet_es.ra_in1k,66.860,33.140,86.710,13.290,5.44,224,0.875,bicubic,-11.198,-7.216,+32 mobilevit_s.cvnets_in1k,66.850,33.150,87.070,12.930,5.58,256,0.900,bicubic,-11.462,-7.078,+15 regnetx_040.pycls_in1k,66.820,33.180,86.740,13.260,22.12,224,0.875,bicubic,-11.672,-7.502,-4 hrnet_w32.ms_in1k,66.780,33.220,87.290,12.710,41.23,224,0.875,bilinear,-11.662,-6.900,0 resnet50.am_in1k,66.780,33.220,86.740,13.260,25.56,224,0.875,bicubic,-12.222,-7.658,-42 tf_mixnet_l.in1k,66.780,33.220,86.480,13.520,7.33,224,0.875,bicubic,-11.996,-7.522,-26 seresnext26t_32x4d.bt_in1k,66.770,33.230,86.720,13.280,16.81,288,0.950,bicubic,-11.974,-7.592,-25 hrnet_w18.ms_aug_in1k,66.760,33.240,87.480,12.520,21.30,224,0.950,bilinear,-11.362,-6.574,+22 repvit_m1.dist_in1k,66.760,33.240,87.180,12.820,5.49,224,0.950,bicubic,-11.778,-6.890,-15 hrnet_w30.ms_in1k,66.760,33.240,86.790,13.210,37.71,224,0.875,bilinear,-11.436,-7.432,+14 selecsls60b.in1k,66.750,33.250,86.540,13.460,32.77,224,0.875,bicubic,-11.662,-7.628,-1 vit_small_patch16_224.augreg_in1k,66.690,33.310,86.720,13.280,22.05,224,0.900,bicubic,-12.158,-7.568,-41 tf_efficientnetv2_b0.in1k,66.690,33.310,86.700,13.300,7.14,224,0.875,bicubic,-11.668,-7.314,+2 wide_resnet101_2.tv_in1k,66.680,33.320,87.030,12.970,126.89,224,0.875,bilinear,-12.162,-7.252,-42 seresnext26d_32x4d.bt_in1k,66.680,33.320,86.830,13.170,16.81,288,0.950,bicubic,-12.134,-7.410,-39 dla60_res2next.in1k,66.660,33.340,87.020,12.980,17.03,224,0.875,bilinear,-11.780,-7.124,-11 wide_resnet50_2.tv_in1k,66.650,33.350,86.800,13.200,68.88,224,0.875,bilinear,-11.826,-7.288,-15 inception_v3.tf_adv_in1k,66.630,33.370,86.580,13.420,23.83,299,0.875,bicubic,-10.962,-7.150,+42 mobilevitv2_100.cvnets_in1k,66.610,33.390,87.020,12.980,4.90,256,0.888,bicubic,-11.470,-7.150,+13 vit_tiny_patch16_384.augreg_in21k_ft_in1k,66.590,33.410,87.250,12.750,5.79,384,1.000,bicubic,-11.834,-7.292,-12 cs3darknet_m.c2ns_in1k,66.580,33.420,87.180,12.820,9.31,288,0.950,bicubic,-11.054,-6.836,+37 levit_128.fb_dist_in1k,66.560,33.440,86.740,13.260,9.21,224,0.900,bicubic,-11.930,-7.272,-22 levit_conv_128.fb_dist_in1k,66.550,33.450,86.730,13.270,9.21,224,0.900,bicubic,-11.944,-7.278,-25 tf_efficientnet_b1.in1k,66.540,33.460,86.700,13.300,7.79,240,0.882,bicubic,-12.022,-7.394,-31 resnet50c.gluon_in1k,66.540,33.460,86.170,13.830,25.58,224,0.875,bicubic,-11.466,-7.822,+13 dla102.in1k,66.530,33.470,86.910,13.090,33.27,224,0.875,bilinear,-11.494,-7.024,+10 hrnet_w18_small_v2.gluon_in1k,66.510,33.490,86.500,13.500,15.60,224,0.875,bicubic,-11.680,-7.402,-3 mobileone_s3.apple_in1k,66.500,33.500,86.370,13.630,10.17,224,0.900,bilinear,-11.492,-7.544,+12 vit_base_patch16_224.augreg_in1k,66.480,33.520,86.260,13.740,86.57,224,0.900,bicubic,-12.674,-7.830,-76 vit_base_patch32_384.augreg_in1k,66.430,33.570,86.960,13.040,88.30,384,1.000,bicubic,-12.326,-7.266,-50 gmixer_24_224.ra3_in1k,66.430,33.570,86.160,13.840,24.72,224,0.875,bicubic,-11.596,-7.508,+5 inception_v3.tf_in1k,66.420,33.580,86.680,13.320,23.83,299,0.875,bicubic,-11.436,-7.186,+17 bat_resnext26ts.ch_in1k,66.390,33.610,86.860,13.140,10.73,256,0.900,bicubic,-11.862,-7.238,-14 hardcorenas_f.miil_green_in1k,66.360,33.640,86.190,13.810,8.20,224,0.875,bilinear,-11.736,-7.612,-3 seresnext26ts.ch_in1k,66.320,33.680,86.700,13.300,10.39,288,1.000,bicubic,-11.950,-7.392,-17 coat_lite_tiny.in1k,66.290,33.710,86.960,13.040,5.72,224,0.900,bicubic,-11.230,-6.962,+27 eca_resnext26ts.ch_in1k,66.270,33.730,86.410,13.590,10.30,288,1.000,bicubic,-11.730,-7.516,+2 legacy_seresnet50.in1k,66.250,33.750,86.300,13.700,28.09,224,0.875,bilinear,-11.394,-7.458,+18 efficientnet_b0.ra_in1k,66.250,33.750,85.970,14.030,5.29,224,0.875,bicubic,-11.444,-7.562,+17 cs3darknet_focus_m.c2ns_in1k,66.240,33.760,87.080,12.920,9.30,288,0.950,bicubic,-11.044,-6.886,+39 selecsls60.in1k,66.220,33.780,86.330,13.670,30.67,224,0.875,bicubic,-11.768,-7.500,0 tf_efficientnet_cc_b0_8e.in1k,66.220,33.780,86.230,13.770,24.01,224,0.875,bicubic,-11.684,-7.432,+4 res2net50_26w_4s.in1k,66.150,33.850,86.610,13.390,25.70,224,0.875,bilinear,-11.800,-7.242,0 tf_efficientnet_em.in1k,66.150,33.850,86.360,13.640,6.90,240,0.882,bicubic,-11.976,-7.688,-16 resnext50_32x4d.tv_in1k,66.150,33.850,86.040,13.960,25.03,224,0.875,bilinear,-11.472,-7.656,+15 densenetblur121d.ra_in1k,66.140,33.860,86.600,13.400,8.00,288,0.950,bicubic,-11.182,-7.188,+28 resmlp_12_224.fb_distilled_in1k,66.120,33.880,86.620,13.380,15.35,224,0.875,bicubic,-11.834,-6.940,-5 inception_v3.tv_in1k,66.110,33.890,86.320,13.680,23.83,299,0.875,bicubic,-11.324,-7.154,+22 resnet50.a3_in1k,66.110,33.890,85.820,14.180,25.56,224,0.950,bicubic,-11.938,-7.960,-15 ghostnetv2_160.in1k,66.080,33.920,86.730,13.270,12.39,224,0.875,bicubic,-11.752,-7.210,0 resnet26t.ra2_in1k,66.080,33.920,86.670,13.330,16.01,320,1.000,bicubic,-12.248,-7.454,-37 regnety_016.pycls_in1k,66.080,33.920,86.370,13.630,11.20,224,0.875,bicubic,-11.788,-7.348,-4 efficientnet_b1_pruned.in1k,66.070,33.930,86.550,13.450,6.33,240,0.882,bicubic,-12.170,-7.284,-32 gcresnext26ts.ch_in1k,66.040,33.960,86.750,13.250,10.48,288,1.000,bicubic,-12.374,-7.286,-45 resnet50.gluon_in1k,66.030,33.970,86.270,13.730,25.56,224,0.875,bicubic,-11.552,-7.450,+7 rexnet_100.nav_in1k,66.020,33.980,86.490,13.510,4.80,224,0.875,bicubic,-11.836,-7.150,-8 tinynet_a.in1k,66.020,33.980,85.780,14.220,6.19,192,0.875,bicubic,-11.628,-7.760,-1 res2net50_14w_8s.in1k,66.000,34.000,86.230,13.770,25.06,224,0.875,bilinear,-12.158,-7.616,-30 poolformerv2_s12.sail_in1k,65.890,34.110,86.510,13.490,11.89,224,1.000,bicubic,-12.112,-7.354,-21 resnet34.a2_in1k,65.870,34.130,86.140,13.860,21.80,288,1.000,bicubic,-11.288,-7.134,+26 densenet161.tv_in1k,65.850,34.150,86.460,13.540,28.68,224,0.875,bicubic,-11.508,-7.182,+12 res2next50.in1k,65.850,34.150,85.830,14.170,24.67,224,0.875,bilinear,-12.392,-8.062,-42 convnextv2_atto.fcmae_ft_in1k,65.840,34.160,86.170,13.830,3.71,288,0.950,bicubic,-11.920,-7.556,-10 hardcorenas_e.miil_green_in1k,65.830,34.170,85.970,14.030,8.07,224,0.875,bilinear,-11.960,-7.730,-12 repvgg_b1g4.rvgg_in1k,65.820,34.180,86.110,13.890,39.97,224,0.875,bilinear,-11.768,-7.726,-4 regnetx_008.tv2_in1k,65.810,34.190,86.210,13.790,7.26,224,0.965,bicubic,-11.496,-7.454,+10 xcit_tiny_12_p16_224.fb_in1k,65.790,34.210,86.220,13.780,6.72,224,1.000,bicubic,-11.350,-7.496,+20 ese_vovnet19b_dw.ra_in1k,65.770,34.230,86.470,13.530,6.54,288,0.950,bicubic,-11.974,-7.314,-14 mobilenetv3_large_100.miil_in21k_ft_in1k,65.770,34.230,85.180,14.820,5.48,224,0.875,bilinear,-12.150,-7.734,-25 skresnet34.ra_in1k,65.740,34.260,85.950,14.050,22.28,224,0.875,bicubic,-11.170,-7.194,+29 resnet101.tv_in1k,65.710,34.290,85.990,14.010,44.55,224,0.875,bilinear,-11.670,-7.556,+1 convnext_tiny.fb_in22k_ft_in1k,65.650,34.350,86.620,13.380,28.59,288,1.000,bicubic,-13.248,-8.054,-103 hardcorenas_d.miil_green_in1k,65.650,34.350,85.450,14.550,7.50,224,0.875,bilinear,-11.784,-8.040,-4 poolformer_s12.sail_in1k,65.630,34.370,86.210,13.790,11.92,224,0.900,bicubic,-11.610,-7.322,+7 dpn68b.mx_in1k,65.620,34.380,85.950,14.050,12.61,224,0.875,bicubic,-11.898,-7.902,-11 selecsls42b.in1k,65.620,34.380,85.840,14.160,32.46,224,0.875,bicubic,-11.550,-7.552,+9 convnext_atto_ols.a2_in1k,65.600,34.400,86.260,13.740,3.70,288,0.950,bicubic,-11.616,-7.416,+5 fastvit_t8.apple_dist_in1k,65.550,34.450,86.160,13.840,4.03,256,0.900,bicubic,-11.626,-7.138,+6 tf_efficientnet_b0.ap_in1k,65.500,34.500,85.570,14.430,5.29,224,0.875,bicubic,-11.590,-7.692,+10 mobileone_s2.apple_in1k,65.440,34.560,85.950,14.050,7.88,224,0.900,bilinear,-12.076,-7.718,-15 tf_efficientnet_lite2.in1k,65.400,34.600,86.020,13.980,6.09,260,0.890,bicubic,-12.062,-7.732,-14 convmixer_1024_20_ks9_p14.in1k,65.390,34.610,85.610,14.390,24.38,224,0.960,bicubic,-11.546,-7.740,+15 res2net50_48w_2s.in1k,65.350,34.650,85.950,14.050,25.29,224,0.875,bilinear,-12.164,-7.600,-17 resnet26d.bt_in1k,65.340,34.660,86.000,14.000,16.01,288,0.950,bicubic,-12.068,-7.638,-13 densenet201.tv_in1k,65.270,34.730,85.670,14.330,20.01,224,0.875,bicubic,-12.016,-7.810,-7 dla60.in1k,65.220,34.780,85.760,14.240,22.04,224,0.875,bilinear,-11.826,-7.558,+6 seresnet50.a3_in1k,65.190,34.810,85.300,14.700,28.09,224,0.950,bicubic,-11.836,-7.772,+6 crossvit_9_dagger_240.in1k,65.170,34.830,86.570,13.430,8.78,240,0.875,bicubic,-11.808,-7.048,+7 gernet_s.idstcv_in1k,65.150,34.850,85.530,14.470,8.17,224,0.875,bilinear,-11.760,-7.786,+11 tf_efficientnet_cc_b0_4e.in1k,65.150,34.850,85.140,14.860,13.31,224,0.875,bicubic,-12.152,-8.196,-13 mobilenetv2_120d.ra_in1k,65.060,34.940,85.990,14.010,5.83,224,0.875,bicubic,-12.248,-7.512,-16 legacy_seresnext26_32x4d.in1k,65.040,34.960,85.630,14.370,16.79,224,0.875,bicubic,-12.068,-7.684,-4 resnet34.bt_in1k,64.940,35.060,86.210,13.790,21.80,288,0.950,bicubic,-11.540,-7.144,+21 convnext_atto.d2_in1k,64.920,35.080,86.230,13.770,3.70,288,0.950,bicubic,-12.088,-7.472,0 hrnet_w18.ms_in1k,64.920,35.080,85.730,14.270,21.30,224,0.875,bilinear,-11.832,-7.714,+8 resnext26ts.ra2_in1k,64.900,35.100,85.710,14.290,10.30,288,1.000,bicubic,-12.278,-7.754,-13 efficientvit_m5.r224_in1k,64.890,35.110,85.390,14.610,12.47,224,0.875,bicubic,-12.168,-7.794,-6 hardcorenas_c.miil_green_in1k,64.870,35.130,85.250,14.750,5.52,224,0.875,bilinear,-12.196,-7.912,-8 repghostnet_150.in1k,64.830,35.170,85.880,14.120,6.58,224,0.875,bicubic,-12.630,-7.630,-31 efficientformerv2_s0.snap_dist_in1k,64.800,35.200,85.650,14.350,3.60,224,0.950,bicubic,-11.314,-7.208,+23 densenet169.tv_in1k,64.790,35.210,85.250,14.750,14.15,224,0.875,bicubic,-11.110,-7.778,+27 fastvit_t8.apple_in1k,64.730,35.270,85.680,14.320,4.03,256,0.900,bicubic,-11.444,-7.372,+19 ghostnetv2_130.in1k,64.720,35.280,85.420,14.580,8.96,224,0.875,bicubic,-12.036,-7.942,-1 mixnet_m.ft_in1k,64.670,35.330,85.460,14.540,5.01,224,0.875,bicubic,-12.590,-7.958,-24 xcit_nano_12_p8_224.fb_dist_in1k,64.610,35.390,85.990,14.010,3.05,224,1.000,bicubic,-11.722,-7.108,+14 levit_128s.fb_dist_in1k,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.936,-8.142,+4 levit_conv_128s.fb_dist_in1k,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.930,-8.136,+4 repvgg_a2.rvgg_in1k,64.470,35.530,85.140,14.860,28.21,224,0.875,bilinear,-11.988,-7.862,+7 xcit_nano_12_p16_384.fb_dist_in1k,64.420,35.580,85.300,14.700,3.05,384,1.000,bicubic,-11.038,-7.398,+31 hardcorenas_b.miil_green_in1k,64.390,35.610,84.880,15.120,5.18,224,0.875,bilinear,-12.158,-7.882,-2 regnetx_016.pycls_in1k,64.380,35.620,85.470,14.530,9.19,224,0.875,bicubic,-12.544,-7.946,-13 tf_efficientnet_lite1.in1k,64.370,35.630,85.460,14.540,5.42,240,0.882,bicubic,-12.274,-7.764,-7 resmlp_12_224.fb_in1k,64.350,35.650,85.590,14.410,15.35,224,0.875,bicubic,-12.298,-7.588,-9 tf_efficientnet_b0.aa_in1k,64.300,35.700,85.280,14.720,5.29,224,0.875,bicubic,-12.544,-7.938,-13 tf_mixnet_m.in1k,64.260,35.740,85.100,14.900,5.01,224,0.875,bicubic,-12.694,-8.054,-19 densenet121.ra_in1k,64.250,35.750,85.820,14.180,7.98,288,0.950,bicubic,-12.250,-7.548,-3 resnet26.bt_in1k,64.200,35.800,85.210,14.790,16.00,288,0.950,bicubic,-12.166,-7.970,0 tf_efficientnet_es.in1k,64.200,35.800,84.740,15.260,5.44,224,0.875,bicubic,-12.398,-8.462,-11 regnety_008.pycls_in1k,64.140,35.860,85.240,14.760,6.26,224,0.875,bicubic,-12.162,-7.822,+1 dpn68.mx_in1k,64.120,35.880,85.080,14.920,12.61,224,0.875,bicubic,-12.226,-7.928,-2 vit_small_patch32_224.augreg_in21k_ft_in1k,64.090,35.910,85.550,14.450,22.88,224,0.900,bicubic,-11.904,-7.250,+3 mobilenetv2_140.ra_in1k,64.070,35.930,85.030,14.970,6.11,224,0.875,bicubic,-12.446,-7.958,-10 repghostnet_130.in1k,63.960,36.040,84.840,15.160,5.48,224,0.875,bicubic,-12.416,-8.052,-7 hardcorenas_a.miil_green_in1k,63.720,36.280,84.410,15.590,5.26,224,0.875,bilinear,-12.218,-8.098,+3 resnest14d.gluon_in1k,63.620,36.380,84.230,15.770,10.61,224,0.875,bilinear,-11.888,-8.278,+12 regnety_004.tv2_in1k,63.600,36.400,84.860,15.140,4.34,224,0.965,bicubic,-11.994,-7.840,+9 mobilevitv2_075.cvnets_in1k,63.590,36.410,84.950,15.050,2.87,256,0.888,bicubic,-12.018,-7.794,+7 tf_mixnet_s.in1k,63.580,36.420,84.260,15.740,4.13,224,0.875,bicubic,-12.072,-8.380,+4 tf_efficientnet_b0.in1k,63.520,36.480,84.870,15.130,5.29,224,0.875,bicubic,-13.010,-8.138,-20 mixnet_s.ft_in1k,63.390,36.610,84.720,15.280,4.13,224,0.875,bicubic,-12.604,-8.550,-5 mobilenetv3_large_100.ra_in1k,63.380,36.620,84.080,15.920,5.48,224,0.875,bicubic,-12.386,-8.458,-1 vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,63.340,36.660,85.270,14.730,6.36,384,1.000,bicubic,-12.620,-7.992,-6 resnet50.tv_in1k,63.330,36.670,84.670,15.330,25.56,224,0.875,bilinear,-12.798,-8.188,-11 efficientnet_es_pruned.in1k,63.310,36.690,84.950,15.050,5.44,224,0.875,bicubic,-11.696,-7.494,+16 mixer_b16_224.goog_in21k_ft_in1k,63.290,36.710,83.340,16.660,59.88,224,0.875,bicubic,-13.312,-8.884,-29 mobilenetv3_rw.rmsp_in1k,63.240,36.760,84.520,15.480,5.48,224,0.875,bicubic,-12.380,-8.184,-4 efficientnet_lite0.ra_in1k,63.240,36.760,84.410,15.590,4.65,224,0.875,bicubic,-12.242,-8.110,+2 mobileone_s1.apple_in1k,63.200,36.800,84.210,15.790,4.83,224,0.900,bilinear,-12.586,-8.582,-9 semnasnet_100.rmsp_in1k,63.160,36.840,84.540,15.460,3.89,224,0.875,bicubic,-12.290,-8.058,+2 vit_tiny_patch16_224.augreg_in21k_ft_in1k,63.130,36.870,84.870,15.130,5.72,224,0.900,bicubic,-12.332,-7.974,-1 regnety_006.pycls_in1k,63.090,36.910,84.240,15.760,6.06,224,0.875,bicubic,-12.178,-8.286,+1 pit_ti_distilled_224.in1k,63.020,36.980,83.810,16.190,5.10,224,0.900,bicubic,-11.236,-8.142,+22 densenet121.tv_in1k,62.940,37.060,84.250,15.750,7.98,224,0.875,bicubic,-11.824,-7.904,+12 mobilevit_xs.cvnets_in1k,62.930,37.070,84.830,15.170,2.32,256,0.900,bicubic,-11.704,-7.518,+14 ghostnetv2_100.in1k,62.900,37.100,84.090,15.910,6.16,224,0.875,bicubic,-12.266,-8.264,-2 legacy_seresnet34.in1k,62.890,37.110,84.230,15.770,21.96,224,0.875,bilinear,-11.912,-7.896,+8 hrnet_w18_small_v2.ms_in1k,62.830,37.170,83.970,16.030,15.60,224,0.875,bilinear,-12.280,-8.446,-1 edgenext_x_small.in1k,62.810,37.190,84.670,15.330,2.34,288,1.000,bicubic,-12.878,-8.096,-17 mobilenetv2_110d.ra_in1k,62.810,37.190,84.480,15.520,4.52,224,0.875,bicubic,-12.244,-7.704,-1 deit_tiny_distilled_patch16_224.fb_in1k,62.790,37.210,83.930,16.070,5.91,224,0.900,bicubic,-11.714,-7.960,+11 resnet18.fb_swsl_ig1b_ft_in1k,62.750,37.250,84.300,15.700,11.69,224,0.875,bilinear,-10.538,-7.430,+30 tinynet_b.in1k,62.720,37.280,84.220,15.780,3.73,188,0.875,bicubic,-12.258,-7.966,-1 repvgg_b0.rvgg_in1k,62.710,37.290,83.880,16.120,15.82,224,0.875,bilinear,-12.434,-8.536,-9 tf_efficientnet_lite0.in1k,62.580,37.420,84.230,15.770,4.65,224,0.875,bicubic,-12.252,-7.940,-1 xcit_nano_12_p8_224.fb_in1k,62.560,37.440,84.210,15.790,3.05,224,1.000,bicubic,-11.350,-7.958,+17 resnet34.gluon_in1k,62.540,37.460,84.000,16.000,21.80,224,0.875,bicubic,-12.040,-7.982,+4 regnetx_008.pycls_in1k,62.490,37.510,84.020,15.980,7.26,224,0.875,bicubic,-12.538,-8.318,-9 dla34.in1k,62.490,37.510,83.930,16.070,15.74,224,0.875,bilinear,-12.150,-8.136,0 fbnetc_100.rmsp_in1k,62.450,37.550,83.370,16.630,5.57,224,0.875,bilinear,-12.680,-9.018,-14 tf_mobilenetv3_large_100.in1k,62.440,37.560,83.950,16.050,5.48,224,0.875,bilinear,-13.076,-8.644,-24 crossvit_9_240.in1k,62.250,37.750,84.240,15.760,8.55,240,0.875,bicubic,-11.710,-7.722,+8 repghostnet_111.in1k,62.250,37.750,83.880,16.120,4.54,224,0.875,bicubic,-12.806,-8.312,-15 crossvit_tiny_240.in1k,62.080,37.920,83.610,16.390,7.01,240,0.875,bicubic,-11.260,-8.298,+16 regnetx_004_tv.tv2_in1k,62.060,37.940,83.770,16.230,5.50,224,0.965,bicubic,-12.540,-8.400,-5 resnet18d.ra2_in1k,62.000,38.000,83.790,16.210,11.71,288,0.950,bicubic,-11.794,-8.048,+9 repvgg_a1.rvgg_in1k,61.970,38.030,83.040,16.960,14.09,224,0.875,bilinear,-12.492,-8.816,-4 efficientvit_m4.r224_in1k,61.950,38.050,83.580,16.420,8.80,224,0.875,bicubic,-12.418,-8.400,-4 mnasnet_100.rmsp_in1k,61.920,38.080,83.700,16.300,4.38,224,0.875,bicubic,-12.732,-8.422,-12 vgg19_bn.tv_in1k,61.870,38.130,83.450,16.550,143.68,224,0.875,bilinear,-12.346,-8.394,-4 regnety_004.pycls_in1k,61.840,38.160,83.410,16.590,4.34,224,0.875,bicubic,-12.186,-8.338,-2 convit_tiny.fb_in1k,61.590,38.410,84.090,15.910,5.71,224,0.875,bicubic,-11.522,-7.622,+12 resnet18.a1_in1k,61.580,38.420,82.470,17.530,11.69,288,1.000,bicubic,-11.578,-8.556,+10 resnet34.a3_in1k,61.490,38.510,82.610,17.390,21.80,224,0.950,bicubic,-11.480,-8.496,+13 resnet18.fb_ssl_yfcc100m_ft_in1k,61.480,38.520,83.330,16.670,11.69,224,0.875,bilinear,-11.118,-8.086,+15 repghostnet_100.in1k,61.380,38.620,82.750,17.250,4.07,224,0.875,bicubic,-12.826,-8.792,-9 regnetx_006.pycls_in1k,61.360,38.640,83.450,16.550,6.20,224,0.875,bicubic,-12.508,-8.228,-3 hrnet_w18_small.gluon_in1k,61.290,38.710,82.280,17.720,13.19,224,0.875,bicubic,-12.630,-8.914,-6 ghostnet_100.in1k,61.250,38.750,82.300,17.700,5.18,224,0.875,bicubic,-12.708,-9.232,-8 spnasnet_100.rmsp_in1k,61.240,38.760,82.760,17.240,4.42,224,0.875,bilinear,-12.854,-9.060,-12 resnet34.tv_in1k,61.200,38.800,82.740,17.260,21.80,224,0.875,bilinear,-12.106,-8.680,0 vit_base_patch32_224.augreg_in1k,61.040,38.960,82.730,17.270,88.22,224,0.900,bicubic,-13.854,-9.048,-29 efficientvit_m3.r224_in1k,61.010,38.990,83.200,16.800,6.90,224,0.875,bicubic,-12.364,-8.148,-5 pit_ti_224.in1k,60.960,39.040,83.850,16.150,4.85,224,0.900,bicubic,-11.950,-7.554,+5 skresnet18.ra_in1k,60.850,39.150,82.850,17.150,11.96,224,0.875,bicubic,-12.184,-8.322,0 vgg16_bn.tv_in1k,60.770,39.230,82.960,17.040,138.37,224,0.875,bilinear,-12.600,-8.554,-7 semnasnet_075.rmsp_in1k,60.720,39.280,82.540,17.460,2.91,224,0.875,bicubic,-12.284,-8.600,-1 tf_mobilenetv3_large_075.in1k,60.380,39.620,81.970,18.030,3.99,224,0.875,bilinear,-13.050,-9.382,-11 xcit_nano_12_p16_224.fb_dist_in1k,60.220,39.780,82.490,17.510,3.05,224,1.000,bicubic,-12.090,-8.370,+7 resnet18.a2_in1k,60.200,39.800,81.840,18.160,11.69,288,1.000,bicubic,-12.172,-8.756,+4 mobilenetv2_100.ra_in1k,60.150,39.850,82.190,17.810,3.50,224,0.875,bicubic,-12.818,-8.826,-3 vit_base_patch32_224.sam_in1k,59.990,40.010,81.220,18.780,88.22,224,0.900,bicubic,-13.704,-9.794,-16 deit_tiny_patch16_224.fb_in1k,59.800,40.200,82.660,17.340,5.72,224,0.900,bicubic,-12.370,-8.456,+7 legacy_seresnet18.in1k,59.790,40.210,81.680,18.320,11.78,224,0.875,bicubic,-11.970,-8.652,+11 repvgg_a0.rvgg_in1k,59.760,40.240,81.250,18.750,9.11,224,0.875,bilinear,-12.648,-9.242,-4 vgg19.tv_in1k,59.710,40.290,81.460,18.540,143.67,224,0.875,bilinear,-12.668,-9.414,-3 edgenext_xx_small.in1k,59.410,40.590,81.840,18.160,1.33,288,1.000,bicubic,-12.468,-8.712,+6 regnetx_004.pycls_in1k,59.380,40.620,81.740,18.260,5.16,224,0.875,bicubic,-13.022,-9.086,-6 vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,59.080,40.920,81.760,18.240,6.34,224,0.900,bicubic,-12.718,-9.064,+4 tf_mobilenetv3_large_minimal_100.in1k,59.080,40.920,81.130,18.870,3.92,224,0.875,bilinear,-13.184,-9.510,-2 repghostnet_080.in1k,59.060,40.940,81.160,18.840,3.28,224,0.875,bicubic,-13.152,-9.324,-2 hrnet_w18_small.ms_in1k,58.960,41.040,81.340,18.660,13.19,224,0.875,bilinear,-13.376,-9.340,-7 vgg13_bn.tv_in1k,58.960,41.040,81.090,18.910,133.05,224,0.875,bilinear,-12.628,-9.288,+4 lcnet_100.ra2_in1k,58.920,41.080,81.200,18.800,2.95,224,0.875,bicubic,-13.182,-9.154,-3 vgg16.tv_in1k,58.840,41.160,81.660,18.340,138.36,224,0.875,bilinear,-12.752,-8.724,+1 pvt_v2_b0.in1k,58.770,41.230,82.120,17.880,3.67,224,0.900,bicubic,-11.890,-8.076,+7 mobileone_s0.apple_in1k,58.580,41.420,80.080,19.920,5.29,224,0.875,bilinear,-12.822,-9.762,+1 efficientvit_m2.r224_in1k,58.420,41.580,81.360,18.640,4.19,224,0.875,bicubic,-12.394,-8.782,+4 resnet18.gluon_in1k,58.340,41.660,80.970,19.030,11.69,224,0.875,bicubic,-12.494,-8.786,+2 xcit_nano_12_p16_224.fb_in1k,58.330,41.670,80.920,19.080,3.05,224,1.000,bicubic,-11.632,-8.842,+7 tinynet_c.in1k,58.200,41.800,80.280,19.720,2.46,184,0.875,bicubic,-13.042,-9.452,-1 resnet14t.c3_in1k,58.190,41.810,80.300,19.700,10.08,224,0.950,bicubic,-14.064,-10.006,-14 efficientvit_b0.r224_in1k,58.050,41.950,79.800,20.200,3.41,224,0.950,bicubic,-13.348,-9.628,-4 mobilevitv2_050.cvnets_in1k,57.700,42.300,80.880,19.120,1.37,256,0.888,bicubic,-12.448,-9.038,+2 vgg11_bn.tv_in1k,57.410,42.590,80.010,19.990,132.87,224,0.875,bilinear,-12.972,-9.798,-1 resnet18.tv_in1k,57.180,42.820,80.190,19.810,11.69,224,0.875,bilinear,-12.580,-8.880,+3 mobilevit_xxs.cvnets_in1k,57.170,42.830,79.760,20.240,1.27,256,0.900,bicubic,-11.748,-9.186,+4 vgg13.tv_in1k,57.140,42.860,79.550,20.450,133.05,224,0.875,bilinear,-12.792,-9.700,0 regnety_002.pycls_in1k,57.010,42.990,79.880,20.120,3.16,224,0.875,bicubic,-13.270,-9.650,-4 mixer_l16_224.goog_in21k_ft_in1k,56.660,43.340,76.010,23.990,208.20,224,0.875,bicubic,-15.394,-11.664,-18 repghostnet_058.in1k,56.110,43.890,78.510,21.490,2.55,224,0.875,bicubic,-12.804,-9.910,+1 regnetx_002.pycls_in1k,56.070,43.930,79.210,20.790,2.68,224,0.875,bicubic,-12.682,-9.332,+2 resnet18.a3_in1k,56.000,44.000,78.970,21.030,11.69,224,0.950,bicubic,-12.252,-9.202,+4 dla60x_c.in1k,55.990,44.010,78.970,21.030,1.32,224,0.875,bilinear,-11.922,-9.462,+5 vgg11.tv_in1k,55.820,44.180,78.830,21.170,132.86,224,0.875,bilinear,-13.202,-9.794,-5 resnet10t.c3_in1k,55.560,44.440,78.440,21.560,5.44,224,0.950,bicubic,-12.804,-9.596,-1 efficientvit_m1.r224_in1k,55.470,44.530,79.150,20.850,2.98,224,0.875,bicubic,-12.836,-9.520,-1 lcnet_075.ra2_in1k,55.440,44.560,78.350,21.650,2.36,224,0.875,bicubic,-13.342,-10.010,-5 mobilenetv3_small_100.lamb_in1k,54.680,45.320,77.770,22.230,2.54,224,0.875,bicubic,-12.978,-9.866,+1 tf_mobilenetv3_small_100.in1k,54.470,45.530,77.070,22.930,2.54,224,0.875,bilinear,-13.452,-10.602,-2 repghostnet_050.in1k,53.550,46.450,76.740,23.260,2.31,224,0.875,bicubic,-13.416,-10.180,+1 tinynet_d.in1k,53.430,46.570,76.320,23.680,2.34,152,0.875,bicubic,-13.542,-10.746,-1 mnasnet_small.lamb_in1k,53.270,46.730,75.910,24.090,2.03,224,0.875,bicubic,-12.926,-10.594,0 dla46x_c.in1k,53.040,46.960,76.840,23.160,1.07,224,0.875,bilinear,-12.952,-10.134,0 mobilenetv2_050.lamb_in1k,52.840,47.160,75.450,24.550,1.97,224,0.875,bicubic,-13.108,-10.634,0 dla46_c.in1k,52.200,47.800,75.670,24.330,1.30,224,0.875,bilinear,-12.672,-10.628,+2 tf_mobilenetv3_small_075.in1k,52.160,47.840,75.450,24.550,2.04,224,0.875,bilinear,-13.566,-10.682,-1 mobilenetv3_small_075.lamb_in1k,51.890,48.110,74.740,25.260,2.04,224,0.875,bicubic,-13.346,-10.706,-1 efficientvit_m0.r224_in1k,50.750,49.250,74.470,25.530,2.35,224,0.875,bicubic,-12.520,-10.706,0 lcnet_050.ra2_in1k,49.980,50.020,73.440,26.560,1.88,224,0.875,bicubic,-13.158,-10.942,0 tf_mobilenetv3_small_minimal_100.in1k,49.530,50.470,73.020,26.980,2.04,224,0.875,bilinear,-13.364,-11.218,0 tinynet_e.in1k,46.730,53.270,70.360,29.640,2.04,106,0.875,bicubic,-13.136,-11.402,0 mobilenetv3_small_050.lamb_in1k,44.850,55.150,67.690,32.310,1.59,224,0.875,bicubic,-13.066,-12.490,0
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv	model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count tinynet_e,10001.12,50.423,512,106,2.04 mobilenetv3_small_050,7406.47,68.392,512,224,1.59 tf_mobilenetv3_small_minimal_100,6438.14,78.983,512,224,2.04 mobilenetv3_small_075,6186.83,82.006,512,224,2.04 tf_mobilenetv3_small_075,5783.46,87.782,512,224,2.04 mobilenetv3_small_100,5749.13,88.315,512,224,2.54 lcnet_035,5673.53,89.75,512,224,1.64 tf_mobilenetv3_small_100,5383.9,94.36,512,224,2.54 levit_128s,5298.88,95.701,512,224,7.78 lcnet_050,5280.37,96.452,512,224,1.88 tinynet_d,5161.83,98.416,512,152,2.34 mixer_s32_224,4696.33,108.475,512,224,19.1 resnet10t,4669.46,109.393,512,176,5.44 vit_small_patch32_224,4447.28,114.289,512,224,22.88 lcnet_075,4278.23,119.175,512,224,2.36 vit_tiny_r_s16_p8_224,4137.87,122.895,512,224,6.34 levit_128,3895.0,130.318,512,224,9.21 regnetx_002,3718.05,137.026,512,224,2.68 lcnet_100,3569.0,142.969,512,224,2.95 mnasnet_small,3450.28,147.453,512,224,2.03 regnety_002,3414.18,149.006,512,224,3.16 cs3darknet_focus_s,3251.91,156.949,512,256,3.27 mobilenetv2_035,3160.04,161.202,512,224,1.68 levit_192,3046.5,166.9,512,224,10.95 gernet_s,3034.31,168.028,512,224,8.17 tinynet_c,2919.98,174.314,512,184,2.46 mnasnet_050,2847.14,179.025,512,224,2.22 cs3darknet_s,2821.27,180.951,512,256,3.28 resnet18,2764.22,184.877,512,224,11.69 ssl_resnet18,2760.71,185.109,512,224,11.69 mobilenetv2_050,2751.58,185.257,512,224,1.97 swsl_resnet18,2742.47,186.338,512,224,11.69 semnasnet_050,2741.67,185.816,512,224,2.08 gluon_resnet18_v1b,2741.53,186.395,512,224,11.69 lcnet_150,2713.5,188.193,512,224,4.5 regnetx_004,2695.23,188.875,512,224,5.16 ese_vovnet19b_slim_dw,2588.37,197.313,512,224,1.9 seresnet18,2562.51,199.293,512,224,11.78 nf_regnet_b0,2561.76,198.646,512,192,8.76 legacy_seresnet18,2500.8,204.207,512,224,11.78 tf_efficientnetv2_b0,2483.22,204.949,512,192,7.14 levit_256,2482.39,205.091,512,224,18.89 mobilenetv3_large_075,2392.41,213.119,512,224,3.99 resnet14t,2385.69,214.281,512,176,10.08 tf_mobilenetv3_large_minimal_100,2347.68,217.368,512,224,3.92 vit_tiny_patch16_224,2293.54,222.408,512,224,5.72 regnetx_006,2293.09,222.433,512,224,6.2 deit_tiny_patch16_224,2290.53,222.68,512,224,5.72 tf_mobilenetv3_large_075,2259.6,225.688,512,224,3.99 deit_tiny_distilled_patch16_224,2253.36,226.358,512,224,5.91 edgenext_xx_small,2231.33,228.598,512,256,1.33 ghostnet_050,2189.91,232.414,512,224,2.59 mobilenetv3_rw,2184.31,233.512,512,224,5.48 mnasnet_075,2176.02,234.492,512,224,3.17 mobilenetv3_large_100,2167.29,235.344,512,224,5.48 mobilenetv3_large_100_miil,2165.63,235.504,512,224,5.48 levit_256d,2159.5,235.516,512,224,26.21 resnet18d,2129.12,240.084,512,224,11.71 hardcorenas_a,2118.32,240.968,512,224,5.26 regnety_004,2100.99,242.536,512,224,4.34 pit_ti_distilled_224,2086.5,244.504,512,224,5.1 pit_ti_224,2079.54,245.311,512,224,4.85 ese_vovnet19b_slim,2066.1,247.446,512,224,3.17 mnasnet_100,2053.84,248.477,512,224,4.38 tf_mobilenetv3_large_100,2053.63,248.437,512,224,5.48 mnasnet_b1,2053.54,248.485,512,224,4.38 semnasnet_075,2008.51,253.986,512,224,2.91 hardcorenas_b,2008.46,253.96,512,224,5.18 mobilenetv2_075,1983.69,257.32,512,224,2.64 hardcorenas_c,1977.37,257.94,512,224,5.52 xcit_nano_12_p16_224_dist,1970.62,258.036,512,224,3.05 xcit_nano_12_p16_224,1969.78,258.084,512,224,3.05 tinynet_b,1965.95,259.368,512,188,3.73 hardcorenas_d,1880.3,271.085,512,224,7.5 tf_efficientnetv2_b1,1876.23,271.395,512,192,8.14 resnetblur18,1872.21,273.11,512,224,11.69 spnasnet_100,1862.13,273.955,512,224,4.42 mnasnet_a1,1859.21,274.476,512,224,3.89 semnasnet_100,1857.75,274.693,512,224,3.89 mobilenetv2_100,1832.14,278.633,512,224,3.5 regnety_006,1809.24,281.912,512,224,6.06 visformer_tiny,1802.41,283.384,512,224,10.32 mixer_b32_224,1784.58,286.101,512,224,60.29 skresnet18,1730.13,295.275,512,224,11.96 tinynet_a,1710.13,298.117,512,192,6.19 vit_base_patch32_224_sam,1703.64,299.668,512,224,88.22 vit_base_patch32_224,1703.57,299.695,512,224,88.22 efficientnet_lite0,1674.68,304.971,512,224,4.65 cs3darknet_focus_m,1668.48,306.209,512,256,9.3 hardcorenas_e,1650.74,309.021,512,224,8.07 hardcorenas_f,1646.88,309.777,512,224,8.2 gluon_resnet34_v1b,1634.03,312.731,512,224,21.8 regnetx_008,1632.2,312.851,512,224,7.26 tv_resnet34,1630.02,313.513,512,224,21.8 resnet34,1622.41,314.992,512,224,21.8 ghostnet_100,1601.5,318.319,512,224,5.18 tf_efficientnet_lite0,1591.79,320.884,512,224,4.65 fbnetc_100,1567.77,325.605,512,224,5.57 pit_xs_distilled_224,1551.83,329.02,512,224,11.0 pit_xs_224,1549.02,329.642,512,224,10.62 mixer_s16_224,1543.23,331.197,512,224,18.53 dla46_c,1532.94,333.18,512,224,1.3 mnasnet_140,1525.17,334.879,512,224,7.12 seresnet34,1505.77,339.147,512,224,21.96 cs3darknet_m,1499.82,340.716,512,256,9.31 regnety_008,1498.63,340.596,512,224,6.26 levit_384,1491.26,342.207,512,224,39.13 edgenext_x_small,1481.71,344.446,512,256,2.34 ese_vovnet19b_dw,1466.46,348.623,512,224,6.54 legacy_seresnet34,1465.81,348.38,512,224,21.96 efficientnet_b0,1459.11,262.1,384,224,5.29 gernet_m,1456.76,350.74,512,224,21.14 vit_small_patch32_384,1448.56,352.604,512,384,22.92 regnetz_005,1448.06,352.165,512,224,7.12 rexnet_100,1447.81,264.049,384,224,4.8 rexnetr_100,1441.71,265.216,384,224,4.88 nf_resnet26,1422.76,359.346,512,224,16.0 hrnet_w18_small,1410.43,361.614,512,224,13.19 selecsls42,1405.04,363.736,512,224,30.35 selecsls42b,1401.22,364.735,512,224,32.46 mobilenetv2_110d,1400.15,273.199,384,224,4.52 tf_efficientnet_b0_ap,1398.67,273.43,384,224,5.29 mobilevitv2_050,1396.45,365.664,512,256,1.37 tf_efficientnet_b0_ns,1395.54,274.064,384,224,5.29 tf_efficientnet_b0,1395.32,274.114,384,224,5.29 tf_efficientnetv2_b2,1392.9,365.948,512,208,10.1 vit_tiny_r_s16_p8_384,1392.75,274.873,384,384,6.36 resnet34d,1379.64,370.514,512,224,21.82 ghostnet_130,1364.55,373.824,512,224,7.36 gmixer_12_224,1352.72,377.701,512,224,12.7 crossvit_tiny_240,1349.19,377.902,512,240,7.01 gmlp_ti16_224,1340.6,284.894,384,224,5.87 semnasnet_140,1340.57,380.992,512,224,6.11 dla46x_c,1338.33,381.81,512,224,1.07 xcit_tiny_12_p16_224,1323.84,384.926,512,224,6.72 xcit_tiny_12_p16_224_dist,1317.19,386.895,512,224,6.72 resnetrs50,1317.01,387.565,512,160,35.69 mobilevit_xxs,1316.84,290.489,384,256,1.27 resnet26,1312.7,389.566,512,224,16.0 efficientnet_b1_pruned,1301.95,391.798,512,240,6.33 mobilenetv2_140,1267.4,302.189,384,224,6.11 dla60x_c,1262.98,404.404,512,224,1.32 crossvit_9_240,1260.08,303.33,384,240,8.55 convnext_nano_hnf,1235.34,413.703,512,224,15.59 convnext_nano_ols,1234.94,413.902,512,224,15.6 poolformer_s12,1234.11,414.201,512,224,11.92 convnext_nano,1233.61,414.261,512,224,15.59 resmlp_12_distilled_224,1232.37,414.645,512,224,15.35 resmlp_12_224,1232.04,414.762,512,224,15.35 fbnetv3_b,1226.89,415.617,512,224,8.6 nf_regnet_b2,1219.45,418.235,512,240,14.31 repvgg_b0,1217.24,419.512,512,224,15.82 selecsls60b,1214.07,420.825,512,224,32.77 selecsls60,1211.7,421.663,512,224,30.67 nf_regnet_b1,1209.03,421.975,512,256,10.22 crossvit_9_dagger_240,1206.16,316.906,384,240,8.78 nf_seresnet26,1198.39,426.558,512,224,17.4 mixnet_s,1181.75,431.958,512,224,4.13 nf_ecaresnet26,1174.85,435.233,512,224,16.0 efficientnet_lite1,1171.46,217.556,256,240,5.42 darknet17,1164.06,439.537,512,256,14.3 efficientnet_es_pruned,1160.76,440.317,512,224,5.44 efficientnet_es,1160.37,440.47,512,224,5.44 regnetx_016,1139.3,448.473,512,224,9.19 fbnetv3_d,1138.14,335.598,384,224,10.31 tf_efficientnet_es,1136.29,449.83,512,224,5.44 rexnetr_130,1133.04,224.76,256,224,7.61 dla34,1132.96,451.315,512,224,15.74 resnet26d,1119.56,456.822,512,224,16.01 tf_mixnet_s,1118.11,456.605,512,224,4.13 tf_efficientnet_lite1,1110.94,229.444,256,240,5.42 edgenext_small,1109.37,460.388,512,256,5.59 convit_tiny,1095.04,466.531,512,224,5.71 rexnet_130,1094.78,232.699,256,224,7.56 mobilenetv2_120d,1078.49,236.158,256,224,5.83 darknet21,1073.87,476.43,512,256,20.86 ecaresnet50d_pruned,1067.01,478.899,512,224,19.94 deit_small_patch16_224,1053.64,363.563,384,224,22.05 vit_small_patch16_224,1052.92,363.872,384,224,22.05 deit_small_distilled_patch16_224,1032.61,370.971,384,224,22.44 sedarknet21,1031.46,495.893,512,256,20.95 gernet_l,1030.31,496.058,512,256,31.08 efficientnet_b1,1030.3,246.963,256,224,7.79 rexnetr_150,1022.06,249.288,256,224,9.78 repvgg_a2,1010.18,506.008,512,224,28.21 edgenext_small_rw,1009.52,506.183,512,256,7.83 skresnet34,1008.96,506.323,512,224,22.28 resnest14d,979.06,522.497,512,224,10.61 cs3darknet_focus_l,977.57,391.957,384,256,21.15 deit3_small_patch16_224,977.26,391.961,384,224,22.06 deit3_small_patch16_224_in21ft1k,976.5,392.276,384,224,22.06 rexnet_150,965.2,264.04,256,224,9.73 regnety_016,954.26,534.657,512,224,11.2 vit_base_patch32_plus_256,951.64,537.091,512,256,119.48 mobilevitv2_075,947.54,269.157,256,256,2.87 legacy_seresnext26_32x4d,946.21,405.17,384,224,16.79 pit_s_224,942.8,270.615,256,224,23.46 pit_s_distilled_224,939.97,271.455,256,224,24.04 vit_srelpos_small_patch16_224,922.29,415.451,384,224,21.97 vit_relpos_small_patch16_224,921.7,415.439,384,224,21.98 efficientnet_b0_g16_evos,909.42,421.149,384,224,8.11 resnext26ts,905.09,423.733,384,256,10.3 cs3darknet_l,902.18,282.891,256,256,21.16 coat_lite_tiny,893.97,428.624,384,224,5.72 resnet26t,890.89,574.188,512,256,16.01 efficientnet_b0_gn,881.54,289.263,256,224,5.29 resnetv2_50,880.1,580.976,512,224,25.55 efficientnet_b2_pruned,874.13,291.317,256,260,8.31 seresnext26ts,867.62,294.407,256,256,10.39 eca_resnext26ts,867.54,294.527,256,256,10.3 tf_efficientnet_b1,863.78,294.816,256,240,7.79 tf_efficientnet_b1_ap,863.54,294.906,256,240,7.79 tf_efficientnet_b1_ns,863.39,294.941,256,240,7.79 cs3sedarknet_l,861.38,444.523,384,256,21.91 tf_efficientnetv2_b3,855.0,297.539,256,240,14.36 efficientnet_lite2,852.1,299.402,256,260,6.09 twins_svt_small,851.73,449.18,384,224,24.06 gcresnext26ts,850.58,300.113,256,256,10.48 efficientnetv2_rw_t,850.16,298.981,256,224,13.65 botnet26t_256,849.43,451.465,384,256,12.49 ecaresnetlight,846.78,603.683,512,224,30.16 seresnext26t_32x4d,845.6,453.458,384,224,16.81 seresnext26tn_32x4d,845.31,453.612,384,224,16.81 seresnext26d_32x4d,844.96,453.775,384,224,16.81 coat_lite_mini,842.06,455.115,384,224,11.01 tf_efficientnet_cc_b0_8e,837.03,457.594,384,224,24.01 ecaresnet101d_pruned,837.02,609.921,512,224,24.88 ecaresnext26t_32x4d,835.25,459.196,384,224,15.41 ecaresnext50t_32x4d,834.39,459.653,384,224,15.41 cspresnet50,830.57,461.498,384,256,21.62 swsl_resnet50,829.79,616.192,512,224,25.56 ssl_resnet50,829.64,616.294,512,224,25.56 gluon_resnet50_v1b,829.63,616.32,512,224,25.56 visformer_small,828.8,462.625,384,224,40.22 tv_resnet50,826.55,618.618,512,224,25.56 resnet50,826.06,618.983,512,224,25.56 vgg11,825.98,619.706,512,224,132.86 halonet26t,824.96,464.902,384,256,12.48 vovnet39a,817.65,625.544,512,224,22.6 tf_efficientnet_lite2,816.76,312.458,256,260,6.09 convnext_tiny_hnf,815.48,312.97,256,224,28.59 convnext_tiny_hnfd,815.19,313.078,256,224,28.59 vit_small_resnet26d_224,813.66,470.891,384,224,63.61 convnext_tiny,813.16,313.859,256,224,28.59 efficientnet_cc_b0_8e,812.96,471.165,384,224,24.01 vit_relpos_base_patch32_plus_rpn_256,811.26,630.0,512,256,119.42 mixnet_m,810.2,630.361,512,224,5.01 efficientnet_cc_b0_4e,808.8,473.577,384,224,13.31 convnext_tiny_in22ft1k,808.5,315.666,256,224,28.59 efficientnet_b2a,800.27,318.401,256,256,9.11 efficientnet_b2,799.96,318.544,256,256,9.11 regnetz_b16,796.72,319.811,256,224,9.72 tresnet_m,792.8,643.233,512,224,31.39 mobilevit_xs,792.23,321.979,256,256,2.32 ecaresnet26t,791.55,484.557,384,256,16.01 gc_efficientnetv2_rw_t,791.43,320.691,256,224,13.68 resnetv2_50t,790.83,646.602,512,224,25.57 resnetv2_50d,790.11,647.216,512,224,25.57 regnetx_032,787.5,486.368,384,224,15.3 ese_vovnet39b,786.37,650.429,512,224,24.57 tf_efficientnet_cc_b0_4e,784.54,488.254,384,224,13.31 eca_botnext26ts_256,781.43,326.976,256,256,10.59 resnet32ts,781.02,327.191,256,256,17.96 tf_mixnet_m,777.93,492.059,384,224,5.01 resnet33ts,767.78,332.812,256,256,19.68 gluon_resnet50_v1c,763.32,502.196,384,224,25.58 eca_halonext26ts,763.09,334.836,256,256,10.76 rexnetr_200,762.4,250.686,192,224,16.52 dpn68b,756.57,506.252,384,224,12.61 lambda_resnet26t,751.39,510.429,384,256,10.96 vit_relpos_small_patch16_rpn_224,751.37,510.029,384,224,21.97 resnet50t,748.03,512.487,384,224,25.57 cspresnet50d,746.91,341.842,256,256,21.64 gluon_resnet50_v1d,746.5,513.543,384,224,25.58 resnet50d,744.99,514.575,384,224,25.58 legacy_seresnet50,744.88,514.356,384,224,28.09 cspresnet50w,744.04,343.166,256,256,28.12 eca_resnet33ts,743.27,343.735,256,256,19.68 efficientnet_b0_g8_gn,743.27,343.315,256,224,6.56 seresnet33ts,742.4,344.003,256,256,19.78 resnetaa50,741.95,516.711,384,224,25.56 selecsls84,740.99,689.736,512,224,50.95 dpn68,739.97,517.747,384,224,12.61 res2net50_48w_2s,738.06,519.427,384,224,25.29 vit_small_r26_s32_224,737.22,345.982,256,224,36.43 eca_vovnet39b,735.59,695.354,512,224,22.6 lambda_resnet26rpt_256,735.09,260.579,192,256,10.99 nf_regnet_b3,732.33,522.471,384,288,18.59 rexnet_200,731.68,261.239,192,224,16.37 densenet121,730.11,348.758,256,224,7.98 resnest26d,728.94,526.039,384,224,17.07 bat_resnext26ts,728.42,350.197,256,256,10.73 mobilevitv2_100,727.72,262.852,192,256,4.9 tv_densenet121,727.58,350.07,256,224,7.98 nf_seresnet50,727.17,526.884,384,224,28.09 gcresnet33ts,725.89,351.666,256,256,19.88 eca_nfnet_l0,723.69,706.434,512,224,24.14 nfnet_l0,719.96,532.162,384,224,35.07 seresnet50,714.65,536.208,384,224,28.09 twins_pcpvt_small,714.45,356.63,256,224,24.11 nf_ecaresnet50,713.69,537.063,384,224,25.56 dla60,709.61,540.13,384,224,22.04 efficientnet_em,708.33,360.423,256,240,6.9 hrnet_w18_small_v2,705.02,723.712,512,224,15.6 resnetblur50d,704.94,362.275,256,224,25.58 vgg11_bn,703.05,545.962,384,224,132.87 resnetblur50,698.61,548.824,384,224,25.56 regnety_032,696.58,549.77,384,224,19.44 nf_resnet50,696.17,550.716,384,256,25.56 efficientnet_b3_pruned,694.05,367.106,256,300,9.86 tf_efficientnet_em,690.66,369.697,256,240,6.9 skresnet50,685.67,371.92,256,224,25.8 xcit_tiny_24_p16_224,683.44,371.201,256,224,12.12 poolformer_s24,681.93,374.176,256,224,21.39 xcit_tiny_24_p16_224_dist,681.85,371.937,256,224,12.12 vit_base_resnet26d_224,680.96,562.594,384,224,101.4 vovnet57a,678.75,564.837,384,224,36.64 densenet121d,678.22,375.614,256,224,8.0 resnetaa50d,673.73,569.117,384,224,25.58 gluon_resnet50_v1s,669.16,573.001,384,224,25.68 gmixer_24_224,666.22,382.715,256,224,24.72 swsl_resnext50_32x4d,663.66,577.766,384,224,25.03 resnext50_32x4d,663.39,577.966,384,224,25.03 ssl_resnext50_32x4d,663.18,578.185,384,224,25.03 tv_resnext50_32x4d,662.37,578.888,384,224,25.03 gluon_resnext50_32x4d,662.06,579.185,384,224,25.03 haloregnetz_b,660.09,386.296,256,224,11.68 ese_vovnet57b,656.27,584.17,384,224,38.61 cspresnext50,656.07,389.365,256,256,20.57 seresnet50t,655.71,584.407,384,224,28.1 vit_relpos_medium_patch16_cls_224,654.69,389.857,256,224,38.76 seresnetaa50d,654.11,390.147,256,224,28.11 densenetblur121d,649.47,392.249,256,224,8.0 res2net50_26w_4s,648.76,590.62,384,224,25.7 fbnetv3_g,647.3,294.603,192,240,16.62 swin_tiny_patch4_window7_224,646.76,394.841,256,224,28.29 ecaresnet50d,643.9,595.437,384,224,25.58 regnety_040,640.15,598.298,384,224,20.65 gmlp_s16_224,638.67,299.017,192,224,19.42 crossvit_small_240,637.47,399.952,256,240,26.86 resnext50d_32x4d,635.21,402.121,256,224,25.05 nfnet_f0,634.03,806.334,512,192,71.49 vit_srelpos_medium_patch16_224,629.85,405.54,256,224,38.74 mobilevit_s,629.67,303.779,192,256,5.58 skresnet50d,628.92,405.574,256,224,25.82 vit_relpos_medium_patch16_224,628.2,406.369,256,224,38.75 resnest50d_1s4x24d,628.12,406.263,256,224,25.68 mixnet_l,627.47,406.445,256,224,7.33 tf_efficientnet_b2_ns,627.11,304.591,192,260,9.11 tf_efficientnet_b2_ap,626.79,304.757,192,260,9.11 tf_efficientnet_b2,626.11,305.153,192,260,9.11 regnetx_040,624.89,613.356,384,224,22.12 regnetv_040,622.71,409.581,256,224,20.64 darknetaa53,614.47,415.819,256,256,36.02 seresnext50_32x4d,613.62,416.021,256,224,27.56 gluon_seresnext50_32x4d,613.35,416.206,256,224,27.56 sehalonet33ts,613.13,416.664,256,256,13.69 legacy_seresnext50_32x4d,612.89,416.52,256,224,27.56 dla60x,612.79,416.731,256,224,17.35 gcresnet50t,611.79,626.12,384,256,25.9 xcit_nano_12_p16_384_dist,611.55,416.81,256,384,3.05 resmlp_24_224,609.69,418.351,256,224,30.02 resmlp_24_distilled_224,609.51,418.474,256,224,30.02 gcresnext50ts,606.82,314.923,192,256,15.67 tf_inception_v3,603.29,635.057,384,299,23.83 gluon_inception_v3,603.22,635.143,384,299,23.83 adv_inception_v3,603.01,635.347,384,299,23.83 inception_v3,602.27,636.205,384,299,23.83 tf_mixnet_l,600.24,424.956,256,224,7.33 dm_nfnet_f0,600.1,638.573,384,192,71.49 xcit_small_12_p16_224,598.44,425.955,256,224,26.25 xcit_small_12_p16_224_dist,598.22,426.013,256,224,26.25 semobilevit_s,597.07,320.258,192,256,5.74 densenet169,592.78,429.221,256,224,14.15 res2next50,591.98,431.144,256,224,24.67 resnetv2_101,590.74,431.806,256,224,44.54 darknet53,590.64,432.606,256,256,41.61 resnetv2_50x1_bit_distilled,587.0,326.262,192,224,25.55 res2net50_14w_8s,586.94,433.992,256,224,25.06 swin_s3_tiny_224,586.39,435.576,256,224,28.33 repvgg_b1g4,584.26,875.234,512,224,39.97 dla60_res2net,583.07,437.618,256,224,20.85 crossvit_15_240,576.62,331.16,192,240,27.53 cait_xxs24_224,576.52,441.46,256,224,11.96 cs3darknet_focus_x,569.86,448.292,256,256,35.02 resnet101,568.98,448.321,256,224,44.55 gluon_resnet101_v1b,568.4,448.834,256,224,44.55 tv_resnet101,566.24,450.547,256,224,44.55 resnetrs101,564.72,451.1,256,192,63.62 efficientnet_cc_b1_8e,564.18,452.061,256,240,39.72 crossvit_15_dagger_240,558.23,342.033,192,240,28.21 vit_base_resnet50d_224,557.61,457.501,256,224,110.97 mobilevitv2_125,557.38,343.473,192,256,7.48 xcit_nano_12_p8_224_dist,555.43,459.069,256,224,3.05 xcit_nano_12_p8_224,555.18,459.311,256,224,3.05 sebotnet33ts_256,554.49,230.012,128,256,13.7 resnet51q,551.31,463.504,256,256,35.7 resnetv2_101d,548.02,465.6,256,224,44.56 tf_efficientnet_cc_b1_8e,547.16,466.173,256,240,39.72 resnetv2_50d_gn,546.54,350.469,192,224,25.57 nf_resnet101,543.9,704.337,384,224,44.55 vit_base_patch32_384,542.76,470.804,256,384,88.3 gluon_resnet101_v1c,537.15,475.0,256,224,44.57 cspdarknet53,537.1,475.617,256,256,27.64 cs3darknet_x,534.86,477.64,256,256,35.05 vit_base_r26_s32_224,534.67,357.767,192,224,101.38 resnest50d,534.66,477.434,256,224,27.48 resnet50_gn,531.78,360.235,192,224,25.56 regnetz_c16,530.35,360.552,192,256,13.46 gluon_resnet101_v1d,528.59,482.76,256,224,44.57 mixer_b16_224,528.02,484.004,256,224,59.88 mixer_l32_224,527.33,362.504,192,224,206.94 mixer_b16_224_miil,526.58,485.347,256,224,59.88 vit_large_patch32_224,521.73,489.021,256,224,306.54 dla60_res2next,520.31,490.572,256,224,17.03 ecaresnet50t,516.29,494.896,256,256,25.57 cs3sedarknet_xdw,516.24,246.008,128,256,21.6 lambda_resnet50ts,515.16,371.658,192,256,21.54 vit_tiny_patch16_384,512.2,249.072,128,384,5.79 resnet61q,510.55,375.027,192,256,36.85 swinv2_cr_tiny_224,505.83,504.823,256,224,28.33 halonet50ts,503.76,380.122,192,256,22.73 repvgg_b1,503.57,1015.623,512,224,57.42 swinv2_cr_tiny_ns_224,502.5,508.144,256,224,28.33 cs3sedarknet_x,501.96,508.547,256,256,35.4 dla102,497.28,513.14,256,224,33.27 wide_resnet50_2,495.68,773.85,384,224,68.88 res2net50_26w_6s,493.57,516.914,256,224,37.05 resnetaa101d,490.51,520.338,256,224,44.57 convnext_small,489.81,390.224,192,224,50.22 convnext_small_in22ft1k,489.45,390.576,192,224,50.22 legacy_seresnet101,487.73,522.616,256,224,49.33 vit_relpos_medium_patch16_rpn_224,485.5,526.221,256,224,38.73 efficientnet_lite3,484.47,263.098,128,300,8.2 gluon_resnet101_v1s,483.47,527.891,256,224,44.67 seresnet101,480.65,530.38,256,224,49.33 cs3edgenet_x,477.59,535.019,256,256,47.82 nest_tiny,476.68,267.593,128,224,17.06 nf_seresnet101,474.46,537.213,256,224,49.33 mobilevitv2_150_in22ft1k,473.86,269.132,128,256,10.59 mobilevitv2_150,473.84,269.144,128,256,10.59 resnetblur101d,472.23,540.497,256,224,44.57 jx_nest_tiny,472.22,270.163,128,224,17.06 nf_ecaresnet101,469.53,543.375,256,224,44.55 vgg13_bn,468.37,546.3,256,224,133.05 twins_pcpvt_base,466.47,408.848,192,224,43.83 tf_efficientnet_lite3,465.4,273.895,128,300,8.2 vgg16,465.36,824.954,384,224,138.36 sequencer2d_s,462.43,412.819,192,224,27.65 mixnet_xl,460.21,554.308,256,224,11.9 coat_lite_small,457.06,418.568,192,224,19.84 efficientnet_b3a,456.95,278.403,128,288,12.23 efficientnet_b3,456.81,278.448,128,288,12.23 regnetx_080,454.56,843.629,384,224,39.57 regnetx_064,452.43,564.953,256,224,26.21 halo2botnet50ts_256,451.35,424.392,192,256,22.64 ecaresnet101d,447.44,570.33,256,224,44.57 densenet201,447.44,425.987,192,224,20.01 nf_regnet_b4,445.8,428.533,192,320,30.21 convit_small,443.63,431.737,192,224,27.78 efficientnetv2_s,433.27,293.157,128,288,21.46 skresnext50_32x4d,432.3,590.802,256,224,27.48 cs3se_edgenet_x,431.68,443.324,192,256,50.72 botnet50ts_256,428.8,297.529,128,256,22.74 ssl_resnext101_32x4d,427.28,447.74,192,224,44.18 resnext101_32x4d,427.18,447.921,192,224,44.18 swsl_resnext101_32x4d,427.16,447.915,192,224,44.18 gluon_resnext101_32x4d,427.13,447.97,192,224,44.18 poolformer_s36,425.0,449.906,192,224,30.86 ese_vovnet39b_evos,421.31,302.862,128,224,24.58 resnet101d,418.0,457.739,192,256,44.57 dla102x,417.16,458.658,192,224,26.31 res2net101_26w_4s,416.51,612.121,256,224,45.21 lamhalobotnet50ts_256,413.09,463.774,192,256,22.57 twins_svt_base,411.8,464.138,192,224,56.07 crossvit_18_240,406.79,312.611,128,240,43.27 tresnet_l,404.84,1261.389,512,224,55.99 efficientnetv2_rw_s,402.34,315.806,128,288,23.94 volo_d1_224,401.47,476.8,192,224,26.63 resmlp_36_224,401.06,476.505,192,224,44.69 res2net50_26w_8s,400.52,636.999,256,224,48.4 resmlp_36_distilled_224,400.14,477.557,192,224,44.69 swin_small_patch4_window7_224,399.67,478.499,192,224,49.61 resnest50d_4s2x40d,396.0,645.092,256,224,30.42 vit_base_patch16_224_miil,395.78,484.311,192,224,86.54 crossvit_18_dagger_240,394.08,322.72,128,240,44.27 deit_base_patch16_224,390.88,490.307,192,224,86.57 vit_base_patch16_224,390.86,490.391,192,224,86.57 vit_base_patch16_224_sam,390.67,490.608,192,224,86.57 mobilevitv2_175_in22ft1k,389.97,327.241,128,256,14.25 mobilevitv2_175,389.95,327.23,128,256,14.25 tf_efficientnetv2_s_in21ft1k,389.66,326.288,128,300,21.46 tf_efficientnetv2_s,389.1,326.713,128,300,21.46 vgg16_bn,388.69,658.276,256,224,138.37 regnety_064,388.46,657.256,256,224,30.58 regnety_080,385.84,662.273,256,224,39.18 deit_base_distilled_patch16_224,385.62,497.03,192,224,87.34 xception,385.56,331.194,128,299,22.86 regnety_040s_gn,384.97,330.927,128,224,20.65 repvgg_b2g4,379.42,1348.329,512,224,61.76 resnetv2_152,379.4,503.868,192,224,60.19 regnetz_d8,378.76,336.282,128,256,23.37 hrnet_w18,378.26,671.883,256,224,21.3 ese_vovnet99b,377.27,677.036,256,224,63.2 vit_small_resnet50d_s16_224,376.52,508.654,192,224,57.53 cait_xxs36_224,375.8,507.032,192,224,17.3 gluon_seresnext101_32x4d,375.02,509.78,192,224,48.96 regnetz_040,374.91,339.544,128,256,27.12 seresnext101_32x4d,374.73,510.176,192,224,48.96 regnetv_064,372.69,513.522,192,224,30.58 regnetz_040h,372.64,341.593,128,256,28.94 legacy_seresnext101_32x4d,372.06,513.705,192,224,48.96 deit3_base_patch16_224_in21ft1k,371.79,515.431,192,224,86.59 deit3_base_patch16_224,371.73,515.464,192,224,86.59 tf_efficientnet_b3,370.15,344.089,128,300,12.23 tf_efficientnet_b3_ap,370.14,344.111,128,300,12.23 tf_efficientnet_b3_ns,370.1,344.134,128,300,12.23 resnet152,370.08,516.516,192,224,60.19 vit_relpos_base_patch16_clsgap_224,369.76,518.105,192,224,86.43 vit_relpos_base_patch16_cls_224,369.34,518.67,192,224,86.43 resnetv2_50d_frn,369.16,345.594,128,224,25.59 gluon_resnet152_v1b,369.02,517.998,192,224,60.19 tv_resnet152,369.0,518.088,192,224,60.19 regnetz_b16_evos,365.3,348.518,128,224,9.74 sequencer2d_m,363.12,525.505,192,224,38.31 ese_vovnet99b_iabn,362.9,1055.043,384,224,63.2 resnetv2_152d,360.99,529.48,192,224,60.2 beit_base_patch16_224,358.29,534.776,192,224,86.53 xcit_tiny_12_p16_384_dist,357.91,534.55,192,384,6.72 vit_relpos_base_patch16_224,355.33,539.194,192,224,86.43 gluon_resnet152_v1c,354.77,538.797,192,224,60.21 regnetz_d32,354.52,359.397,128,256,27.58 swinv2_tiny_window8_256,354.35,540.569,192,256,28.35 resnetv2_50d_evos,353.36,270.506,96,224,25.59 dpn92,353.0,723.617,256,224,37.67 vgg19,352.0,1090.655,384,224,143.67 gluon_resnet152_v1d,351.06,544.563,192,224,60.21 densenet161,346.02,367.416,128,224,28.68 xception41p,344.85,370.318,128,299,26.91 gluon_resnet152_v1s,344.7,368.96,128,224,60.32 mobilevitv2_200,342.4,372.843,128,256,18.45 tnt_s_patch16_224,342.25,559.037,192,224,23.76 mobilevitv2_200_in22ft1k,342.08,373.147,128,256,18.45 eca_nfnet_l1,341.07,561.084,192,256,41.41 hrnet_w32,340.54,747.043,256,224,41.23 dla169,338.11,565.259,192,224,53.39 convnext_base_in22ft1k,337.76,377.102,128,224,88.59 convnext_base,336.98,378.091,128,224,88.59 repvgg_b2,335.01,1527.215,512,224,89.02 repvgg_b3g4,334.01,1148.557,384,224,83.83 vgg13,331.37,1544.923,512,224,133.05 pit_b_224,331.17,385.577,128,224,73.76 vgg19_bn,330.96,773.109,256,224,143.68 pit_b_distilled_224,329.46,387.568,128,224,74.79 regnetx_120,327.41,780.952,256,224,46.11 twins_pcpvt_large,322.96,392.17,128,224,60.99 hrnet_w30,321.86,790.607,256,224,37.71 legacy_seresnet152,319.56,397.245,128,224,66.82 inception_v4,316.87,603.734,192,299,42.68 seresnet152,313.75,608.677,192,224,66.82 vit_small_patch16_36x1_224,310.56,409.448,128,224,64.67 dla102x2,309.09,412.537,128,224,41.28 xcit_small_24_p16_224_dist,307.83,412.466,128,224,47.67 convmixer_1024_20_ks9_p14,307.81,830.813,256,224,24.38 vit_small_patch16_18x2_224,307.61,413.3,128,224,64.67 xcit_small_24_p16_224,307.46,412.867,128,224,47.67 regnety_120,307.05,623.971,192,224,51.82 poolformer_m36,303.34,420.132,128,224,56.17 efficientnet_el_pruned,301.49,423.464,128,300,10.59 efficientnet_el,301.45,423.5,128,300,10.59 swinv2_cr_small_ns_224,300.41,423.619,128,224,49.7 swinv2_cr_small_224,297.65,427.521,128,224,49.7 mixnet_xxl,297.33,428.503,128,224,23.96 cait_s24_224,296.96,428.341,128,224,46.92 nest_small,296.72,321.888,96,224,38.35 coat_tiny,296.44,429.708,128,224,5.5 tf_efficientnet_el,295.51,432.07,128,300,10.59 jx_nest_small,294.83,323.932,96,224,38.35 efficientnet_b4,293.1,325.442,96,320,19.34 xception41,293.07,435.505,128,299,26.97 xcit_tiny_12_p8_224_dist,291.52,437.287,128,224,6.71 tresnet_xl,291.4,875.028,256,224,78.44 resnext101_64x4d,291.33,437.816,128,224,83.46 gluon_resnext101_64x4d,291.25,437.881,128,224,83.46 swin_s3_small_224,289.76,439.818,128,224,49.74 wide_resnet101_2,289.62,661.356,192,224,126.89 xcit_tiny_12_p8_224,289.33,440.549,128,224,6.71 twins_svt_large,289.11,440.647,128,224,99.27 resnet152d,281.47,452.46,128,256,60.21 swin_base_patch4_window7_224,279.66,455.817,128,224,87.77 convnext_tiny_384_in22ft1k,278.8,343.389,96,384,28.59 resnet200,276.62,459.688,128,224,64.67 ssl_resnext101_32x8d,276.52,461.341,128,224,88.79 ig_resnext101_32x8d,276.39,461.582,128,224,88.79 resnext101_32x8d,276.22,461.854,128,224,88.79 swsl_resnext101_32x8d,276.22,461.764,128,224,88.79 repvgg_b3,271.93,1411.039,384,224,123.09 nfnet_f1,271.43,705.161,192,224,132.63 resnetv2_50d_evob,268.92,355.729,96,224,25.59 gmlp_b16_224,268.22,356.298,96,224,73.08 dpn98,267.62,476.602,128,224,61.57 regnetx_160,266.55,719.244,192,224,54.28 regnety_160,264.44,724.758,192,224,83.59 gluon_seresnext101_64x4d,264.12,482.344,128,224,88.23 ens_adv_inception_resnet_v2,261.47,730.952,192,299,55.84 inception_resnet_v2,261.44,730.919,192,299,55.84 xception65p,259.32,492.32,128,299,39.82 efficientnet_lite4,255.23,249.374,64,380,13.01 vit_base_patch16_rpn_224,254.51,753.593,192,224,86.54 resnest101e,253.9,501.575,128,256,48.28 crossvit_base_240,253.73,376.737,96,240,105.03 seresnext101_32x8d,251.44,506.792,128,224,93.57 vit_relpos_base_patch16_rpn_224,250.62,765.002,192,224,86.41 vit_base_patch16_plus_240,248.4,514.352,128,240,117.56 tf_efficientnet_lite4,247.32,257.437,64,380,13.01 efficientnet_b3_gn,245.44,258.998,64,288,11.73 dm_nfnet_f1,244.51,521.138,128,224,132.63 seresnext101d_32x8d,242.49,525.503,128,224,93.59 seresnet152d,242.25,392.75,96,256,66.84 xcit_tiny_24_p16_384_dist,241.62,526.318,128,384,12.12 vit_small_patch16_384,239.05,266.865,64,384,22.2 vit_relpos_base_patch16_plus_240,238.57,535.322,128,240,117.38 vit_large_r50_s32_224,237.94,401.033,96,224,328.99 resnetrs152,237.63,535.199,128,256,86.62 swinv2_tiny_window16_256,237.41,403.072,96,256,28.35 seresnextaa101d_32x8d,228.0,559.15,128,224,93.59 xcit_medium_24_p16_224_dist,227.91,558.239,128,224,84.4 deit3_small_patch16_384_in21ft1k,227.77,280.008,64,384,22.21 deit3_small_patch16_384,227.76,280.015,64,384,22.21 xcit_medium_24_p16_224,227.75,558.491,128,224,84.4 vit_small_r26_s32_384,227.25,280.302,64,384,36.47 convit_base,224.86,568.198,128,224,86.54 gluon_xception65,224.1,426.474,96,299,39.92 swin_s3_base_224,223.36,426.944,96,224,71.13 tnt_b_patch16_224,222.94,572.213,128,224,65.41 xception65,222.93,428.728,96,299,39.92 coat_mini,222.88,572.209,128,224,10.34 volo_d2_224,222.28,430.111,96,224,58.68 xcit_small_12_p16_384_dist,221.79,430.984,96,384,26.25 poolformer_m48,220.53,432.741,96,224,73.47 hrnet_w40,219.45,869.959,192,224,57.56 vit_base_r50_s16_224,215.41,443.988,96,224,98.66 swinv2_cr_base_ns_224,213.88,446.428,96,224,87.88 sequencer2d_l,213.86,444.098,96,224,54.3 swinv2_small_window8_256,212.59,449.01,96,256,49.73 swinv2_cr_base_224,211.39,451.682,96,224,87.88 mobilevitv2_150_384_in22ft1k,210.38,303.23,64,384,10.59 nest_base,210.2,302.774,64,224,67.72 tresnet_m_448,209.55,913.447,192,448,31.39 efficientnetv2_m,207.96,304.545,64,320,54.14 jx_nest_base,207.78,306.371,64,224,67.72 regnetz_c16_evos,207.35,306.824,64,256,13.49 hrnet_w44,206.45,925.026,192,224,67.06 resnet200d,204.47,623.017,128,256,64.69 efficientnet_b3_g8_gn,203.44,312.836,64,288,14.25 hrnet_w48,202.15,628.427,128,224,77.47 densenet264,202.1,470.789,96,224,72.69 dpn131,198.25,643.486,128,224,79.25 tf_efficientnet_b4,194.83,326.399,64,380,19.34 tf_efficientnet_b4_ap,194.65,326.738,64,380,19.34 tf_efficientnet_b4_ns,194.23,327.375,64,380,19.34 xcit_nano_12_p8_384_dist,187.76,338.965,64,384,3.05 efficientnetv2_rw_m,187.31,338.14,64,320,53.24 dpn107,187.14,682.151,128,224,86.92 convnext_large_in22ft1k,187.05,511.402,96,224,197.77 convnext_large,187.01,511.523,96,224,197.77 nf_regnet_b5,186.49,512.09,96,384,49.74 xcit_tiny_24_p8_224_dist,183.21,520.533,96,224,12.11 xcit_tiny_24_p8_224,183.21,520.609,96,224,12.11 halonet_h1,177.48,359.151,64,256,8.1 hrnet_w64,176.04,722.362,128,224,128.06 mobilevitv2_175_384_in22ft1k,175.76,363.135,64,384,14.25 senet154,174.83,545.792,96,224,115.09 regnety_320,174.41,732.528,128,224,145.05 gluon_senet154,174.03,548.162,96,224,115.09 regnetz_e8,173.89,365.999,64,256,57.7 legacy_senet154,170.27,560.493,96,224,115.09 xception71,168.81,376.911,64,299,42.34 xcit_small_12_p8_224,168.52,377.961,64,224,26.21 xcit_small_12_p8_224_dist,168.32,378.375,64,224,26.21 vit_large_patch32_384,168.05,569.595,96,384,306.63 convnext_small_384_in22ft1k,164.94,386.292,64,384,50.22 mixer_l16_224,164.43,582.335,96,224,208.2 ecaresnet200d,161.74,392.334,64,256,64.69 seresnet200d,161.56,391.892,64,256,71.86 resnetrs200,160.52,394.222,64,256,93.21 densenet264d_iabn,158.12,804.924,128,224,72.74 regnetx_320,155.94,819.702,128,224,107.81 swin_large_patch4_window7_224,153.79,414.255,64,224,196.53 volo_d3_224,152.73,416.584,64,224,86.33 mobilevitv2_200_384_in22ft1k,150.68,317.559,48,384,18.45 swinv2_base_window8_256,150.28,423.39,64,256,87.92 resnetv2_50x1_bitm,149.04,321.21,48,448,25.55 nfnet_f2,148.92,641.446,96,256,193.78 swinv2_small_window16_256,142.83,445.591,64,256,49.73 tf_efficientnetv2_m,142.41,333.833,48,384,54.14 eca_nfnet_l2,142.2,672.291,96,320,56.72 tf_efficientnetv2_m_in21ft1k,141.35,336.246,48,384,54.14 regnetz_d8_evos,132.2,360.995,48,256,23.46 swinv2_cr_tiny_384,131.5,485.388,64,384,28.33 ig_resnext101_32x16d,130.47,734.203,96,224,194.03 ssl_resnext101_32x16d,130.4,734.63,96,224,194.03 swsl_resnext101_32x16d,130.37,734.771,96,224,194.03 xcit_large_24_p16_224,126.98,500.577,64,224,189.1 xcit_large_24_p16_224_dist,126.97,500.662,64,224,189.1 seresnet269d,125.7,503.318,64,256,113.67 dm_nfnet_f2,125.2,507.681,64,256,193.78 swinv2_cr_large_224,124.7,510.736,64,224,196.68 xcit_tiny_12_p8_384_dist,122.38,390.412,48,384,6.71 resnetrs270,121.5,520.761,64,256,129.86 crossvit_15_dagger_408,117.57,270.29,32,408,28.5 vit_large_patch16_224,117.08,544.981,64,224,304.33 vit_base_patch16_18x2_224,116.55,546.378,64,224,256.73 convnext_base_384_in22ft1k,115.97,412.084,48,384,88.59 convnext_xlarge_in22ft1k,115.91,550.445,64,224,350.2 deit3_large_patch16_224_in21ft1k,113.19,563.501,64,224,304.37 deit3_large_patch16_224,113.17,563.634,64,224,304.37 xcit_small_24_p16_384_dist,112.88,421.839,48,384,47.67 beit_large_patch16_224,107.8,591.544,64,224,304.43 swinv2_base_window16_256,103.68,460.461,48,256,87.92 swinv2_base_window12to16_192to256_22kft1k,103.56,461.021,48,256,87.92 tresnet_l_448,103.2,1236.839,128,448,55.99 volo_d1_384,99.39,320.613,32,384,26.78 cait_xxs24_384,97.83,488.033,48,384,12.03 vit_base_patch16_384,96.96,329.192,32,384,86.86 deit_base_patch16_384,96.37,331.171,32,384,86.86 volo_d4_224,95.75,498.748,48,224,192.96 deit_base_distilled_patch16_384,94.83,336.556,32,384,87.63 efficientnet_b5,93.71,338.901,32,456,30.39 deit3_base_patch16_384,93.22,342.328,32,384,86.88 deit3_base_patch16_384_in21ft1k,92.68,344.327,32,384,86.88 tf_efficientnet_b5,92.16,344.785,32,456,30.39 tf_efficientnet_b5_ns,92.11,344.939,32,456,30.39 tf_efficientnet_b5_ap,91.98,345.405,32,456,30.39 resnetv2_152x2_bit_teacher,89.37,355.711,32,224,236.34 crossvit_18_dagger_408,88.76,358.492,32,408,44.61 xcit_small_24_p8_224,87.25,546.829,48,224,47.63 xcit_small_24_p8_224_dist,86.87,549.24,48,224,47.63 convmixer_768_32,85.53,1121.025,96,224,21.11 vit_large_patch14_224,85.27,561.239,48,224,304.2 eca_nfnet_l3,84.61,563.702,48,352,72.04 resnetv2_101x1_bitm,84.61,187.399,16,448,44.54 beit_base_patch16_384,83.67,381.291,32,384,86.74 resnest200e,83.33,570.802,48,320,70.2 tf_efficientnetv2_l_in21ft1k,83.27,379.678,32,384,118.52 efficientnetv2_l,83.27,379.867,32,384,118.52 tf_efficientnetv2_l,82.74,382.367,32,384,118.52 ecaresnet269d,82.15,579.477,48,320,102.09 tresnet_xl_448,78.31,1222.487,96,448,78.44 xcit_medium_24_p16_384_dist,77.9,407.346,32,384,84.4 vit_large_r50_s32_384,77.49,410.426,32,384,329.09 swinv2_cr_small_384,76.31,416.793,32,384,49.7 swin_base_patch4_window12_384,74.03,430.327,32,384,87.9 pnasnet5large,68.77,461.392,32,331,86.06 resnetrs350,68.24,460.967,32,288,163.96 nfnet_f3,67.87,703.087,48,320,254.92 nasnetalarge,67.38,469.785,32,331,88.75 resmlp_big_24_distilled_224,67.03,475.867,32,224,129.14 resmlp_big_24_224_in22ft1k,67.03,475.857,32,224,129.14 resmlp_big_24_224,67.02,475.97,32,224,129.14 cait_xs24_384,65.59,485.229,32,384,26.67 convnext_large_384_in22ft1k,63.62,501.159,32,384,197.77 vit_base_patch8_224,63.42,377.591,24,224,86.58 cait_xxs36_384,63.23,502.345,32,384,17.37 ig_resnext101_32x32d,62.72,508.666,32,224,468.53 xcit_tiny_24_p8_384_dist,62.14,511.676,32,384,12.11 volo_d5_224,61.58,516.467,32,224,295.46 vit_base_resnet50_384,61.06,391.43,24,384,98.95 vit_base_r50_s16_384,61.0,391.773,24,384,98.95 swinv2_large_window12to16_192to256_22kft1k,60.93,391.352,24,256,196.74 xcit_medium_24_p8_224,60.43,526.111,32,224,84.32 xcit_medium_24_p8_224_dist,60.03,529.652,32,224,84.32 xcit_small_12_p8_384_dist,57.75,413.763,24,384,26.21 dm_nfnet_f3,57.26,554.375,32,320,254.92 volo_d2_384,55.56,286.247,16,384,58.87 efficientnet_b6,54.98,288.003,16,528,43.04 swinv2_cr_base_384,54.71,436.265,24,384,87.88 tf_efficientnet_b6,54.38,291.338,16,528,43.04 tf_efficientnet_b6_ns,54.21,292.241,16,528,43.04 tf_efficientnet_b6_ap,54.17,292.479,16,528,43.04 efficientnetv2_xl,53.77,291.666,16,384,208.12 tf_efficientnetv2_xl_in21ft1k,53.07,295.611,16,384,208.12 convmixer_1536_20,50.1,957.271,48,224,51.63 swinv2_cr_huge_224,49.51,482.114,24,224,657.83 cait_s24_384,49.37,483.331,24,384,47.06 resnetrs420,48.12,489.4,24,320,191.89 xcit_large_24_p16_384_dist,45.6,522.909,24,384,189.1 swin_large_patch4_window12_384,41.65,382.145,16,384,196.74 convnext_xlarge_384_in22ft1k,40.18,595.51,24,384,350.2 vit_huge_patch14_224,39.94,398.436,16,224,632.05 deit3_huge_patch14_224_in21ft1k,38.36,414.578,16,224,632.13 deit3_huge_patch14_224,38.33,414.855,16,224,632.13 nfnet_f4,36.85,646.047,24,384,316.07 resnest269e,35.75,664.499,24,416,110.93 resnetv2_50x3_bitm,34.88,457.822,16,448,217.32 xcit_large_24_p8_224_dist,33.7,471.344,16,224,188.93 xcit_large_24_p8_224,33.68,471.512,16,224,188.93 resnetv2_152x2_bit_teacher_384,32.69,487.138,16,384,236.34 ig_resnext101_32x48d,32.39,492.417,16,224,828.41 swinv2_cr_large_384,32.36,491.839,16,384,196.68 cait_s36_384,31.92,497.404,16,384,68.37 efficientnet_b7,31.74,248.492,8,600,66.35 dm_nfnet_f4,31.4,758.349,24,384,316.07 tf_efficientnet_b7,31.4,251.12,8,600,66.35 tf_efficientnet_b7_ns,31.37,251.394,8,600,66.35 tf_efficientnet_b7_ap,31.35,251.548,8,600,66.35 xcit_small_24_p8_384_dist,29.2,544.65,16,384,47.63 vit_large_patch16_384,29.07,411.127,12,384,304.72 deit3_large_patch16_384,28.22,423.365,12,384,304.76 deit3_large_patch16_384_in21ft1k,28.19,423.825,12,384,304.76 swinv2_base_window12to24_192to384_22kft1k,28.14,423.938,12,384,87.92 beit_large_patch16_384,25.12,475.56,12,384,305.0 volo_d3_448,23.79,333.686,8,448,86.63 nfnet_f5,22.84,694.067,16,416,377.21 resnetv2_152x2_bitm,22.69,350.236,8,448,236.34 vit_giant_patch14_224,22.29,356.113,8,224,1012.61 dm_nfnet_f5,20.95,756.72,16,416,377.21 xcit_medium_24_p8_384_dist,19.97,397.272,8,384,84.32 efficientnet_b8,19.9,297.659,6,672,87.41 tf_efficientnet_b8_ap,19.66,301.198,6,672,87.41 tf_efficientnet_b8,19.65,301.246,6,672,87.41 nfnet_f6,18.5,641.193,12,448,438.36 resnetv2_101x3_bitm,18.0,442.742,8,448,387.93 volo_d4_448,16.87,353.154,6,448,193.41 swinv2_large_window12to24_192to384_22kft1k,16.59,359.187,6,384,196.74 dm_nfnet_f6,15.07,522.261,8,448,438.36 swinv2_cr_huge_384,12.92,461.964,6,384,657.94 nfnet_f7,12.67,622.861,8,480,499.5 cait_m36_384,11.76,506.439,6,384,271.22 xcit_large_24_p8_384_dist,11.53,516.755,6,384,188.93 volo_d5_448,11.08,357.783,4,448,295.91 tf_efficientnet_l2_ns_475,10.91,360.832,4,475,480.31 beit_large_patch16_512,9.42,422.333,4,512,305.67 volo_d5_512,7.72,385.462,3,512,296.09 resnetv2_152x4_bitm,4.91,404.529,2,480,936.53 cait_m48_448,4.71,419.69,2,448,356.46 efficientnet_l2,3.43,285.826,1,800,480.31 tf_efficientnet_l2_ns,3.42,287.247,1,800,480.31
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt111-cu113-rtx3090.csv	model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count tinynet_e,10725.36,46.047,512,106,2.04 mobilenetv3_small_050,9864.52,50.786,512,224,1.59 lcnet_035,9593.72,52.888,512,224,1.64 lcnet_050,8283.82,61.296,512,224,1.88 tf_mobilenetv3_small_minimal_100,8178.73,62.055,512,224,2.04 tinynet_d,7987.22,63.336,512,152,2.34 mobilenetv3_small_075,7734.29,65.482,512,224,2.04 mobilenetv3_small_100,7481.49,67.702,512,224,2.54 tf_mobilenetv3_small_075,7093.89,71.455,512,224,2.04 tf_mobilenetv3_small_100,6879.11,73.705,512,224,2.54 levit_128s,6303.14,80.293,512,224,7.78 lcnet_075,5742.95,88.676,512,224,2.36 lcnet_100,5331.75,95.531,512,224,2.95 mixer_s32_224,4714.36,108.029,512,224,19.1 mnasnet_small,4652.19,109.156,512,224,2.03 mnasnet_050,4534.41,112.14,512,224,2.22 levit_128,4434.56,114.332,512,224,9.21 vit_small_patch32_224,4334.06,117.284,512,224,22.88 mobilenetv2_035,4197.24,121.203,512,224,1.68 tinynet_c,4165.97,121.921,512,184,2.46 gernet_s,4117.74,123.649,512,224,8.17 semnasnet_050,4027.14,126.223,512,224,2.08 vit_tiny_r_s16_p8_224,3857.49,131.88,512,224,6.34 levit_192,3823.94,132.765,512,224,10.95 lcnet_150,3663.02,139.3,512,224,4.5 resnet18,3584.19,142.504,512,224,11.69 gluon_resnet18_v1b,3584.07,142.508,512,224,11.69 swsl_resnet18,3583.72,142.531,512,224,11.69 ssl_resnet18,3558.1,143.543,512,224,11.69 mobilenetv2_050,3541.93,143.76,512,224,1.97 mobilenetv3_large_075,3343.71,152.255,512,224,3.99 ese_vovnet19b_slim_dw,3243.14,157.395,512,224,1.9 tf_mobilenetv3_large_minimal_100,3227.09,157.922,512,224,3.92 seresnet18,3222.19,158.398,512,224,11.78 legacy_seresnet18,3130.77,163.021,512,224,11.78 tf_mobilenetv3_large_075,3109.15,163.824,512,224,3.99 mnasnet_075,3102.76,164.235,512,224,3.17 ghostnet_050,3069.54,165.437,512,224,2.59 mobilenetv3_rw,3020.36,168.644,512,224,5.48 mobilenetv3_large_100,2997.0,169.969,512,224,5.48 mobilenetv3_large_100_miil,2996.51,169.991,512,224,5.48 levit_256,2923.52,174.041,512,224,18.89 hardcorenas_a,2875.54,177.351,512,224,5.26 resnet18d,2830.02,180.547,512,224,11.71 mnasnet_b1,2826.42,180.324,512,224,4.38 mnasnet_100,2810.01,181.39,512,224,4.38 tf_mobilenetv3_large_100,2800.51,181.961,512,224,5.48 tinynet_b,2773.33,183.58,512,188,3.73 hardcorenas_b,2665.39,191.158,512,224,5.18 semnasnet_075,2649.51,192.342,512,224,2.91 hardcorenas_c,2643.17,192.764,512,224,5.52 ese_vovnet19b_slim,2613.26,195.551,512,224,3.17 mobilenetv2_075,2538.45,200.896,512,224,2.64 tf_efficientnetv2_b0,2507.59,202.986,512,224,7.14 spnasnet_100,2504.82,203.411,512,224,4.42 levit_256d,2485.6,204.456,512,224,26.21 hardcorenas_d,2483.8,204.983,512,224,7.5 semnasnet_100,2411.15,211.44,512,224,3.89 mnasnet_a1,2396.96,212.676,512,224,3.89 mobilenetv2_100,2381.65,214.209,512,224,3.5 regnetx_002,2371.97,215.169,512,224,2.68 tinynet_a,2274.6,223.875,512,192,6.19 regnety_002,2255.16,226.095,512,224,3.16 ghostnet_100,2251.56,226.062,512,224,5.18 fbnetc_100,2248.09,226.804,512,224,5.57 deit_tiny_patch16_224,2233.8,228.385,512,224,5.72 vit_tiny_patch16_224,2229.44,228.819,512,224,5.72 efficientnet_lite0,2209.28,231.003,512,224,4.65 hardcorenas_f,2207.45,230.839,512,224,8.2 deit_tiny_distilled_patch16_224,2193.62,232.567,512,224,5.91 hardcorenas_e,2183.35,233.392,512,224,8.07 xcit_nano_12_p16_224_dist,2148.4,236.588,512,224,3.05 xcit_nano_12_p16_224,2147.35,236.626,512,224,3.05 tv_resnet34,2081.13,245.45,512,224,21.8 resnet34,2080.93,245.474,512,224,21.8 gluon_resnet34_v1b,2070.75,246.674,512,224,21.8 pit_ti_distilled_224,2069.0,246.563,512,224,5.1 pit_ti_224,2067.14,246.798,512,224,4.85 tf_efficientnet_lite0,2051.57,248.83,512,224,4.65 skresnet18,2011.18,253.956,512,224,11.96 resnet26,1965.66,259.994,512,224,16.0 resnetblur18,1945.89,262.773,512,224,11.69 gernet_m,1920.15,265.947,512,224,21.14 ese_vovnet19b_dw,1905.39,268.216,512,224,6.54 nf_resnet26,1877.61,272.201,512,224,16.0 hrnet_w18_small,1858.6,274.127,512,224,13.19 seresnet34,1854.8,275.134,512,224,21.96 mnasnet_140,1835.61,278.136,512,224,7.12 legacy_seresnet34,1814.57,281.284,512,224,21.96 efficientnet_b0,1800.57,212.181,384,224,5.29 levit_384,1799.63,283.374,512,224,39.13 resnet34d,1799.03,284.0,512,224,21.82 mobilenetv2_110d,1768.64,216.1,384,224,4.52 rexnetr_100,1759.23,217.141,384,224,4.88 selecsls42,1754.3,291.22,512,224,30.35 selecsls42b,1748.59,292.176,512,224,32.46 mixer_b32_224,1728.02,295.478,512,224,60.29 tf_efficientnet_b0_ns,1702.0,224.532,384,224,5.29 tf_efficientnet_b0_ap,1700.69,224.751,384,224,5.29 tf_efficientnet_b0,1700.24,224.78,384,224,5.29 mixer_s16_224,1649.18,309.899,512,224,18.53 semnasnet_140,1640.99,311.119,512,224,6.11 tf_efficientnet_es,1622.88,314.744,512,224,5.44 efficientnet_es,1618.83,315.528,512,224,5.44 efficientnet_es_pruned,1616.07,316.054,512,224,5.44 vit_base_patch32_224_sam,1613.76,316.429,512,224,88.22 vit_base_patch32_224,1612.96,316.587,512,224,88.22 resnet26d,1609.37,317.63,512,224,16.01 tf_efficientnetv2_b1,1607.05,237.525,384,240,8.14 ghostnet_130,1600.19,318.63,512,224,7.36 pit_xs_distilled_224,1591.2,320.859,512,224,11.0 pit_xs_224,1589.39,321.258,512,224,10.62 repvgg_b0,1586.15,321.714,512,224,15.82 resmlp_12_224,1552.02,329.099,512,224,15.35 resmlp_12_distilled_224,1551.98,329.119,512,224,15.35 gmixer_12_224,1551.47,329.195,512,224,12.7 mobilenetv2_140,1539.46,248.646,384,224,6.11 mobilevit_xxs,1515.36,252.293,384,256,1.27 selecsls60,1486.56,343.544,512,224,30.67 selecsls60b,1480.82,344.867,512,224,32.77 nf_seresnet26,1471.47,347.302,512,224,17.4 xcit_tiny_12_p16_224,1422.37,358.218,512,224,6.72 xcit_tiny_12_p16_224_dist,1420.76,358.558,512,224,6.72 efficientnet_lite1,1418.67,179.483,256,240,5.42 vit_small_patch32_384,1414.68,361.045,512,384,22.92 efficientnet_b1_pruned,1384.32,368.407,512,240,6.33 gmlp_ti16_224,1378.39,276.995,384,224,5.87 dla46_c,1367.99,373.533,512,224,1.3 nf_ecaresnet26,1361.11,375.605,512,224,16.0 poolformer_s12,1359.83,375.831,512,224,11.92 rexnetr_130,1350.37,188.438,256,224,7.61 tf_efficientnet_lite1,1343.78,189.557,256,240,5.42 crossvit_tiny_240,1320.33,386.159,512,240,7.01 mobilenetv2_120d,1309.71,194.276,256,224,5.83 resnetv2_50,1296.0,394.281,512,224,25.55 gernet_l,1283.32,398.106,512,256,31.08 rexnet_100,1277.92,299.324,384,224,4.8 crossvit_9_240,1236.5,309.16,384,240,8.55 resnet26t,1227.85,416.475,512,256,16.01 ssl_resnet50,1223.5,417.643,512,224,25.56 resnet50,1222.61,417.956,512,224,25.56 tv_resnet50,1222.03,418.147,512,224,25.56 swsl_resnet50,1222.0,418.189,512,224,25.56 gluon_resnet50_v1b,1221.97,418.181,512,224,25.56 crossvit_9_dagger_240,1195.59,319.731,384,240,8.78 vit_tiny_r_s16_p8_384,1193.45,320.912,384,384,6.36 rexnetr_150,1185.63,214.799,256,224,9.78 fbnetv3_b,1184.86,322.487,384,256,8.6 botnet26t_256,1168.73,327.971,384,256,12.49 tf_efficientnetv2_b2,1157.41,219.649,256,260,10.1 regnetx_004,1150.82,443.834,512,224,5.16 repvgg_a2,1142.14,447.42,512,224,28.21 skresnet34,1140.48,447.803,512,224,22.28 resnetv2_50t,1134.35,450.541,512,224,25.57 fbnetv3_d,1133.65,223.966,256,256,10.31 resnetv2_50d,1131.93,451.499,512,224,25.57 gluon_resnet50_v1c,1131.41,338.54,384,224,25.58 halonet26t,1122.34,341.57,384,256,12.48 convit_tiny,1108.09,461.02,512,224,5.71 efficientnet_lite2,1096.26,232.544,256,260,6.09 dla34,1094.33,467.287,512,224,15.74 convnext_nano_hnf,1075.54,356.227,384,224,15.59 resnet50d,1070.21,357.929,384,224,25.58 gluon_resnet50_v1d,1070.13,357.997,384,224,25.58 resnet50t,1068.62,358.508,384,224,25.57 mixnet_s,1051.2,485.837,512,224,4.13 legacy_seresnext26_32x4d,1051.18,486.414,512,224,16.79 tf_efficientnet_lite2,1045.58,243.879,256,260,6.09 vit_small_resnet26d_224,1042.37,367.395,384,224,63.61 deit_small_patch16_224,1032.62,371.017,384,224,22.05 vit_small_patch16_224,1027.63,372.823,384,224,22.05 regnety_004,1027.25,497.255,512,224,4.34 tf_efficientnet_b1_ns,1026.16,247.955,256,240,7.79 tf_efficientnet_b1_ap,1026.11,248.006,256,240,7.79 tf_efficientnet_b1,1025.11,248.193,256,240,7.79 resnet32ts,1021.77,249.96,256,256,17.96 deit_small_distilled_patch16_224,1010.79,379.058,384,224,22.44 resnet33ts,1009.39,252.998,256,256,19.68 res2net50_48w_2s,1006.25,380.82,384,224,25.29 vovnet39a,1004.46,509.092,512,224,22.6 seresnext26d_32x4d,1002.27,382.471,384,224,16.81 seresnext26t_32x4d,1001.74,382.665,384,224,16.81 seresnext26tn_32x4d,1001.47,382.779,384,224,16.81 legacy_seresnet50,993.86,385.256,384,224,28.09 tf_efficientnet_em,979.63,260.356,256,240,6.9 efficientnet_em,978.46,260.687,256,240,6.9 dla46x_c,973.77,525.047,512,224,1.07 eca_resnet33ts,964.39,264.788,256,256,19.68 pit_s_224,961.0,265.507,256,224,23.46 pit_s_distilled_224,960.14,265.718,256,224,24.04 tf_mixnet_s,958.59,532.877,512,224,4.13 seresnet50,956.87,400.165,384,224,28.09 efficientnet_b1,954.91,266.596,256,256,7.79 seresnet33ts,954.85,267.313,256,256,19.78 ecaresnetlight,952.86,536.422,512,224,30.16 vit_base2_patch32_256,950.48,537.853,512,256,119.46 ese_vovnet39b,947.73,539.578,512,224,24.57 ecaresnext50t_32x4d,947.69,404.65,384,224,15.41 ecaresnext26t_32x4d,947.22,404.844,384,224,15.41 dla60,945.45,405.196,384,224,22.04 gluon_resnet50_v1s,943.31,406.227,384,224,25.68 resnetaa50d,941.94,406.82,384,224,25.58 eca_vovnet39b,939.18,544.514,512,224,22.6 vgg11,930.6,550.023,512,224,132.86 gcresnet33ts,927.58,274.995,256,256,19.88 lambda_resnet26rpt_256,921.55,207.755,192,256,10.99 dla60x_c,921.01,554.951,512,224,1.32 ecaresnet50d_pruned,911.85,560.565,512,224,19.94 resnetblur50,909.58,421.362,384,224,25.56 mobilevit_xs,909.51,210.0,192,256,2.32 cspresnet50,906.79,422.612,384,256,21.62 rexnetr_200,896.75,212.966,192,224,16.52 coat_lite_tiny,890.79,430.193,384,224,5.72 nf_seresnet50,886.81,431.807,384,224,28.09 dpn68b,878.29,436.039,384,224,12.61 selecsls84,872.56,585.545,512,224,50.95 twins_svt_small,868.85,440.366,384,224,24.06 hrnet_w18_small_v2,867.42,587.948,512,224,15.6 seresnet50t,865.51,442.504,384,224,28.1 cspresnext50,862.61,444.309,384,224,20.57 resnetrs50,861.96,444.354,384,224,35.69 cspresnet50w,860.12,445.567,384,256,28.12 cspresnet50d,849.09,451.363,384,256,21.64 densenet121,845.28,301.077,256,224,7.98 tv_densenet121,845.28,301.063,256,224,7.98 rexnet_150,842.23,302.82,256,224,9.73 tv_resnext50_32x4d,836.18,458.41,384,224,25.03 swsl_resnext50_32x4d,836.09,458.464,384,224,25.03 res2net50_26w_4s,835.77,458.208,384,224,25.7 coat_lite_mini,833.77,459.672,384,224,11.01 vit_base_resnet26d_224,833.37,459.491,384,224,101.4 resnext50_32x4d,832.87,460.244,384,224,25.03 ssl_resnext50_32x4d,832.34,460.521,384,224,25.03 dpn68,831.95,460.44,384,224,12.61 gluon_resnext50_32x4d,831.77,460.799,384,224,25.03 vovnet57a,828.64,616.994,512,224,36.64 efficientnet_b2_pruned,825.77,308.457,256,260,8.31 resnetblur50d,818.48,311.928,256,224,25.58 skresnet50,810.12,472.628,384,224,25.8 tf_efficientnet_b2_ap,809.72,235.646,192,260,9.11 tf_efficientnet_b2_ns,809.32,235.717,192,260,9.11 tf_efficientnet_b2,809.06,235.843,192,260,9.11 vgg11_bn,805.38,476.555,384,224,132.87 densenet121d,805.31,316.045,256,224,8.0 nf_ecaresnet50,804.89,476.102,384,224,25.56 rexnet_130,801.42,318.304,256,224,7.56 ecaresnet50d,793.07,483.245,384,224,25.58 ese_vovnet57b,790.94,484.567,384,224,38.61 regnetx_006,790.56,646.782,512,224,6.2 gcresnet50t,788.09,323.372,256,256,25.9 regnety_006,787.76,648.873,512,224,6.06 convnext_tiny,781.17,326.737,256,224,28.59 tf_inception_v3,774.78,494.217,384,299,23.83 gluon_inception_v3,774.48,494.404,384,299,23.83 seresnetaa50d,773.82,329.682,256,224,28.11 inception_v3,772.99,495.38,384,299,23.83 adv_inception_v3,769.85,497.351,384,299,23.83 resmlp_24_distilled_224,767.92,331.893,256,224,30.02 resnext50d_32x4d,765.61,333.516,256,224,25.05 resmlp_24_224,762.25,334.383,256,224,30.02 gmixer_24_224,759.72,335.461,256,224,24.72 resnetv2_101,757.49,336.449,256,224,44.54 res2net50_14w_8s,756.34,336.303,256,224,25.06 xcit_nano_12_p16_384_dist,754.95,337.338,256,384,3.05 sehalonet33ts,749.52,340.746,256,256,13.69 densenetblur121d,739.98,344.167,256,224,8.0 dla60_res2net,736.45,346.205,256,224,20.85 skresnet50d,736.23,346.324,256,224,25.82 mobilevit_s,734.66,260.236,192,256,5.58 gluon_resnet101_v1b,731.02,348.601,256,224,44.55 tv_resnet101,727.65,350.31,256,224,44.55 resnet101,727.5,350.369,256,224,44.55 xcit_tiny_24_p16_224,726.25,349.143,256,224,12.12 xcit_tiny_24_p16_224_dist,725.28,349.489,256,224,12.12 efficientnet_b2,724.21,263.654,192,288,9.11 twins_pcpvt_small,724.17,351.883,256,224,24.11 efficientnet_b2a,723.56,263.856,192,288,9.11 ecaresnet101d_pruned,714.19,715.116,512,224,24.88 nf_resnet50,710.87,539.322,384,288,25.56 nf_resnet101,707.69,540.968,384,224,44.55 seresnext50_32x4d,706.91,361.01,256,224,27.56 gluon_seresnext50_32x4d,706.58,361.114,256,224,27.56 efficientnet_b0_gn,705.84,361.599,256,224,5.29 legacy_seresnext50_32x4d,704.96,362.028,256,224,27.56 nf_regnet_b0,703.19,726.933,512,256,8.76 darknet53,701.97,363.892,256,256,41.61 resnetv2_101d,699.92,364.177,256,224,44.56 densenet169,698.35,364.121,256,224,14.15 gluon_resnet101_v1c,697.85,365.313,256,224,44.57 dla60x,694.54,367.645,256,224,17.35 poolformer_s24,690.11,369.675,256,224,21.39 semobilevit_s,684.71,279.143,192,256,5.74 efficientnetv2_rw_t,684.48,278.424,192,288,13.65 vit_small_r26_s32_224,682.34,373.878,256,224,36.43 convnext_tiny_hnf,681.83,374.501,256,224,28.59 gluon_resnet101_v1d,674.08,378.237,256,224,44.57 tf_efficientnetv2_b3,670.7,284.467,192,300,14.36 xcit_small_12_p16_224,670.22,380.208,256,224,26.25 xcit_small_12_p16_224_dist,669.97,380.26,256,224,26.25 sebotnet33ts_256,666.38,191.3,128,256,13.7 rexnet_200,665.99,287.162,192,224,16.37 vgg13,663.21,578.818,384,224,133.05 regnety_008,661.82,772.637,512,224,6.26 wide_resnet50_2,661.04,580.088,384,224,68.88 dla102,650.38,392.047,256,224,33.27 gmlp_s16_224,648.9,294.316,192,224,19.42 vit_base_resnet50d_224,646.39,394.444,256,224,110.97 swin_tiny_patch4_window7_224,639.35,399.428,256,224,28.29 ecaresnet26t,628.75,406.586,256,320,16.01 repvgg_b1,627.31,815.089,512,224,57.42 gluon_resnet101_v1s,624.3,408.497,256,224,44.67 crossvit_small_240,624.04,408.587,256,240,26.86 resnetaa101d,621.23,410.532,256,224,44.57 eca_botnext26ts_256,618.03,413.613,256,256,10.59 resnext26ts,615.62,623.252,384,256,10.3 gc_efficientnetv2_rw_t,605.04,314.593,192,288,13.68 eca_halonext26ts,604.41,422.932,256,256,10.76 seresnext26ts,598.53,427.051,256,256,10.39 eca_resnext26ts,598.25,427.346,256,256,10.3 convnext_tiny_hnfd,592.58,430.992,256,224,28.63 regnetx_008,591.8,864.35,512,224,7.26 resnetv2_50x1_bit_distilled,589.64,324.794,192,224,25.55 legacy_seresnet101,588.41,432.899,256,224,49.33 gcresnext26ts,585.17,436.656,256,256,10.48 halonet50ts,584.39,327.573,192,256,22.73 xcit_nano_12_p8_224,583.42,437.05,256,224,3.05 cait_xxs24_224,582.93,436.673,256,224,11.96 xcit_nano_12_p8_224_dist,581.01,438.848,256,224,3.05 mixer_b16_224,580.44,440.268,256,224,59.88 mixer_b16_224_miil,579.94,440.653,256,224,59.88 swin_s3_tiny_224,578.76,441.339,256,224,28.33 seresnet101,574.05,443.691,256,224,49.33 mixnet_m,573.23,891.634,512,224,5.01 res2net50_26w_6s,569.28,447.908,256,224,37.05 vgg13_bn,568.79,449.813,256,224,133.05 crossvit_15_240,568.46,335.957,192,240,27.53 resnetblur101d,567.71,449.352,256,224,44.57 cspdarknet53,565.64,451.547,256,256,27.64 efficientnet_lite3,563.01,226.244,128,300,8.2 tf_efficientnet_lite3,561.05,227.067,128,300,8.2 crossvit_15_dagger_240,549.96,347.22,192,240,28.21 resnext101_32x4d,548.6,465.031,256,224,44.18 tf_mixnet_m,547.03,700.438,384,224,5.01 swsl_resnext101_32x4d,546.13,467.192,256,224,44.18 gluon_resnext101_32x4d,545.31,467.899,256,224,44.18 ssl_resnext101_32x4d,545.31,467.944,256,224,44.18 densenet201,539.32,353.029,192,224,20.01 vgg16,536.49,715.548,384,224,138.36 bat_resnext26ts,533.44,478.688,256,256,10.73 nf_seresnet101,532.2,478.733,256,224,49.33 vit_base_r26_s32_224,528.57,361.981,192,224,101.38 resnetv2_152,524.49,485.916,256,224,60.19 botnet50ts_256,524.41,243.109,128,256,22.74 res2net101_26w_4s,523.46,486.536,256,224,45.21 efficientnet_b3_pruned,519.43,491.117,256,300,9.86 vit_base_patch32_384,517.32,494.013,256,384,88.3 vit_large_patch32_224,511.41,498.976,256,224,306.54 halo2botnet50ts_256,510.64,375.017,192,256,22.64 mixer_l32_224,510.1,374.908,192,224,206.94 swin_v2_cr_tiny_224,506.89,377.55,192,224,28.33 resmlp_36_distilled_224,505.72,377.435,192,224,44.69 resmlp_36_224,505.39,377.735,192,224,44.69 vit_tiny_patch16_384,503.94,253.174,128,384,5.79 res2next50,502.84,507.818,256,224,24.67 dla102x,501.98,380.938,192,224,26.31 swin_v2_cr_tiny_ns_224,501.36,381.688,192,224,28.33 resnet152,499.83,381.783,192,224,60.19 gluon_resnet152_v1b,499.79,381.933,192,224,60.19 tv_resnet152,496.47,384.401,192,224,60.19 xception,494.06,258.29,128,299,22.86 visformer_tiny,491.83,1040.332,512,224,10.32 mixnet_l,488.23,784.993,384,224,7.33 gluon_resnet152_v1c,485.58,393.139,192,224,60.21 resnet50_gn,484.73,395.256,192,224,25.56 resnetv2_152d,484.59,393.938,192,224,60.2 twins_pcpvt_base,480.18,397.102,192,224,43.83 nest_tiny,477.31,267.267,128,224,17.06 res2net50_26w_8s,476.93,534.581,256,224,48.4 ecaresnet101d,475.58,536.508,256,224,44.57 convnext_small,473.89,403.445,192,224,50.22 gluon_resnet152_v1d,473.52,403.216,192,224,60.21 tf_mixnet_l,470.86,542.123,256,224,7.33 jx_nest_tiny,470.71,271.015,128,224,17.06 vgg16_bn,469.97,544.386,256,224,138.37 nf_ecaresnet101,465.87,547.628,256,224,44.55 coat_lite_small,463.34,412.922,192,224,19.84 poolformer_s36,458.24,417.134,192,224,30.86 efficientnet_el,455.42,279.993,128,300,10.59 efficientnet_el_pruned,454.32,280.643,128,300,10.59 vgg19,451.86,849.574,384,224,143.67 convit_small,450.19,425.458,192,224,27.78 fbnetv3_g,449.02,283.045,128,288,16.62 ese_vovnet99b,448.97,568.658,256,224,63.2 seresnext101_32x4d,448.16,426.163,192,224,48.96 gluon_seresnext101_32x4d,447.49,426.94,192,224,48.96 gluon_resnet152_v1s,446.73,427.49,192,224,60.32 legacy_seresnext101_32x4d,446.01,428.263,192,224,48.96 nf_regnet_b3,442.03,577.378,256,320,18.59 tf_efficientnet_el,441.61,288.785,128,300,10.59 ese_vovnet39b_evos,439.23,290.493,128,224,24.58 dla60_res2next,437.89,583.208,256,224,17.03 volo_d1_224,437.25,437.756,192,224,26.63 dla169,436.98,436.866,192,224,53.39 skresnext50_32x4d,435.09,586.972,256,224,27.48 hrnet_w32,433.46,438.263,192,224,41.23 vit_small_resnet50d_s16_224,432.94,442.239,192,224,57.53 twins_svt_base,429.83,444.617,192,224,56.07 hrnet_w18,419.32,605.846,256,224,21.3 crossvit_18_240,400.15,317.858,128,240,43.27 vgg19_bn,399.74,640.035,256,224,143.68 ecaresnet50t,399.44,319.549,128,320,25.57 inception_v4,397.78,480.469,192,299,42.68 tf_efficientnet_b3,393.44,323.695,128,300,12.23 swin_small_patch4_window7_224,393.38,486.272,192,224,49.61 legacy_seresnet152,392.87,485.406,192,224,66.82 tf_efficientnet_b3_ap,392.08,324.783,128,300,12.23 vit_base_patch16_224_miil,391.87,489.169,192,224,86.54 tf_efficientnet_b3_ns,391.61,325.176,128,300,12.23 crossvit_18_dagger_240,387.95,327.891,128,240,44.27 vit_base_patch16_224,385.23,497.575,192,224,86.57 vit_base_patch16_224_sam,384.98,497.891,192,224,86.57 deit_base_patch16_224,384.15,498.956,192,224,86.57 cait_xxs36_224,380.29,501.012,192,224,17.3 repvgg_b2,380.03,1346.183,512,224,89.02 regnetx_016,379.21,1349.264,512,224,9.19 deit_base_distilled_patch16_224,378.28,506.739,192,224,87.34 densenet161,376.13,337.907,128,224,28.68 haloregnetz_b,375.52,680.247,256,224,11.68 xcit_tiny_12_p8_224,374.86,339.659,128,224,6.71 xcit_tiny_12_p8_224_dist,374.8,339.672,128,224,6.71 seresnet152,372.93,339.954,128,224,66.82 dla102x2,362.92,351.15,128,224,41.28 wide_resnet101_2,360.46,531.121,192,224,126.89 gluon_resnext101_64x4d,357.83,356.147,128,224,83.46 efficientnet_b3a,354.6,359.335,128,320,12.23 resnet200,354.57,357.998,128,224,64.67 xception41p,353.88,360.846,128,299,26.91 regnety_016,353.36,1447.082,512,224,11.2 efficientnet_b3,353.18,360.796,128,320,12.23 beit_base_patch16_224,352.29,543.93,192,224,86.53 resnest14d,349.84,1463.045,512,224,10.61 hrnet_w30,346.82,733.381,256,224,37.71 ens_adv_inception_resnet_v2,341.5,558.819,192,299,55.84 inception_resnet_v2,341.03,559.722,192,299,55.84 xcit_small_24_p16_224_dist,341.01,371.894,128,224,47.67 tnt_s_patch16_224,340.3,562.32,192,224,23.76 xcit_small_24_p16_224,339.02,373.952,128,224,47.67 efficientnet_lite4,334.87,189.779,64,380,13.01 dpn92,332.25,769.002,256,224,37.67 nf_regnet_b1,330.04,1549.911,512,288,10.22 twins_pcpvt_large,327.73,386.698,128,224,60.99 resnet101d,327.41,389.353,128,320,44.57 convnext_small_in22ft1k,327.31,389.301,128,224,88.59 convnext_base,327.3,389.374,128,224,88.59 convnext_base_in22ft1k,326.61,390.177,128,224,88.59 convnext_tiny_in22ft1k,324.94,392.181,128,224,88.59 tf_efficientnet_lite4,322.62,197.041,64,380,13.01 resnetrs101,321.74,395.61,128,288,63.62 pit_b_224,318.92,400.391,128,224,73.76 pit_b_distilled_224,318.09,401.455,128,224,74.79 gcresnext50ts,316.73,604.706,192,256,15.67 repvgg_b3,315.56,1215.795,384,224,123.09 gluon_seresnext101_64x4d,313.23,406.424,128,224,88.23 regnetz_d8,311.3,204.013,64,320,23.37 poolformer_m36,307.82,413.95,128,224,56.17 xception41,305.16,418.228,128,299,26.97 resnetv2_50d_gn,304.62,419.347,128,288,25.57 coat_tiny,304.05,418.955,128,224,5.5 vit_small_patch16_36x1_224,302.22,420.928,128,224,64.67 swin_v2_cr_small_224,302.05,421.43,128,224,49.7 cait_s24_224,301.09,422.497,128,224,46.92 mixnet_xl,300.81,849.169,256,224,11.9 vit_small_patch16_18x2_224,299.9,424.102,128,224,64.67 resnetv2_50d_frn,299.64,426.047,128,224,25.59 efficientnetv2_s,299.05,318.819,96,384,21.46 twins_svt_large,298.59,426.599,128,224,99.27 tf_efficientnetv2_s,297.45,320.544,96,384,21.46 tf_efficientnetv2_s_in21ft1k,295.84,322.238,96,384,21.46 efficientnetv2_rw_s,295.58,214.343,64,384,23.94 nest_small,295.19,323.571,96,224,38.35 jx_nest_small,293.04,325.974,96,224,38.35 hrnet_w40,291.68,653.529,192,224,57.56 regnetz_005,286.8,1783.819,512,224,7.12 nf_regnet_b2,284.19,1799.964,512,272,14.31 dpn98,283.21,450.397,128,224,61.57 gluon_xception65,280.82,339.911,96,299,39.92 xception65,279.18,341.922,96,299,39.92 resnet51q,277.93,689.969,192,288,35.7 nf_regnet_b4,277.28,459.47,128,384,30.21 swin_s3_small_224,277.05,344.678,96,224,49.74 swin_base_patch4_window7_224,275.83,462.244,128,224,87.77 xception65p,274.95,464.241,128,299,39.82 gmlp_b16_224,270.89,352.851,96,224,73.08 hrnet_w48,266.5,475.648,128,224,77.47 resnest26d,258.35,1485.604,384,224,17.07 resnest50d_1s4x24d,258.11,990.508,256,224,25.68 xcit_tiny_24_p16_384_dist,251.56,378.207,96,384,12.12 regnetz_c16,250.16,510.248,128,320,13.46 crossvit_base_240,249.99,382.366,96,240,105.03 coat_mini,247.34,515.482,128,224,10.34 xcit_medium_24_p16_224,244.65,519.851,128,224,84.4 xcit_medium_24_p16_224_dist,244.08,521.069,128,224,84.4 hrnet_w44,241.77,789.352,192,224,67.06 efficientnet_b4,241.47,262.953,64,384,19.34 volo_d2_224,238.72,400.292,96,224,58.68 tf_efficientnet_b4,236.52,268.528,64,380,19.34 tf_efficientnet_b4_ap,236.39,268.69,64,380,19.34 tf_efficientnet_b4_ns,236.1,269.028,64,380,19.34 vit_small_patch16_384,235.52,270.897,64,384,22.2 resnetv2_50d_evob,235.19,406.951,96,224,25.59 tresnet_m,234.84,2177.505,512,224,31.39 nfnet_l0,233.22,1096.449,256,288,35.07 visformer_small,232.72,1649.37,384,224,40.22 xcit_small_12_p16_384_dist,230.87,414.048,96,384,26.25 vit_large_r50_s32_224,228.88,417.083,96,224,328.99 convit_base,228.65,558.76,128,224,86.54 eca_nfnet_l0,226.92,1127.142,256,288,24.14 resnetv2_50d_evos,223.6,285.051,64,288,25.59 tnt_b_patch16_224,222.54,573.324,128,224,65.41 vit_small_r26_s32_384,221.41,287.746,64,384,36.47 densenet264,220.0,432.388,96,224,72.69 swin_s3_base_224,219.07,435.557,96,224,71.13 hrnet_w64,218.91,579.973,128,224,128.06 resnext101_64x4d,217.22,440.378,96,288,83.46 resnet152d,216.09,441.927,96,320,60.21 xception71,215.62,294.681,64,299,42.34 swin_v2_cr_base_224,215.23,443.662,96,224,87.88 dpn131,211.54,603.038,128,224,79.25 nest_base,210.35,302.564,64,224,67.72 vit_base_r50_s16_224,208.87,457.959,96,224,98.66 jx_nest_base,208.37,305.48,64,224,67.72 resnet61q,207.91,614.63,128,288,36.85 mixnet_xxl,202.79,629.315,128,224,23.96 xcit_nano_12_p8_384_dist,196.18,324.445,64,384,3.05 poolformer_m48,193.91,492.638,96,224,73.47 xcit_tiny_24_p8_224,190.8,499.85,96,224,12.11 xcit_tiny_24_p8_224_dist,190.62,500.3,96,224,12.11 seresnet200d,190.31,500.165,96,256,71.86 ecaresnet200d,182.23,523.49,96,256,64.69 regnetz_b16,181.6,1055.83,192,288,9.72 convnext_large_in22ft1k,180.86,529.028,96,224,197.77 convnext_large,180.64,529.707,96,224,197.77 convmixer_768_32,179.25,534.249,96,224,21.11 repvgg_b1g4,178.41,2868.637,512,224,39.97 regnety_032,178.01,1436.656,256,288,19.44 regnetx_032,177.68,2160.015,384,224,15.3 resnest50d,177.64,1439.786,256,224,27.48 gluon_senet154,177.29,538.24,96,224,115.09 senet154,176.81,539.67,96,224,115.09 halonet_h1,175.85,362.526,64,256,8.1 legacy_senet154,175.45,543.752,96,224,115.09 xcit_small_12_p8_224,174.99,363.954,64,224,26.21 xcit_small_12_p8_224_dist,174.85,364.256,64,224,26.21 seresnet152d,173.74,364.967,64,320,66.84 dpn107,173.23,552.482,96,224,86.92 mixer_l16_224,172.58,554.751,96,224,208.2 resnetrs152,171.14,370.433,64,320,86.62 resnest50d_4s2x40d,167.22,1529.64,256,224,30.42 resnet200d,166.19,382.131,64,320,64.69 volo_d3_224,162.25,391.709,64,224,86.33 regnetx_040,161.87,2371.182,384,224,22.12 vit_large_patch32_384,161.83,591.666,96,384,306.63 efficientnet_b3_gn,160.22,397.781,64,320,11.73 swin_large_patch4_window7_224,151.35,420.987,64,224,196.53 regnetx_080,150.02,2558.515,384,224,39.57 regnety_040s_gn,149.61,853.983,128,224,20.65 efficientnetv2_m,149.31,318.263,48,416,54.14 ssl_resnext101_32x8d,147.46,866.477,128,224,88.79 resnext101_32x8d,147.32,867.312,128,224,88.79 swsl_resnext101_32x8d,147.16,868.264,128,224,88.79 ig_resnext101_32x8d,146.91,869.691,128,224,88.79 regnetz_e8,146.27,326.163,48,320,57.7 resnetv2_50x1_bitm,140.77,340.162,48,448,25.55 seresnet269d,137.16,460.633,64,256,113.67 xcit_large_24_p16_224,136.77,464.553,64,224,189.1 xcit_large_24_p16_224_dist,136.73,464.738,64,224,189.1 xcit_tiny_12_p8_384_dist,128.06,373.098,48,384,6.71 efficientnetv2_rw_m,126.21,250.013,32,416,53.24 regnetx_064,124.13,2061.422,256,224,26.21 resnetrs200,123.75,383.587,48,320,93.21 dm_nfnet_f0,121.58,2104.417,256,256,71.49 swin_v2_cr_large_224,120.98,394.327,48,224,196.68 regnety_040,120.25,1595.17,192,288,20.65 nfnet_f0,119.63,2138.729,256,256,71.49 regnetv_040,118.92,1074.873,128,288,20.64 ese_vovnet99b_iabn,118.27,3243.951,384,224,63.2 xcit_small_24_p16_384_dist,117.41,405.393,48,384,47.67 regnetz_b16_evos,117.22,544.08,64,288,9.74 crossvit_15_dagger_408,116.43,273.026,32,408,28.5 efficientnet_b0_g8_gn,115.43,2216.717,256,224,6.56 vit_large_patch16_224,115.39,553.095,64,224,304.33 regnetz_c16_evos,115.15,414.978,48,320,13.49 vit_base_patch16_18x2_224,114.12,558.126,64,224,256.73 convnext_xlarge_in22ft1k,114.03,559.475,64,224,350.2 convnext_tiny_384_in22ft1k,112.53,424.859,48,384,88.59 convnext_small_384_in22ft1k,112.49,424.983,48,384,88.59 convnext_base_384_in22ft1k,112.43,425.187,48,384,88.59 swin_v2_cr_tiny_384,111.07,286.85,32,384,28.33 tf_efficientnetv2_m,109.5,289.071,32,480,54.14 tf_efficientnetv2_m_in21ft1k,109.37,289.435,32,480,54.14 beit_large_patch16_224,106.59,598.365,64,224,304.43 volo_d1_384,104.96,303.527,32,384,26.78 tresnet_l,104.79,4882.686,512,224,55.99 repvgg_b2g4,102.33,5002.104,512,224,61.76 eca_nfnet_l1,101.26,1262.246,128,320,41.41 volo_d4_224,101.17,471.916,48,224,192.96 cspdarknet53_iabn,98.65,3890.155,384,256,27.64 cait_xxs24_384,98.52,484.701,48,384,12.03 efficientnet_b5,97.28,326.485,32,456,30.39 tf_efficientnet_b5,95.75,331.792,32,456,30.39 tf_efficientnet_b5_ns,95.73,331.833,32,456,30.39 vit_base_patch16_384,95.69,333.568,32,384,86.86 deit_base_patch16_384,95.67,333.658,32,384,86.86 regnetz_d8_evos,95.66,332.456,32,320,23.46 tf_efficientnet_b5_ap,95.45,332.725,32,456,30.39 regnetz_040,94.56,674.98,64,320,27.12 regnetz_040h,94.14,678.01,64,320,28.94 deit_base_distilled_patch16_384,93.65,340.87,32,384,87.63 tresnet_xl,90.76,4227.401,384,224,78.44 cspresnext50_iabn,89.98,4265.102,384,256,20.57 resnest101e,89.71,1424.366,128,256,48.28 crossvit_18_dagger_408,87.99,361.633,32,408,44.61 xcit_small_24_p8_224,87.73,361.414,32,224,47.63 xcit_small_24_p8_224_dist,87.7,361.441,32,224,47.63 resnetv2_101x1_bitm,86.89,366.637,32,448,44.54 nf_regnet_b5,86.53,736.892,64,456,49.74 resnetv2_152x2_bit_teacher,86.4,368.012,32,224,236.34 repvgg_b3g4,84.75,4530.071,384,224,83.83 vit_large_patch14_224,84.3,567.814,48,224,304.2 beit_base_patch16_384,82.73,385.724,32,384,86.74 seresnext101_32x8d,81.65,781.61,64,288,93.57 xcit_medium_24_p16_384_dist,81.08,391.168,32,384,84.4 ecaresnet269d,77.88,406.425,32,352,102.09 regnetx_120,77.54,3300.773,256,224,46.11 pnasnet5large,76.44,414.721,32,331,86.06 vit_large_r50_s32_384,75.76,419.984,32,384,329.09 regnety_120,75.34,2547.007,192,224,51.82 resnetrs270,75.27,419.128,32,352,129.86 swin_base_patch4_window12_384,73.61,432.836,32,384,87.9 regnety_064,72.69,1759.173,128,288,30.58 regnetz_d32,72.18,885.049,64,320,27.58 regnetv_064,71.81,1780.738,128,288,30.58 resmlp_big_24_224,68.34,466.717,32,224,129.14 resmlp_big_24_224_in22ft1k,68.31,466.955,32,224,129.14 resmlp_big_24_distilled_224,68.26,467.278,32,224,129.14 regnety_320,67.49,1895.092,128,224,145.05 nasnetalarge,66.93,473.19,32,331,88.75 swin_v2_cr_small_384,66.26,359.874,24,384,49.7 cait_xs24_384,65.98,482.447,32,384,26.67 regnety_080,65.45,1954.421,128,288,39.18 regnetx_160,65.01,2952.336,192,224,54.28 xcit_tiny_24_p8_384_dist,64.61,492.002,32,384,12.11 volo_d5_224,64.26,494.733,32,224,295.46 cait_xxs36_384,63.75,498.023,32,384,17.37 vit_base_patch8_224,62.96,380.293,24,224,86.58 xcit_medium_24_p8_224,62.71,506.97,32,224,84.32 xcit_medium_24_p8_224_dist,62.63,507.407,32,224,84.32 efficientnet_b3_g8_gn,62.43,1023.448,64,320,14.25 convnext_large_384_in22ft1k,61.7,516.836,32,384,197.77 convmixer_1024_20_ks9_p14,61.37,4170.722,256,224,24.38 efficientnet_b0_g16_evos,60.46,6350.16,384,224,8.11 tf_efficientnetv2_l_in21ft1k,60.25,261.19,16,480,118.52 xcit_small_12_p8_384_dist,60.04,398.02,24,384,26.21 efficientnetv2_l,59.19,265.957,16,480,118.52 vit_base_resnet50_384,59.0,405.136,24,384,98.95 tf_efficientnetv2_l,58.87,267.382,16,480,118.52 vit_base_r50_s16_384,58.85,406.196,24,384,98.95 volo_d2_384,58.21,273.125,16,384,58.87 tresnet_m_448,53.55,3582.526,192,448,31.39 cait_s24_384,49.8,479.385,24,384,47.06 regnety_160,48.14,1993.0,96,288,83.59 ig_resnext101_32x16d,47.52,2018.737,96,224,194.03 swsl_resnext101_32x16d,47.43,2022.391,96,224,194.03 xcit_large_24_p16_384_dist,47.39,502.961,24,384,189.1 ssl_resnext101_32x16d,47.29,2028.601,96,224,194.03 resnetrs350,47.22,500.442,24,384,163.96 swin_v2_cr_base_384,47.19,336.677,16,384,87.88 swin_v2_cr_huge_224,46.26,343.404,16,224,657.83 regnetx_320,46.16,2771.769,128,224,107.81 eca_nfnet_l2,44.82,1425.204,64,384,56.72 efficientnet_b6,42.73,371.597,16,528,43.04 tf_efficientnet_b6,41.54,382.29,16,528,43.04 tf_efficientnet_b6_ns,41.5,382.621,16,528,43.04 tf_efficientnet_b6_ap,41.36,384.088,16,528,43.04 swin_large_patch4_window12_384,41.02,388.265,16,384,196.74 nfnet_f1,40.44,2371.478,96,320,132.63 vit_huge_patch14_224,39.64,401.543,16,224,632.05 dm_nfnet_f1,38.26,1670.645,64,320,132.63 convnext_xlarge_384_in22ft1k,37.04,430.195,16,384,350.2 efficientnet_b7,36.68,214.625,8,600,66.35 efficientnetv2_xl,36.54,322.755,12,512,208.12 tf_efficientnetv2_xl_in21ft1k,36.36,324.371,12,512,208.12 tf_efficientnet_b7_ap,36.21,217.402,8,600,66.35 tf_efficientnet_b7,36.05,218.422,8,600,66.35 tf_efficientnet_b7_ns,35.41,221.975,8,600,66.35 xcit_large_24_p8_224,34.96,454.269,16,224,188.93 xcit_large_24_p8_224_dist,34.92,454.696,16,224,188.93 resnetrs420,32.2,487.619,16,416,191.89 cait_s36_384,32.09,494.814,16,384,68.37 resnest200e,32.0,1494.763,48,320,70.2 densenet264d_iabn,31.93,4003.992,128,224,72.74 resnetv2_50x3_bitm,31.81,502.194,16,448,217.32 xcit_small_24_p8_384_dist,29.97,396.971,12,384,47.63 resnetv2_152x2_bit_teacher_384,29.92,398.698,12,384,236.34 vit_large_patch16_384,28.85,414.372,12,384,304.72 swin_v2_cr_large_384,28.47,419.18,12,384,196.68 tresnet_l_448,25.64,4988.984,128,448,55.99 beit_large_patch16_384,25.11,475.839,12,384,305.0 volo_d3_448,24.79,320.233,8,448,86.63 eca_nfnet_l3,24.19,1319.468,32,448,72.04 tresnet_xl_448,23.17,4140.191,96,448,78.44 nfnet_f2,22.36,2143.929,48,352,193.78 vit_giant_patch14_224,22.25,356.944,8,224,1012.61 dm_nfnet_f2,22.12,2166.49,48,352,193.78 efficientnet_cc_b0_8e,21.78,44.083,1,224,24.01 resnetv2_152x2_bitm,21.74,365.619,8,448,236.34 tf_efficientnet_cc_b0_4e,21.44,44.859,1,224,13.31 tf_efficientnet_cc_b0_8e,20.98,45.875,1,224,24.01 xcit_medium_24_p8_384_dist,20.42,388.21,8,384,84.32 efficientnet_cc_b0_4e,20.36,47.276,1,224,13.31 ig_resnext101_32x32d,18.17,1760.063,32,224,468.53 volo_d4_448,17.6,338.311,6,448,193.41 resnetv2_101x3_bitm,17.53,454.801,8,448,387.93 tf_efficientnet_cc_b1_8e,17.15,56.006,1,240,39.72 efficientnet_cc_b1_8e,16.21,59.398,1,240,39.72 resnest269e,13.09,1826.778,24,416,110.93 tf_efficientnet_b8_ap,12.18,488.771,6,672,87.41 efficientnet_b8,12.12,491.31,6,672,87.41 nfnet_f3,12.08,1982.52,24,416,254.92 xcit_large_24_p8_384_dist,11.91,500.622,6,384,188.93 tf_efficientnet_b8,11.9,500.516,6,672,87.41 cait_m36_384,11.87,501.638,6,384,271.22 dm_nfnet_f3,11.74,2040.434,24,416,254.92 volo_d5_448,11.52,343.952,4,448,295.91 swin_v2_cr_huge_384,10.9,364.423,4,384,657.94 convmixer_1536_20,9.63,4981.706,48,224,51.63 beit_large_patch16_512,9.42,422.773,4,512,305.67 tf_efficientnet_l2_ns_475,9.0,327.787,3,475,480.31 ig_resnext101_32x48d,8.5,1880.808,16,224,828.41 volo_d5_512,8.05,369.448,3,512,296.09 nfnet_f4,6.47,1849.874,12,512,316.07 dm_nfnet_f4,6.2,1929.065,12,512,316.07 cait_m48_448,4.75,415.897,2,448,356.46 nfnet_f5,4.63,1719.792,8,544,377.21 resnetv2_152x4_bitm,4.49,443.185,2,480,936.53 dm_nfnet_f5,4.47,1782.137,8,544,377.21 nfnet_f6,3.5,1707.387,6,576,438.36 dm_nfnet_f6,3.39,1759.608,6,576,438.36 nfnet_f7,2.67,1489.771,4,608,499.5 efficientnet_l2,2.09,473.733,1,800,480.31 tf_efficientnet_l2_ns,2.09,474.031,1,800,480.31
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt210-cu121-rtx3090.csv	model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count tinynet_e,106,1024.0,75290.96,13.591,0.03,0.69,2.04 mobilenetv3_small_050,224,1024.0,56785.93,18.023,0.03,0.92,1.59 efficientvit_m0,224,1024.0,50656.23,20.205,0.08,0.91,2.35 lcnet_035,224,1024.0,48853.22,20.951,0.03,1.04,1.64 lcnet_050,224,1024.0,42147.98,24.285,0.05,1.26,1.88 mobilenetv3_small_075,224,1024.0,42002.46,24.369,0.05,1.3,2.04 mobilenetv3_small_100,224,1024.0,38516.23,26.573,0.06,1.42,2.54 tinynet_d,152,1024.0,37989.71,26.944,0.05,1.42,2.34 efficientvit_m1,224,1024.0,37486.44,27.306,0.17,1.33,2.98 tf_mobilenetv3_small_minimal_100,224,1024.0,33948.13,30.153,0.06,1.41,2.04 efficientvit_m2,224,1024.0,33551.67,30.51,0.2,1.47,4.19 tf_mobilenetv3_small_075,224,1024.0,33262.15,30.775,0.05,1.3,2.04 tf_mobilenetv3_small_100,224,1024.0,31002.71,33.019,0.06,1.42,2.54 lcnet_075,224,1024.0,30664.19,33.384,0.1,1.99,2.36 efficientvit_m3,224,1024.0,29423.78,34.792,0.27,1.62,6.9 efficientvit_m4,224,1024.0,27882.1,36.716,0.3,1.7,8.8 mnasnet_small,224,1024.0,25015.02,40.925,0.07,2.16,2.03 regnetx_002,224,1024.0,24564.71,41.67,0.2,2.16,2.68 lcnet_100,224,1024.0,24268.72,42.183,0.16,2.52,2.95 levit_128s,224,1024.0,22705.11,45.089,0.31,1.88,7.78 regnety_002,224,1024.0,22248.91,46.012,0.2,2.17,3.16 resnet10t,176,1024.0,22236.3,46.04,0.7,1.51,5.44 mobilenetv2_035,224,1024.0,22055.42,46.418,0.07,2.86,1.68 levit_conv_128s,224,1024.0,21863.15,46.826,0.31,1.88,7.78 ghostnet_050,224,1024.0,20782.95,49.261,0.05,1.77,2.59 mnasnet_050,224,1024.0,20672.17,49.525,0.11,3.07,2.22 repghostnet_050,224,1024.0,20617.05,49.657,0.05,2.02,2.31 efficientvit_m5,224,1024.0,19010.14,53.856,0.53,2.41,12.47 tinynet_c,184,1024.0,18737.07,54.641,0.11,2.87,2.46 efficientvit_b0,224,1024.0,18023.56,56.804,0.1,2.87,3.41 semnasnet_050,224,1024.0,17573.38,58.26,0.11,3.44,2.08 mobilenetv2_050,224,1024.0,17491.5,58.532,0.1,3.64,1.97 regnetx_004,224,1024.0,17164.74,59.647,0.4,3.14,5.16 repghostnet_058,224,1024.0,16947.81,60.41,0.07,2.59,2.55 regnetx_004_tv,224,1024.0,16485.73,62.101,0.42,3.17,5.5 vit_small_patch32_224,224,1024.0,16428.86,62.319,1.12,2.09,22.88 cs3darknet_focus_s,256,1024.0,16333.25,62.684,0.69,2.7,3.27 lcnet_150,224,1024.0,15841.02,64.632,0.34,3.79,4.5 gernet_s,224,1024.0,15617.62,65.556,0.75,2.65,8.17 cs3darknet_s,256,1024.0,15597.89,65.64,0.72,2.97,3.28 levit_128,224,1024.0,15372.6,66.601,0.41,2.71,9.21 vit_tiny_r_s16_p8_224,224,1024.0,15191.19,67.397,0.43,1.85,6.34 levit_conv_128,224,1024.0,14904.31,68.695,0.41,2.71,9.21 mobilenetv3_large_075,224,1024.0,14843.63,68.964,0.16,4.0,3.99 pit_ti_distilled_224,224,1024.0,14746.15,69.432,0.51,2.77,5.1 pit_ti_224,224,1024.0,14700.08,69.649,0.5,2.75,4.85 mixer_s32_224,224,1024.0,14362.24,71.288,1.0,2.28,19.1 resnet10t,224,1024.0,14254.88,71.825,1.1,2.43,5.44 repghostnet_080,224,1024.0,13967.84,73.293,0.1,3.22,3.28 tf_efficientnetv2_b0,192,1024.0,13629.52,75.121,0.54,3.51,7.14 mobilenetv3_rw,224,1024.0,13582.75,75.38,0.23,4.41,5.48 levit_192,224,1024.0,13511.34,75.778,0.66,3.2,10.95 mnasnet_075,224,1024.0,13417.36,76.309,0.23,4.77,3.17 mobilenetv3_large_100,224,1024.0,13322.79,76.851,0.23,4.41,5.48 hardcorenas_a,224,1024.0,13314.34,76.899,0.23,4.38,5.26 levit_conv_192,224,1024.0,12952.02,79.05,0.66,3.2,10.95 regnety_004,224,1024.0,12651.55,80.929,0.41,3.89,4.34 tf_mobilenetv3_large_075,224,1024.0,12636.69,81.023,0.16,4.0,3.99 nf_regnet_b0,192,1024.0,12264.41,83.481,0.37,3.15,8.76 tinynet_b,188,1024.0,12262.56,83.495,0.21,4.44,3.73 tf_mobilenetv3_large_minimal_100,224,1024.0,12182.74,84.043,0.22,4.4,3.92 hardcorenas_b,224,1024.0,12118.5,84.488,0.26,5.09,5.18 hardcorenas_c,224,1024.0,12088.28,84.699,0.28,5.01,5.52 resnet14t,176,1024.0,11843.82,86.448,1.07,3.61,10.08 mnasnet_100,224,1024.0,11686.43,87.612,0.33,5.46,4.38 regnety_006,224,1024.0,11675.48,87.69,0.61,4.33,6.06 ese_vovnet19b_slim_dw,224,1024.0,11663.91,87.781,0.4,5.28,1.9 repghostnet_100,224,1024.0,11508.79,88.956,0.15,3.98,4.07 tf_mobilenetv3_large_100,224,1024.0,11443.62,89.472,0.23,4.41,5.48 vit_tiny_patch16_224,224,1024.0,11342.82,90.267,1.08,4.12,5.72 hardcorenas_d,224,1024.0,11329.99,90.369,0.3,4.93,7.5 deit_tiny_distilled_patch16_224,224,1024.0,11311.9,90.514,1.09,4.15,5.91 deit_tiny_patch16_224,224,1024.0,11286.31,90.719,1.08,4.12,5.72 semnasnet_075,224,1024.0,11132.28,91.974,0.23,5.54,2.91 resnet18,224,1024.0,11101.69,92.228,1.82,2.48,11.69 ghostnet_100,224,1024.0,11039.87,92.744,0.15,3.55,5.18 mobilenetv2_075,224,1024.0,10984.87,93.208,0.22,5.86,2.64 spnasnet_100,224,1024.0,10557.11,96.986,0.35,6.03,4.42 tf_efficientnetv2_b1,192,1024.0,10473.04,97.765,0.76,4.59,8.14 regnetx_008,224,1024.0,10422.45,98.23,0.81,5.15,7.26 seresnet18,224,1024.0,10416.31,98.297,1.82,2.49,11.78 tf_efficientnetv2_b0,224,1024.0,10174.51,100.633,0.73,4.77,7.14 legacy_seresnet18,224,1024.0,10133.12,101.044,1.82,2.49,11.78 repghostnet_111,224,1024.0,10094.28,101.428,0.18,4.38,4.54 hardcorenas_f,224,1024.0,10012.95,102.257,0.35,5.57,8.2 tinynet_a,192,1024.0,9946.05,102.945,0.35,5.41,6.19 dla46_c,224,1024.0,9943.77,102.967,0.58,4.5,1.3 hardcorenas_e,224,1024.0,9851.75,103.931,0.35,5.65,8.07 semnasnet_100,224,1024.0,9823.16,104.233,0.32,6.23,3.89 levit_256,224,1024.0,9811.76,104.354,1.13,4.23,18.89 repvgg_a0,224,1024.0,9709.7,105.449,1.52,3.59,9.11 mobilenetv2_100,224,1024.0,9654.78,106.051,0.31,6.68,3.5 regnety_008,224,1024.0,9643.2,106.178,0.81,5.25,6.26 fbnetc_100,224,1024.0,9552.51,107.186,0.4,6.51,5.57 efficientnet_lite0,224,1024.0,9466.4,108.161,0.4,6.74,4.65 levit_conv_256,224,1024.0,9461.49,108.218,1.13,4.23,18.89 resnet18d,224,1024.0,9458.4,108.253,2.06,3.29,11.71 pit_xs_224,224,1024.0,9332.33,109.714,1.1,4.12,10.62 ese_vovnet19b_slim,224,1024.0,9277.16,110.369,1.69,3.52,3.17 regnety_008_tv,224,1024.0,9213.78,111.127,0.84,5.42,6.43 pit_xs_distilled_224,224,1024.0,9203.86,111.241,1.11,4.15,11.0 convnext_atto,224,1024.0,9104.06,112.467,0.55,3.81,3.7 repghostnet_130,224,1024.0,8873.05,115.395,0.25,5.24,5.48 ghostnet_130,224,1024.0,8870.81,115.424,0.24,4.6,7.36 convnext_atto_ols,224,1024.0,8829.55,115.964,0.58,4.11,3.7 regnetz_005,224,1024.0,8796.44,116.392,0.52,5.86,7.12 xcit_nano_12_p16_224,224,1024.0,8604.96,118.991,0.56,4.17,3.05 levit_256d,224,1024.0,8322.97,123.022,1.4,4.93,26.21 regnetx_006,224,1024.0,8320.1,123.064,0.61,3.98,6.2 tf_efficientnet_lite0,224,1024.0,8163.21,125.431,0.4,6.74,4.65 fbnetv3_b,224,1024.0,8152.31,125.598,0.42,6.97,8.6 efficientnet_b0,224,1024.0,8085.72,126.633,0.4,6.75,5.29 levit_conv_256d,224,1024.0,8055.13,127.113,1.4,4.93,26.21 edgenext_xx_small,256,1024.0,8014.51,127.757,0.26,3.33,1.33 mnasnet_140,224,1024.0,7984.3,128.241,0.6,7.71,7.12 convnext_femto,224,1024.0,7977.79,128.346,0.79,4.57,5.22 tf_efficientnetv2_b2,208,1024.0,7861.13,130.251,1.06,6.0,10.1 mobilevit_xxs,256,1024.0,7827.79,130.801,0.34,5.74,1.27 repghostnet_150,224,1024.0,7766.69,131.835,0.32,6.0,6.58 convnext_femto_ols,224,1024.0,7757.32,131.994,0.82,4.87,5.23 rexnetr_100,224,1024.0,7545.9,135.692,0.43,7.72,4.88 repvit_m1,224,1024.0,7543.44,135.728,0.83,7.45,5.49 resnet14t,224,1024.0,7466.4,137.137,1.69,5.8,10.08 mobilenetv2_110d,224,1024.0,7331.32,139.66,0.45,8.71,4.52 hrnet_w18_small,224,1024.0,7298.3,140.296,1.61,5.72,13.19 cs3darknet_focus_m,256,1024.0,7202.61,142.16,1.98,4.89,9.3 repvit_m0_9,224,1024.0,7165.5,142.888,0.83,7.45,5.49 crossvit_tiny_240,240,1024.0,7123.68,143.735,1.3,5.67,7.01 efficientvit_b1,224,1024.0,7109.59,144.02,0.53,7.25,9.1 tf_efficientnet_b0,224,1024.0,7104.21,144.129,0.4,6.75,5.29 crossvit_9_240,240,1024.0,7025.32,145.747,1.55,5.59,8.55 nf_regnet_b0,256,1024.0,6992.1,146.441,0.64,5.58,8.76 repvgg_a1,224,1024.0,6942.64,147.483,2.64,4.74,14.09 mobilevitv2_050,256,1024.0,6935.55,147.628,0.48,8.04,1.37 cs3darknet_m,256,1024.0,6929.59,147.762,2.08,5.28,9.31 efficientnet_b1_pruned,240,1024.0,6922.7,147.909,0.4,6.21,6.33 gernet_m,224,1024.0,6840.64,149.682,3.02,5.24,21.14 fbnetv3_d,224,1024.0,6784.35,150.925,0.52,8.5,10.31 semnasnet_140,224,1024.0,6771.35,151.215,0.6,8.87,6.11 crossvit_9_dagger_240,240,1024.0,6704.51,152.722,1.68,6.03,8.78 tf_efficientnetv2_b1,240,1024.0,6611.54,154.87,1.21,7.34,8.14 mobilenetv2_140,224,1024.0,6588.7,155.407,0.6,9.57,6.11 resnet34,224,1024.0,6504.25,157.425,3.67,3.74,21.8 ese_vovnet19b_dw,224,1024.0,6406.95,159.816,1.34,8.25,6.54 selecsls42,224,1024.0,6366.41,160.834,2.94,4.62,30.35 resnet18,288,1024.0,6354.7,161.13,3.01,4.11,11.69 selecsls42b,224,1024.0,6344.62,161.386,2.98,4.62,32.46 efficientnet_b0_g16_evos,224,1024.0,6342.4,161.442,1.01,7.42,8.11 edgenext_xx_small,288,1024.0,6334.97,161.631,0.33,4.21,1.33 efficientnet_lite1,240,1024.0,6268.15,163.355,0.62,10.14,5.42 pvt_v2_b0,224,1024.0,6254.52,163.711,0.53,7.01,3.67 visformer_tiny,224,1024.0,6218.29,164.665,1.27,5.72,10.32 convnext_pico,224,1024.0,6208.02,164.938,1.37,6.1,9.05 fbnetv3_b,256,1024.0,6192.25,165.357,0.55,9.1,8.6 efficientnet_es_pruned,224,1024.0,6175.39,165.809,1.81,8.73,5.44 efficientnet_es,224,1024.0,6170.12,165.95,1.81,8.73,5.44 rexnet_100,224,1024.0,6170.05,165.953,0.41,7.44,4.8 ghostnetv2_100,224,1024.0,6155.62,166.342,0.18,4.55,6.16 seresnet34,224,1024.0,6069.09,168.714,3.67,3.74,21.96 convnext_pico_ols,224,1024.0,6043.01,169.442,1.43,6.5,9.06 seresnet18,288,1024.0,5998.94,170.686,3.01,4.11,11.78 dla46x_c,224,1024.0,5992.19,170.877,0.54,5.66,1.07 dla34,224,1024.0,5954.72,171.952,3.07,5.02,15.74 repghostnet_200,224,1024.0,5934.75,172.524,0.54,7.96,9.8 resnet26,224,1024.0,5916.33,173.07,2.36,7.35,16.0 levit_384,224,1024.0,5897.4,173.625,2.36,6.26,39.13 resnet34d,224,1024.0,5884.13,174.017,3.91,4.54,21.82 cs3darknet_focus_m,288,1024.0,5878.89,174.173,2.51,6.19,9.3 legacy_seresnet34,224,1024.0,5873.4,174.335,3.67,3.74,21.96 repvit_m2,224,1024.0,5866.53,174.53,1.36,9.43,8.8 vit_base_patch32_224,224,1024.0,5866.04,174.553,4.37,4.19,88.22 vit_base_patch32_clip_224,224,1024.0,5864.79,174.59,4.37,4.19,88.22 repvit_m1_0,224,1024.0,5862.26,174.66,1.13,8.69,7.3 tf_efficientnet_es,224,1024.0,5831.76,175.58,1.81,8.73,5.44 rexnetr_130,224,1024.0,5827.09,175.72,0.68,9.81,7.61 resnetrs50,160,1024.0,5819.33,175.954,2.29,6.2,35.69 dla60x_c,224,1024.0,5709.85,179.326,0.59,6.01,1.32 vit_small_patch32_384,384,1024.0,5700.23,179.631,3.26,6.07,22.92 levit_conv_384,224,1024.0,5694.64,179.807,2.36,6.26,39.13 tiny_vit_5m_224,224,1024.0,5681.84,180.212,1.18,9.32,12.08 efficientnet_b1,224,1024.0,5671.54,180.54,0.59,9.36,7.79 cs3darknet_m,288,1024.0,5670.5,180.573,2.63,6.69,9.31 resnetblur18,224,1024.0,5631.98,181.808,2.34,3.39,11.69 tf_efficientnet_lite1,240,1024.0,5588.09,183.236,0.62,10.14,5.42 repvit_m1_1,224,1024.0,5584.25,183.355,1.36,9.43,8.8 mixnet_s,224,1024.0,5566.85,183.931,0.25,6.25,4.13 convnext_atto,288,1024.0,5556.64,184.274,0.91,6.3,3.7 darknet17,256,1024.0,5525.94,185.298,3.26,7.18,14.3 pit_s_224,224,1024.0,5520.06,185.491,2.42,6.18,23.46 resnet18d,288,1024.0,5497.35,186.262,3.41,5.43,11.71 selecsls60,224,1024.0,5496.69,186.283,3.59,5.52,30.67 pit_s_distilled_224,224,1024.0,5494.69,186.349,2.45,6.22,24.04 xcit_tiny_12_p16_224,224,1024.0,5472.11,187.12,1.24,6.29,6.72 selecsls60b,224,1024.0,5466.97,187.296,3.63,5.52,32.77 skresnet18,224,1024.0,5432.07,188.499,1.82,3.24,11.96 convnext_atto_ols,288,1024.0,5378.78,190.367,0.96,6.8,3.7 resmlp_12_224,224,1024.0,5371.14,190.637,3.01,5.5,15.35 regnetz_005,288,1024.0,5353.96,191.249,0.86,9.68,7.12 mobilenetv2_120d,224,1024.0,5347.39,191.484,0.69,11.97,5.83 convnextv2_atto,224,1024.0,5293.77,193.425,0.55,3.81,3.71 repvgg_b0,224,1024.0,5265.8,194.451,3.41,6.15,15.82 mixer_b32_224,224,1024.0,5245.72,195.191,3.24,6.29,60.29 vit_tiny_r_s16_p8_384,384,1024.0,5235.72,195.568,1.25,5.39,6.36 nf_regnet_b1,256,1024.0,5226.46,195.915,0.82,7.27,10.22 nf_regnet_b2,240,1024.0,5223.53,196.02,0.97,7.23,14.31 vit_base_patch32_clip_quickgelu_224,224,1024.0,5220.87,196.124,4.37,4.19,87.85 resnetaa34d,224,1024.0,5205.31,196.711,4.43,5.07,21.82 resnet26d,224,1024.0,5169.81,198.062,2.6,8.15,16.01 tf_mixnet_s,224,1024.0,5128.65,199.652,0.25,6.25,4.13 rexnetr_150,224,1024.0,5105.32,200.564,0.89,11.13,9.78 gmixer_12_224,224,1024.0,5083.79,201.414,2.67,7.26,12.7 fbnetv3_d,256,1024.0,5047.63,202.856,0.68,11.1,10.31 edgenext_x_small,256,1024.0,5018.94,204.014,0.54,5.93,2.34 mixer_s16_224,224,1024.0,5009.58,204.393,3.79,5.97,18.53 regnetz_b16,224,1024.0,5008.24,204.437,1.45,9.95,9.72 gmlp_ti16_224,224,1024.0,4999.44,204.811,1.34,7.55,5.87 darknet21,256,1024.0,4956.17,206.601,3.93,7.47,20.86 eva02_tiny_patch14_224,224,1024.0,4940.45,207.258,1.4,6.17,5.5 ghostnetv2_130,224,1024.0,4896.55,209.116,0.28,5.9,8.96 convnext_femto,288,1024.0,4844.52,211.362,1.3,7.56,5.22 nf_resnet26,224,1024.0,4822.21,212.339,2.41,7.35,16.0 efficientnet_lite2,260,1024.0,4817.66,212.541,0.89,12.9,6.09 tf_efficientnetv2_b2,260,1024.0,4797.27,213.444,1.72,9.84,10.1 efficientnet_cc_b0_8e,224,1024.0,4749.51,215.591,0.42,9.42,24.01 sedarknet21,256,1024.0,4747.46,215.684,3.93,7.47,20.95 efficientnet_cc_b0_4e,224,1024.0,4720.11,216.933,0.41,9.42,13.31 efficientnet_b2_pruned,260,1024.0,4716.64,217.093,0.73,9.13,8.31 convnext_femto_ols,288,1024.0,4709.5,217.422,1.35,8.06,5.23 resnext26ts,256,1024.0,4668.94,219.311,2.43,10.52,10.3 tiny_vit_11m_224,224,1024.0,4649.32,220.237,1.9,10.73,20.35 ecaresnet50d_pruned,224,1024.0,4636.78,220.832,2.53,6.43,19.94 deit_small_patch16_224,224,1024.0,4620.93,221.59,4.25,8.25,22.05 efficientformer_l1,224,1024.0,4616.64,221.795,1.3,5.53,12.29 vit_small_patch16_224,224,1024.0,4614.32,221.907,4.25,8.25,22.05 dpn48b,224,1024.0,4588.67,223.146,1.69,8.92,9.13 deit_small_distilled_patch16_224,224,1024.0,4587.3,223.214,4.27,8.29,22.44 vit_base_patch32_clip_256,256,1024.0,4547.51,225.168,5.68,5.44,87.86 convnextv2_femto,224,1024.0,4545.73,225.256,0.79,4.57,5.23 mobilevitv2_075,256,1024.0,4537.95,225.638,1.05,12.06,2.87 eca_resnext26ts,256,1024.0,4521.18,226.479,2.43,10.52,10.3 seresnext26ts,256,1024.0,4517.43,226.666,2.43,10.52,10.39 efficientnetv2_rw_t,224,1024.0,4511.98,226.94,1.93,9.94,13.65 legacy_seresnext26_32x4d,224,1024.0,4489.21,228.092,2.49,9.39,16.79 gernet_l,256,1024.0,4474.96,228.817,4.57,8.0,31.08 gcresnext26ts,256,1024.0,4472.11,228.964,2.43,10.53,10.48 rexnet_130,224,1024.0,4453.51,229.92,0.68,9.71,7.56 tf_efficientnet_b1,240,1024.0,4442.45,230.492,0.71,10.88,7.79 tf_efficientnet_cc_b0_8e,224,1024.0,4391.83,233.15,0.42,9.42,24.01 convnext_nano,224,1024.0,4389.78,233.258,2.46,8.37,15.59 gc_efficientnetv2_rw_t,224,1024.0,4373.41,234.132,1.94,9.97,13.68 tf_efficientnet_cc_b0_4e,224,1024.0,4373.37,234.134,0.41,9.42,13.31 tf_efficientnetv2_b3,240,1024.0,4372.06,234.204,1.93,9.95,14.36 tf_efficientnet_lite2,260,1024.0,4324.79,236.764,0.89,12.9,6.09 efficientnet_b1,256,1024.0,4298.75,238.198,0.77,12.22,7.79 deit3_small_patch16_224,224,1024.0,4270.38,239.779,4.25,8.25,22.06 cs3darknet_focus_l,256,1024.0,4230.07,242.066,4.66,8.03,21.15 nf_regnet_b1,288,1024.0,4135.98,247.568,1.02,9.2,10.22 convnext_nano_ols,224,1024.0,4118.16,248.644,2.65,9.38,15.65 nf_seresnet26,224,1024.0,4112.79,248.966,2.41,7.36,17.4 nf_ecaresnet26,224,1024.0,4107.39,249.292,2.41,7.36,16.0 efficientnet_b2,256,1024.0,4105.27,249.424,0.89,12.81,9.11 cs3darknet_l,256,1024.0,4101.41,249.66,4.86,8.55,21.16 nf_regnet_b2,272,1024.0,4097.18,249.913,1.22,9.27,14.31 ecaresnext50t_32x4d,224,1024.0,4074.12,251.332,2.7,10.09,15.41 ecaresnext26t_32x4d,224,1024.0,4072.14,251.454,2.7,10.09,15.41 seresnext26t_32x4d,224,1024.0,4061.05,252.141,2.7,10.09,16.81 repvgg_a2,224,1024.0,4049.32,252.867,5.7,6.26,28.21 poolformer_s12,224,1024.0,4047.55,252.981,1.82,5.53,11.92 seresnext26d_32x4d,224,1024.0,4037.54,253.609,2.73,10.19,16.81 regnetx_016,224,1024.0,4025.84,254.342,1.62,7.93,9.19 resnet26t,256,1024.0,4021.85,254.598,3.35,10.52,16.01 flexivit_small,240,1024.0,4011.8,255.236,4.88,9.46,22.06 edgenext_x_small,288,1024.0,3990.87,256.573,0.68,7.5,2.34 rexnet_150,224,1024.0,3983.48,257.051,0.9,11.21,9.73 vit_relpos_small_patch16_rpn_224,224,1024.0,3975.32,257.575,4.24,9.38,21.97 repvit_m3,224,1024.0,3966.18,258.164,1.89,13.94,10.68 vit_relpos_small_patch16_224,224,1024.0,3948.05,259.358,4.24,9.38,21.98 vit_srelpos_small_patch16_224,224,1024.0,3937.22,260.07,4.23,8.49,21.97 mobileone_s1,224,1024.0,3931.71,260.434,0.86,9.67,4.83 resnetv2_50,224,1024.0,3890.29,263.208,4.11,11.11,25.55 eca_botnext26ts_256,256,1024.0,3883.93,263.639,2.46,11.6,10.59 cs3sedarknet_l,256,1024.0,3835.91,266.94,4.86,8.56,21.91 ghostnetv2_160,224,1024.0,3826.79,267.576,0.42,7.23,12.39 resnet34,288,1024.0,3820.15,268.041,6.07,6.18,21.8 edgenext_small,256,1024.0,3794.31,269.865,1.26,9.07,5.59 dpn68,224,1024.0,3788.79,270.258,2.35,10.47,12.61 ese_vovnet19b_dw,288,1024.0,3782.88,270.682,2.22,13.63,6.54 fbnetv3_g,240,1024.0,3779.41,270.931,1.28,14.87,16.62 convnext_pico,288,1024.0,3777.8,271.046,2.27,10.08,9.05 ecaresnetlight,224,1024.0,3759.77,272.346,4.11,8.42,30.16 eca_halonext26ts,256,1024.0,3745.07,273.414,2.44,11.46,10.76 dpn68b,224,1024.0,3719.51,275.293,2.35,10.47,12.61 mixnet_m,224,1024.0,3687.37,277.689,0.36,8.19,5.01 resnet50,224,1024.0,3687.18,277.708,4.11,11.11,25.56 efficientnet_em,240,1024.0,3685.78,277.814,3.04,14.34,6.9 convnext_pico_ols,288,1024.0,3673.49,278.743,2.37,10.74,9.06 resnet32ts,256,1024.0,3641.96,281.156,4.63,11.58,17.96 bat_resnext26ts,256,1024.0,3638.35,281.435,2.53,12.51,10.73 efficientnet_b3_pruned,300,1024.0,3633.29,281.827,1.04,11.86,9.86 botnet26t_256,256,1024.0,3632.31,281.904,3.32,11.98,12.49 hrnet_w18_small_v2,224,1024.0,3631.33,281.979,2.62,9.65,15.6 ecaresnet101d_pruned,224,1024.0,3611.37,283.538,3.48,7.69,24.88 ecaresnet26t,256,1024.0,3599.02,284.511,3.35,10.53,16.01 regnetv_040,224,1024.0,3598.04,284.583,4.0,12.29,20.64 seresnet34,288,1024.0,3583.61,285.735,6.07,6.18,21.96 resnetv2_50t,224,1024.0,3573.26,286.561,4.32,11.82,25.57 pvt_v2_b1,224,1024.0,3571.19,286.726,2.04,14.01,14.01 regnety_016,224,1024.0,3567.37,287.031,1.63,8.04,11.2 resnext26ts,288,1024.0,3565.74,287.167,3.07,13.31,10.3 regnety_040,224,1024.0,3565.62,287.173,4.0,12.29,20.65 resnet33ts,256,1024.0,3563.66,287.335,4.76,11.66,19.68 resnetv2_50d,224,1024.0,3553.44,288.159,4.35,11.92,25.57 tf_efficientnet_em,240,1024.0,3544.42,288.894,3.04,14.34,6.9 halonet26t,256,1024.0,3541.55,289.129,3.19,11.69,12.48 dla60,224,1024.0,3527.55,290.275,4.26,10.16,22.04 tf_mixnet_m,224,1024.0,3524.0,290.567,0.36,8.19,5.01 resnet50c,224,1024.0,3521.04,290.812,4.35,11.92,25.58 edgenext_small_rw,256,1024.0,3501.76,292.411,1.58,9.51,7.83 resnet34d,288,1024.0,3491.3,293.29,6.47,7.51,21.82 convnextv2_pico,224,1024.0,3480.58,294.194,1.37,6.1,9.07 vit_small_resnet26d_224,224,1024.0,3476.26,294.557,5.04,10.65,63.61 convit_tiny,224,1024.0,3460.49,295.901,1.26,7.94,5.71 tresnet_m,224,1024.0,3457.69,296.14,5.75,7.31,31.39 resnet26,288,1024.0,3457.48,296.158,3.9,12.15,16.0 seresnext26ts,288,1024.0,3455.43,296.333,3.07,13.32,10.39 vit_relpos_base_patch32_plus_rpn_256,256,1024.0,3447.98,296.974,7.59,6.63,119.42 seresnet33ts,256,1024.0,3444.98,297.233,4.76,11.66,19.78 eca_resnext26ts,288,1024.0,3443.01,297.404,3.07,13.32,10.3 eca_resnet33ts,256,1024.0,3442.23,297.471,4.76,11.66,19.68 tf_efficientnet_b2,260,1024.0,3440.99,297.578,1.02,13.83,9.11 gcresnet33ts,256,1024.0,3424.64,298.998,4.76,11.68,19.88 gcresnext26ts,288,1024.0,3414.23,299.91,3.07,13.33,10.48 resnet50t,224,1024.0,3401.57,301.026,4.32,11.82,25.57 vovnet39a,224,1024.0,3395.56,301.56,7.09,6.73,22.6 resnet50d,224,1024.0,3380.59,302.894,4.35,11.92,25.58 efficientvit_b2,224,1024.0,3359.89,304.76,1.6,14.62,24.33 resnest14d,224,1024.0,3357.89,304.943,2.76,7.33,10.61 vit_base_patch32_plus_256,256,1024.0,3354.04,305.293,7.7,6.35,119.48 efficientnet_b0_gn,224,1024.0,3353.74,305.319,0.42,6.75,5.29 cs3darknet_focus_l,288,1024.0,3340.22,306.556,5.9,10.16,21.15 selecsls84,224,1024.0,3335.07,307.029,5.9,7.57,50.95 vit_tiny_patch16_384,384,1024.0,3332.37,307.277,3.16,12.08,5.79 legacy_seresnet50,224,1024.0,3325.14,307.946,3.88,10.6,28.09 coatnet_nano_cc_224,224,1024.0,3301.24,310.176,2.13,13.1,13.76 fastvit_t8,256,1024.0,3298.88,310.398,0.7,8.63,4.03 resnetblur18,288,1024.0,3292.39,311.01,3.87,5.6,11.69 repvit_m1_5,224,1024.0,3281.4,312.05,2.31,15.7,14.64 ese_vovnet39b,224,1024.0,3276.58,312.51,7.09,6.74,24.57 levit_512,224,1024.0,3274.29,312.728,5.64,10.22,95.17 haloregnetz_b,224,1024.0,3272.82,312.869,1.97,11.94,11.68 mobilevit_xs,256,1024.0,3272.76,312.87,0.93,13.62,2.32 coat_lite_tiny,224,1024.0,3257.39,314.352,1.6,11.65,5.72 coatnext_nano_rw_224,224,1024.0,3256.31,314.455,2.36,10.68,14.7 eca_vovnet39b,224,1024.0,3252.14,314.859,7.09,6.74,22.6 efficientnet_b2,288,1024.0,3249.31,315.132,1.12,16.2,9.11 resnetaa50,224,1024.0,3245.58,315.495,5.15,11.64,25.56 coatnet_nano_rw_224,224,1024.0,3238.25,316.209,2.29,13.29,15.14 cs3darknet_l,288,1024.0,3236.81,316.35,6.16,10.83,21.16 convnextv2_atto,288,1024.0,3226.1,317.401,0.91,6.3,3.71 mobileone_s2,224,1024.0,3211.19,318.869,1.34,11.55,7.88 seresnet50,224,1024.0,3200.07,319.981,4.11,11.13,28.09 nf_regnet_b3,288,1024.0,3185.16,321.477,1.67,11.84,18.59 crossvit_small_240,240,1024.0,3184.9,321.506,5.09,11.34,26.86 res2net50_48w_2s,224,1024.0,3168.87,323.132,4.18,11.72,25.29 resnetaa34d,288,1024.0,3155.87,324.463,7.33,8.38,21.82 vit_small_r26_s32_224,224,1024.0,3124.44,327.727,3.54,9.44,36.43 dla60x,224,1024.0,3106.99,329.567,3.54,13.8,17.35 efficientnet_b0_g8_gn,224,1024.0,3104.31,329.853,0.66,6.75,6.56 resnext50_32x4d,224,1024.0,3099.2,330.397,4.26,14.4,25.03 levit_conv_512,224,1024.0,3078.02,332.67,5.64,10.22,95.17 skresnet34,224,1024.0,3073.03,333.21,3.67,5.13,22.28 coat_lite_mini,224,1024.0,3058.66,334.777,2.0,12.25,11.01 resnet26d,288,1024.0,3053.73,335.317,4.29,13.48,16.01 mobileone_s0,224,1024.0,3053.01,335.391,1.09,15.48,5.29 levit_512d,224,1024.0,3045.04,336.274,5.85,11.3,92.5 cs3sedarknet_l,288,1024.0,3026.08,338.38,6.16,10.83,21.91 resnetaa50d,224,1024.0,3022.22,338.813,5.39,12.44,25.58 convnext_tiny,224,1024.0,3015.62,339.555,4.47,13.44,28.59 eca_nfnet_l0,224,1024.0,3011.21,340.052,4.35,10.47,24.14 xcit_nano_12_p16_384,384,1024.0,3011.18,340.055,1.64,12.14,3.05 nfnet_l0,224,1024.0,3000.78,341.23,4.36,10.47,35.07 resnetrs50,224,1024.0,2989.89,342.477,4.48,12.14,35.69 efficientnet_cc_b1_8e,240,1024.0,2988.69,342.615,0.75,15.44,39.72 regnetz_b16,288,1024.0,2987.05,342.79,2.39,16.43,9.72 seresnet50t,224,1024.0,2984.21,343.128,4.32,11.83,28.1 ecaresnet50d,224,1024.0,2975.54,344.128,4.35,11.93,25.58 regnetz_c16,256,1024.0,2971.35,344.607,2.51,16.57,13.46 densenet121,224,1024.0,2967.84,345.021,2.87,6.9,7.98 crossvit_15_240,240,1024.0,2967.06,345.11,5.17,12.01,27.53 resnet50s,224,1024.0,2958.0,346.169,5.47,13.52,25.68 rexnetr_200,224,1024.0,2955.32,346.483,1.59,15.11,16.52 mixnet_l,224,1024.0,2926.26,349.918,0.58,10.84,7.33 xcit_tiny_24_p16_224,224,1024.0,2925.33,350.035,2.34,11.82,12.12 levit_conv_512d,224,1024.0,2899.99,353.091,5.85,11.3,92.5 gcresnext50ts,256,1024.0,2897.54,353.393,3.75,15.46,15.67 lambda_resnet26rpt_256,256,1024.0,2887.51,354.621,3.16,11.87,10.99 resnext50d_32x4d,224,1024.0,2876.86,355.933,4.5,15.2,25.05 resnet32ts,288,1024.0,2868.64,356.953,5.86,14.65,17.96 crossvit_15_dagger_240,240,1024.0,2848.99,359.413,5.5,12.68,28.21 tiny_vit_21m_224,224,1024.0,2842.09,360.287,4.08,15.96,33.22 vit_base_resnet26d_224,224,1024.0,2837.87,360.821,6.93,12.34,101.4 tf_efficientnet_cc_b1_8e,240,1024.0,2835.77,361.09,0.75,15.44,39.72 cspresnet50,256,1024.0,2834.55,361.245,4.54,11.5,21.62 mobilevitv2_100,256,1024.0,2833.62,361.358,1.84,16.08,4.9 resnet33ts,288,1024.0,2829.43,361.9,6.02,14.75,19.68 vovnet57a,224,1024.0,2821.83,362.874,8.95,7.52,36.64 deit3_medium_patch16_224,224,1024.0,2805.09,365.038,7.53,10.99,38.85 inception_next_tiny,224,1024.0,2798.9,365.847,4.19,11.98,28.06 tf_mixnet_l,224,1024.0,2798.14,365.947,0.58,10.84,7.33 res2next50,224,1024.0,2797.04,366.091,4.2,13.71,24.67 dla60_res2next,224,1024.0,2795.54,366.285,3.49,13.17,17.03 coatnet_pico_rw_224,224,1024.0,2793.27,366.584,1.96,12.91,10.85 convnext_tiny_hnf,224,1024.0,2770.64,369.577,4.47,13.44,28.59 gcresnet50t,256,1024.0,2767.9,369.943,5.42,14.67,25.9 convnextv2_femto,288,1024.0,2762.62,370.652,1.3,7.56,5.23 tf_efficientnetv2_b3,300,1024.0,2757.15,371.387,3.04,15.74,14.36 legacy_seresnext50_32x4d,224,1024.0,2750.41,372.297,4.26,14.42,27.56 ecaresnet50d_pruned,288,1024.0,2749.78,372.383,4.19,10.61,19.94 res2net50_26w_4s,224,1024.0,2749.69,372.394,4.28,12.61,25.7 seresnext50_32x4d,224,1024.0,2749.17,372.464,4.26,14.42,27.56 vgg11_bn,224,1024.0,2746.28,372.857,7.62,7.44,132.87 resmlp_24_224,224,1024.0,2745.97,372.9,5.96,10.91,30.02 resnetv2_50x1_bit,224,1024.0,2742.41,373.383,4.23,11.11,25.55 eca_resnet33ts,288,1024.0,2737.24,374.089,6.02,14.76,19.68 efficientnetv2_rw_t,288,1024.0,2736.91,374.133,3.19,16.42,13.65 seresnet33ts,288,1024.0,2734.83,374.417,6.02,14.76,19.78 nfnet_f0,192,1024.0,2731.03,374.934,7.21,10.16,71.49 res2net50_14w_8s,224,1024.0,2724.75,375.804,4.21,13.28,25.06 visformer_small,224,1024.0,2720.95,376.328,4.88,11.43,40.22 ese_vovnet57b,224,1024.0,2711.8,377.598,8.95,7.52,38.61 gcresnet33ts,288,1024.0,2705.39,378.493,6.02,14.78,19.88 cspresnet50d,256,1024.0,2702.61,378.881,4.86,12.55,21.64 twins_svt_small,224,1024.0,2696.15,379.788,2.82,10.7,24.06 efficientvit_l1,224,1024.0,2692.51,380.303,5.27,15.85,52.65 resnetblur50,224,1024.0,2689.65,380.707,5.16,12.02,25.56 seresnetaa50d,224,1024.0,2682.26,381.757,5.4,12.46,28.11 fbnetv3_g,288,1024.0,2673.23,383.046,1.77,21.09,16.62 cspresnet50w,256,1024.0,2671.97,383.228,5.04,12.19,28.12 dla60_res2net,224,1024.0,2669.84,383.53,4.15,12.34,20.85 convnext_nano,288,1024.0,2669.05,383.645,4.06,13.84,15.59 gc_efficientnetv2_rw_t,288,1024.0,2659.37,385.042,3.2,16.45,13.68 gcvit_xxtiny,224,1024.0,2658.4,385.182,2.14,15.36,12.0 poolformerv2_s12,224,1024.0,2624.04,390.223,1.83,5.53,11.89 vit_relpos_medium_patch16_rpn_224,224,1024.0,2618.88,390.989,7.5,12.13,38.73 mobileone_s3,224,1024.0,2616.83,391.296,1.94,13.85,10.17 davit_tiny,224,1024.0,2612.7,391.92,4.47,17.08,28.36 vit_relpos_medium_patch16_224,224,1024.0,2603.89,393.246,7.5,12.13,38.75 resnet51q,256,1024.0,2602.52,393.454,6.38,16.55,35.7 gmixer_24_224,224,1024.0,2594.59,394.657,5.28,14.45,24.72 maxvit_pico_rw_256,256,768.0,2593.58,296.105,1.68,18.77,7.46 vit_srelpos_medium_patch16_224,224,1024.0,2591.17,395.176,7.49,11.32,38.74 vit_relpos_medium_patch16_cls_224,224,1024.0,2587.16,395.789,7.55,13.3,38.76 maxvit_rmlp_pico_rw_256,256,768.0,2587.02,296.857,1.69,21.32,7.52 nf_regnet_b3,320,1024.0,2582.41,396.514,2.05,14.61,18.59 res2net50d,224,1024.0,2577.65,397.25,4.52,13.41,25.72 cs3darknet_focus_x,256,1024.0,2569.33,398.536,8.03,10.69,35.02 densenetblur121d,224,1024.0,2559.52,400.063,3.11,7.9,8.0 inception_v3,299,1024.0,2546.29,402.143,5.73,8.97,23.83 coatnet_0_rw_224,224,1024.0,2545.57,402.256,4.23,15.1,27.44 repvgg_b1g4,224,1024.0,2545.06,402.332,8.15,10.64,39.97 regnetx_032,224,1024.0,2534.07,404.077,3.2,11.37,15.3 twins_pcpvt_small,224,1024.0,2533.92,404.104,3.68,15.51,24.11 resnetblur50d,224,1024.0,2528.9,404.909,5.4,12.82,25.58 rexnet_200,224,1024.0,2519.88,406.358,1.56,14.91,16.37 resnetrs101,192,1024.0,2505.12,408.751,6.04,12.7,63.62 resnet26t,320,1024.0,2502.87,409.119,5.24,16.44,16.01 nf_ecaresnet50,224,1024.0,2502.03,409.253,4.21,11.13,25.56 convnext_nano_ols,288,1024.0,2497.73,409.961,4.38,15.5,15.65 convnextv2_nano,224,1024.0,2497.72,409.963,2.46,8.37,15.62 nf_seresnet50,224,1024.0,2494.79,410.425,4.21,11.13,28.09 regnety_032,224,1024.0,2483.68,412.275,3.2,11.26,19.44 vit_medium_patch16_gap_240,240,1024.0,2477.36,413.332,8.6,12.57,44.4 cs3darknet_x,256,1024.0,2475.51,413.641,8.38,11.35,35.05 densenet169,224,1024.0,2463.83,415.603,3.4,7.3,14.15 xcit_small_12_p16_224,224,1024.0,2460.07,416.237,4.82,12.57,26.25 cspresnext50,256,1024.0,2452.36,417.546,4.05,15.86,20.57 mobilevit_s,256,1024.0,2447.35,418.395,1.86,17.03,5.58 darknet53,256,1024.0,2439.82,419.693,9.31,12.39,41.61 darknetaa53,256,1024.0,2432.07,421.03,7.97,12.39,36.02 edgenext_small,320,1024.0,2429.25,421.516,1.97,14.16,5.59 seresnext26t_32x4d,288,1024.0,2412.74,424.404,4.46,16.68,16.81 sehalonet33ts,256,1024.0,2403.77,425.986,3.55,14.7,13.69 seresnext26d_32x4d,288,1024.0,2391.16,428.231,4.51,16.85,16.81 resnet61q,256,1024.0,2368.17,432.39,7.8,17.01,36.85 fastvit_t12,256,1024.0,2356.34,434.562,1.42,12.42,7.55 vit_base_r26_s32_224,224,1024.0,2354.84,434.838,6.76,11.54,101.38 focalnet_tiny_srf,224,1024.0,2353.35,435.113,4.42,16.32,28.43 resnetv2_101,224,1024.0,2342.24,437.176,7.83,16.23,44.54 cs3sedarknet_x,256,1024.0,2329.01,439.66,8.38,11.35,35.4 nf_resnet50,256,1024.0,2318.52,441.645,5.46,14.52,25.56 xcit_nano_12_p8_224,224,1024.0,2310.67,443.15,2.16,15.71,3.05 resnest26d,224,1024.0,2309.28,443.418,3.64,9.97,17.07 coatnet_rmlp_nano_rw_224,224,1024.0,2308.34,443.598,2.51,18.21,15.15 resnetv2_50,288,1024.0,2302.9,444.644,6.79,18.37,25.55 ecaresnet50t,256,1024.0,2299.59,445.285,5.64,15.45,25.57 gmlp_s16_224,224,1024.0,2291.16,446.925,4.42,15.1,19.42 efficientnet_lite3,300,1024.0,2290.17,447.117,1.65,21.85,8.2 dm_nfnet_f0,192,1024.0,2271.28,450.836,7.21,10.16,71.49 resnet101,224,1024.0,2263.99,452.287,7.83,16.23,44.55 ecaresnet26t,320,1024.0,2258.47,453.393,5.24,16.44,16.01 edgenext_base,256,1024.0,2256.96,453.695,3.85,15.58,18.51 efficientnetv2_s,288,1024.0,2251.36,454.825,4.75,20.13,21.46 skresnet50,224,1024.0,2250.82,454.933,4.11,12.5,25.8 dla102,224,1024.0,2248.24,455.455,7.19,14.18,33.27 edgenext_small_rw,320,1024.0,2240.98,456.929,2.46,14.85,7.83 ecaresnetlight,288,1024.0,2235.21,458.11,6.79,13.91,30.16 dpn68b,288,1024.0,2234.13,458.331,3.89,17.3,12.61 gcresnext50ts,288,1024.0,2232.45,458.676,4.75,19.57,15.67 fastvit_s12,256,1024.0,2229.72,459.239,1.82,13.67,9.47 fastvit_sa12,256,1024.0,2225.03,460.206,1.96,13.83,11.58 focalnet_tiny_lrf,224,1024.0,2222.33,460.766,4.49,17.76,28.65 resnetv2_101d,224,1024.0,2216.51,461.976,8.07,17.04,44.56 resnet101c,224,1024.0,2202.12,464.995,8.08,17.04,44.57 vit_base_resnet50d_224,224,1024.0,2199.36,465.578,8.68,16.1,110.97 regnetv_040,288,1024.0,2190.89,467.375,6.6,20.3,20.64 vit_medium_patch16_gap_256,256,1024.0,2190.03,467.563,9.78,14.29,38.86 resnet50,288,1024.0,2185.5,468.532,6.8,18.37,25.56 gcresnet50t,288,1024.0,2180.99,469.5,6.86,18.57,25.9 regnety_040,288,1024.0,2169.28,472.031,6.61,20.3,20.65 vgg13,224,1024.0,2159.6,474.15,11.31,12.25,133.05 eva02_small_patch14_224,224,1024.0,2151.59,475.915,5.53,12.34,21.62 vit_medium_patch16_reg4_gap_256,256,1024.0,2149.02,476.485,9.93,14.51,38.87 efficientnetv2_rw_s,288,1024.0,2146.83,476.971,4.91,21.41,23.94 ecaresnet101d_pruned,288,1024.0,2141.83,478.084,5.75,12.71,24.88 mobilevitv2_125,256,1024.0,2139.71,478.555,2.86,20.1,7.48 vit_medium_patch16_reg4_256,256,1024.0,2136.17,479.352,9.97,14.56,38.87 skresnet50d,224,1024.0,2134.1,479.815,4.36,13.31,25.82 pvt_v2_b2,224,1024.0,2119.72,483.066,3.9,24.96,25.36 hrnet_w18_ssld,224,1024.0,2114.47,484.27,4.32,16.31,21.3 convnextv2_pico,288,1024.0,2113.62,484.464,2.27,10.08,9.07 eva02_tiny_patch14_336,336,1024.0,2113.11,484.582,3.14,13.85,5.76 efficientvit_l2,224,1024.0,2109.14,485.494,6.97,19.58,63.71 hrnet_w18,224,1024.0,2100.77,487.428,4.32,16.31,21.3 regnetx_040,224,1024.0,2099.85,487.636,3.99,12.2,22.12 tf_efficientnet_lite3,300,1024.0,2090.5,489.823,1.65,21.85,8.2 wide_resnet50_2,224,1024.0,2081.66,491.904,11.43,14.4,68.88 resnet51q,288,1024.0,2069.71,494.744,8.07,20.94,35.7 poolformer_s24,224,1024.0,2067.46,495.278,3.41,10.68,21.39 sebotnet33ts_256,256,512.0,2066.45,247.758,3.89,17.46,13.7 efficientformer_l3,224,1024.0,2064.62,495.963,3.93,12.01,31.41 resnest50d_1s4x24d,224,1024.0,2057.55,497.667,4.43,13.57,25.68 gcvit_xtiny,224,1024.0,2053.45,498.662,2.93,20.26,19.98 cspdarknet53,256,1024.0,2048.51,499.863,6.57,16.81,27.64 crossvit_18_240,240,1024.0,2029.53,504.539,8.21,16.14,43.27 mixnet_xl,224,1024.0,2029.05,504.653,0.93,14.57,11.9 vit_base_patch32_384,384,1024.0,2028.15,504.881,12.67,12.14,88.3 efficientnet_b3,288,1024.0,2027.72,504.989,1.63,21.49,12.23 vit_base_patch32_clip_384,384,1024.0,2026.31,505.34,12.67,12.14,88.3 resnet50t,288,1024.0,2024.16,505.879,7.14,19.53,25.57 dla102x,224,1024.0,2023.35,506.08,5.89,19.42,26.31 legacy_seresnet101,224,1024.0,2012.58,508.788,7.61,15.74,49.33 resnet50d,288,1024.0,2012.14,508.9,7.19,19.7,25.58 cs3edgenet_x,256,1024.0,2002.36,511.384,11.53,12.92,47.82 resnetaa101d,224,1024.0,1994.67,513.346,9.12,17.56,44.57 repvgg_b1,224,1024.0,1994.42,513.418,13.16,10.64,57.42 res2net50_26w_6s,224,1024.0,1979.48,517.295,6.33,15.28,37.05 regnetz_d32,256,1024.0,1978.14,517.642,5.98,23.74,27.58 cs3sedarknet_xdw,256,1024.0,1970.5,519.653,5.97,17.18,21.6 resnetaa50,288,1024.0,1968.61,520.152,8.52,19.24,25.56 seresnet101,224,1024.0,1966.15,520.803,7.84,16.27,49.33 resnet101s,224,1024.0,1964.56,521.226,9.19,18.64,44.67 cs3darknet_x,288,1024.0,1958.87,522.739,10.6,14.36,35.05 crossvit_18_dagger_240,240,1024.0,1955.55,523.625,8.65,16.91,44.27 swin_tiny_patch4_window7_224,224,1024.0,1951.67,524.668,4.51,17.06,28.29 tresnet_v2_l,224,1024.0,1947.69,525.738,8.85,16.34,46.17 ese_vovnet39b,288,1024.0,1941.03,527.543,11.71,11.13,24.57 regnetz_d8,256,1024.0,1940.13,527.785,3.97,23.74,23.37 tf_efficientnetv2_s,300,1024.0,1939.51,527.958,5.35,22.73,21.46 regnetz_c16,320,1024.0,1933.29,529.65,3.92,25.88,13.46 coatnet_bn_0_rw_224,224,1024.0,1926.49,531.525,4.48,18.41,27.44 darknet53,288,1024.0,1924.44,532.092,11.78,15.68,41.61 resnext101_32x4d,224,1024.0,1923.83,532.261,8.01,21.23,44.18 coatnet_rmlp_0_rw_224,224,1024.0,1920.22,533.259,4.52,21.26,27.45 xcit_tiny_12_p16_384,384,1024.0,1917.57,533.997,3.64,18.25,6.72 darknetaa53,288,1024.0,1915.93,534.454,10.08,15.68,36.02 mobileone_s4,224,1024.0,1915.84,534.474,3.04,17.74,14.95 maxxvit_rmlp_nano_rw_256,256,768.0,1913.61,401.326,4.17,21.53,16.78 nest_tiny,224,1024.0,1909.31,536.303,5.24,14.75,17.06 regnetz_040,256,1024.0,1906.99,536.946,4.06,24.19,27.12 nf_regnet_b4,320,1024.0,1906.99,536.957,3.29,19.88,30.21 seresnet50,288,1024.0,1902.22,538.306,6.8,18.39,28.09 pvt_v2_b2_li,224,1024.0,1897.86,539.539,3.77,25.04,22.55 regnetz_040_h,256,1024.0,1896.27,539.981,4.12,24.29,28.94 densenet201,224,1024.0,1895.14,540.319,4.34,7.85,20.01 halonet50ts,256,1024.0,1887.53,542.495,5.3,19.2,22.73 nest_tiny_jx,224,1024.0,1885.06,543.199,5.24,14.75,17.06 vgg13_bn,224,1024.0,1884.94,543.241,11.33,12.25,133.05 regnetx_080,224,1024.0,1883.47,543.661,8.02,14.06,39.57 vit_large_patch32_224,224,1024.0,1882.39,543.977,15.27,11.11,305.51 ecaresnet101d,224,1024.0,1880.92,544.404,8.08,17.07,44.57 resnet61q,288,1024.0,1874.14,546.373,9.87,21.52,36.85 nf_resnet101,224,1024.0,1864.42,549.218,8.01,16.23,44.55 cs3se_edgenet_x,256,1024.0,1859.86,550.568,11.53,12.94,50.72 repvit_m2_3,224,1024.0,1852.95,552.61,4.57,26.21,23.69 resmlp_36_224,224,1024.0,1843.66,555.406,8.91,16.33,44.69 cs3sedarknet_x,288,1024.0,1843.16,555.556,10.6,14.37,35.4 resnext50_32x4d,288,1024.0,1841.23,556.139,7.04,23.81,25.03 convnext_small,224,1024.0,1838.66,556.915,8.71,21.56,50.22 convnext_tiny,288,1024.0,1835.18,557.972,7.39,22.21,28.59 resnetv2_50d_gn,224,1024.0,1829.29,559.767,4.38,11.92,25.57 resnetaa50d,288,1024.0,1827.2,560.408,8.92,20.57,25.58 pit_b_224,224,1024.0,1823.77,561.458,10.56,16.6,73.76 eca_nfnet_l0,288,1024.0,1822.69,561.796,7.12,17.29,24.14 nfnet_l0,288,1024.0,1817.7,563.332,7.13,17.29,35.07 sequencer2d_s,224,1024.0,1816.41,563.738,4.96,11.31,27.65 pit_b_distilled_224,224,1024.0,1810.4,565.6,10.63,16.67,74.79 nf_resnet50,288,1024.0,1794.38,570.655,6.88,18.37,25.56 twins_pcpvt_base,224,1024.0,1790.37,571.935,6.46,21.35,43.83 rexnetr_200,288,768.0,1782.92,430.745,2.62,24.96,16.52 seresnet50t,288,1024.0,1780.59,575.079,7.14,19.55,28.1 cait_xxs24_224,224,1024.0,1779.24,575.513,2.53,20.29,11.96 swin_s3_tiny_224,224,1024.0,1777.31,576.139,4.64,19.13,28.33 resnet50_gn,224,1024.0,1776.88,576.279,4.14,11.11,25.56 ecaresnet50d,288,1024.0,1775.84,576.616,7.19,19.72,25.58 resnetblur101d,224,1024.0,1765.86,579.878,9.12,17.94,44.57 densenet121,288,1024.0,1761.12,581.437,4.74,11.41,7.98 coat_lite_small,224,1024.0,1760.12,581.767,3.96,22.09,19.84 mixer_b16_224,224,1024.0,1758.48,582.299,12.62,14.53,59.88 mobilevitv2_150,256,768.0,1748.31,439.266,4.09,24.11,10.59 efficientvit_b3,224,1024.0,1742.56,587.628,3.99,26.9,48.65 rexnetr_300,224,1024.0,1736.82,589.571,3.39,22.16,34.81 vgg16,224,1024.0,1730.88,591.595,15.47,13.56,138.36 maxxvitv2_nano_rw_256,256,768.0,1724.32,445.384,6.12,19.66,23.7 res2net101_26w_4s,224,1024.0,1723.01,594.296,8.1,18.45,45.21 resnext50d_32x4d,288,1024.0,1717.01,596.374,7.44,25.13,25.05 maxvit_nano_rw_256,256,768.0,1709.05,449.363,4.26,25.76,15.45 legacy_seresnext101_32x4d,224,1024.0,1707.02,599.865,8.02,21.26,48.96 seresnext101_32x4d,224,1024.0,1706.74,599.963,8.02,21.26,48.96 maxvit_rmlp_nano_rw_256,256,768.0,1705.93,450.183,4.28,27.4,15.5 resnetv2_50d_frn,224,1024.0,1703.71,601.028,4.33,11.92,25.59 mobilevitv2_175,256,512.0,1701.95,300.817,5.54,28.13,14.25 tf_efficientnet_b3,300,1024.0,1694.25,604.385,1.87,23.83,12.23 convnext_tiny_hnf,288,1024.0,1681.52,608.96,7.39,22.21,28.59 ese_vovnet39b_evos,224,1024.0,1671.22,612.716,7.07,6.74,24.58 res2net50_26w_8s,224,1024.0,1656.9,618.009,8.37,17.95,48.4 resnet101d,256,1024.0,1654.59,618.871,10.55,22.25,44.57 tresnet_l,224,1024.0,1652.13,619.794,10.9,11.9,55.99 res2net101d,224,1024.0,1652.09,619.808,8.35,19.25,45.23 mixer_l32_224,224,1024.0,1651.22,620.129,11.27,19.86,206.94 regnetz_b16_evos,224,1024.0,1648.87,621.016,1.43,9.95,9.74 botnet50ts_256,256,512.0,1645.51,311.14,5.54,22.23,22.74 efficientnet_b3,320,1024.0,1641.76,623.708,2.01,26.52,12.23 seresnext50_32x4d,288,1024.0,1638.34,625.012,7.04,23.82,27.56 coatnet_0_224,224,512.0,1634.58,313.22,4.43,21.14,25.04 swinv2_cr_tiny_224,224,1024.0,1629.27,628.491,4.66,28.45,28.33 inception_next_small,224,1024.0,1628.58,628.755,8.36,19.27,49.37 resnetv2_152,224,1024.0,1628.46,628.801,11.55,22.56,60.19 regnetx_064,224,1024.0,1628.2,628.898,6.49,16.37,26.21 hrnet_w32,224,1024.0,1627.55,629.157,8.97,22.02,41.23 convnextv2_tiny,224,1024.0,1627.26,629.266,4.47,13.44,28.64 seresnetaa50d,288,1024.0,1622.33,631.178,8.92,20.59,28.11 davit_small,224,1024.0,1614.32,634.313,8.69,27.54,49.75 regnety_040_sgn,224,1024.0,1612.57,634.996,4.03,12.29,20.65 legacy_xception,299,768.0,1604.43,478.663,8.4,35.83,22.86 swinv2_cr_tiny_ns_224,224,1024.0,1600.49,639.793,4.66,28.45,28.33 resnetblur50,288,1024.0,1598.7,640.511,8.52,19.87,25.56 efficientnet_el,300,1024.0,1595.26,641.889,8.0,30.7,10.59 efficientnet_el_pruned,300,1024.0,1592.53,642.988,8.0,30.7,10.59 resnet152,224,1024.0,1589.58,644.183,11.56,22.56,60.19 deit_base_patch16_224,224,1024.0,1581.19,647.603,16.87,16.49,86.57 cs3edgenet_x,288,1024.0,1577.26,649.216,14.59,16.36,47.82 deit_base_distilled_patch16_224,224,1024.0,1575.74,649.842,16.95,16.58,87.34 vit_base_patch16_224,224,1024.0,1574.94,650.173,16.87,16.49,86.57 vit_base_patch16_224_miil,224,1024.0,1574.63,650.301,16.88,16.5,94.4 vit_base_patch16_clip_224,224,1024.0,1574.46,650.371,16.87,16.49,86.57 vit_base_patch16_siglip_224,224,1024.0,1571.54,651.577,17.02,16.71,92.88 resnetv2_152d,224,1024.0,1564.52,654.501,11.8,23.36,60.2 vit_base_patch16_gap_224,224,1024.0,1563.13,655.085,16.78,16.41,86.57 halo2botnet50ts_256,256,1024.0,1562.09,655.52,5.02,21.78,22.64 resnet152c,224,1024.0,1558.11,657.195,11.8,23.36,60.21 ese_vovnet99b,224,1024.0,1554.99,658.512,16.51,11.27,63.2 vit_small_resnet50d_s16_224,224,1024.0,1551.97,659.792,13.0,21.12,57.53 nf_seresnet101,224,1024.0,1549.92,660.662,8.02,16.27,49.33 nf_ecaresnet101,224,1024.0,1549.88,660.683,8.01,16.27,44.55 tf_efficientnet_el,300,1024.0,1543.58,663.384,8.0,30.7,10.59 coatnet_rmlp_1_rw_224,224,1024.0,1542.97,663.643,7.44,28.08,41.69 nfnet_f0,256,1024.0,1541.8,664.144,12.62,18.05,71.49 vgg16_bn,224,1024.0,1533.25,667.85,15.5,13.56,138.37 resnest50d,224,1024.0,1530.42,669.084,5.4,14.36,27.48 caformer_s18,224,1024.0,1528.28,670.023,3.9,15.18,26.34 pvt_v2_b3,224,1024.0,1527.57,670.328,6.71,33.8,45.24 densenetblur121d,288,1024.0,1521.38,673.062,5.14,13.06,8.0 maxvit_tiny_rw_224,224,768.0,1520.98,504.928,4.93,28.54,29.06 mvitv2_tiny,224,1024.0,1518.09,674.509,4.7,21.16,24.17 vit_base_patch16_rpn_224,224,1024.0,1516.7,675.134,16.78,16.41,86.54 convnextv2_nano,288,768.0,1514.74,507.006,4.06,13.84,15.62 regnety_032,288,1024.0,1514.59,676.077,5.29,18.61,19.44 rexnet_300,224,1024.0,1508.74,678.701,3.44,22.4,34.71 resnetblur50d,288,1024.0,1506.45,679.732,8.92,21.19,25.58 deit3_base_patch16_224,224,1024.0,1497.14,683.959,16.87,16.49,86.59 convit_small,224,1024.0,1494.54,685.148,5.76,17.87,27.78 vit_base_patch32_clip_448,448,1024.0,1493.83,685.476,17.21,16.49,88.34 dla169,224,1024.0,1487.25,688.504,11.6,20.2,53.39 skresnext50_32x4d,224,1024.0,1470.99,696.12,4.5,17.18,27.48 xcit_tiny_12_p8_224,224,1024.0,1465.13,698.903,4.81,23.6,6.71 vit_small_patch16_36x1_224,224,1024.0,1460.65,701.044,12.63,24.59,64.67 ecaresnet50t,320,1024.0,1451.46,705.484,8.82,24.13,25.57 beitv2_base_patch16_224,224,1024.0,1448.02,707.161,16.87,16.49,86.53 vgg19,224,1024.0,1441.93,710.149,19.63,14.86,143.67 beit_base_patch16_224,224,1024.0,1440.48,710.862,16.87,16.49,86.53 hrnet_w30,224,1024.0,1436.17,712.996,8.15,21.21,37.71 edgenext_base,320,1024.0,1435.98,713.087,6.01,24.32,18.51 resnet152s,224,1024.0,1434.4,713.876,12.92,24.96,60.32 convformer_s18,224,1024.0,1427.19,717.481,3.96,15.82,26.77 resnetv2_50d_evos,224,1024.0,1426.57,717.793,4.33,11.92,25.59 focalnet_small_srf,224,1024.0,1426.35,717.904,8.62,26.26,49.89 sequencer2d_m,224,1024.0,1413.9,724.228,6.55,14.26,38.31 vit_relpos_base_patch16_rpn_224,224,1024.0,1408.36,727.069,16.8,17.63,86.41 volo_d1_224,224,1024.0,1407.83,727.348,6.94,24.43,26.63 regnety_080,224,1024.0,1407.5,727.512,8.0,17.97,39.18 vit_small_patch16_18x2_224,224,1024.0,1407.09,727.729,12.63,24.59,64.67 gcvit_tiny,224,1024.0,1405.32,728.65,4.79,29.82,28.22 dpn92,224,1024.0,1404.08,729.292,6.54,18.21,37.67 vit_relpos_base_patch16_224,224,1024.0,1402.98,729.864,16.8,17.63,86.43 resnetv2_101,288,1024.0,1402.28,730.227,12.94,26.83,44.54 regnetx_160,224,1024.0,1400.84,730.974,15.99,25.52,54.28 dla102x2,224,1024.0,1395.12,733.975,9.34,29.91,41.28 legacy_seresnet152,224,1024.0,1394.86,734.109,11.33,22.08,66.82 vit_relpos_base_patch16_clsgap_224,224,1024.0,1394.83,734.131,16.88,17.72,86.43 vit_relpos_base_patch16_cls_224,224,1024.0,1392.12,735.556,16.88,17.72,86.43 vit_small_patch16_384,384,1024.0,1390.73,736.291,12.45,24.15,22.2 poolformer_s36,224,1024.0,1388.46,737.493,5.0,15.82,30.86 vit_base_patch16_clip_quickgelu_224,224,1024.0,1388.13,737.672,16.87,16.49,86.19 densenet161,224,1024.0,1384.23,739.75,7.79,11.06,28.68 flexivit_base,240,1024.0,1380.45,741.777,19.35,18.92,86.59 efficientformerv2_s0,224,1024.0,1377.72,743.244,0.41,5.3,3.6 seresnet152,224,1024.0,1371.27,746.737,11.57,22.61,66.82 poolformerv2_s24,224,1024.0,1356.43,754.905,3.42,10.68,21.34 resnet101,288,1024.0,1354.29,756.102,12.95,26.83,44.55 focalnet_small_lrf,224,1024.0,1339.63,764.378,8.74,28.61,50.34 inception_v4,299,1024.0,1338.22,765.183,12.28,15.09,42.68 repvgg_b2,224,1024.0,1336.97,765.895,20.45,12.9,89.02 nf_regnet_b4,384,1024.0,1327.28,771.488,4.7,28.61,30.21 repvgg_b2g4,224,1024.0,1323.55,773.658,12.63,12.9,61.76 eca_nfnet_l1,256,1024.0,1319.97,775.763,9.62,22.04,41.41 fastvit_sa24,256,1024.0,1310.4,781.428,3.79,23.92,21.55 xcit_small_24_p16_224,224,1024.0,1307.21,783.335,9.1,23.63,47.67 twins_pcpvt_large,224,1024.0,1303.57,785.524,9.53,30.21,60.99 vit_base_patch16_xp_224,224,1024.0,1302.82,785.975,16.85,16.49,86.51 maxvit_tiny_tf_224,224,768.0,1301.05,590.28,5.42,31.21,30.92 deit3_small_patch16_384,384,1024.0,1298.34,788.686,12.45,24.15,22.21 coatnet_rmlp_1_rw2_224,224,1024.0,1296.36,789.892,7.71,32.74,41.72 coatnet_1_rw_224,224,1024.0,1295.8,790.234,7.63,27.22,41.72 regnety_080_tv,224,1024.0,1291.63,792.778,8.51,19.73,39.38 vgg19_bn,224,1024.0,1290.82,793.286,19.66,14.86,143.68 mixnet_xxl,224,768.0,1286.88,596.774,2.04,23.43,23.96 dm_nfnet_f0,256,1024.0,1286.75,795.79,12.62,18.05,71.49 efficientnet_b4,320,768.0,1280.17,599.91,3.13,34.76,19.34 hrnet_w18_ssld,288,1024.0,1279.49,800.308,7.14,26.96,21.3 maxxvit_rmlp_tiny_rw_256,256,768.0,1274.84,602.417,6.36,32.69,29.64 efficientformerv2_s1,224,1024.0,1271.59,805.28,0.67,7.66,6.19 convnext_base,224,1024.0,1268.86,807.011,15.38,28.75,88.59 mobilevitv2_200,256,512.0,1268.57,403.59,7.22,32.15,18.45 regnetz_d32,320,1024.0,1265.97,808.844,9.33,37.08,27.58 efficientnetv2_s,384,1024.0,1265.12,809.401,8.44,35.77,21.46 twins_svt_base,224,1024.0,1261.93,811.442,8.36,20.42,56.07 wide_resnet50_2,288,1024.0,1242.89,823.878,18.89,23.81,68.88 regnetz_d8,320,1024.0,1242.36,824.221,6.19,37.08,23.37 regnetz_040,320,512.0,1238.82,413.274,6.35,37.78,27.12 regnetz_040_h,320,512.0,1231.07,415.879,6.43,37.94,28.94 nest_small,224,1024.0,1230.37,832.252,9.41,22.88,38.35 tf_efficientnetv2_s,384,1024.0,1224.58,836.191,8.44,35.77,21.46 nest_small_jx,224,1024.0,1220.76,838.798,9.41,22.88,38.35 maxvit_tiny_rw_256,256,768.0,1213.37,632.937,6.44,37.27,29.07 maxvit_rmlp_tiny_rw_256,256,768.0,1210.44,634.468,6.47,39.84,29.15 vit_base_patch16_siglip_256,256,1024.0,1208.23,847.511,22.23,21.83,92.93 efficientnetv2_rw_s,384,1024.0,1208.22,847.514,8.72,38.03,23.94 resnetaa101d,288,1024.0,1207.75,847.844,15.07,29.03,44.57 swin_small_patch4_window7_224,224,1024.0,1206.81,848.507,8.77,27.47,49.61 dpn98,224,1024.0,1206.02,849.061,11.73,25.2,61.57 swinv2_tiny_window8_256,256,1024.0,1197.34,855.217,5.96,24.57,28.35 cs3se_edgenet_x,320,1024.0,1196.49,855.827,18.01,20.21,50.72 resnext101_64x4d,224,1024.0,1196.17,856.053,15.52,31.21,83.46 cait_xxs36_224,224,1024.0,1193.04,858.302,3.77,30.34,17.3 resnext101_32x8d,224,1024.0,1188.06,861.896,16.48,31.21,88.79 seresnet101,288,1024.0,1178.9,868.597,12.95,26.87,49.33 resnet152d,256,1024.0,1177.58,869.569,15.41,30.51,60.21 wide_resnet101_2,224,1024.0,1172.43,873.387,22.8,21.23,126.89 crossvit_base_240,240,1024.0,1171.25,874.269,20.13,22.67,105.03 resnet200,224,1024.0,1159.72,882.961,15.07,32.19,64.67 inception_resnet_v2,299,1024.0,1156.1,885.722,13.18,25.06,55.84 rexnetr_300,288,512.0,1153.3,443.932,5.59,36.61,34.81 resnetrs101,288,1024.0,1142.76,896.066,13.56,28.53,63.62 davit_base,224,1024.0,1141.57,896.996,15.36,36.72,87.95 tresnet_xl,224,1024.0,1136.08,901.333,15.2,15.34,78.44 coat_tiny,224,1024.0,1135.01,902.184,4.35,27.2,5.5 tnt_s_patch16_224,224,1024.0,1134.91,902.262,5.24,24.37,23.76 mvitv2_small,224,1024.0,1131.08,905.308,7.0,28.08,34.87 ecaresnet101d,288,1024.0,1130.54,905.749,13.35,28.19,44.57 vit_base_patch16_reg8_gap_256,256,1024.0,1124.62,910.517,22.6,22.09,86.62 maxvit_tiny_pm_256,256,768.0,1121.86,684.565,6.31,40.82,30.09 hrnet_w40,224,1024.0,1119.9,914.356,12.75,25.29,57.56 convnext_small,288,1024.0,1119.4,914.761,14.39,35.65,50.22 nfnet_f1,224,1024.0,1117.42,916.384,17.87,22.94,132.63 efficientnet_lite4,380,768.0,1117.23,687.403,4.04,45.66,13.01 pvt_v2_b4,224,1024.0,1107.81,924.328,9.83,48.14,62.56 seresnext101_64x4d,224,1024.0,1107.71,924.416,15.53,31.25,88.23 seresnext101_32x8d,224,1024.0,1101.53,929.602,16.48,31.25,93.57 resnetv2_50d_gn,288,1024.0,1100.54,930.437,7.24,19.7,25.57 coatnet_1_224,224,512.0,1098.68,466.003,8.28,31.3,42.23 repvgg_b3g4,224,1024.0,1097.61,932.923,17.89,15.1,83.83 samvit_base_patch16_224,224,1024.0,1097.38,933.118,16.83,17.2,86.46 eva02_base_patch16_clip_224,224,1024.0,1094.75,935.361,16.9,18.91,86.26 mvitv2_small_cls,224,1024.0,1086.56,942.407,7.04,28.17,34.87 vit_large_r50_s32_224,224,1024.0,1082.13,946.268,19.45,22.22,328.99 inception_next_base,224,1024.0,1079.66,948.435,14.85,25.69,86.67 resnet50_gn,288,1024.0,1076.3,951.4,6.85,18.37,25.56 pvt_v2_b5,224,1024.0,1073.94,953.474,11.39,44.23,81.96 seresnext101d_32x8d,224,1024.0,1071.41,955.74,16.72,32.05,93.59 efficientnetv2_m,320,1024.0,1070.2,956.818,11.01,39.97,54.14 vit_small_r26_s32_384,384,1024.0,1066.07,960.526,10.24,27.67,36.47 resnetblur101d,288,1024.0,1059.66,966.334,15.07,29.65,44.57 resnet101d,320,1024.0,1045.1,979.801,16.48,34.77,44.57 regnetz_e8,256,1024.0,1042.94,981.82,9.91,40.94,57.7 tf_efficientnet_lite4,380,768.0,1038.99,739.169,4.04,45.66,13.01 xception41p,299,768.0,1034.81,742.157,9.25,39.86,26.91 repvgg_b3,224,1024.0,1031.23,992.974,29.16,15.1,123.09 xcit_tiny_24_p16_384,384,1024.0,1026.84,997.227,6.87,34.29,12.12 resnetrs152,256,1024.0,1024.28,999.711,15.59,30.83,86.62 seresnet152d,256,1024.0,1022.13,1001.814,15.42,30.56,66.84 swinv2_cr_small_224,224,1024.0,1005.65,1018.232,9.07,50.27,49.7 vit_base_patch16_plus_240,240,1024.0,1004.91,1018.982,26.31,22.07,117.56 regnetz_b16_evos,288,768.0,997.65,769.796,2.36,16.43,9.74 focalnet_base_srf,224,1024.0,995.12,1029.007,15.28,35.01,88.15 swinv2_cr_small_ns_224,224,1024.0,993.65,1030.528,9.08,50.27,49.7 convnextv2_small,224,1024.0,992.07,1032.17,8.71,21.56,50.32 convnextv2_tiny,288,768.0,989.58,776.074,7.39,22.21,28.64 vit_small_patch8_224,224,1024.0,985.02,1039.56,16.76,32.86,21.67 regnety_040_sgn,288,1024.0,979.5,1045.407,6.67,20.3,20.65 regnetz_c16_evos,256,768.0,978.11,785.174,2.48,16.57,13.49 vit_base_r50_s16_224,224,1024.0,971.42,1054.108,20.94,27.88,97.89 hrnet_w44,224,1024.0,967.41,1058.48,14.94,26.92,67.06 efficientformer_l7,224,1024.0,966.26,1059.742,10.17,24.45,82.23 hrnet_w48_ssld,224,1024.0,963.59,1062.678,17.34,28.56,77.47 hrnet_w48,224,1024.0,962.72,1063.645,17.34,28.56,77.47 poolformer_m36,224,1024.0,959.97,1066.674,8.8,22.02,56.17 resnet152,288,1024.0,955.06,1072.17,19.11,37.28,60.19 cait_s24_224,224,1024.0,951.69,1075.97,9.35,40.58,46.92 tiny_vit_21m_384,384,512.0,946.04,541.193,11.94,46.84,21.23 focalnet_base_lrf,224,1024.0,946.02,1082.418,15.43,38.13,88.75 dm_nfnet_f1,224,1024.0,943.8,1084.958,17.87,22.94,132.63 efficientnet_b3_gn,288,512.0,943.58,542.602,1.74,23.35,11.73 efficientnetv2_rw_m,320,1024.0,934.42,1095.856,12.72,47.14,53.24 vit_relpos_base_patch16_plus_240,240,1024.0,933.99,1096.357,26.21,23.41,117.38 gmlp_b16_224,224,1024.0,931.13,1099.724,15.78,30.21,73.08 fastvit_sa36,256,1024.0,928.53,1102.809,5.62,34.02,31.53 xception41,299,768.0,927.7,827.842,9.28,39.86,26.97 eva02_small_patch14_336,336,1024.0,926.94,1104.696,12.41,27.7,22.13 maxvit_rmlp_small_rw_224,224,768.0,923.72,831.408,10.48,42.44,64.9 sequencer2d_l,224,1024.0,917.56,1115.991,9.74,22.12,54.3 poolformerv2_s36,224,1024.0,914.51,1119.704,5.01,15.82,30.79 xcit_medium_24_p16_224,224,1024.0,901.57,1135.786,16.13,31.71,84.4 coat_mini,224,1024.0,900.78,1136.787,6.82,33.68,10.34 coat_lite_medium,224,1024.0,898.48,1139.693,9.81,40.06,44.57 swin_s3_small_224,224,768.0,882.63,870.118,9.43,37.84,49.74 efficientnet_b3_g8_gn,288,512.0,882.63,580.072,2.59,23.35,14.25 dpn131,224,1024.0,878.67,1165.389,16.09,32.97,79.25 levit_384_s8,224,512.0,874.93,585.181,9.98,35.86,39.12 efficientnet_b4,384,512.0,874.47,585.489,4.51,50.04,19.34 vit_medium_patch16_gap_384,384,1024.0,873.17,1172.722,22.01,32.15,39.03 nest_base,224,1024.0,871.22,1175.339,16.71,30.51,67.72 nf_regnet_b5,384,1024.0,867.94,1179.793,7.95,42.9,49.74 resnet200d,256,1024.0,866.43,1181.848,20.0,43.09,64.69 maxvit_small_tf_224,224,512.0,864.97,591.915,11.39,46.31,68.93 nest_base_jx,224,1024.0,863.51,1185.835,16.71,30.51,67.72 xcit_small_12_p16_384,384,1024.0,860.6,1189.852,14.14,36.5,26.25 resnetv2_50d_evos,288,1024.0,857.98,1193.488,7.15,19.7,25.59 swin_base_patch4_window7_224,224,1024.0,857.23,1194.527,15.47,36.63,87.77 gcvit_small,224,1024.0,850.2,1204.416,8.57,41.61,51.09 crossvit_15_dagger_408,408,1024.0,849.94,1204.779,16.07,37.0,28.5 eca_nfnet_l1,320,1024.0,845.79,1210.693,14.92,34.42,41.41 tf_efficientnet_b4,380,512.0,836.31,612.204,4.49,49.49,19.34 regnety_080,288,1024.0,834.08,1227.682,13.22,29.69,39.18 levit_conv_384_s8,224,512.0,831.47,615.767,9.98,35.86,39.12 twins_svt_large,224,1024.0,829.67,1234.208,14.84,27.23,99.27 seresnet152,288,1024.0,826.68,1238.676,19.11,37.34,66.82 xception65p,299,768.0,826.46,929.251,13.91,52.48,39.82 eva02_base_patch14_224,224,1024.0,822.18,1245.459,22.0,24.67,85.76 caformer_s36,224,1024.0,811.28,1262.182,7.55,29.29,39.3 maxxvit_rmlp_small_rw_256,256,768.0,805.75,953.134,14.21,47.76,66.01 coatnet_2_rw_224,224,512.0,802.77,637.783,14.55,39.37,73.87 swinv2_base_window12_192,192,1024.0,801.77,1277.157,11.9,39.72,109.28 mvitv2_base,224,1024.0,789.29,1297.348,10.16,40.5,51.47 densenet264d,224,1024.0,784.72,1304.914,13.57,14.0,72.74 resnest50d_4s2x40d,224,1024.0,782.94,1307.879,4.4,17.94,30.42 swinv2_tiny_window16_256,256,512.0,779.51,656.811,6.68,39.02,28.35 volo_d2_224,224,1024.0,778.59,1315.191,14.34,41.34,58.68 dpn107,224,1024.0,773.9,1323.149,18.38,33.46,86.92 xcit_tiny_24_p8_224,224,1024.0,770.47,1329.042,9.21,45.38,12.11 convnext_base,288,1024.0,769.28,1331.103,25.43,47.53,88.59 coatnet_rmlp_2_rw_224,224,512.0,762.93,671.09,14.64,44.94,73.88 mvitv2_base_cls,224,1024.0,760.58,1346.32,10.23,40.65,65.44 convit_base,224,1024.0,757.3,1352.149,17.52,31.77,86.54 convformer_s36,224,1024.0,757.3,1352.161,7.67,30.5,40.01 coatnet_2_224,224,384.0,753.79,509.418,15.94,42.41,74.68 hrnet_w64,224,1024.0,748.82,1367.478,28.97,35.09,128.06 resnet152d,320,1024.0,747.67,1369.57,24.08,47.67,60.21 ecaresnet200d,256,1024.0,744.16,1376.037,20.0,43.15,64.69 seresnet200d,256,1024.0,743.64,1376.992,20.01,43.15,71.86 resnetrs200,256,1024.0,743.56,1377.137,20.18,43.42,93.21 swinv2_small_window8_256,256,1024.0,740.78,1382.313,11.58,40.14,49.73 xception65,299,768.0,738.05,1040.572,13.96,52.48,39.92 fastvit_ma36,256,1024.0,734.46,1394.207,7.85,40.39,44.07 swinv2_cr_small_ns_256,256,1024.0,733.6,1395.843,12.07,76.21,49.7 senet154,224,1024.0,731.81,1399.262,20.77,38.69,115.09 maxvit_rmlp_small_rw_256,256,768.0,731.54,1049.835,13.69,55.48,64.9 legacy_senet154,224,1024.0,730.99,1400.828,20.77,38.69,115.09 tf_efficientnetv2_m,384,1024.0,728.54,1405.529,15.85,57.52,54.14 xcit_nano_12_p8_384,384,1024.0,723.54,1415.249,6.34,46.06,3.05 poolformer_m48,224,1024.0,722.45,1417.374,11.59,29.17,73.47 tnt_b_patch16_224,224,1024.0,722.04,1418.187,14.09,39.01,65.41 efficientvit_l3,224,1024.0,720.55,1421.127,27.62,39.16,246.04 swinv2_cr_base_224,224,1024.0,719.69,1422.825,15.86,59.66,87.88 efficientnet_b3_g8_gn,320,512.0,718.69,712.395,3.2,28.83,14.25 resnest101e,256,1024.0,718.12,1425.925,13.38,28.66,48.28 swin_s3_base_224,224,1024.0,717.57,1427.034,13.69,48.26,71.13 resnext101_64x4d,288,1024.0,717.4,1427.37,25.66,51.59,83.46 swinv2_cr_base_ns_224,224,1024.0,713.5,1435.162,15.86,59.66,87.88 convnextv2_base,224,768.0,711.23,1079.807,15.38,28.75,88.72 resnet200,288,1024.0,697.53,1468.023,24.91,53.21,64.67 efficientnet_b3_gn,320,512.0,695.5,736.148,2.14,28.83,11.73 coat_small,224,1024.0,694.03,1475.431,12.61,44.25,21.69 convnext_large,224,1024.0,690.43,1483.117,34.4,43.13,197.77 regnetz_e8,320,1024.0,670.8,1526.503,15.46,63.94,57.7 efficientformerv2_s2,224,1024.0,670.26,1527.748,1.27,11.77,12.71 seresnext101_32x8d,288,1024.0,656.14,1560.626,27.24,51.63,93.57 resnetrs152,320,1024.0,655.8,1561.431,24.34,48.14,86.62 xcit_small_12_p8_224,224,1024.0,655.5,1562.148,18.69,47.19,26.21 maxxvitv2_rmlp_base_rw_224,224,768.0,651.85,1178.173,23.88,54.39,116.09 seresnet152d,320,1024.0,649.85,1575.74,24.09,47.72,66.84 vit_large_patch32_384,384,1024.0,647.57,1581.281,44.28,32.22,306.63 poolformerv2_m36,224,1024.0,646.73,1583.338,8.81,22.02,56.08 resnext101_32x16d,224,1024.0,641.29,1596.767,36.27,51.18,194.03 seresnext101d_32x8d,288,1024.0,639.61,1600.97,27.64,52.95,93.59 regnetz_d8_evos,256,1024.0,638.02,1604.938,4.5,24.92,23.46 davit_large,224,1024.0,634.07,1614.963,34.37,55.08,196.81 efficientnetv2_m,416,1024.0,633.12,1617.367,18.6,67.5,54.14 regnety_064,224,1024.0,632.1,1619.968,6.39,16.41,30.58 regnetv_064,224,1024.0,629.87,1625.704,6.39,16.41,30.58 regnetz_c16_evos,320,512.0,622.61,822.333,3.86,25.88,13.49 gcvit_base,224,1024.0,620.94,1649.111,14.87,55.48,90.32 nf_regnet_b5,456,512.0,602.97,849.111,11.7,61.95,49.74 seresnextaa101d_32x8d,288,1024.0,601.98,1701.035,28.51,56.44,93.59 xception71,299,768.0,600.76,1278.366,18.09,69.92,42.34 eca_nfnet_l2,320,1024.0,593.89,1724.216,20.95,47.43,56.72 nfnet_f2,256,1024.0,593.31,1725.904,33.76,41.85,193.78 crossvit_18_dagger_408,408,1024.0,585.92,1747.666,25.31,49.38,44.61 hrnet_w48_ssld,288,1024.0,585.32,1749.444,28.66,47.21,77.47 ecaresnet200d,288,1024.0,584.36,1752.321,25.31,54.59,64.69 seresnet200d,288,1024.0,583.25,1755.672,25.32,54.6,71.86 caformer_m36,224,1024.0,582.88,1756.773,12.75,40.61,56.2 levit_512_s8,224,256.0,582.77,439.271,21.82,52.28,74.05 maxvit_rmlp_base_rw_224,224,768.0,582.44,1318.589,22.63,79.3,116.14 seresnet269d,256,1024.0,581.62,1760.578,26.59,53.6,113.67 convmixer_768_32,224,1024.0,580.09,1765.235,19.55,25.95,21.11 resnetrs270,256,1024.0,565.62,1810.398,27.06,55.84,129.86 mixer_l16_224,224,1024.0,553.36,1850.484,44.6,41.69,208.2 levit_conv_512_s8,224,256.0,552.47,463.363,21.82,52.28,74.05 efficientnetv2_rw_m,416,1024.0,552.47,1853.491,21.49,79.62,53.24 resnet200d,320,1024.0,551.74,1855.93,31.25,67.33,64.69 nfnet_f1,320,1024.0,548.82,1865.795,35.97,46.77,132.63 convformer_m36,224,1024.0,548.78,1865.947,12.89,42.05,57.05 volo_d3_224,224,1024.0,541.9,1889.619,20.78,60.09,86.33 swinv2_base_window8_256,256,1024.0,530.42,1930.519,20.37,52.59,87.92 maxvit_base_tf_224,224,512.0,517.72,988.937,23.52,81.67,119.47 xcit_large_24_p16_224,224,1024.0,511.16,2003.26,35.86,47.26,189.1 convmixer_1024_20_ks9_p14,224,1024.0,510.74,2004.929,5.55,5.51,24.38 dm_nfnet_f2,256,1024.0,503.11,2035.325,33.76,41.85,193.78 swin_large_patch4_window7_224,224,768.0,494.53,1552.967,34.53,54.94,196.53 vit_base_patch16_18x2_224,224,1024.0,494.1,2072.443,50.37,49.17,256.73 deit_base_patch16_384,384,1024.0,493.77,2073.808,49.4,48.3,86.86 vit_base_patch16_384,384,1024.0,493.5,2074.946,49.4,48.3,86.86 deit_base_distilled_patch16_384,384,1024.0,493.31,2075.754,49.49,48.39,87.63 vit_base_patch16_clip_384,384,1024.0,492.52,2079.081,49.41,48.3,86.86 eva_large_patch14_196,196,1024.0,491.4,2083.813,59.66,43.77,304.14 vit_base_patch16_siglip_384,384,1024.0,490.82,2086.272,50.0,49.11,93.18 vit_large_patch16_224,224,1024.0,489.19,2093.231,59.7,43.77,304.33 halonet_h1,256,256.0,487.96,524.621,3.0,51.17,8.1 tiny_vit_21m_512,512,256.0,487.73,524.868,21.23,83.26,21.27 seresnextaa101d_32x8d,320,768.0,487.6,1575.053,35.19,69.67,93.59 swinv2_large_window12_192,192,768.0,487.6,1575.036,26.17,56.53,228.77 swinv2_small_window16_256,256,512.0,487.58,1050.071,12.82,66.29,49.73 poolformerv2_m48,224,1024.0,487.33,2101.208,11.59,29.17,73.35 resnetrs200,320,1024.0,476.69,2148.152,31.51,67.81,93.21 xcit_tiny_12_p8_384,384,1024.0,472.87,2165.479,14.12,69.12,6.71 vit_small_patch14_dinov2,518,1024.0,470.72,2175.374,29.46,57.34,22.06 deit3_base_patch16_384,384,1024.0,469.96,2178.883,49.4,48.3,86.88 vit_small_patch14_reg4_dinov2,518,1024.0,469.28,2182.048,29.55,57.51,22.06 deit3_large_patch16_224,224,1024.0,468.18,2187.162,59.7,43.77,304.37 tf_efficientnetv2_m,480,1024.0,466.8,2193.627,24.76,89.84,54.14 dm_nfnet_f1,320,1024.0,463.74,2208.099,35.97,46.77,132.63 xcit_small_24_p16_384,384,1024.0,458.11,2235.247,26.72,68.57,47.67 seresnet269d,288,1024.0,457.25,2239.451,33.65,67.81,113.67 beit_large_patch16_224,224,1024.0,453.95,2255.726,59.7,43.77,304.43 beitv2_large_patch16_224,224,1024.0,453.79,2256.515,59.7,43.77,304.43 regnetx_120,224,1024.0,452.56,2262.648,12.13,21.37,46.11 efficientnet_b5,448,512.0,444.06,1152.996,9.59,93.56,30.39 regnety_120,224,1024.0,444.03,2306.127,12.14,21.38,51.82 efficientformerv2_l,224,1024.0,441.81,2317.703,2.59,18.54,26.32 coatnet_3_rw_224,224,384.0,441.21,870.327,32.63,59.07,181.81 resnetv2_152x2_bit,224,1024.0,439.95,2327.532,46.95,45.11,236.34 convnext_xlarge,224,768.0,438.91,1749.766,60.98,57.5,350.2 coatnet_rmlp_3_rw_224,224,256.0,438.69,583.549,32.75,64.7,165.15 coatnet_3_224,224,256.0,431.52,593.24,35.72,63.61,166.97 convnextv2_base,288,512.0,430.66,1188.858,25.43,47.53,88.72 flexivit_large,240,1024.0,427.93,2392.897,68.48,50.22,304.36 convnextv2_large,224,512.0,424.61,1205.798,34.4,43.13,197.96 swinv2_cr_large_224,224,768.0,424.12,1810.813,35.1,78.42,196.68 swinv2_cr_tiny_384,384,256.0,420.98,608.099,15.34,161.01,28.33 caformer_b36,224,768.0,420.2,1827.698,22.5,54.14,98.75 maxvit_tiny_tf_384,384,256.0,419.78,609.84,16.0,94.22,30.98 convnext_large,288,768.0,417.93,1837.619,56.87,71.29,197.77 regnety_160,224,1024.0,417.09,2455.096,15.96,23.04,83.59 eca_nfnet_l2,384,1024.0,412.81,2480.539,30.05,68.28,56.72 maxxvitv2_rmlp_large_rw_224,224,768.0,411.22,1867.582,43.69,75.4,215.42 efficientnetv2_l,384,1024.0,409.83,2498.611,36.1,101.16,118.52 davit_huge,224,768.0,407.6,1884.205,60.93,73.44,348.92 tf_efficientnetv2_l,384,1024.0,405.08,2527.906,36.1,101.16,118.52 regnety_320,224,1024.0,403.27,2539.241,32.34,30.26,145.05 regnetz_d8_evos,320,768.0,403.13,1905.094,7.03,38.92,23.46 beit_base_patch16_384,384,1024.0,402.61,2543.386,49.4,48.3,86.74 convformer_b36,224,768.0,397.77,1930.749,22.69,56.06,99.88 tf_efficientnet_b5,456,384.0,394.74,972.77,10.46,98.86,30.39 eca_nfnet_l3,352,1024.0,378.23,2707.314,32.57,73.12,72.04 vit_large_patch16_siglip_256,256,1024.0,375.52,2726.866,78.12,57.42,315.96 ecaresnet269d,320,1024.0,372.48,2749.133,41.53,83.69,102.09 vit_large_r50_s32_384,384,1024.0,369.32,2772.633,56.4,64.88,329.09 maxvit_large_tf_224,224,384.0,359.98,1066.726,42.99,109.57,211.79 vit_large_patch14_224,224,1024.0,359.62,2847.449,77.83,57.11,304.2 vit_large_patch14_clip_224,224,1024.0,359.62,2847.409,77.83,57.11,304.2 swinv2_base_window16_256,256,384.0,359.2,1069.042,22.02,84.71,87.92 swinv2_base_window12to16_192to256,256,384.0,359.01,1069.609,22.02,84.71,87.92 nasnetalarge,331,384.0,356.97,1075.708,23.89,90.56,88.75 resnetrs350,288,1024.0,356.46,2872.642,43.67,87.09,163.96 vit_base_patch8_224,224,1024.0,351.76,2911.045,66.87,65.71,86.58 volo_d4_224,224,1024.0,343.2,2983.708,44.34,80.22,192.96 xcit_small_24_p8_224,224,1024.0,342.74,2987.714,35.81,90.77,47.63 volo_d1_384,384,512.0,340.3,1504.541,22.75,108.55,26.78 convnext_large_mlp,320,512.0,338.23,1513.736,70.21,88.02,200.13 repvgg_d2se,320,1024.0,335.87,3048.766,74.57,46.82,133.33 vit_large_patch14_clip_quickgelu_224,224,1024.0,324.37,3156.896,77.83,57.11,303.97 vit_base_r50_s16_384,384,1024.0,315.28,3247.919,61.29,81.77,98.95 nfnet_f2,352,1024.0,313.79,3263.314,63.22,79.06,193.78 xcit_medium_24_p16_384,384,1024.0,313.38,3267.626,47.39,91.63,84.4 vit_large_patch14_xp_224,224,1024.0,311.53,3287.018,77.77,57.11,304.06 ecaresnet269d,352,1024.0,307.84,3326.422,50.25,101.25,102.09 coat_lite_medium_384,384,512.0,301.48,1698.273,28.73,116.7,44.57 regnety_064,288,1024.0,298.91,3425.709,10.56,27.11,30.58 resnetrs270,352,1024.0,298.81,3426.892,51.13,105.48,129.86 regnetv_064,288,1024.0,298.12,3434.809,10.55,27.11,30.58 resnext101_32x32d,224,512.0,296.06,1729.362,87.29,91.12,468.53 nfnet_f3,320,1024.0,290.3,3527.352,68.77,83.93,254.92 efficientnetv2_xl,384,1024.0,290.02,3530.821,52.81,139.2,208.12 tf_efficientnetv2_xl,384,1024.0,287.47,3562.138,52.81,139.2,208.12 cait_xxs24_384,384,1024.0,284.02,3605.396,9.63,122.65,12.03 maxvit_small_tf_384,384,192.0,274.58,699.228,33.58,139.86,69.02 coatnet_4_224,224,256.0,274.31,933.246,60.81,98.85,275.43 convnext_xlarge,288,512.0,265.38,1929.279,100.8,95.05,350.2 dm_nfnet_f2,352,1024.0,265.36,3858.944,63.22,79.06,193.78 vit_base_patch16_siglip_512,512,512.0,263.16,1945.545,88.89,87.3,93.52 vit_so400m_patch14_siglip_224,224,1024.0,262.63,3898.968,106.18,70.45,427.68 efficientnetv2_l,480,512.0,261.08,1961.059,56.4,157.99,118.52 swinv2_cr_small_384,384,256.0,258.97,988.525,29.7,298.03,49.7 convnextv2_large,288,384.0,257.89,1488.981,56.87,71.29,197.96 tf_efficientnetv2_l,480,512.0,257.78,1986.206,56.4,157.99,118.52 eva02_large_patch14_224,224,1024.0,256.9,3985.935,77.9,65.52,303.27 eva02_large_patch14_clip_224,224,1024.0,253.93,4032.531,77.93,65.52,304.11 regnety_120,288,768.0,253.81,3025.924,20.06,35.34,51.82 xcit_tiny_24_p8_384,384,1024.0,248.2,4125.63,27.05,132.94,12.11 coatnet_rmlp_2_rw_384,384,192.0,247.61,775.41,43.04,132.57,73.88 dm_nfnet_f3,320,1024.0,247.07,4144.617,68.77,83.93,254.92 resnetrs420,320,1024.0,244.54,4187.355,64.2,126.56,191.89 mvitv2_large,224,512.0,243.6,2101.832,43.87,112.02,217.99 mvitv2_large_cls,224,512.0,241.75,2117.866,42.17,111.69,234.58 resmlp_big_24_224,224,1024.0,241.59,4238.519,100.23,87.31,129.14 regnety_160,288,768.0,237.71,3230.76,26.37,38.07,83.59 xcit_medium_24_p8_224,224,768.0,234.01,3281.941,63.52,121.22,84.32 eca_nfnet_l3,448,512.0,233.43,2193.322,52.55,118.4,72.04 volo_d5_224,224,1024.0,228.8,4475.542,72.4,118.11,295.46 swin_base_patch4_window12_384,384,256.0,227.46,1125.454,47.19,134.78,87.9 xcit_small_12_p8_384,384,384.0,223.23,1720.206,54.92,138.25,26.21 swinv2_large_window12to16_192to256,256,256.0,219.08,1168.537,47.81,121.53,196.74 maxxvitv2_rmlp_base_rw_384,384,384.0,217.17,1768.16,70.18,160.22,116.09 efficientnet_b6,528,256.0,205.22,1247.45,19.4,167.39,43.04 regnetx_320,224,768.0,200.5,3830.333,31.81,36.3,107.81 resnetrs350,384,1024.0,199.92,5122.143,77.59,154.74,163.96 cait_xs24_384,384,768.0,198.76,3863.971,19.28,183.98,26.67 maxvit_xlarge_tf_224,224,256.0,198.54,1289.412,96.49,164.37,506.99 tf_efficientnet_b6,528,192.0,198.54,967.028,19.4,167.39,43.04 focalnet_huge_fl3,224,512.0,191.39,2675.182,118.26,104.8,745.28 volo_d2_384,384,384.0,190.85,2012.066,46.17,184.51,58.87 cait_xxs36_384,384,1024.0,189.78,5395.721,14.35,183.7,17.37 eva02_base_patch14_448,448,512.0,189.58,2700.759,87.74,98.4,87.12 vit_huge_patch14_gap_224,224,1024.0,186.27,5497.294,161.36,94.7,630.76 swinv2_cr_base_384,384,256.0,185.05,1383.395,50.57,333.68,87.88 swinv2_cr_huge_224,224,384.0,182.04,2109.357,115.97,121.08,657.83 maxvit_rmlp_base_rw_384,384,384.0,179.65,2137.52,66.51,233.79,116.14 vit_huge_patch14_224,224,1024.0,179.6,5701.574,161.99,95.07,630.76 vit_huge_patch14_clip_224,224,1024.0,179.43,5706.842,161.99,95.07,632.05 xcit_large_24_p16_384,384,1024.0,177.48,5769.692,105.34,137.15,189.1 vit_base_patch14_dinov2,518,512.0,176.68,2897.828,117.11,114.68,86.58 vit_base_patch14_reg4_dinov2,518,512.0,175.98,2909.337,117.45,115.02,86.58 deit3_huge_patch14_224,224,1024.0,173.53,5900.889,161.99,95.07,632.13 nfnet_f3,416,768.0,171.77,4471.127,115.58,141.78,254.92 maxvit_tiny_tf_512,512,128.0,170.91,748.92,28.66,172.66,31.05 seresnextaa201d_32x8d,384,512.0,170.35,3005.583,101.11,199.72,149.39 maxvit_base_tf_384,384,192.0,166.63,1152.259,69.34,247.75,119.65 vit_huge_patch14_clip_quickgelu_224,224,1024.0,165.5,6187.275,161.99,95.07,632.08 efficientnetv2_xl,512,512.0,163.45,3132.529,93.85,247.32,208.12 nfnet_f4,384,768.0,163.26,4704.17,122.14,147.57,316.07 tf_efficientnetv2_xl,512,512.0,161.63,3167.699,93.85,247.32,208.12 vit_huge_patch14_xp_224,224,1024.0,159.72,6411.21,161.88,95.07,631.8 eva_large_patch14_336,336,768.0,155.72,4931.845,174.74,128.21,304.53 vit_large_patch14_clip_336,336,768.0,155.28,4945.947,174.74,128.21,304.53 vit_large_patch16_384,384,768.0,155.12,4950.906,174.85,128.21,304.72 vit_large_patch16_siglip_384,384,768.0,154.94,4956.619,175.76,129.18,316.28 convnext_xxlarge,256,384.0,153.59,2500.071,198.09,124.45,846.47 vit_giant_patch16_gap_224,224,1024.0,153.47,6672.363,198.14,103.64,1011.37 cait_s24_384,384,512.0,153.12,3343.821,32.17,245.3,47.06 davit_giant,224,384.0,152.05,2525.491,192.34,138.2,1406.47 deit3_large_patch16_384,384,1024.0,148.73,6884.872,174.85,128.21,304.76 coatnet_5_224,224,192.0,147.83,1298.762,142.72,143.69,687.47 dm_nfnet_f3,416,512.0,146.0,3506.787,115.58,141.78,254.92 resnetrs420,416,768.0,144.59,5311.727,108.45,213.79,191.89 vit_large_patch14_clip_quickgelu_336,336,768.0,141.12,5441.998,174.74,128.21,304.29 dm_nfnet_f4,384,768.0,139.13,5519.969,122.14,147.57,316.07 swin_large_patch4_window12_384,384,128.0,135.95,941.498,104.08,202.16,196.74 xcit_large_24_p8_224,224,512.0,131.73,3886.696,141.22,181.53,188.93 beit_large_patch16_384,384,768.0,129.79,5917.023,174.84,128.21,305.0 efficientnet_b7,600,192.0,128.05,1499.407,38.33,289.94,66.35 tf_efficientnet_b7,600,192.0,124.56,1541.433,38.33,289.94,66.35 focalnet_huge_fl4,224,512.0,123.26,4153.862,118.9,113.34,686.46 eva_giant_patch14_clip_224,224,1024.0,116.99,8753.07,259.74,135.89,1012.59 eva_giant_patch14_224,224,1024.0,116.91,8758.747,259.74,135.89,1012.56 nfnet_f5,416,768.0,116.91,6569.029,170.71,204.56,377.21 xcit_small_24_p8_384,384,384.0,116.73,3289.571,105.23,265.87,47.63 maxvit_large_tf_384,384,128.0,116.56,1098.144,126.61,332.3,212.03 vit_giant_patch14_224,224,1024.0,114.32,8957.604,259.74,135.89,1012.61 vit_giant_patch14_clip_224,224,1024.0,114.12,8973.257,259.74,135.89,1012.65 swinv2_cr_large_384,384,192.0,113.51,1691.47,108.96,404.96,196.68 eva02_large_patch14_clip_336,336,768.0,110.42,6955.361,174.97,147.1,304.43 mvitv2_huge_cls,224,384.0,105.54,3638.368,120.67,243.63,694.8 maxvit_small_tf_512,512,96.0,104.89,915.238,60.02,256.36,69.13 cait_s36_384,384,512.0,102.28,5005.663,47.99,367.39,68.37 dm_nfnet_f5,416,512.0,99.59,5141.209,170.71,204.56,377.21 swinv2_base_window12to24_192to384,384,96.0,96.5,994.841,55.25,280.36,87.92 focalnet_large_fl3,384,256.0,93.78,2729.925,105.06,168.04,239.13 nfnet_f4,512,512.0,91.69,5583.92,216.26,262.26,316.07 focalnet_large_fl4,384,256.0,90.64,2824.324,105.2,181.78,239.32 nfnet_f6,448,512.0,86.88,5893.345,229.7,273.62,438.36 efficientnet_b8,672,128.0,85.75,1492.768,63.48,442.89,87.41 tf_efficientnet_b8,672,128.0,83.71,1529.068,63.48,442.89,87.41 volo_d3_448,448,128.0,81.1,1578.235,96.33,446.83,86.63 vit_so400m_patch14_siglip_384,384,512.0,80.75,6340.618,302.34,200.62,428.23 xcit_medium_24_p8_384,384,256.0,80.25,3189.919,186.67,354.69,84.32 dm_nfnet_f4,512,384.0,78.23,4908.575,216.26,262.26,316.07 vit_huge_patch14_clip_336,336,512.0,75.44,6786.84,363.7,213.44,632.46 dm_nfnet_f6,448,512.0,74.17,6903.248,229.7,273.62,438.36 maxvit_base_tf_512,512,96.0,72.37,1326.47,123.93,456.26,119.88 nfnet_f5,544,384.0,68.39,5614.643,290.97,349.71,377.21 nfnet_f7,480,512.0,66.61,7686.561,300.08,355.86,499.5 vit_gigantic_patch14_224,224,512.0,66.24,7729.406,473.4,204.12,1844.44 vit_gigantic_patch14_clip_224,224,512.0,66.15,7739.524,473.41,204.12,1844.91 focalnet_xlarge_fl3,384,192.0,65.92,2912.463,185.61,223.99,408.79 maxvit_xlarge_tf_384,384,96.0,64.9,1479.208,283.86,498.45,475.32 focalnet_xlarge_fl4,384,192.0,63.63,3017.361,185.79,242.31,409.03 beit_large_patch16_512,512,256.0,61.48,4163.85,310.6,227.76,305.67 volo_d4_448,448,192.0,60.99,3147.895,197.13,527.35,193.41 regnety_640,384,192.0,60.97,3149.012,188.47,124.83,281.38 convnextv2_huge,384,96.0,60.92,1575.922,337.96,232.35,660.29 swinv2_large_window12to24_192to384,384,48.0,60.75,790.151,116.15,407.83,196.74 eva02_large_patch14_448,448,512.0,59.67,8581.221,310.69,261.32,305.08 dm_nfnet_f5,544,384.0,58.35,6580.773,290.97,349.71,377.21 vit_huge_patch14_clip_378,378,512.0,58.14,8806.389,460.13,270.04,632.68 convmixer_1536_20,224,1024.0,56.99,17967.01,48.68,33.03,51.63 vit_large_patch14_dinov2,518,384.0,56.83,6757.154,414.89,304.42,304.37 vit_large_patch14_reg4_dinov2,518,384.0,56.64,6779.944,416.1,305.31,304.37 maxvit_large_tf_512,512,64.0,54.68,1170.494,225.96,611.85,212.33 tf_efficientnet_l2,475,96.0,54.05,1776.14,172.11,609.89,480.31 vit_huge_patch14_clip_quickgelu_378,378,384.0,53.95,7117.573,460.13,270.04,632.68 vit_huge_patch16_gap_448,448,512.0,52.86,9685.108,494.35,290.02,631.67 nfnet_f6,576,384.0,52.55,7307.184,378.69,452.2,438.36 swinv2_cr_giant_224,224,192.0,52.45,3660.551,483.85,309.15,2598.76 eva_giant_patch14_336,336,512.0,49.65,10312.606,583.14,305.1,1013.01 swinv2_cr_huge_384,384,96.0,49.62,1934.539,352.04,583.18,657.94 xcit_large_24_p8_384,384,192.0,45.19,4249.177,415.0,531.74,188.93 dm_nfnet_f6,576,256.0,44.83,5710.109,378.69,452.2,438.36 volo_d5_448,448,192.0,42.49,4518.905,315.06,737.92,295.91 nfnet_f7,608,256.0,41.52,6165.283,480.39,570.85,499.5 cait_m36_384,384,256.0,33.1,7733.448,173.11,734.79,271.22 resnetv2_152x4_bit,480,96.0,32.12,2989.13,844.84,414.26,936.53 maxvit_xlarge_tf_512,512,48.0,30.41,1578.222,505.95,917.77,475.77 regnety_2560,384,128.0,30.25,4231.43,747.83,296.49,1282.6 volo_d5_512,512,128.0,29.54,4332.489,425.09,1105.37,296.09 samvit_base_patch16,1024,16.0,23.81,671.88,371.55,403.08,89.67 regnety_1280,384,128.0,22.93,5583.053,374.99,210.2,644.81 efficientnet_l2,800,48.0,19.03,2521.932,479.12,1707.39,480.31 vit_giant_patch14_dinov2,518,192.0,17.15,11193.542,1553.56,871.89,1136.48 vit_giant_patch14_reg4_dinov2,518,192.0,17.12,11212.072,1558.09,874.43,1136.48 swinv2_cr_giant_384,384,32.0,15.04,2127.877,1450.71,1394.86,2598.76 eva_giant_patch14_560,560,192.0,15.03,12771.913,1618.04,846.56,1014.45 cait_m48_448,448,128.0,13.96,9172.063,329.4,1708.21,356.46 samvit_large_patch16,1024,12.0,10.64,1127.934,1317.08,1055.58,308.28 samvit_huge_patch16,1024,8.0,6.61,1210.638,2741.59,1727.57,637.03
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv	model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count tinynet_e,11915.85,41.681,512,106,2.04 mobilenetv3_small_050,11290.99,44.293,512,224,1.59 lcnet_035,10015.98,50.125,512,224,1.64 lcnet_050,9286.37,54.37,512,224,1.88 tf_mobilenetv3_small_minimal_100,9042.22,55.986,512,224,2.04 mobilenetv3_small_075,8679.98,58.254,512,224,2.04 mobilenetv3_small_100,8035.08,62.981,512,224,2.54 tinynet_d,7990.69,63.223,512,152,2.34 tf_mobilenetv3_small_075,7930.1,63.8,512,224,2.04 tf_mobilenetv3_small_100,7330.24,69.047,512,224,2.54 lcnet_075,6950.91,73.156,512,224,2.36 levit_128s,6539.16,77.346,512,224,7.78 resnet10t,6318.63,80.774,512,176,5.44 mnasnet_small,5607.09,90.422,512,224,2.03 lcnet_100,5354.67,95.126,512,224,2.95 mixer_s32_224,4943.04,103.013,512,224,19.1 mobilenetv2_035,4789.43,106.101,512,224,1.68 mnasnet_050,4680.08,108.62,512,224,2.22 levit_128,4558.28,111.213,512,224,9.21 cs3darknet_focus_s,4469.48,114.041,512,256,3.27 vit_small_patch32_224,4445.76,114.324,512,224,22.88 tinynet_c,4167.16,121.826,512,184,2.46 gernet_s,4165.03,122.198,512,224,8.17 cs3darknet_s,4110.51,124.007,512,256,3.28 regnetx_002,4105.04,124.027,512,224,2.68 mobilenetv2_050,4051.14,125.606,512,224,1.97 vit_tiny_r_s16_p8_224,4025.23,126.328,512,224,6.34 semnasnet_050,3904.91,130.185,512,224,2.08 regnety_002,3777.81,134.562,512,224,3.16 levit_192,3727.29,136.213,512,224,10.95 ghostnet_050,3670.99,138.144,512,224,2.59 ese_vovnet19b_slim_dw,3629.92,140.575,512,224,1.9 lcnet_150,3576.28,142.665,512,224,4.5 gluon_resnet18_v1b,3482.17,146.691,512,224,11.69 resnet18,3481.78,146.713,512,224,11.69 swsl_resnet18,3480.5,146.765,512,224,11.69 ssl_resnet18,3477.04,146.904,512,224,11.69 resnet14t,3472.37,147.102,512,176,10.08 tf_efficientnetv2_b0,3428.08,148.143,512,192,7.14 tf_mobilenetv3_large_minimal_100,3366.45,151.356,512,224,3.92 mnasnet_075,3238.88,157.273,512,224,3.17 tf_mobilenetv3_large_075,3189.08,159.67,512,224,3.99 seresnet18,3138.91,162.608,512,224,11.78 mobilenetv3_large_075,3095.0,164.56,512,224,3.99 legacy_seresnet18,3076.04,165.928,512,224,11.78 hardcorenas_a,2971.63,171.576,512,224,5.26 levit_256,2956.43,172.043,512,224,18.89 mnasnet_b1,2930.02,173.933,512,224,4.38 mnasnet_100,2929.31,173.976,512,224,4.38 tf_mobilenetv3_large_100,2907.93,175.204,512,224,5.48 resnet18d,2875.3,177.69,512,224,11.71 tinynet_b,2851.82,178.435,512,188,3.73 hardcorenas_b,2772.42,183.73,512,224,5.18 hardcorenas_c,2763.94,184.272,512,224,5.52 mobilenetv3_rw,2754.46,184.981,512,224,5.48 nf_regnet_b0,2740.89,185.595,512,192,8.76 mobilenetv3_large_100_miil,2733.62,186.4,512,224,5.48 mobilenetv3_large_100,2732.43,186.472,512,224,5.48 ese_vovnet19b_slim,2684.58,190.344,512,224,3.17 spnasnet_100,2610.47,195.171,512,224,4.42 mobilenetv2_075,2609.91,195.379,512,224,2.64 semnasnet_075,2603.1,195.762,512,224,2.91 hardcorenas_d,2566.48,198.271,512,224,7.5 tf_efficientnetv2_b1,2548.95,199.349,512,192,8.14 levit_256d,2522.09,201.424,512,224,26.21 fbnetc_100,2397.58,212.548,512,224,5.57 tinynet_a,2334.41,218.035,512,192,6.19 mobilenetv2_100,2313.1,220.563,512,224,3.5 vit_tiny_patch16_224,2299.56,221.804,512,224,5.72 mnasnet_a1,2291.94,222.453,512,224,3.89 deit_tiny_patch16_224,2290.33,222.697,512,224,5.72 semnasnet_100,2279.15,223.737,512,224,3.89 edgenext_xx_small,2271.04,224.572,512,256,1.33 dla46_c,2266.89,225.115,512,224,1.3 hardcorenas_f,2252.64,226.141,512,224,8.2 deit_tiny_distilled_patch16_224,2248.67,226.799,512,224,5.91 hardcorenas_e,2245.94,226.861,512,224,8.07 xcit_nano_12_p16_224_dist,2177.52,233.052,512,224,3.05 xcit_nano_12_p16_224,2170.17,234.054,512,224,3.05 tf_efficientnet_lite0,2134.89,239.057,512,224,4.65 ghostnet_100,2129.82,239.0,512,224,5.18 hrnet_w18_small,2121.96,239.906,512,224,13.19 regnety_004,2085.76,244.311,512,224,4.34 efficientnet_lite0,2079.28,245.485,512,224,4.65 cs3darknet_focus_m,2062.98,247.547,512,256,9.3 pit_ti_distilled_224,2061.94,247.414,512,224,5.1 mnasnet_140,2060.59,247.645,512,224,7.12 pit_ti_224,2057.02,247.989,512,224,4.85 gluon_resnet34_v1b,2039.68,250.446,512,224,21.8 tv_resnet34,2038.39,250.573,512,224,21.8 resnet34,2036.51,250.813,512,224,21.8 ese_vovnet19b_dw,1999.58,255.562,512,224,6.54 resnet26,1962.0,260.488,512,224,16.0 tf_efficientnetv2_b2,1951.52,260.748,512,208,10.1 skresnet18,1943.81,262.753,512,224,11.96 cs3darknet_m,1940.79,263.122,512,256,9.31 regnetz_005,1916.17,265.765,512,224,7.12 resnetblur18,1897.99,269.406,512,224,11.69 rexnetr_100,1893.12,201.724,384,224,4.88 nf_resnet26,1869.64,273.344,512,224,16.0 mobilenetv2_110d,1868.27,204.505,384,224,4.52 visformer_tiny,1861.63,274.356,512,224,10.32 mixer_b32_224,1856.75,274.965,512,224,60.29 seresnet34,1837.21,277.783,512,224,21.96 fbnetv3_b,1825.5,278.744,512,224,8.6 mobilevitv2_050,1824.87,279.552,512,256,1.37 gernet_m,1822.12,280.293,512,224,21.14 resnet34d,1813.16,281.758,512,224,21.82 levit_384,1801.0,283.153,512,224,39.13 legacy_seresnet34,1781.32,286.529,512,224,21.96 regnetx_004,1780.61,286.47,512,224,5.16 tf_efficientnet_b0_ns,1779.24,214.77,384,224,5.29 tf_efficientnet_b0,1779.02,214.765,384,224,5.29 tf_efficientnet_b0_ap,1777.73,214.898,384,224,5.29 efficientnet_b0,1751.72,291.183,512,224,5.29 selecsls42,1718.76,297.231,512,224,30.35 selecsls42b,1710.18,298.726,512,224,32.46 vit_base_patch32_224,1708.63,298.818,512,224,88.22 vit_base_patch32_224_sam,1707.5,298.997,512,224,88.22 efficientnet_es_pruned,1687.32,302.688,512,224,5.44 resnetrs50,1686.45,302.42,512,160,35.69 efficientnet_es,1686.11,302.906,512,224,5.44 mixer_s16_224,1660.76,307.737,512,224,18.53 darknet17,1654.01,309.253,512,256,14.3 mobilenetv2_140,1637.57,233.691,384,224,6.11 fbnetv3_d,1634.54,233.136,384,224,10.31 tf_efficientnet_es,1623.9,314.542,512,224,5.44 resnet26d,1623.31,314.899,512,224,16.01 mobilevit_xxs,1602.81,238.427,384,256,1.27 resmlp_12_distilled_224,1577.54,323.769,512,224,15.35 resmlp_12_224,1577.31,323.803,512,224,15.35 pit_xs_224,1555.66,328.198,512,224,10.62 pit_xs_distilled_224,1555.48,328.255,512,224,11.0 semnasnet_140,1546.19,330.184,512,224,6.11 ghostnet_130,1542.47,330.535,512,224,7.36 repvgg_b0,1538.07,331.828,512,224,15.82 efficientnet_lite1,1530.99,166.26,256,240,5.42 dla34,1524.02,335.337,512,224,15.74 edgenext_x_small,1512.48,337.399,512,256,2.34 darknet21,1486.14,344.159,512,256,20.86 selecsls60,1482.76,344.397,512,224,30.67 selecsls60b,1478.62,345.378,512,224,32.77 nf_seresnet26,1473.71,346.754,512,224,17.4 vit_small_patch32_384,1455.89,350.818,512,384,22.92 gmixer_12_224,1448.32,352.721,512,224,12.7 efficientnet_b1_pruned,1446.82,352.35,512,240,6.33 tf_efficientnet_lite1,1443.47,176.394,256,240,5.42 nf_ecaresnet26,1440.41,354.896,512,224,16.0 xcit_tiny_12_p16_224_dist,1426.36,357.157,512,224,6.72 xcit_tiny_12_p16_224,1426.18,357.168,512,224,6.72 sedarknet21,1401.98,364.696,512,256,20.95 rexnetr_130,1388.84,183.199,256,224,7.61 dla46x_c,1388.59,367.953,512,224,1.07 gmlp_ti16_224,1381.11,276.449,384,224,5.87 mixnet_s,1365.54,373.667,512,224,4.13 rexnet_100,1364.31,280.319,384,224,4.8 regnety_006,1361.43,374.963,512,224,6.06 mobilenetv2_120d,1352.9,188.013,256,224,5.83 legacy_seresnext26_32x4d,1349.26,378.798,512,224,16.79 crossvit_tiny_240,1348.01,378.219,512,240,7.01 vit_tiny_r_s16_p8_384,1345.31,284.562,384,384,6.36 poolformer_s12,1342.54,380.659,512,224,11.92 dla60x_c,1341.77,380.621,512,224,1.32 efficientnet_b1,1325.85,191.544,256,224,7.79 resnetv2_50,1288.61,396.553,512,224,25.55 regnetx_006,1286.44,397.176,512,224,6.2 crossvit_9_240,1258.73,303.637,384,240,8.55 convnext_nano_ols,1252.33,408.151,512,224,15.6 convnext_nano,1249.89,408.864,512,224,15.59 convnext_nano_hnf,1249.05,409.138,512,224,15.59 resnet26t,1237.34,413.275,512,256,16.01 tf_mixnet_s,1236.15,412.905,512,224,4.13 nf_regnet_b2,1229.56,414.759,512,240,14.31 rexnetr_150,1224.57,207.878,256,224,9.78 gluon_resnet50_v1b,1219.23,419.12,512,224,25.56 tv_resnet50,1218.99,419.17,512,224,25.56 crossvit_9_dagger_240,1218.38,313.701,384,240,8.78 resnet50,1218.01,419.528,512,224,25.56 swsl_resnet50,1217.39,419.737,512,224,25.56 ssl_resnet50,1217.38,419.757,512,224,25.56 cs3darknet_focus_l,1216.61,314.788,384,256,21.15 repvgg_a2,1214.87,420.579,512,224,28.21 cs3darknet_l,1203.14,318.267,384,256,21.16 gernet_l,1201.09,425.379,512,256,31.08 efficientnet_lite2,1191.67,213.855,256,260,6.09 nf_regnet_b1,1181.15,431.966,512,256,10.22 seresnext26d_32x4d,1178.86,325.051,384,224,16.81 botnet26t_256,1178.34,325.281,384,256,12.49 seresnext26tn_32x4d,1177.85,325.355,384,224,16.81 seresnext26t_32x4d,1176.65,325.669,384,224,16.81 mobilevitv2_075,1174.29,217.001,256,256,2.87 ecaresnext50t_32x4d,1159.52,330.605,384,224,15.41 ecaresnext26t_32x4d,1158.26,330.961,384,224,15.41 gluon_resnet50_v1c,1147.86,333.697,384,224,25.58 halonet26t,1136.15,337.402,384,256,12.48 resnetv2_50d,1134.86,450.316,512,224,25.57 resnetv2_50t,1132.89,451.133,512,224,25.57 edgenext_small,1127.71,452.849,512,256,5.59 tf_efficientnet_lite2,1121.02,227.403,256,260,6.09 convit_tiny,1118.98,456.53,512,224,5.71 skresnet34,1113.08,458.799,512,224,22.28 tf_efficientnet_b1,1099.77,231.299,256,240,7.79 tf_efficientnet_b1_ap,1099.37,231.402,256,240,7.79 efficientnetv2_rw_t,1098.86,230.78,256,224,13.65 tf_efficientnet_b1_ns,1098.29,231.567,256,240,7.79 ecaresnetlight,1091.16,468.275,512,224,30.16 gluon_resnet50_v1d,1084.38,353.226,384,224,25.58 dpn68b,1083.77,353.123,384,224,12.61 cs3sedarknet_l,1083.42,353.12,384,256,21.91 resnet50d,1078.0,355.348,384,224,25.58 resnet50t,1076.81,355.721,384,224,25.57 resnet32ts,1075.86,237.337,256,256,17.96 resnet33ts,1061.36,240.599,256,256,19.68 vit_small_patch16_224,1057.92,362.157,384,224,22.05 resnetaa50,1057.73,362.204,384,224,25.56 vit_small_resnet26d_224,1057.57,362.04,384,224,63.61 deit_small_patch16_224,1050.7,364.638,384,224,22.05 cspresnet50,1042.19,367.617,384,256,21.62 tf_efficientnetv2_b3,1041.71,243.94,256,240,14.36 regnetx_008,1034.73,493.971,512,224,7.26 ecaresnet26t,1033.34,371.048,384,256,16.01 deit_small_distilled_patch16_224,1028.8,372.398,384,224,22.44 vit_relpos_base_patch32_plus_rpn_256,1021.86,499.989,512,256,119.42 dla60,1020.05,375.488,384,224,22.04 res2net50_48w_2s,1018.83,376.079,384,224,25.29 gc_efficientnetv2_rw_t,1014.65,249.524,256,224,13.68 vit_relpos_small_patch16_rpn_224,1013.69,377.786,384,224,21.97 edgenext_small_rw,1011.18,505.339,512,256,7.83 pit_s_224,1010.83,378.943,384,224,23.46 seresnet33ts,1007.26,253.362,256,256,19.78 efficientnet_em,1007.19,253.179,256,240,6.9 vovnet39a,1006.62,507.995,512,224,22.6 legacy_seresnet50,1003.5,381.52,384,224,28.09 gluon_resnext50_32x4d,1001.3,382.689,384,224,25.03 tv_resnext50_32x4d,1001.18,382.711,384,224,25.03 resnext50_32x4d,1001.03,382.776,384,224,25.03 ssl_resnext50_32x4d,1000.68,382.908,384,224,25.03 eca_resnet33ts,999.77,255.368,256,256,19.68 swsl_resnext50_32x4d,997.37,384.186,384,224,25.03 regnety_008,993.3,514.408,512,224,6.26 dpn68,992.27,385.859,384,224,12.61 deit3_small_patch16_224,987.86,387.777,384,224,22.06 deit3_small_patch16_224_in21ft1k,987.15,388.058,384,224,22.06 gcresnet33ts,985.12,258.855,256,256,19.88 efficientnet_b2a,980.29,259.63,256,256,9.11 tf_efficientnet_em,980.0,260.253,256,240,6.9 efficientnet_b2,978.68,260.092,256,256,9.11 seresnet50,971.79,394.011,384,224,28.09 gluon_resnet50_v1s,970.71,394.714,384,224,25.68 vit_srelpos_small_patch16_224,969.18,395.281,384,224,21.97 vit_relpos_small_patch16_224,965.13,396.742,384,224,21.98 ecaresnet50d_pruned,964.18,530.07,512,224,19.94 cspresnet50d,956.82,266.672,256,256,21.64 vgg11,954.03,536.508,512,224,132.86 cspresnet50w,952.27,267.927,256,256,28.12 ese_vovnet39b,951.93,537.173,512,224,24.57 vit_base_patch32_plus_256,951.5,537.138,512,256,119.48 resnetaa50d,950.79,403.026,384,224,25.58 eca_vovnet39b,948.4,539.184,512,224,22.6 lambda_resnet26rpt_256,942.15,203.17,192,256,10.99 pit_s_distilled_224,934.29,273.079,256,224,24.04 mobilevit_xs,924.5,275.792,256,256,2.32 tv_densenet121,917.93,277.067,256,224,7.98 densenet121,913.65,278.353,256,224,7.98 resnetblur50,911.91,420.254,384,224,25.56 hrnet_w18_small_v2,910.26,559.998,512,224,15.6 coat_lite_tiny,909.29,421.406,384,224,5.72 mobilevitv2_100,907.45,281.094,256,256,4.9 nf_resnet50,900.11,425.722,384,256,25.56 resnext50d_32x4d,894.57,285.293,256,224,25.05 nf_seresnet50,892.73,428.967,384,224,28.09 rexnetr_200,890.57,214.407,192,224,16.52 efficientnet_cc_b0_4e,890.34,430.073,384,224,13.31 efficientnet_cc_b0_8e,889.37,430.553,384,224,24.01 dla60x,886.5,287.775,256,224,17.35 twins_svt_small,885.48,432.048,384,224,24.06 seresnet50t,879.71,435.29,384,224,28.1 mixnet_m,878.04,581.529,512,224,5.01 nf_ecaresnet50,875.38,437.674,384,224,25.56 efficientnet_b2_pruned,873.9,291.355,256,260,8.31 densenet121d,873.44,291.238,256,224,8.0 cspresnext50,868.23,294.006,256,256,20.57 rexnet_150,866.26,294.391,256,224,9.73 ecaresnet50d,862.65,444.205,384,224,25.58 fbnetv3_g,862.32,220.642,192,240,16.62 regnetz_b16,862.05,295.457,256,224,9.72 tf_efficientnet_cc_b0_4e,861.1,444.691,384,224,13.31 tf_efficientnet_cc_b0_8e,857.16,446.822,384,224,24.01 gcresnet50t,854.99,447.633,384,256,25.9 res2net50_26w_4s,851.03,449.921,384,224,25.7 coat_lite_mini,849.82,450.985,384,224,11.01 tf_efficientnet_b2_ap,849.52,224.466,192,260,9.11 tf_efficientnet_b2,848.58,224.736,192,260,9.11 tf_efficientnet_b2_ns,847.86,224.983,192,260,9.11 vit_base_resnet26d_224,844.62,453.315,384,224,101.4 vgg11_bn,832.74,460.889,384,224,132.87 vovnet57a,832.06,614.449,512,224,36.64 selecsls84,830.17,615.492,512,224,50.95 resnetblur50d,826.31,308.964,256,224,25.58 convnext_tiny_hnfd,820.9,310.941,256,224,28.59 convnext_tiny_hnf,819.46,311.471,256,224,28.59 convnext_tiny,819.24,311.536,256,224,28.59 convnext_tiny_in22ft1k,818.81,311.724,256,224,28.59 rexnet_130,816.78,312.226,256,224,7.56 seresnext50_32x4d,814.69,313.102,256,224,27.56 legacy_seresnext50_32x4d,813.61,313.477,256,224,27.56 gluon_seresnext50_32x4d,813.13,313.678,256,224,27.56 skresnet50,808.8,473.357,384,224,25.8 visformer_small,806.27,475.588,384,224,40.22 res2net50_14w_8s,794.56,319.93,256,224,25.06 densenetblur121d,789.33,322.521,256,224,8.0 seresnetaa50d,785.32,324.779,256,224,28.11 gluon_inception_v3,782.59,489.263,384,299,23.83 inception_v3,782.35,489.427,384,299,23.83 adv_inception_v3,778.18,491.976,384,299,23.83 resmlp_24_distilled_224,777.24,327.895,256,224,30.02 resmlp_24_224,776.95,327.972,256,224,30.02 tf_inception_v3,775.41,493.776,384,299,23.83 ese_vovnet57b,774.18,495.058,384,224,38.61 tf_mixnet_m,773.08,495.127,384,224,5.01 resnetv2_101,772.45,329.834,256,224,44.54 dla60_res2net,767.35,332.099,256,224,20.85 nf_regnet_b3,766.23,499.321,384,288,18.59 sehalonet33ts,763.66,334.4,256,256,13.69 ecaresnet101d_pruned,754.9,676.449,512,224,24.88 darknet53,753.16,339.081,256,256,41.61 densenet169,752.52,337.551,256,224,14.15 resnet101,747.89,340.74,256,224,44.55 gluon_resnet101_v1b,747.04,341.055,256,224,44.55 tv_resnet101,746.84,341.219,256,224,44.55 skresnet50d,739.17,344.891,256,224,25.82 twins_pcpvt_small,738.11,345.194,256,224,24.11 vit_small_r26_s32_224,733.9,347.477,256,224,36.43 mobilevit_s,733.0,260.821,192,256,5.58 darknetaa53,732.7,348.577,256,256,36.02 xcit_tiny_24_p16_224_dist,727.98,348.335,256,224,12.12 xcit_tiny_24_p16_224,727.1,348.63,256,224,12.12 efficientnet_b0_gn,724.56,352.174,256,224,5.29 efficientnet_b3_pruned,722.23,352.701,256,300,9.86 gluon_resnet101_v1c,717.66,355.143,256,224,44.57 resnext26ts,717.15,534.946,384,256,10.3 resnetv2_101d,715.45,356.238,256,224,44.56 gmixer_24_224,714.67,356.582,256,224,24.72 resnetrs101,714.37,356.071,256,192,63.62 nf_resnet101,712.1,537.603,384,224,44.55 efficientnet_lite3,702.44,181.104,128,300,8.2 mixnet_l,702.18,545.327,384,224,7.33 eca_resnext26ts,694.05,368.289,256,256,10.3 semobilevit_s,692.92,368.16,256,256,5.74 seresnext26ts,691.18,369.699,256,256,10.39 poolformer_s24,689.84,369.792,256,224,21.39 gluon_resnet101_v1d,688.26,370.323,256,224,44.57 dla102,688.03,370.524,256,224,33.27 vit_relpos_medium_patch16_rpn_224,687.13,371.514,256,224,38.73 sebotnet33ts_256,686.07,279.058,192,256,13.7 gcresnext26ts,683.09,373.929,256,256,10.48 regnetx_016,682.73,749.012,512,224,9.19 haloregnetz_b,680.78,374.495,256,224,11.68 cspdarknet53,679.01,375.961,256,256,27.64 vgg13,677.45,566.653,384,224,133.05 xcit_nano_12_p16_384_dist,671.72,379.231,256,384,3.05 wide_resnet50_2,668.78,573.358,384,224,68.88 tf_efficientnet_lite3,665.78,191.165,128,300,8.2 vit_relpos_medium_patch16_cls_224,661.77,385.665,256,224,38.76 vit_srelpos_medium_patch16_224,659.88,386.996,256,224,38.74 rexnet_200,659.06,290.146,192,224,16.37 vit_base_resnet50d_224,658.84,386.945,256,224,110.97 ecaresnet50t,657.78,388.237,256,256,25.57 gmlp_s16_224,657.63,290.408,192,224,19.42 vit_relpos_medium_patch16_224,657.05,388.484,256,224,38.75 tf_efficientnet_cc_b1_8e,654.82,389.25,256,240,39.72 regnety_016,650.07,785.757,512,224,11.2 swin_tiny_patch4_window7_224,648.69,393.641,256,224,28.29 xcit_small_12_p16_224,640.82,397.688,256,224,26.25 gluon_resnet101_v1s,640.79,397.908,256,224,44.67 xcit_small_12_p16_224_dist,639.99,398.193,256,224,26.25 crossvit_small_240,638.8,399.076,256,240,26.86 efficientnet_cc_b1_8e,637.42,399.94,256,240,39.72 resnetaa101d,634.86,401.619,256,224,44.57 cs3sedarknet_xdw,630.82,302.41,192,256,21.6 repvgg_b1,623.55,820.034,512,224,57.42 mobilevitv2_125,620.51,308.406,192,256,7.48 bat_resnext26ts,613.61,415.954,256,256,10.73 gluon_resnext101_32x4d,609.67,418.333,256,224,44.18 swsl_resnext101_32x4d,609.02,418.731,256,224,44.18 resnext101_32x4d,609.01,418.74,256,224,44.18 tf_mixnet_l,606.88,420.297,256,224,7.33 ssl_resnext101_32x4d,606.28,420.718,256,224,44.18 legacy_seresnet101,601.55,423.316,256,224,49.33 cs3darknet_focus_x,600.02,425.715,256,256,35.02 dla102x,598.42,319.231,192,224,26.31 halonet50ts,597.8,320.205,192,256,22.73 xcit_nano_12_p8_224,595.07,428.358,256,224,3.05 xcit_nano_12_p8_224_dist,593.27,429.695,256,224,3.05 cait_xxs24_224,593.22,428.92,256,224,11.96 seresnet101,590.42,431.41,256,224,49.33 swin_s3_tiny_224,588.57,433.98,256,224,28.33 resnetv2_50x1_bit_distilled,585.83,326.889,192,224,25.55 efficientnet_b0_g8_gn,582.67,438.264,256,224,6.56 crossvit_15_240,580.46,328.975,192,240,27.53 resnetblur101d,576.87,442.155,256,224,44.57 res2net50_26w_6s,573.8,444.339,256,224,37.05 vgg13_bn,573.47,446.125,256,224,133.05 efficientnet_b3a,572.29,221.925,128,288,12.23 efficientnet_b3,572.18,221.941,128,288,12.23 cs3darknet_x,571.12,447.259,256,256,35.05 densenet201,562.52,338.221,192,224,20.01 crossvit_15_dagger_240,562.49,339.489,192,240,28.21 efficientnetv2_s,559.15,226.702,128,288,21.46 eca_botnext26ts_256,558.6,457.666,256,256,10.59 mixer_b16_224,556.6,459.152,256,224,59.88 mixer_b16_224_miil,556.5,459.202,256,224,59.88 eca_halonext26ts,547.96,466.574,256,256,10.76 ecaresnet101d,546.58,466.555,256,224,44.57 vgg16,546.06,702.994,384,224,138.36 mixer_l32_224,543.38,351.819,192,224,206.94 vit_base_patch32_384,543.37,470.294,256,384,88.3 nf_seresnet101,540.53,471.014,256,224,49.33 resnetv2_152,536.63,474.697,256,224,60.19 botnet50ts_256,534.71,238.412,128,256,22.74 mobilevitv2_150,533.38,238.97,128,256,10.59 vit_base_r26_s32_224,533.28,358.697,192,224,101.38 mobilevitv2_150_in22ft1k,532.99,239.183,128,256,10.59 cs3sedarknet_x,531.85,479.872,256,256,35.4 nf_ecaresnet101,531.3,479.947,256,224,44.55 cs3edgenet_x,529.37,482.632,256,256,47.82 res2next50,528.59,483.023,256,224,24.67 res2net101_26w_4s,527.01,483.179,256,224,45.21 vit_large_patch32_224,524.74,486.172,256,224,306.54 resnet101d,523.85,364.964,192,256,44.57 efficientnetv2_rw_s,520.77,243.564,128,288,23.94 halo2botnet50ts_256,517.51,369.975,192,256,22.64 resmlp_36_distilled_224,513.23,371.84,192,224,44.69 vit_tiny_patch16_384,510.99,249.657,128,384,5.79 resmlp_36_224,509.53,374.55,192,224,44.69 swinv2_cr_tiny_224,506.82,503.861,256,224,28.33 mixnet_xl,505.67,504.387,256,224,11.9 resnetv2_50d_gn,505.25,379.149,192,224,25.57 swinv2_cr_tiny_ns_224,504.1,506.527,256,224,28.33 gluon_resnet152_v1b,502.02,380.204,192,224,60.19 regnetz_d8,501.8,253.463,128,256,23.37 resnet152,501.44,380.547,192,224,60.19 tv_resnet152,501.12,380.811,192,224,60.19 xception,497.64,256.367,128,299,22.86 regnety_032,496.85,771.405,384,224,19.44 tf_efficientnet_b3_ap,496.0,256.348,128,300,12.23 tf_efficientnet_b3,494.58,257.101,128,300,12.23 tf_efficientnet_b3_ns,492.45,258.213,128,300,12.23 convnext_small_in22ft1k,490.79,389.411,192,224,50.22 res2net50_26w_8s,489.22,520.921,256,224,48.4 tf_efficientnetv2_s_in21ft1k,488.77,259.622,128,300,21.46 tf_efficientnetv2_s,488.25,259.918,128,300,21.46 gluon_resnet152_v1c,488.2,390.894,192,224,60.21 convnext_small,487.15,392.394,192,224,50.22 twins_pcpvt_base,487.13,391.314,192,224,43.83 resnetv2_152d,486.82,392.059,192,224,60.2 legacy_seresnext101_32x4d,484.7,393.829,192,224,48.96 resnet50_gn,482.45,397.141,192,224,25.56 gluon_seresnext101_32x4d,480.63,397.197,192,224,48.96 hrnet_w32,480.24,528.215,256,224,41.23 sequencer2d_s,480.16,264.213,128,224,27.65 seresnext101_32x4d,479.03,398.526,192,224,48.96 nest_tiny,477.97,266.889,128,224,17.06 dla60_res2next,477.74,534.408,256,224,17.03 gluon_resnet152_v1d,476.32,400.788,192,224,60.21 regnetz_c16,475.79,402.061,192,256,13.46 hrnet_w18,473.42,535.867,256,224,21.3 jx_nest_tiny,472.84,269.807,128,224,17.06 regnetz_d32,471.85,269.596,128,256,27.58 regnetz_040,471.81,269.412,128,256,27.12 xception41p,471.79,270.424,128,299,26.91 vgg16_bn,470.3,543.999,256,224,138.37 regnetz_040h,469.27,270.846,128,256,28.94 poolformer_s36,467.39,408.832,192,224,30.86 resnet51q,463.81,551.102,256,256,35.7 efficientnet_el_pruned,461.98,275.957,128,300,10.59 efficientnet_el,461.97,275.94,128,300,10.59 coat_lite_small,461.36,414.606,192,224,19.84 nf_regnet_b4,457.97,417.049,192,320,30.21 vgg19,457.37,839.347,384,224,143.67 cs3se_edgenet_x,457.05,418.615,192,256,50.72 dla169,455.51,419.044,192,224,53.39 convit_small,454.72,421.197,192,224,27.78 gluon_resnet152_v1s,452.53,421.917,192,224,60.32 tf_efficientnet_el,449.8,283.446,128,300,10.59 gcresnext50ts,445.12,429.834,192,256,15.67 regnetx_040,442.35,866.937,384,224,22.12 vit_small_resnet50d_s16_224,437.26,437.826,192,224,57.53 volo_d1_224,437.04,437.842,192,224,26.63 mobilevitv2_175_in22ft1k,434.9,293.339,128,256,14.25 mobilevitv2_175,434.88,293.341,128,256,14.25 resnet61q,433.95,441.405,192,256,36.85 ese_vovnet99b,433.24,589.371,256,224,63.2 ese_vovnet39b_evos,430.54,296.328,128,224,24.58 twins_svt_base,425.02,449.64,192,224,56.07 resnest14d,411.87,1242.634,512,224,10.61 dla102x2,405.87,313.766,128,224,41.28 mobilevitv2_200_in22ft1k,405.83,314.414,128,256,18.45 mobilevitv2_200,405.76,314.447,128,256,18.45 inception_v4,405.43,471.399,192,299,42.68 crossvit_18_240,404.58,314.332,128,240,43.27 swin_small_patch4_window7_224,400.37,477.632,192,224,49.61 densenet161,399.17,318.189,128,224,28.68 vgg19_bn,398.5,642.012,256,224,143.68 legacy_seresnet152,398.4,478.588,192,224,66.82 vit_base_patch16_224_miil,397.86,481.79,192,224,86.54 sequencer2d_m,396.83,480.626,192,224,38.31 crossvit_18_dagger_240,396.31,320.926,128,240,44.27 resnetv2_50d_frn,394.18,323.553,128,224,25.59 vit_base_patch16_224,392.99,487.729,192,224,86.57 vit_base_patch16_224_sam,392.92,487.774,192,224,86.57 vit_base_patch16_rpn_224,391.32,489.846,192,224,86.54 xception41,391.05,326.045,128,299,26.97 deit_base_patch16_224,390.04,491.437,192,224,86.57 cait_xxs36_224,387.24,492.066,192,224,17.3 efficientnet_b0_g16_evos,386.23,993.13,384,224,8.11 deit_base_distilled_patch16_224,384.06,499.086,192,224,87.34 xcit_tiny_12_p16_384_dist,383.09,499.371,192,384,6.72 vit_relpos_base_patch16_rpn_224,382.95,500.328,192,224,86.41 seresnet152,379.38,334.127,128,224,66.82 resnetv2_50d_evos,374.27,340.812,128,224,25.59 deit3_base_patch16_224,374.05,512.341,192,224,86.59 deit3_base_patch16_224_in21ft1k,373.84,512.639,192,224,86.59 vit_relpos_base_patch16_clsgap_224,370.76,516.704,192,224,86.43 vit_relpos_base_patch16_cls_224,370.22,517.437,192,224,86.43 hrnet_w30,369.35,688.202,256,224,37.71 vit_relpos_base_patch16_224,368.93,519.279,192,224,86.43 gluon_resnext101_64x4d,363.93,350.084,128,224,83.46 resnext101_64x4d,363.79,350.21,128,224,83.46 beit_base_patch16_224,358.77,534.02,192,224,86.53 ens_adv_inception_resnet_v2,358.6,532.08,192,299,55.84 wide_resnet101_2,358.56,533.868,192,224,126.89 inception_resnet_v2,358.54,532.143,192,299,55.84 resnet200,357.55,355.04,128,224,64.67 resnet152d,357.54,355.729,128,256,60.21 swinv2_tiny_window8_256,357.0,536.56,192,256,28.35 efficientnet_b4,354.99,268.381,96,320,19.34 dpn92,353.0,723.721,256,224,37.67 repvgg_b2,352.05,1453.222,512,224,89.02 resnest50d_1s4x24d,349.97,730.142,256,224,25.68 regnetz_b16_evos,347.19,366.83,128,224,9.74 tnt_s_patch16_224,342.71,558.25,192,224,23.76 xception65p,341.43,373.588,128,299,39.82 convnext_base_in22ft1k,339.67,374.996,128,224,88.59 convnext_base,338.68,376.119,128,224,88.59 efficientnet_lite4,338.39,187.783,64,380,13.01 twins_pcpvt_large,333.52,379.633,128,224,60.99 pit_b_224,331.08,385.636,128,224,73.76 pit_b_distilled_224,328.87,388.208,128,224,74.79 xcit_small_24_p16_224_dist,326.41,388.817,128,224,47.67 xcit_small_24_p16_224,326.38,388.806,128,224,47.67 tf_efficientnet_lite4,324.6,195.748,64,380,13.01 eca_nfnet_l0,319.84,1599.745,512,224,24.14 nfnet_l0,319.69,1600.317,512,224,35.07 gluon_seresnext101_64x4d,319.51,398.398,128,224,88.23 repvgg_b3,316.57,1211.922,384,224,123.09 skresnext50_32x4d,315.84,809.121,256,224,27.48 poolformer_m36,315.69,403.496,128,224,56.17 ssl_resnext101_32x8d,313.35,406.924,128,224,88.79 resnext101_32x8d,312.8,407.622,128,224,88.79 swsl_resnext101_32x8d,312.76,407.724,128,224,88.79 ig_resnext101_32x8d,311.13,409.865,128,224,88.79 vit_small_patch16_36x1_224,309.04,411.365,128,224,64.67 regnetx_032,308.88,1241.936,384,224,15.3 vit_small_patch16_18x2_224,306.57,414.654,128,224,64.67 xcit_tiny_12_p8_224,306.37,415.93,128,224,6.71 cait_s24_224,305.74,415.965,128,224,46.92 xcit_tiny_12_p8_224_dist,304.23,418.886,128,224,6.71 swinv2_cr_small_ns_224,300.99,422.86,128,224,49.7 twins_svt_large,300.69,423.548,128,224,99.27 swinv2_cr_small_224,299.81,424.482,128,224,49.7 coat_tiny,298.83,426.275,128,224,5.5 resnest26d,298.23,1286.829,384,224,17.07 nest_small,296.79,321.765,96,224,38.35 jx_nest_small,293.75,325.094,96,224,38.35 swin_s3_small_224,290.56,438.612,128,224,49.74 dpn98,290.11,439.591,128,224,61.57 resnetv2_50d_evob,289.65,330.197,96,224,25.59 seresnet152d,283.9,447.414,128,256,66.84 gluon_xception65,283.18,337.068,96,299,39.92 convnext_tiny_384_in22ft1k,282.39,338.982,96,384,28.59 resnetrs152,282.26,450.046,128,256,86.62 xception65,281.11,339.548,96,299,39.92 swin_base_patch4_window7_224,281.0,453.662,128,224,87.77 hrnet_w48,279.44,682.135,192,224,77.47 mixnet_xxl,278.4,457.833,128,224,23.96 seresnext101_32x8d,278.13,458.033,128,224,93.57 gmlp_b16_224,275.97,346.253,96,224,73.08 seresnext101d_32x8d,270.35,471.144,128,224,93.59 resnet200d,267.1,476.272,128,256,64.69 nfnet_f0,265.61,1926.394,512,192,71.49 regnetz_e8,256.51,247.489,64,256,57.7 xcit_tiny_24_p16_384_dist,255.73,371.975,96,384,12.12 crossvit_base_240,254.6,375.374,96,240,105.03 dm_nfnet_f0,251.38,1526.301,384,192,71.49 hrnet_w40,249.23,765.525,192,224,57.56 vit_base_patch16_plus_240,246.96,517.368,128,240,117.56 efficientnetv2_m,246.55,256.379,64,320,54.14 vit_relpos_base_patch16_plus_240,244.88,521.493,128,240,117.38 seresnextaa101d_32x8d,243.89,522.629,128,224,93.59 tf_efficientnet_b4_ap,242.14,262.218,64,380,19.34 tf_efficientnet_b4,241.83,262.52,64,380,19.34 tf_efficientnet_b4_ns,241.46,263.01,64,380,19.34 xcit_medium_24_p16_224,241.39,526.926,128,224,84.4 xcit_medium_24_p16_224_dist,241.08,527.466,128,224,84.4 xcit_small_12_p16_384_dist,240.61,397.192,96,384,26.25 vit_small_patch16_384,239.06,266.856,64,384,22.2 volo_d2_224,238.89,400.019,96,224,58.68 swinv2_tiny_window16_256,238.79,400.76,96,256,28.35 mobilevitv2_150_384_in22ft1k,238.1,267.77,64,384,10.59 vit_large_r50_s32_224,236.27,403.797,96,224,328.99 tresnet_m,233.24,2192.365,512,224,31.39 hrnet_w44,232.48,820.975,192,224,67.06 poolformer_m48,232.43,410.471,96,224,73.47 densenet264,231.27,411.12,96,224,72.69 convit_base,231.06,552.947,128,224,86.54 nf_regnet_b5,228.54,417.354,96,384,49.74 deit3_small_patch16_384,226.74,281.318,64,384,22.21 deit3_small_patch16_384_in21ft1k,226.44,281.652,64,384,22.21 vit_small_r26_s32_384,226.15,281.726,64,384,36.47 coat_mini,225.14,566.497,128,224,10.34 efficientnetv2_rw_m,224.46,281.565,64,320,53.24 swin_s3_base_224,224.0,425.728,96,224,71.13 tnt_b_patch16_224,223.52,570.669,128,224,65.41 hrnet_w64,223.29,568.417,128,224,128.06 sequencer2d_l,220.03,286.022,64,224,54.3 dpn131,216.53,588.962,128,224,79.25 vit_base_r50_s16_224,215.49,443.851,96,224,98.66 swinv2_cr_base_ns_224,214.73,444.647,96,224,87.88 xception71,214.09,296.77,64,299,42.34 swinv2_cr_base_224,213.21,447.81,96,224,87.88 swinv2_small_window8_256,213.06,448.048,96,256,49.73 nest_base,210.25,302.717,64,224,67.72 jx_nest_base,209.06,304.441,64,224,67.72 seresnet200d,203.53,467.209,96,256,71.86 resnetrs200,201.84,471.293,96,256,93.21 resnest50d,201.6,1268.493,256,224,27.48 ecaresnet200d,201.55,472.938,96,256,64.69 xcit_nano_12_p8_384_dist,201.45,315.854,64,384,3.05 efficientnet_b3_gn,197.65,322.123,64,288,11.73 xcit_tiny_24_p8_224_dist,195.37,488.075,96,224,12.11 xcit_tiny_24_p8_224,195.11,488.622,96,224,12.11 dpn107,194.08,492.913,96,224,86.92 regnetz_c16_evos,193.89,328.188,64,256,13.49 regnety_040,190.14,2017.916,384,224,20.65 mobilevitv2_175_384_in22ft1k,189.6,336.534,64,384,14.25 regnetv_040,188.29,1358.084,256,224,20.64 convnext_large,187.93,509.087,96,224,197.77 convnext_large_in22ft1k,187.83,509.365,96,224,197.77 convmixer_768_32,187.17,511.603,96,224,21.11 regnetx_080,181.41,1409.979,256,224,39.57 resnest50d_4s2x40d,180.44,1417.38,256,224,30.42 xcit_small_12_p8_224,179.5,354.768,64,224,26.21 xcit_small_12_p8_224_dist,179.34,355.047,64,224,26.21 halonet_h1,176.7,360.706,64,256,8.1 tf_efficientnetv2_m_in21ft1k,175.14,270.794,48,384,54.14 mobilevitv2_200_384_in22ft1k,175.13,273.08,48,384,18.45 tf_efficientnetv2_m,173.37,273.617,48,384,54.14 mixer_l16_224,171.41,558.471,96,224,208.2 efficientnet_b3_g8_gn,168.79,377.376,64,288,14.25 repvgg_b1g4,167.59,3053.943,512,224,39.97 vit_large_patch32_384,167.04,573.058,96,384,306.63 convnext_small_384_in22ft1k,165.65,384.557,64,384,50.22 volo_d3_224,162.19,392.021,64,224,86.33 regnetz_d8_evos,155.31,307.002,48,256,23.46 swin_large_patch4_window7_224,153.79,414.289,64,224,196.53 swinv2_base_window8_256,151.21,420.663,64,256,87.92 convmixer_1024_20_ks9_p14,149.3,1713.726,256,224,24.38 resnetv2_50x1_bitm,147.75,215.764,32,448,25.55 seresnet269d,145.59,433.61,64,256,113.67 resnetrs270,144.14,437.83,64,256,129.86 swinv2_small_window16_256,143.52,443.487,64,256,49.73 regnety_040s_gn,142.58,896.132,128,224,20.65 repvgg_b2g4,133.72,3827.892,512,224,61.76 eca_nfnet_l1,133.59,1435.413,192,256,41.41 xcit_large_24_p16_224,132.6,479.222,64,224,189.1 swinv2_cr_tiny_384,131.94,483.86,64,384,28.33 xcit_large_24_p16_224_dist,131.66,482.65,64,224,189.1 xcit_tiny_12_p8_384_dist,131.64,362.75,48,384,6.71 regnetx_064,129.82,1970.916,256,224,26.21 swinv2_cr_large_224,124.15,513.018,64,224,196.68 xcit_small_24_p16_384_dist,120.64,394.328,48,384,47.67 regnety_064,119.44,2141.523,256,224,30.58 regnety_080,117.88,2170.37,256,224,39.18 crossvit_15_dagger_408,117.86,269.618,32,408,28.5 vit_large_patch16_224,117.2,544.512,64,224,304.33 regnetv_064,117.03,1638.944,192,224,30.58 ese_vovnet99b_iabn,117.02,3278.167,384,224,63.2 convnext_xlarge_in22ft1k,116.37,548.167,64,224,350.2 vit_base_patch16_18x2_224,116.0,548.972,64,224,256.73 convnext_base_384_in22ft1k,115.58,413.454,48,384,88.59 efficientnet_b5,113.63,279.129,32,456,30.39 deit3_large_patch16_224_in21ft1k,112.51,567.041,64,224,304.37 deit3_large_patch16_224,112.48,567.139,64,224,304.37 tf_efficientnet_b5,111.42,284.665,32,456,30.39 tf_efficientnet_b5_ap,111.14,285.451,32,456,30.39 tf_efficientnet_b5_ns,111.14,285.33,32,456,30.39 legacy_senet154,110.98,861.567,96,224,115.09 senet154,110.82,862.828,96,224,115.09 gluon_senet154,110.77,863.12,96,224,115.09 beit_large_patch16_224,109.02,584.818,64,224,304.43 repvgg_b3g4,108.77,3529.239,384,224,83.83 regnetx_160,107.6,1783.261,192,224,54.28 nfnet_f1,107.01,1791.907,192,224,132.63 volo_d1_384,105.69,301.347,32,384,26.78 swinv2_base_window16_256,103.88,459.56,48,256,87.92 swinv2_base_window12to16_192to256_22kft1k,103.79,460.002,48,256,87.92 tresnet_l,102.82,4975.916,512,224,55.99 dm_nfnet_f1,101.59,1257.525,128,224,132.63 volo_d4_224,101.08,472.359,48,224,192.96 cait_xxs24_384,99.39,480.268,48,384,12.03 ecaresnet269d,99.06,479.988,48,320,102.09 efficientnetv2_l,98.76,319.521,32,384,118.52 tf_efficientnetv2_l_in21ft1k,98.35,320.759,32,384,118.52 tf_efficientnetv2_l,97.56,323.47,32,384,118.52 deit_base_patch16_384,97.3,328.042,32,384,86.86 vit_base_patch16_384,97.1,328.712,32,384,86.86 resnest101e,96.09,1329.413,128,256,48.28 deit_base_distilled_patch16_384,94.63,337.315,32,384,87.63 regnetx_120,94.03,2721.558,256,224,46.11 deit3_base_patch16_384,93.5,341.294,32,384,86.88 deit3_base_patch16_384_in21ft1k,93.49,341.309,32,384,86.88 xcit_small_24_p8_224_dist,92.61,514.968,48,224,47.63 xcit_small_24_p8_224,92.51,515.466,48,224,47.63 regnety_120,92.07,2083.952,192,224,51.82 tresnet_xl,91.15,4209.119,384,224,78.44 crossvit_18_dagger_408,89.17,356.787,32,408,44.61 resnetv2_152x2_bit_teacher,89.16,356.538,32,224,236.34 vit_large_patch14_224,85.06,562.673,48,224,304.2 resnetv2_101x1_bitm,84.72,187.286,16,448,44.54 resnetrs350,84.14,372.211,32,288,163.96 beit_base_patch16_384,83.87,380.424,32,384,86.74 regnety_160,83.24,2305.144,192,224,83.59 pnasnet5large,83.16,380.801,32,331,86.06 xcit_medium_24_p16_384_dist,82.74,383.266,32,384,84.4 vit_large_r50_s32_384,77.34,411.186,32,384,329.09 nasnetalarge,77.32,408.633,32,331,88.75 swinv2_cr_small_384,76.42,416.277,32,384,49.7 swin_base_patch4_window12_384,74.73,426.327,32,384,87.9 resmlp_big_24_distilled_224,70.88,449.95,32,224,129.14 resmlp_big_24_224_in22ft1k,70.88,449.933,32,224,129.14 resmlp_big_24_224,70.41,452.99,32,224,129.14 regnety_320,66.33,1928.357,128,224,145.05 xcit_tiny_24_p8_384_dist,66.29,479.361,32,384,12.11 cait_xs24_384,66.24,480.525,32,384,26.67 ig_resnext101_32x16d,65.9,1455.165,96,224,194.03 ssl_resnext101_32x16d,65.74,1458.688,96,224,194.03 swsl_resnext101_32x16d,65.74,1458.738,96,224,194.03 volo_d5_224,64.41,493.535,32,224,295.46 cait_xxs36_384,64.34,493.602,32,384,17.37 efficientnet_b6,64.08,246.77,16,528,43.04 xcit_medium_24_p8_224,63.96,496.86,32,224,84.32 xcit_medium_24_p8_224_dist,63.93,497.194,32,224,84.32 convnext_large_384_in22ft1k,63.85,499.388,32,384,197.77 vit_base_patch8_224,63.45,377.425,24,224,86.58 tf_efficientnet_b6_ns,63.1,250.577,16,528,43.04 tf_efficientnet_b6,62.84,251.669,16,528,43.04 tf_efficientnet_b6_ap,62.76,252.073,16,528,43.04 efficientnetv2_xl,62.18,251.438,16,384,208.12 tf_efficientnetv2_xl_in21ft1k,62.14,251.721,16,384,208.12 xcit_small_12_p8_384_dist,61.84,386.224,24,384,26.21 vit_base_r50_s16_384,61.01,391.67,24,384,98.95 vit_base_resnet50_384,60.98,391.903,24,384,98.95 swinv2_large_window12to16_192to256_22kft1k,60.98,391.098,24,256,196.74 eca_nfnet_l2,58.72,1632.112,96,320,56.72 volo_d2_384,58.5,271.766,16,384,58.87 resnetrs420,56.49,415.629,24,320,191.89 swinv2_cr_base_384,55.08,433.269,24,384,87.88 nfnet_f2,54.73,1750.573,96,256,193.78 tresnet_m_448,53.52,3584.333,192,448,31.39 dm_nfnet_f2,51.26,1245.084,64,256,193.78 cait_s24_384,50.14,476.064,24,384,47.06 swinv2_cr_huge_224,49.63,481.092,24,224,657.83 regnetx_320,48.06,2662.024,128,224,107.81 xcit_large_24_p16_384_dist,48.02,496.416,24,384,189.1 swin_large_patch4_window12_384,41.75,381.31,16,384,196.74 convnext_xlarge_384_in22ft1k,40.45,591.485,24,384,350.2 deit3_huge_patch14_224_in21ft1k,38.41,414.036,16,224,632.13 deit3_huge_patch14_224,38.4,414.103,16,224,632.13 efficientnet_b7,37.97,207.232,8,600,66.35 tf_efficientnet_b7_ap,37.27,210.986,8,600,66.35 tf_efficientnet_b7_ns,37.25,211.249,8,600,66.35 tf_efficientnet_b7,37.22,211.329,8,600,66.35 eca_nfnet_l3,35.61,1344.526,48,352,72.04 xcit_large_24_p8_224_dist,35.32,449.68,16,224,188.93 xcit_large_24_p8_224,35.06,452.952,16,224,188.93 resnetv2_50x3_bitm,34.68,460.605,16,448,217.32 swinv2_cr_large_384,32.56,488.949,16,384,196.68 cait_s36_384,32.3,491.641,16,384,68.37 densenet264d_iabn,32.11,3982.48,128,224,72.74 resnetv2_152x2_bit_teacher_384,31.17,382.508,12,384,236.34 xcit_small_24_p8_384_dist,31.12,510.761,16,384,47.63 resnest200e,30.3,1579.149,48,320,70.2 vit_large_patch16_384,29.14,410.147,12,384,304.72 deit3_large_patch16_384,28.26,422.768,12,384,304.76 deit3_large_patch16_384_in21ft1k,28.25,422.94,12,384,304.76 swinv2_base_window12to24_192to384_22kft1k,28.19,423.281,12,384,87.92 nfnet_f3,26.1,1834.868,48,320,254.92 beit_large_patch16_384,25.3,472.219,12,384,305.0 tresnet_l_448,25.28,5060.321,128,448,55.99 volo_d3_448,24.7,321.403,8,448,86.63 dm_nfnet_f3,24.56,1297.967,32,320,254.92 tresnet_xl_448,23.27,4122.323,96,448,78.44 efficientnet_b8,22.93,257.589,6,672,87.41 tf_efficientnet_b8,22.71,260.18,6,672,87.41 tf_efficientnet_b8_ap,22.69,260.555,6,672,87.41 resnetv2_152x2_bitm,22.58,351.774,8,448,236.34 vit_giant_patch14_224,22.32,355.766,8,224,1012.61 ig_resnext101_32x32d,21.03,1519.993,32,224,468.53 xcit_medium_24_p8_384_dist,21.03,376.997,8,384,84.32 convmixer_1536_20,20.83,2303.059,48,224,51.63 resnetv2_101x3_bitm,18.05,441.706,8,448,387.93 volo_d4_448,17.57,338.789,6,448,193.41 swinv2_large_window12to24_192to384_22kft1k,16.62,358.548,6,384,196.74 resnest269e,16.0,1493.085,24,416,110.93 nfnet_f4,14.17,1687.74,24,384,316.07 swinv2_cr_huge_384,13.13,454.627,6,384,657.94 dm_nfnet_f4,12.96,1229.019,16,384,316.07 xcit_large_24_p8_384_dist,12.19,488.758,6,384,188.93 cait_m36_384,11.91,500.148,6,384,271.22 volo_d5_448,11.43,346.654,4,448,295.91 ig_resnext101_32x48d,11.2,1427.437,16,224,828.41 tf_efficientnet_l2_ns_475,10.96,267.912,3,475,480.31 dm_nfnet_f5,9.76,1222.345,12,416,377.21 beit_large_patch16_512,9.42,422.548,4,512,305.67 volo_d5_512,8.0,371.847,3,512,296.09 nfnet_f5,8.0,1992.337,16,416,377.21 dm_nfnet_f6,7.45,1065.231,8,448,438.36 nfnet_f6,5.82,2052.248,12,448,438.36 nfnet_f7,5.73,1387.07,8,480,499.5 resnetv2_152x4_bitm,4.89,406.668,2,480,936.53 cait_m48_448,4.76,414.936,2,448,356.46 efficientnet_l2,3.95,247.515,1,800,480.31 tf_efficientnet_l2_ns,3.93,248.975,1,800,480.31
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt111-cu113-rtx3090.csv	model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count tinynet_e,9380.97,53.881,512,106,2.04 mobilenetv3_small_050,7276.68,69.643,512,224,1.59 tf_mobilenetv3_small_minimal_100,6334.14,80.291,512,224,2.04 mobilenetv3_small_075,5920.21,85.765,512,224,2.04 lcnet_035,5760.61,88.397,512,224,1.64 mobilenetv3_small_100,5583.48,90.99,512,224,2.54 tf_mobilenetv3_small_075,5569.37,91.204,512,224,2.04 levit_128s,5426.95,93.402,512,224,7.78 lcnet_050,5425.23,93.895,512,224,1.88 tf_mobilenetv3_small_100,5275.47,96.328,512,224,2.54 tinynet_d,4879.29,104.179,512,152,2.34 mixer_s32_224,4491.84,113.42,512,224,19.1 vit_small_patch32_224,4321.32,117.658,512,224,22.88 lcnet_075,4147.31,122.971,512,224,2.36 levit_128,3971.6,127.764,512,224,9.21 vit_tiny_r_s16_p8_224,3957.53,128.516,512,224,6.34 lcnet_100,3524.02,144.807,512,224,2.95 mnasnet_small,3488.23,145.878,512,224,2.03 levit_192,3436.74,147.842,512,224,10.95 regnetx_002,3423.6,148.87,512,224,2.68 regnety_002,3178.35,160.128,512,224,3.16 mobilenetv2_035,3069.55,166.033,512,224,1.68 gernet_s,2949.06,172.893,512,224,8.17 mnasnet_050,2821.34,180.673,512,224,2.22 ssl_resnet18,2720.94,187.823,512,224,11.69 swsl_resnet18,2720.55,187.842,512,224,11.69 gluon_resnet18_v1b,2718.42,187.998,512,224,11.69 resnet18,2711.83,188.459,512,224,11.69 semnasnet_050,2700.91,188.67,512,224,2.08 tinynet_c,2654.89,191.851,512,184,2.46 mobilenetv2_050,2650.81,192.34,512,224,1.97 levit_256,2620.65,194.218,512,224,18.89 lcnet_150,2613.69,195.4,512,224,4.5 regnetx_004,2532.79,201.109,512,224,5.16 seresnet18,2519.64,202.716,512,224,11.78 legacy_seresnet18,2461.74,207.479,512,224,11.78 ese_vovnet19b_slim_dw,2456.97,207.908,512,224,1.9 mobilenetv3_large_075,2342.92,217.677,512,224,3.99 tf_mobilenetv3_large_minimal_100,2311.18,220.824,512,224,3.92 vit_tiny_patch16_224,2249.49,226.757,512,224,5.72 levit_256d,2248.95,226.128,512,224,26.21 deit_tiny_patch16_224,2244.74,227.248,512,224,5.72 tf_mobilenetv3_large_075,2222.25,229.522,512,224,3.99 deit_tiny_distilled_patch16_224,2207.23,231.107,512,224,5.91 ghostnet_050,2193.43,232.046,512,224,2.59 mnasnet_075,2163.05,235.897,512,224,3.17 mobilenetv3_rw,2147.03,237.6,512,224,5.48 mobilenetv3_large_100_miil,2135.44,238.882,512,224,5.48 mobilenetv3_large_100,2134.66,238.979,512,224,5.48 resnet18d,2086.64,244.99,512,224,11.71 pit_ti_distilled_224,2085.77,244.59,512,224,5.1 pit_ti_224,2083.57,244.847,512,224,4.85 hardcorenas_a,2050.08,249.015,512,224,5.26 regnetx_006,2048.56,249.103,512,224,6.2 tf_mobilenetv3_large_100,2046.24,249.335,512,224,5.48 ese_vovnet19b_slim,1997.73,255.915,512,224,3.17 mnasnet_100,1996.94,255.609,512,224,4.38 mnasnet_b1,1993.53,256.025,512,224,4.38 xcit_nano_12_p16_224_dist,1946.62,261.255,512,224,3.05 xcit_nano_12_p16_224,1946.1,261.308,512,224,3.05 semnasnet_075,1927.77,264.67,512,224,2.91 hardcorenas_b,1912.19,266.758,512,224,5.18 mobilenetv2_075,1888.29,270.368,512,224,2.64 hardcorenas_c,1885.12,270.645,512,224,5.52 tf_efficientnetv2_b0,1880.38,271.08,512,224,7.14 tinynet_b,1857.15,274.614,512,188,3.73 regnety_004,1855.83,274.756,512,224,4.34 resnetblur18,1846.1,276.978,512,224,11.69 regnety_006,1808.76,282.043,512,224,6.06 hardcorenas_d,1808.52,281.873,512,224,7.5 mnasnet_a1,1796.84,284.044,512,224,3.89 semnasnet_100,1784.41,286.019,512,224,3.89 spnasnet_100,1782.41,286.288,512,224,4.42 skresnet18,1776.65,287.57,512,224,11.96 mobilenetv2_100,1761.25,289.902,512,224,3.5 mixer_b32_224,1711.23,298.43,512,224,60.29 regnetx_008,1669.43,305.887,512,224,7.26 levit_384,1662.81,306.81,512,224,39.13 vit_base_patch32_224,1658.09,307.969,512,224,88.22 vit_base_patch32_224_sam,1657.82,308.015,512,224,88.22 efficientnet_lite0,1636.56,312.086,512,224,4.65 visformer_tiny,1629.51,313.524,512,224,10.32 gluon_resnet34_v1b,1626.25,314.261,512,224,21.8 resnet34,1624.32,314.625,512,224,21.8 tv_resnet34,1622.42,314.985,512,224,21.8 ghostnet_100,1610.26,316.618,512,224,5.18 hardcorenas_f,1606.37,317.568,512,224,8.2 pit_xs_distilled_224,1596.23,319.885,512,224,11.0 pit_xs_224,1595.39,320.023,512,224,10.62 hardcorenas_e,1595.11,319.841,512,224,8.07 tinynet_a,1592.8,320.187,512,192,6.19 resmlp_12_distilled_224,1562.43,326.888,512,224,15.35 resmlp_12_224,1562.2,326.933,512,224,15.35 regnety_008,1550.03,329.315,512,224,6.26 tf_efficientnet_lite0,1547.08,330.18,512,224,4.65 mixer_s16_224,1538.34,332.27,512,224,18.53 fbnetc_100,1532.35,333.142,512,224,5.57 seresnet34,1494.24,341.776,512,224,21.96 mnasnet_140,1493.96,341.916,512,224,7.12 nf_regnet_b0,1481.15,344.453,512,256,8.76 legacy_seresnet34,1458.06,350.271,512,224,21.96 gmixer_12_224,1449.53,352.403,512,224,12.7 gernet_m,1442.79,354.129,512,224,21.14 ese_vovnet19b_dw,1440.24,354.984,512,224,6.54 vit_small_patch32_384,1423.05,358.931,512,384,22.92 nf_resnet26,1422.6,359.386,512,224,16.0 efficientnet_b0,1413.79,270.572,384,224,5.29 dla46_c,1408.04,362.893,512,224,1.3 rexnetr_100,1385.71,275.935,384,224,4.88 mobilenetv2_110d,1384.57,276.313,384,224,4.52 rexnet_100,1381.06,276.903,384,224,4.8 vit_tiny_r_s16_p8_384,1371.38,279.143,384,384,6.36 ghostnet_130,1367.64,372.982,512,224,7.36 resnet34d,1367.17,373.876,512,224,21.82 tf_efficientnet_b0,1353.9,282.522,384,224,5.29 tf_efficientnet_b0_ap,1353.18,282.683,384,224,5.29 tf_efficientnet_b0_ns,1352.63,282.744,384,224,5.29 selecsls42,1349.71,378.706,512,224,30.35 selecsls42b,1347.34,379.337,512,224,32.46 gmlp_ti16_224,1340.35,284.915,384,224,5.87 regnetz_005,1326.54,384.587,512,224,7.12 crossvit_tiny_240,1322.74,385.447,512,240,7.01 resnet26,1321.4,386.974,512,224,16.0 semnasnet_140,1315.96,388.179,512,224,6.11 xcit_tiny_12_p16_224,1309.91,389.1,512,224,6.72 xcit_tiny_12_p16_224_dist,1303.81,390.894,512,224,6.72 hrnet_w18_small,1302.26,391.765,512,224,13.19 efficientnet_b1_pruned,1277.21,399.368,512,240,6.33 mobilevit_xxs,1270.89,301.046,384,256,1.27 mobilenetv2_140,1250.7,306.236,384,224,6.11 poolformer_s12,1245.33,410.466,512,224,11.92 crossvit_9_240,1236.49,309.127,384,240,8.55 nf_seresnet26,1207.14,423.49,512,224,17.4 tf_efficientnetv2_b1,1198.02,319.027,384,240,8.14 repvgg_b0,1184.29,431.28,512,224,15.82 crossvit_9_dagger_240,1177.9,324.572,384,240,8.78 selecsls60,1152.93,443.193,512,224,30.67 mixnet_s,1150.64,443.717,512,224,4.13 selecsls60b,1149.12,444.678,512,224,32.77 efficientnet_es,1142.77,447.293,512,224,5.44 efficientnet_es_pruned,1141.22,447.892,512,224,5.44 nf_ecaresnet26,1137.4,449.596,512,224,16.0 resnet26d,1125.5,454.383,512,224,16.01 tf_efficientnet_es,1120.47,456.18,512,224,5.44 efficientnet_lite1,1109.69,229.723,256,240,5.42 rexnetr_130,1097.15,232.191,256,224,7.61 tf_mixnet_s,1090.03,468.412,512,224,4.13 convit_tiny,1087.94,469.614,512,224,5.71 convnext_nano_hnf,1075.51,356.223,384,224,15.59 dla34,1072.51,476.787,512,224,15.74 tf_efficientnet_lite1,1061.0,240.288,256,240,5.42 dla46x_c,1058.5,482.972,512,224,1.07 rexnet_130,1051.04,242.43,256,224,7.56 regnetx_016,1042.11,490.415,512,224,9.19 mobilenetv2_120d,1036.53,245.787,256,224,5.83 skresnet34,1033.13,494.425,512,224,22.28 vit_small_patch16_224,1032.86,370.943,384,224,22.05 deit_small_patch16_224,1031.61,371.386,384,224,22.05 deit_small_distilled_patch16_224,1011.7,378.721,384,224,22.44 dla60x_c,1011.12,505.401,512,224,1.32 ecaresnet50d_pruned,1009.57,506.231,512,224,19.94 gernet_l,1007.43,507.303,512,256,31.08 efficientnet_b0_g16_evos,992.51,385.814,384,224,8.11 rexnetr_150,973.93,261.684,256,224,9.78 vit_base2_patch32_256,970.35,526.818,512,256,119.46 pit_s_distilled_224,963.9,264.693,256,224,24.04 pit_s_224,963.25,264.885,256,224,23.46 fbnetv3_b,936.5,408.4,384,256,8.6 rexnet_150,934.54,272.774,256,224,9.73 repvgg_a2,931.84,548.616,512,224,28.21 legacy_seresnext26_32x4d,930.69,411.955,384,224,16.79 regnety_016,921.99,553.558,512,224,11.2 resnest14d,909.12,562.716,512,224,10.61 efficientnet_cc_b0_4e,892.75,428.931,384,224,13.31 efficientnet_cc_b0_8e,889.3,430.599,384,224,24.01 coat_lite_tiny,885.61,432.738,384,224,5.72 nf_regnet_b1,883.44,578.138,512,288,10.22 resnetv2_50,883.05,579.001,512,224,25.55 resnext26ts,882.89,434.405,384,256,10.3 resnet26t,880.13,581.227,512,256,16.01 tf_efficientnetv2_b2,877.18,290.276,256,260,10.1 fbnetv3_d,875.63,290.586,256,256,10.31 nf_regnet_b2,875.24,583.366,512,272,14.31 tf_efficientnet_cc_b0_4e,872.5,438.913,384,224,13.31 tf_efficientnet_cc_b0_8e,867.49,441.45,384,224,24.01 efficientnet_b0_gn,860.35,296.448,256,224,5.29 eca_resnext26ts,852.81,299.643,256,256,10.3 seresnext26ts,848.06,301.17,256,256,10.39 botnet26t_256,835.32,459.139,384,256,12.49 gluon_resnet50_v1b,835.19,458.946,384,224,25.56 twins_svt_small,835.02,458.264,384,224,24.06 swsl_resnet50,834.38,459.391,384,224,25.56 resnet50,833.17,460.051,384,224,25.56 tf_efficientnet_b1_ap,832.71,305.903,256,240,7.79 tf_efficientnet_b1,832.37,306.044,256,240,7.79 tf_efficientnet_b1_ns,831.62,306.366,256,240,7.79 tv_resnet50,831.54,460.947,384,224,25.56 efficientnet_b2_pruned,831.45,306.38,256,260,8.31 seresnext26tn_32x4d,831.09,461.371,384,224,16.81 seresnext26d_32x4d,830.66,461.598,384,224,16.81 gcresnext26ts,830.6,307.372,256,256,10.48 seresnext26t_32x4d,830.1,461.943,384,224,16.81 ssl_resnet50,828.96,462.397,384,224,25.56 coat_lite_mini,825.96,464.063,384,224,11.01 vgg11,818.58,625.301,512,224,132.86 halonet26t,813.08,471.69,384,256,12.48 vit_small_resnet26d_224,812.32,471.613,384,224,63.61 eca_botnext26ts_256,804.88,317.468,256,256,10.59 efficientnet_lite2,800.14,318.978,256,260,6.09 efficientnet_b1,797.8,319.366,256,256,7.79 resnetv2_50t,793.9,644.111,512,224,25.57 ecaresnext26t_32x4d,792.87,483.752,384,224,15.41 ecaresnext50t_32x4d,790.88,484.99,384,224,15.41 resnetv2_50d,790.5,646.873,512,224,25.57 tresnet_m,787.27,647.568,512,224,31.39 convnext_tiny,782.73,326.109,256,224,28.59 eca_halonext26ts,781.32,327.03,256,256,10.76 vovnet39a,779.14,656.53,512,224,22.6 ecaresnet101d_pruned,778.75,655.702,512,224,24.88 mixnet_m,778.63,491.619,384,224,5.01 cspresnet50,774.32,495.068,384,256,21.62 tf_efficientnet_lite2,771.04,331.043,256,260,6.09 gluon_resnet50_v1c,769.5,498.184,384,224,25.58 ecaresnetlight,767.59,666.098,512,224,30.16 resmlp_24_224,766.31,332.575,256,224,30.02 resmlp_24_distilled_224,766.21,332.58,256,224,30.02 cspresnext50,765.28,500.93,384,224,20.57 resnet50t,753.13,509.003,384,224,25.57 resnet50d,752.95,509.12,384,224,25.58 gluon_resnet50_v1d,752.94,509.122,384,224,25.58 legacy_seresnet50,751.28,510.001,384,224,28.09 mobilevit_xs,751.23,339.638,256,256,2.32 tf_mixnet_m,748.19,511.684,384,224,5.01 ese_vovnet39b,748.06,683.786,512,224,24.57 resnet32ts,744.11,343.453,256,256,17.96 visformer_small,743.29,515.928,384,224,40.22 dpn68b,737.5,519.461,384,224,12.61 selecsls84,735.99,694.378,512,224,50.95 resnet33ts,732.63,348.809,256,256,19.68 nf_seresnet50,732.06,523.375,384,224,28.09 rexnetr_200,724.64,263.786,192,224,16.52 lambda_resnet26t,724.61,529.342,384,256,10.96 seresnet50,722.32,530.5,384,224,28.09 dpn68,721.48,531.095,384,224,12.61 res2net50_48w_2s,717.64,534.245,384,224,25.29 gmixer_24_224,717.31,355.331,256,224,24.72 eca_resnet33ts,712.11,358.824,256,256,19.68 seresnet33ts,709.27,360.12,256,256,19.78 bat_resnext26ts,708.45,360.137,256,256,10.73 rexnet_200,703.57,271.754,192,224,16.37 resnetblur50,702.19,546.026,384,224,25.56 cspresnet50d,701.76,546.305,384,256,21.64 vgg11_bn,698.67,549.368,384,224,132.87 twins_pcpvt_small,698.21,364.986,256,224,24.11 eca_vovnet39b,697.82,733.074,512,224,22.6 efficientnet_em,694.64,551.807,384,240,6.9 dla60,693.8,552.499,384,224,22.04 tf_efficientnet_em,692.96,368.419,256,240,6.9 cspresnet50w,692.76,553.433,384,256,28.12 resnest26d,688.5,556.976,384,224,17.07 gcresnet33ts,687.02,371.584,256,256,19.88 tv_densenet121,686.65,371.007,256,224,7.98 densenet121,686.51,371.091,256,224,7.98 vit_small_r26_s32_224,685.79,371.994,256,224,36.43 convnext_tiny_hnf,683.74,373.449,256,224,28.59 xcit_nano_12_p16_384_dist,682.7,373.202,256,384,3.05 xcit_tiny_24_p16_224_dist,681.26,372.348,256,224,12.12 lambda_resnet26rpt_256,679.63,281.896,192,256,10.99 xcit_tiny_24_p16_224,679.38,373.443,256,224,12.12 vit_base_resnet26d_224,679.21,563.967,384,224,101.4 resnetaa50d,678.8,564.84,384,224,25.58 nf_ecaresnet50,673.73,568.984,384,224,25.56 hrnet_w18_small_v2,672.52,758.874,512,224,15.6 gluon_resnet50_v1s,671.98,570.574,384,224,25.68 efficientnet_b0_g8_gn,661.67,385.796,256,224,6.56 seresnet50t,659.6,581.03,384,224,28.1 efficientnet_b3_pruned,657.34,387.781,256,300,9.86 haloregnetz_b,656.48,388.419,256,224,11.68 tv_resnext50_32x4d,651.27,588.791,384,224,25.03 swsl_resnext50_32x4d,651.11,588.925,384,224,25.03 gluon_resnext50_32x4d,650.97,589.059,384,224,25.03 ssl_resnext50_32x4d,650.9,589.136,384,224,25.03 resnext50_32x4d,649.99,589.957,384,224,25.03 resnetrs50,648.71,590.781,384,224,35.69 densenet121d,646.33,394.275,256,224,8.0 regnetx_032,643.77,595.296,384,224,15.3 resnetblur50d,642.77,397.429,256,224,25.58 res2net50_26w_4s,636.71,601.86,384,224,25.7 swin_tiny_patch4_window7_224,635.13,402.044,256,224,28.29 skresnet50,634.96,603.369,384,224,25.8 poolformer_s24,634.53,402.132,256,224,21.39 gmlp_s16_224,632.58,301.939,192,224,19.42 ese_vovnet57b,632.35,606.334,384,224,38.61 crossvit_small_240,625.73,407.457,256,240,26.86 xcit_small_12_p16_224_dist,625.08,407.723,256,224,26.25 xcit_small_12_p16_224,624.67,407.992,256,224,26.25 densenetblur121d,617.78,412.569,256,224,8.0 ecaresnet50d,612.94,625.548,384,224,25.58 tf_efficientnet_b2_ns,610.27,313.114,192,260,9.11 tf_efficientnet_b2_ap,610.1,313.168,192,260,9.11 tf_efficientnet_b2,609.89,313.274,192,260,9.11 mixnet_l,603.6,634.707,384,224,7.33 gluon_inception_v3,601.71,636.771,384,299,23.83 inception_v3,601.07,637.475,384,299,23.83 adv_inception_v3,601.02,637.509,384,299,23.83 tf_inception_v3,600.65,637.912,384,299,23.83 sehalonet33ts,599.96,425.883,256,256,13.69 seresnetaa50d,599.53,425.823,256,224,28.11 resnext50d_32x4d,598.58,426.825,256,224,25.05 mobilevit_s,596.24,320.895,192,256,5.58 resnetv2_50x1_bit_distilled,588.8,325.252,192,224,25.55 gcresnet50t,583.98,436.858,256,256,25.9 skresnet50d,583.54,437.238,256,224,25.82 swin_s3_tiny_224,576.57,442.971,256,224,28.33 seresnext50_32x4d,576.37,443.036,256,224,27.56 gluon_seresnext50_32x4d,576.3,443.051,256,224,27.56 tf_mixnet_l,576.22,442.743,256,224,7.33 legacy_seresnext50_32x4d,575.52,443.681,256,224,27.56 res2next50,575.07,443.835,256,224,24.67 repvgg_b1g4,572.25,893.649,512,224,39.97 cspresnext50_iabn,572.06,669.169,384,256,20.57 convnext_tiny_hnfd,570.41,447.777,256,224,28.63 res2net50_14w_8s,569.05,447.639,256,224,25.06 resnest50d_1s4x24d,568.93,448.655,256,224,25.68 cait_xxs24_224,568.71,447.7,256,224,11.96 crossvit_15_240,567.12,336.74,192,240,27.53 semobilevit_s,566.94,337.375,192,256,5.74 densenet169,563.82,451.519,256,224,14.15 efficientnet_b2,561.37,340.497,192,288,9.11 efficientnet_cc_b1_8e,559.34,455.785,256,240,39.72 efficientnet_b2a,558.58,342.255,192,288,9.11 darknet53,556.85,458.909,256,256,41.61 dla60_res2net,555.54,459.405,256,224,20.85 dla60x,553.4,461.598,256,224,17.35 mixer_b16_224,552.46,462.598,256,224,59.88 nf_resnet101,550.61,695.793,384,224,44.55 tf_efficientnet_cc_b1_8e,549.81,463.949,256,240,39.72 regnetx_040,549.5,697.703,384,224,22.12 mixer_b16_224_miil,549.25,465.267,256,224,59.88 crossvit_15_dagger_240,547.66,348.734,192,240,28.21 nf_regnet_b3,547.65,465.691,256,320,18.59 vovnet57a,547.59,934.12,512,224,36.64 resnetv2_101,544.49,468.639,256,224,44.54 xcit_nano_12_p8_224,542.16,470.425,256,224,3.05 xcit_nano_12_p8_224_dist,541.55,470.928,256,224,3.05 tf_efficientnetv2_b3,541.54,352.751,192,300,14.36 sebotnet33ts_256,540.04,236.225,128,256,13.7 gcresnext50ts,538.97,354.583,192,256,15.67 nf_resnet50,536.27,715.168,384,288,25.56 resnet50_gn,532.97,359.41,192,224,25.56 vit_base_r26_s32_224,532.84,359.064,192,224,101.38 vit_base_patch32_384,531.15,481.156,256,384,88.3 efficientnetv2_rw_t,527.04,362.193,192,288,13.65 resnet101,526.31,484.87,256,224,44.55 gluon_resnet101_v1b,525.74,485.353,256,224,44.55 tv_resnet101,523.69,487.267,256,224,44.55 vit_large_patch32_224,515.11,495.389,256,224,306.54 vit_base_resnet50d_224,514.44,495.989,256,224,110.97 mixer_l32_224,507.93,376.544,192,224,206.94 resnetv2_101d,506.46,503.899,256,224,44.56 swin_v2_cr_tiny_224,505.66,378.391,192,224,28.33 vit_tiny_patch16_384,505.36,252.456,128,384,5.79 resmlp_36_224,505.0,378.005,192,224,44.69 resmlp_36_distilled_224,504.83,378.088,192,224,44.69 swin_v2_cr_tiny_ns_224,503.02,380.384,192,224,28.33 repvgg_b1,501.28,1020.314,512,224,57.42 dla60_res2next,500.08,510.484,256,224,17.03 gluon_resnet101_v1c,498.53,511.943,256,224,44.57 wide_resnet50_2,491.97,779.7,384,224,68.88 gluon_resnet101_v1d,491.71,519.004,256,224,44.57 resnest50d,484.85,526.671,256,224,27.48 vgg13,482.75,795.25,384,224,133.05 cspdarknet53,481.82,530.306,256,256,27.64 gc_efficientnetv2_rw_t,480.93,396.503,192,288,13.68 convnext_small,478.98,399.137,192,224,50.22 efficientnet_lite3,478.82,266.239,128,300,8.2 ecaresnet26t,478.49,534.465,256,320,16.01 nest_tiny,477.21,267.341,128,224,17.06 regnetz_b16,476.53,401.488,192,288,9.72 dla102,475.31,537.042,256,224,33.27 cspdarknet53_iabn,474.61,806.628,384,256,27.64 res2net50_26w_6s,474.4,537.903,256,224,37.05 jx_nest_tiny,469.53,271.701,128,224,17.06 twins_pcpvt_base,465.48,409.697,192,224,43.83 vgg13_bn,465.35,549.842,256,224,133.05 halonet50ts,463.11,413.595,192,256,22.73 regnetx_080,462.29,829.538,384,224,39.57 lambda_resnet50ts,461.63,414.909,192,256,21.54 coat_lite_small,461.47,414.609,192,224,19.84 legacy_seresnet101,460.47,553.804,256,224,49.33 vgg16,459.25,557.204,256,224,138.36 tf_efficientnet_lite3,458.84,277.849,128,300,8.2 resnetaa101d,458.35,556.916,256,224,44.57 gluon_resnet101_v1s,456.37,559.352,256,224,44.67 xcit_tiny_12_p16_384_dist,451.6,423.4,192,384,6.72 seresnet101,447.82,569.466,256,224,49.33 densenet201,447.49,426.067,192,224,20.01 mixnet_xl,447.09,570.692,256,224,11.9 nf_seresnet101,444.89,573.162,256,224,49.33 convit_small,441.84,433.517,192,224,27.78 resnetblur101d,441.53,578.26,256,224,44.57 nfnet_l0,426.47,599.103,256,288,35.07 skresnext50_32x4d,424.37,601.881,256,224,27.48 poolformer_s36,424.29,450.68,192,224,30.86 botnet50ts_256,421.6,302.649,128,256,22.74 halo2botnet50ts_256,418.8,457.379,192,256,22.64 gluon_resnext101_32x4d,418.26,610.512,256,224,44.18 resnext101_32x4d,417.6,611.486,256,224,44.18 ssl_resnext101_32x4d,417.52,611.578,256,224,44.18 swsl_resnext101_32x4d,417.45,611.654,256,224,44.18 fbnetv3_g,415.62,305.885,128,288,16.62 twins_svt_base,413.84,461.835,192,224,56.07 ese_vovnet39b_evos,413.01,308.979,128,224,24.58 res2net101_26w_4s,405.25,629.29,256,224,45.21 eca_nfnet_l0,404.73,631.533,256,288,24.14 tresnet_l,403.54,1265.42,512,224,55.99 lamhalobotnet50ts_256,403.43,474.897,192,256,22.57 volo_d1_224,400.45,478.074,192,224,26.63 crossvit_18_240,400.32,317.735,128,240,43.27 resnet51q,397.98,481.556,192,288,35.7 nf_ecaresnet101,396.22,644.266,256,224,44.55 dla102x,392.86,487.139,192,224,26.31 vit_base_patch16_224_miil,392.0,489.026,192,224,86.54 swin_small_patch4_window7_224,391.85,488.136,192,224,49.61 regnety_032,391.81,651.958,256,288,19.44 regnetx_064,390.82,654.137,256,224,26.21 res2net50_26w_8s,389.26,655.478,256,224,48.4 vgg16_bn,388.47,658.66,256,224,138.37 deit_base_patch16_224,386.56,495.833,192,224,86.57 crossvit_18_dagger_240,386.08,329.466,128,240,44.27 vit_base_patch16_224,385.82,496.783,192,224,86.57 resnest50d_4s2x40d,385.78,662.242,256,224,30.42 xception,384.95,331.72,128,299,22.86 vit_base_patch16_224_sam,384.94,497.961,192,224,86.57 ecaresnet101d,381.61,669.061,256,224,44.57 deit_base_distilled_patch16_224,379.86,504.583,192,224,87.34 resnetv2_152,379.0,673.236,256,224,60.19 repvgg_b2g4,377.95,1353.629,512,224,61.76 ese_vovnet99b,373.94,683.067,256,224,63.2 vit_small_resnet50d_s16_224,373.61,512.677,192,224,57.53 cait_xxs36_224,371.71,512.777,192,224,17.3 resnet152,371.52,514.561,192,224,60.19 tv_resnet152,371.15,515.007,192,224,60.19 gluon_resnet152_v1b,368.93,518.118,192,224,60.19 gluon_seresnext101_32x4d,368.19,519.228,192,224,48.96 seresnext101_32x4d,367.5,520.242,192,224,48.96 nfnet_f0,366.97,696.402,256,256,71.49 legacy_seresnext101_32x4d,366.06,522.375,192,224,48.96 tf_efficientnet_b3_ap,364.58,349.402,128,300,12.23 tf_efficientnet_b3_ns,364.49,349.53,128,300,12.23 tf_efficientnet_b3,364.34,349.633,128,300,12.23 resnetv2_152d,361.2,529.264,192,224,60.2 resnet61q,358.48,356.024,128,288,36.85 resnetv2_50d_frn,357.45,356.923,128,224,25.59 ese_vovnet99b_iabn,357.31,1071.637,384,224,63.2 gluon_resnet152_v1c,355.88,537.206,192,224,60.21 hrnet_w18,355.73,714.824,256,224,21.3 efficientnet_b3,355.08,358.849,128,320,12.23 efficientnet_b3a,354.83,359.115,128,320,12.23 regnety_040,354.78,539.617,192,288,20.65 beit_base_patch16_224,353.55,541.973,192,224,86.53 gluon_resnet152_v1d,353.52,540.757,192,224,60.21 regnety_040s_gn,353.11,360.919,128,224,20.65 vgg19,352.0,1090.659,384,224,143.67 xcit_tiny_12_p8_224,351.32,362.59,128,224,6.71 xcit_tiny_12_p8_224_dist,351.06,362.838,128,224,6.71 xception41p,343.38,371.913,128,299,26.91 regnetv_040,342.33,372.402,128,288,20.64 tnt_s_patch16_224,339.73,563.196,192,224,23.76 repvgg_b2,339.2,1508.352,512,224,89.02 vgg19_bn,336.08,761.352,256,224,143.68 gluon_resnet152_v1s,334.98,570.781,192,224,60.32 densenet161,332.41,382.595,128,224,28.68 dm_nfnet_f0,331.82,770.266,256,256,71.49 dla169,331.14,577.405,192,224,53.39 resnetv2_50d_gn,330.91,385.985,128,288,25.57 convnext_base_in22ft1k,328.04,388.471,128,224,88.59 convnext_tiny_in22ft1k,327.7,388.845,128,224,88.59 convnext_small_in22ft1k,326.36,390.508,128,224,88.59 convnext_base,325.78,391.109,128,224,88.59 xcit_small_24_p16_224_dist,322.26,393.856,128,224,47.67 xcit_small_24_p16_224,321.22,395.027,128,224,47.67 repvgg_b3g4,321.08,1194.912,384,224,83.83 pit_b_224,319.89,399.203,128,224,73.76 twins_pcpvt_large,319.03,397.109,128,224,60.99 pit_b_distilled_224,318.55,400.853,128,224,74.79 legacy_seresnet152,315.59,605.069,192,224,66.82 dpn92,314.54,812.398,256,224,37.67 inception_v4,312.24,612.734,192,299,42.68 regnetx_120,309.13,827.171,256,224,46.11 hrnet_w32,308.88,823.922,256,224,41.23 convmixer_1024_20_ks9_p14,308.12,830.025,256,224,24.38 ecaresnet50t,306.63,416.493,128,320,25.57 seresnet152,305.84,415.318,128,224,66.82 coat_tiny,303.51,419.779,128,224,5.5 vit_small_patch16_36x1_224,303.33,419.341,128,224,64.67 regnetz_c16,302.72,421.37,128,320,13.46 vit_small_patch16_18x2_224,302.0,421.199,128,224,64.67 hrnet_w30,301.17,845.104,256,224,37.71 swin_v2_cr_small_224,300.16,424.024,128,224,49.7 nest_small,296.43,322.186,96,224,38.35 tresnet_xl,295.35,1296.648,384,224,78.44 jx_nest_small,294.56,324.278,96,224,38.35 efficientnet_el,291.47,438.031,128,300,10.59 xception41,291.46,437.936,128,299,26.97 efficientnet_el_pruned,291.16,438.564,128,300,10.59 regnety_120,291.14,658.173,192,224,51.82 cait_s24_224,290.55,438.065,128,224,46.92 nf_regnet_b4,288.27,441.948,128,384,30.21 twins_svt_large,288.14,442.123,128,224,99.27 wide_resnet101_2,287.9,665.343,192,224,126.89 mixnet_xxl,287.84,442.746,128,224,23.96 tf_efficientnet_el,286.59,445.507,128,300,10.59 poolformer_m36,284.22,448.517,128,224,56.17 swin_s3_small_224,277.99,343.436,96,224,49.74 repvgg_b3,276.27,1388.871,384,224,123.09 swin_base_patch4_window7_224,273.38,466.273,128,224,87.77 gmlp_b16_224,266.77,358.289,96,224,73.08 gluon_resnext101_64x4d,266.55,478.624,128,224,83.46 dla102x2,265.99,479.601,128,224,41.28 resnet200,264.41,481.093,128,224,64.67 resnetv2_50d_evob,261.14,366.354,96,224,25.59 regnetx_160,260.8,735.124,192,224,54.28 xception65p,258.86,493.161,128,299,39.82 inception_resnet_v2,255.7,747.606,192,299,55.84 ens_adv_inception_resnet_v2,255.53,748.108,192,299,55.84 resnetrs101,253.22,503.169,128,288,63.62 resnext101_32x8d,252.1,506.144,128,224,88.79 ig_resnext101_32x8d,251.82,506.789,128,224,88.79 ssl_resnext101_32x8d,251.44,507.525,128,224,88.79 swsl_resnext101_32x8d,251.43,507.504,128,224,88.79 crossvit_base_240,250.95,380.881,96,240,105.03 dpn98,249.4,511.692,128,224,61.57 efficientnet_lite4,247.4,257.372,64,380,13.01 coat_mini,246.33,517.632,128,224,10.34 efficientnetv2_s,245.94,388.167,96,384,21.46 gluon_seresnext101_64x4d,243.95,522.441,128,224,88.23 tf_efficientnetv2_s_in21ft1k,243.54,391.981,96,384,21.46 tf_efficientnetv2_s,242.89,393.02,96,384,21.46 resnet101d,240.52,530.628,128,320,44.57 resnest101e,240.15,530.415,128,256,48.28 tf_efficientnet_lite4,239.36,265.97,64,380,13.01 vit_small_patch16_384,235.44,270.99,64,384,22.2 xcit_tiny_24_p16_384_dist,233.56,407.54,96,384,12.12 efficientnetv2_rw_s,232.48,273.03,64,384,23.94 regnety_064,232.21,549.529,128,288,30.58 xcit_medium_24_p16_224_dist,231.18,550.33,128,224,84.4 xcit_medium_24_p16_224,231.11,550.432,128,224,84.4 vit_large_r50_s32_224,229.5,415.85,96,224,328.99 regnetv_064,225.87,564.974,128,288,30.58 vit_small_r26_s32_384,225.43,282.622,64,384,36.47 convit_base,225.0,567.909,128,224,86.54 gluon_xception65,223.32,427.945,96,299,39.92 xception65,222.42,429.727,96,299,39.92 tnt_b_patch16_224,222.32,573.844,128,224,65.41 volo_d2_224,221.58,431.459,96,224,58.68 regnety_080,221.2,577.488,128,288,39.18 hrnet_w40,220.15,867.238,192,224,57.56 swin_s3_base_224,219.92,433.728,96,224,71.13 xcit_small_12_p16_384_dist,216.0,442.656,96,384,26.25 swin_v2_cr_base_224,214.24,445.643,96,224,87.88 hrnet_w48,213.16,895.867,192,224,77.47 resnetv2_50d_evos,211.1,226.199,48,288,25.59 nest_base,210.29,302.692,64,224,67.72 jx_nest_base,209.17,304.343,64,224,67.72 vit_base_r50_s16_224,208.73,458.333,96,224,98.66 tresnet_m_448,206.95,924.987,192,448,31.39 hrnet_w44,206.72,924.031,192,224,67.06 efficientnet_b4,203.38,312.633,64,384,19.34 regnetz_b16_evos,201.27,316.106,64,288,9.74 regnetz_040h,199.85,318.401,64,320,28.94 regnetz_040,199.66,318.691,64,320,27.12 efficientnet_b3_gn,199.47,319.133,64,320,11.73 densenet264,199.22,477.999,96,224,72.69 regnetz_d8,197.46,322.524,64,320,23.37 eca_nfnet_l1,189.9,672.191,128,320,41.41 tf_efficientnet_b4,188.81,336.904,64,380,19.34 tf_efficientnet_b4_ns,188.45,337.501,64,380,19.34 tf_efficientnet_b4_ap,188.4,337.642,64,380,19.34 poolformer_m48,187.63,509.206,96,224,73.47 dpn131,186.23,685.159,128,224,79.25 regnetz_d32,186.08,342.366,64,320,27.58 xcit_nano_12_p8_384_dist,183.21,347.477,64,384,3.05 convnext_large_in22ft1k,182.13,525.349,96,224,197.77 convnext_large,180.72,529.451,96,224,197.77 xcit_tiny_24_p8_224_dist,179.19,532.437,96,224,12.11 xcit_tiny_24_p8_224,179.14,532.524,96,224,12.11 dpn107,174.56,731.55,128,224,86.92 resnet152d,172.45,554.35,96,320,60.21 hrnet_w64,170.75,744.856,128,224,128.06 halonet_h1,170.55,373.795,64,256,8.1 xception71,168.15,378.514,64,299,42.34 mixer_l16_224,167.46,571.737,96,224,208.2 regnety_320,166.3,768.299,128,224,145.05 vit_large_patch32_384,165.23,579.454,96,384,306.63 densenet264d_iabn,165.07,1158.795,192,224,72.74 xcit_small_12_p8_224,164.33,387.646,64,224,26.21 xcit_small_12_p8_224_dist,164.16,388.107,64,224,26.21 efficientnet_b3_g8_gn,153.99,413.949,64,320,14.25 swin_large_patch4_window7_224,152.41,417.988,64,224,196.53 volo_d3_224,152.25,417.779,64,224,86.33 ecaresnet200d,150.97,632.522,96,256,64.69 seresnet200d,149.9,422.786,64,256,71.86 regnetx_320,146.61,871.937,128,224,107.81 seresnet152d,143.56,442.443,64,320,66.84 resnetv2_50x1_bitm,142.06,337.009,48,448,25.55 gluon_senet154,141.6,674.51,96,224,115.09 resnetrs152,141.55,448.821,64,320,86.62 senet154,141.16,676.729,96,224,115.09 legacy_senet154,139.88,683.0,96,224,115.09 regnety_160,136.04,704.372,96,288,83.59 xcit_large_24_p16_224_dist,130.76,486.175,64,224,189.1 xcit_large_24_p16_224,130.33,487.671,64,224,189.1 seresnext101_32x8d,126.87,502.294,64,288,93.57 nfnet_f1,125.34,763.802,96,320,132.63 resnet200d,124.61,510.654,64,320,64.69 regnetz_c16_evos,123.93,385.448,48,320,13.49 resnext101_64x4d,122.56,781.647,96,288,83.46 efficientnetv2_m,120.01,396.838,48,416,54.14 swin_v2_cr_large_224,119.95,397.782,48,224,196.68 xcit_tiny_12_p8_384_dist,119.32,400.563,48,384,6.71 vit_large_patch16_224,116.44,548.026,64,224,304.33 crossvit_15_dagger_408,116.23,273.461,32,408,28.5 seresnet269d,116.02,545.703,64,256,113.67 vit_base_patch16_18x2_224,115.21,552.796,64,224,256.73 convnext_xlarge_in22ft1k,113.61,561.618,64,224,350.2 convnext_base_384_in22ft1k,112.88,423.483,48,384,88.59 dm_nfnet_f1,112.71,565.567,64,320,132.63 convnext_tiny_384_in22ft1k,112.65,424.398,48,384,88.59 convnext_small_384_in22ft1k,112.59,424.604,48,384,88.59 nf_regnet_b5,112.22,567.655,64,456,49.74 swin_v2_cr_tiny_384,111.16,286.572,32,384,28.33 xcit_small_24_p16_384_dist,110.42,431.325,48,384,47.67 beit_large_patch16_224,107.44,593.678,64,224,304.43 regnetz_e8,103.46,461.955,48,320,57.7 efficientnetv2_rw_m,103.11,307.008,32,416,53.24 tresnet_l_448,101.52,1257.535,128,448,55.99 swsl_resnext101_32x16d,99.54,962.862,96,224,194.03 ig_resnext101_32x16d,99.3,965.129,96,224,194.03 ssl_resnext101_32x16d,99.25,965.706,96,224,194.03 volo_d1_384,98.94,322.06,32,384,26.78 resnetrs200,98.6,482.325,48,320,93.21 cait_xxs24_384,97.24,491.195,48,384,12.03 deit_base_patch16_384,95.92,332.748,32,384,86.86 vit_base_patch16_384,95.88,332.951,32,384,86.86 volo_d4_224,95.4,500.622,48,224,192.96 eca_nfnet_l2,94.36,675.671,64,384,56.72 deit_base_distilled_patch16_384,93.97,339.685,32,384,87.63 efficientnet_b5,90.42,351.458,32,456,30.39 tf_efficientnetv2_m,89.36,354.724,32,480,54.14 tf_efficientnetv2_m_in21ft1k,89.12,355.723,32,480,54.14 tf_efficientnet_b5,88.7,358.215,32,456,30.39 tf_efficientnet_b5_ap,88.68,358.368,32,456,30.39 tf_efficientnet_b5_ns,88.58,358.774,32,456,30.39 crossvit_18_dagger_408,87.86,362.177,32,408,44.61 resnetv2_101x1_bitm,87.29,364.971,32,448,44.54 convmixer_768_32,87.13,1100.537,96,224,21.11 resnetv2_152x2_bit_teacher,87.1,364.899,32,224,236.34 vit_large_patch14_224,84.6,565.739,48,224,304.2 regnetz_d8_evos,83.97,379.015,32,320,23.46 beit_base_patch16_384,82.88,385.038,32,384,86.74 xcit_small_24_p8_224_dist,82.62,383.85,32,224,47.63 xcit_small_24_p8_224,82.5,384.488,32,224,47.63 resnest200e,79.64,597.77,48,320,70.2 tresnet_xl_448,77.44,1236.244,96,448,78.44 xcit_medium_24_p16_384_dist,76.61,414.275,32,384,84.4 vit_large_r50_s32_384,76.27,417.132,32,384,329.09 swin_base_patch4_window12_384,73.39,434.11,32,384,87.9 nfnet_f2,71.5,668.121,48,352,193.78 resmlp_big_24_224_in22ft1k,68.15,468.085,32,224,129.14 resmlp_big_24_224,68.13,468.141,32,224,129.14 resmlp_big_24_distilled_224,68.08,468.542,32,224,129.14 pnasnet5large,66.87,474.686,32,331,86.06 swin_v2_cr_small_384,66.28,359.629,24,384,49.7 nasnetalarge,65.59,482.923,32,331,88.75 cait_xs24_384,65.34,487.167,32,384,26.67 dm_nfnet_f2,64.55,740.206,48,352,193.78 cait_xxs36_384,62.84,505.392,32,384,17.37 vit_base_patch8_224,62.83,381.142,24,224,86.58 convnext_large_384_in22ft1k,61.61,517.714,32,384,197.77 ecaresnet269d,61.52,515.695,32,352,102.09 volo_d5_224,61.49,517.153,32,224,295.46 xcit_tiny_24_p8_384_dist,60.46,525.849,32,384,12.11 xcit_medium_24_p8_224,59.24,536.775,32,224,84.32 xcit_medium_24_p8_224_dist,59.23,536.852,32,224,84.32 vit_base_resnet50_384,58.93,405.608,24,384,98.95 vit_base_r50_s16_384,58.91,405.68,24,384,98.95 resnetrs270,58.9,537.38,32,352,129.86 xcit_small_12_p8_384_dist,56.03,426.632,24,384,26.21 volo_d2_384,55.33,287.363,16,384,58.87 ig_resnext101_32x32d,52.68,605.908,32,224,468.53 eca_nfnet_l3,50.62,628.636,32,448,72.04 convmixer_1536_20,49.63,966.338,48,224,51.63 cait_s24_384,49.12,486.034,24,384,47.06 efficientnet_b6,48.5,327.112,16,528,43.04 tf_efficientnet_b6_ns,48.02,330.391,16,528,43.04 tf_efficientnet_b6_ap,47.83,331.608,16,528,43.04 tf_efficientnet_b6,47.78,331.966,16,528,43.04 efficientnetv2_l,47.17,334.927,16,480,118.52 swin_v2_cr_base_384,47.07,337.445,16,384,87.88 tf_efficientnetv2_l_in21ft1k,46.85,337.127,16,480,118.52 tf_efficientnetv2_l,46.53,339.329,16,480,118.52 swin_v2_cr_huge_224,46.2,343.813,16,224,657.83 xcit_large_24_p16_384_dist,44.95,530.527,24,384,189.1 swin_large_patch4_window12_384,41.24,386.07,16,384,196.74 vit_huge_patch14_224,39.65,401.44,16,224,632.05 resnetrs350,37.15,638.194,24,384,163.96 convnext_xlarge_384_in22ft1k,36.94,431.409,16,384,350.2 nfnet_f3,35.43,673.367,24,416,254.92 resnest269e,33.7,705.124,24,416,110.93 xcit_large_24_p8_224_dist,33.34,476.606,16,224,188.93 xcit_large_24_p8_224,33.33,476.71,16,224,188.93 dm_nfnet_f3,32.28,738.882,24,416,254.92 resnetv2_50x3_bitm,31.96,499.813,16,448,217.32 cait_s36_384,31.71,500.682,16,384,68.37 resnetv2_152x2_bit_teacher_384,31.41,506.876,16,384,236.34 tf_efficientnetv2_xl_in21ft1k,31.07,380.33,12,512,208.12 efficientnetv2_xl,30.57,386.62,12,512,208.12 efficientnet_b7,30.54,258.47,8,600,66.35 tf_efficientnet_b7,30.24,261.049,8,600,66.35 tf_efficientnet_b7_ap,30.18,261.545,8,600,66.35 tf_efficientnet_b7_ns,30.17,261.527,8,600,66.35 vit_large_patch16_384,29.1,410.723,12,384,304.72 xcit_small_24_p8_384_dist,28.47,558.499,16,384,47.63 swin_v2_cr_large_384,28.33,421.122,12,384,196.68 ig_resnext101_32x48d,27.82,573.581,16,224,828.41 resnetrs420,25.59,615.408,16,416,191.89 beit_large_patch16_384,24.96,478.806,12,384,305.0 volo_d3_448,23.79,333.801,8,448,86.63 vit_giant_patch14_224,22.41,354.342,8,224,1012.61 resnetv2_152x2_bitm,21.79,364.675,8,448,236.34 xcit_medium_24_p8_384_dist,19.42,408.574,8,384,84.32 nfnet_f4,18.89,630.122,12,512,316.07 resnetv2_101x3_bitm,17.61,452.554,8,448,387.93 dm_nfnet_f4,17.17,693.324,12,512,316.07 volo_d4_448,16.84,353.766,6,448,193.41 efficientnet_b8,14.4,412.697,6,672,87.41 tf_efficientnet_b8_ap,14.32,415.19,6,672,87.41 tf_efficientnet_b8,14.24,417.31,6,672,87.41 nfnet_f5,12.31,643.8,8,544,377.21 cait_m36_384,11.75,507.058,6,384,271.22 xcit_large_24_p8_384_dist,11.36,524.55,6,384,188.93 dm_nfnet_f5,11.21,706.654,8,544,377.21 volo_d5_448,11.01,359.686,4,448,295.91 swin_v2_cr_huge_384,10.89,364.734,4,384,657.94 tf_efficientnet_l2_ns_475,10.43,377.703,4,475,480.31 nfnet_f6,10.18,778.679,8,576,438.36 beit_large_patch16_512,9.37,425.057,4,512,305.67 dm_nfnet_f6,8.56,692.978,6,576,438.36 volo_d5_512,7.76,383.588,3,512,296.09 nfnet_f7,7.54,787.101,6,608,499.5 cait_m48_448,4.71,419.695,2,448,356.46 resnetv2_152x4_bitm,4.53,438.64,2,480,936.53 tf_efficientnet_l2_ns,2.96,331.907,1,800,480.31 efficientnet_l2,2.92,336.419,1,800,480.31
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/results-imagenet-r-clean.csv	model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.150,1.850,99.880,0.120,305.08,448,1.000,bicubic eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.030,1.970,99.890,0.110,305.08,448,1.000,bicubic eva_giant_patch14_560.m30m_ft_in22k_in1k,98.000,2.000,99.860,0.140,"1,014.45",560,1.000,bicubic eva_giant_patch14_336.m30m_ft_in22k_in1k,97.990,2.010,99.900,0.100,"1,013.01",336,1.000,bicubic convnextv2_huge.fcmae_ft_in22k_in1k_384,97.870,2.130,99.910,0.090,660.29,384,1.000,bicubic eva_large_patch14_336.in22k_ft_in22k_in1k,97.860,2.140,99.880,0.120,304.53,336,1.000,bicubic eva02_large_patch14_448.mim_in22k_ft_in1k,97.860,2.140,99.800,0.200,305.08,448,1.000,bicubic eva_giant_patch14_336.clip_ft_in1k,97.860,2.140,99.790,0.210,"1,013.01",336,1.000,bicubic eva02_large_patch14_448.mim_m38m_ft_in1k,97.830,2.170,99.820,0.180,305.08,448,1.000,bicubic convnextv2_huge.fcmae_ft_in22k_in1k_512,97.810,2.190,99.860,0.140,660.29,512,1.000,bicubic eva_large_patch14_336.in22k_ft_in1k,97.810,2.190,99.840,0.160,304.53,336,1.000,bicubic beit_large_patch16_384.in22k_ft_in22k_in1k,97.810,2.190,99.790,0.210,305.00,384,1.000,bicubic tf_efficientnet_l2.ns_jft_in1k,97.780,2.220,99.890,0.110,480.31,800,0.960,bicubic regnety_1280.swag_ft_in1k,97.780,2.220,99.860,0.140,644.81,384,1.000,bicubic beit_large_patch16_512.in22k_ft_in22k_in1k,97.780,2.220,99.820,0.180,305.67,512,1.000,bicubic maxvit_base_tf_512.in21k_ft_in1k,97.760,2.240,99.860,0.140,119.88,512,1.000,bicubic maxvit_xlarge_tf_512.in21k_ft_in1k,97.760,2.240,99.820,0.180,475.77,512,1.000,bicubic tf_efficientnet_l2.ns_jft_in1k_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic convnext_xxlarge.clip_laion2b_soup_ft_in1k,97.750,2.250,99.810,0.190,846.47,256,1.000,bicubic beitv2_large_patch16_224.in1k_ft_in22k_in1k,97.750,2.250,99.790,0.210,304.43,224,0.950,bicubic maxvit_xlarge_tf_384.in21k_ft_in1k,97.740,2.260,99.850,0.150,475.32,384,1.000,bicubic eva02_base_patch14_448.mim_in22k_ft_in1k,97.720,2.280,99.760,0.240,87.12,448,1.000,bicubic maxvit_large_tf_512.in21k_ft_in1k,97.670,2.330,99.730,0.270,212.33,512,1.000,bicubic caformer_b36.sail_in22k_ft_in1k_384,97.660,2.340,99.860,0.140,98.75,384,1.000,bicubic maxvit_large_tf_384.in21k_ft_in1k,97.660,2.340,99.820,0.180,212.03,384,1.000,bicubic convnextv2_large.fcmae_ft_in22k_in1k_384,97.630,2.370,99.800,0.200,197.96,384,1.000,bicubic eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,97.610,2.390,99.820,0.180,87.12,448,1.000,bicubic eva_large_patch14_196.in22k_ft_in22k_in1k,97.610,2.390,99.810,0.190,304.14,196,1.000,bicubic vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,97.610,2.390,99.780,0.220,632.46,336,1.000,bicubic vit_large_patch14_clip_224.openai_ft_in12k_in1k,97.610,2.390,99.730,0.270,304.20,224,1.000,bicubic vit_large_patch14_clip_336.openai_ft_in12k_in1k,97.600,2.400,99.730,0.270,304.53,336,1.000,bicubic convnext_xlarge.fb_in22k_ft_in1k_384,97.590,2.410,99.770,0.230,350.20,384,1.000,bicubic deit3_large_patch16_384.fb_in22k_ft_in1k,97.580,2.420,99.710,0.290,304.76,384,1.000,bicubic eva_giant_patch14_224.clip_ft_in1k,97.570,2.430,99.710,0.290,"1,012.56",224,0.900,bicubic maxvit_base_tf_384.in21k_ft_in1k,97.560,2.440,99.760,0.240,119.65,384,1.000,bicubic eva_large_patch14_196.in22k_ft_in1k,97.520,2.480,99.790,0.210,304.14,196,1.000,bicubic convformer_b36.sail_in22k_ft_in1k_384,97.490,2.510,99.760,0.240,99.88,384,1.000,bicubic beit_large_patch16_224.in22k_ft_in22k_in1k,97.480,2.520,99.690,0.310,304.43,224,0.900,bicubic convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,97.470,2.530,99.760,0.240,200.13,384,1.000,bicubic vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,97.460,2.540,99.780,0.220,304.53,336,1.000,bicubic convnext_xlarge.fb_in22k_ft_in1k,97.450,2.550,99.820,0.180,350.20,288,1.000,bicubic maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,97.450,2.550,99.760,0.240,116.09,384,1.000,bicubic vit_large_patch14_clip_224.openai_ft_in1k,97.440,2.560,99.680,0.320,304.20,224,1.000,bicubic vit_large_patch16_384.augreg_in21k_ft_in1k,97.410,2.590,99.780,0.220,304.72,384,1.000,bicubic vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,97.390,2.610,99.740,0.260,304.20,224,1.000,bicubic convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,97.390,2.610,99.730,0.270,200.13,384,1.000,bicubic regnety_1280.swag_lc_in1k,97.390,2.610,99.730,0.270,644.81,224,0.965,bicubic regnety_320.swag_ft_in1k,97.380,2.620,99.760,0.240,145.05,384,1.000,bicubic convnextv2_base.fcmae_ft_in22k_in1k_384,97.380,2.620,99.720,0.280,88.72,384,1.000,bicubic caformer_m36.sail_in22k_ft_in1k_384,97.370,2.630,99.790,0.210,56.20,384,1.000,bicubic coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,97.370,2.630,99.700,0.300,73.88,384,1.000,bicubic convformer_m36.sail_in22k_ft_in1k_384,97.370,2.630,99.680,0.320,57.05,384,1.000,bicubic caformer_b36.sail_in22k_ft_in1k,97.360,2.640,99.830,0.170,98.75,224,1.000,bicubic vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,97.360,2.640,99.800,0.200,632.05,224,1.000,bicubic maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,97.340,2.660,99.690,0.310,116.14,384,1.000,bicubic tf_efficientnetv2_xl.in21k_ft_in1k,97.330,2.670,99.600,0.400,208.12,512,1.000,bicubic beit_base_patch16_384.in22k_ft_in22k_in1k,97.320,2.680,99.720,0.280,86.74,384,1.000,bicubic tf_efficientnetv2_l.in21k_ft_in1k,97.320,2.680,99.640,0.360,118.52,480,1.000,bicubic convnextv2_large.fcmae_ft_in22k_in1k,97.310,2.690,99.760,0.240,197.96,288,1.000,bicubic beitv2_large_patch16_224.in1k_ft_in1k,97.310,2.690,99.740,0.260,304.43,224,0.950,bicubic deit3_large_patch16_224.fb_in22k_ft_in1k,97.310,2.690,99.680,0.320,304.37,224,1.000,bicubic convnext_large.fb_in22k_ft_in1k_384,97.300,2.700,99.760,0.240,197.77,384,1.000,bicubic volo_d5_512.sail_in1k,97.300,2.700,99.760,0.240,296.09,512,1.150,bicubic seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,97.290,2.710,99.780,0.220,93.59,320,1.000,bicubic seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,97.290,2.710,99.750,0.250,149.39,384,1.000,bicubic caformer_s36.sail_in22k_ft_in1k_384,97.290,2.710,99.720,0.280,39.30,384,1.000,bicubic swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,97.280,2.720,99.780,0.220,196.74,384,1.000,bicubic swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,97.270,2.730,99.790,0.210,87.92,384,1.000,bicubic convformer_b36.sail_in22k_ft_in1k,97.260,2.740,99.750,0.250,99.88,224,1.000,bicubic convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,97.260,2.740,99.740,0.260,200.13,320,1.000,bicubic convnext_base.fb_in22k_ft_in1k_384,97.260,2.740,99.710,0.290,88.59,384,1.000,bicubic convnextv2_huge.fcmae_ft_in1k,97.250,2.750,99.720,0.280,660.29,288,1.000,bicubic deit3_huge_patch14_224.fb_in22k_ft_in1k,97.250,2.750,99.720,0.280,632.13,224,1.000,bicubic volo_d5_448.sail_in1k,97.240,2.760,99.740,0.260,295.91,448,1.150,bicubic swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,97.240,2.760,99.710,0.290,196.74,256,0.900,bicubic deit3_base_patch16_384.fb_in22k_ft_in1k,97.240,2.760,99.670,0.330,86.88,384,1.000,bicubic vit_large_patch14_clip_336.laion2b_ft_in1k,97.230,2.770,99.720,0.280,304.53,336,1.000,bicubic convnext_large.fb_in22k_ft_in1k,97.220,2.780,99.730,0.270,197.77,288,1.000,bicubic vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,97.220,2.780,99.700,0.300,86.86,384,1.000,bicubic convnext_base.fb_in22k_ft_in1k,97.200,2.800,99.760,0.240,88.59,288,1.000,bicubic convnextv2_base.fcmae_ft_in22k_in1k,97.200,2.800,99.760,0.240,88.72,288,1.000,bicubic maxvit_small_tf_512.in1k,97.200,2.800,99.620,0.380,69.13,512,1.000,bicubic tf_efficientnet_b7.ns_jft_in1k,97.190,2.810,99.700,0.300,66.35,600,0.949,bicubic coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,97.180,2.820,99.650,0.350,73.88,224,0.950,bicubic regnety_160.swag_ft_in1k,97.170,2.830,99.780,0.220,83.59,384,1.000,bicubic seresnextaa101d_32x8d.sw_in12k_ft_in1k,97.170,2.830,99.740,0.260,93.59,288,1.000,bicubic swin_large_patch4_window12_384.ms_in22k_ft_in1k,97.170,2.830,99.680,0.320,196.74,384,1.000,bicubic maxvit_base_tf_512.in1k,97.170,2.830,99.640,0.360,119.88,512,1.000,bicubic caformer_b36.sail_in1k_384,97.160,2.840,99.610,0.390,98.75,384,1.000,bicubic swin_base_patch4_window12_384.ms_in22k_ft_in1k,97.130,2.870,99.780,0.220,87.90,384,1.000,bicubic convnext_large_mlp.clip_laion2b_augreg_ft_in1k,97.130,2.870,99.720,0.280,200.13,256,1.000,bicubic vit_base_patch16_clip_384.openai_ft_in12k_in1k,97.120,2.880,99.640,0.360,86.86,384,0.950,bicubic maxvit_base_tf_384.in1k,97.120,2.880,99.570,0.430,119.65,384,1.000,bicubic convnext_small.fb_in22k_ft_in1k_384,97.110,2.890,99.640,0.360,50.22,384,1.000,bicubic maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,97.110,2.890,99.600,0.400,116.14,224,0.950,bicubic vit_huge_patch14_clip_224.laion2b_ft_in1k,97.100,2.900,99.690,0.310,632.05,224,1.000,bicubic maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,97.090,2.910,99.680,0.320,116.09,224,0.950,bicubic vit_base_patch8_224.augreg_in21k_ft_in1k,97.090,2.910,99.610,0.390,86.58,224,0.900,bicubic volo_d4_448.sail_in1k,97.070,2.930,99.750,0.250,193.41,448,1.150,bicubic convformer_m36.sail_in22k_ft_in1k,97.070,2.930,99.630,0.370,57.05,224,1.000,bicubic convformer_s36.sail_in22k_ft_in1k_384,97.060,2.940,99.710,0.290,40.01,384,1.000,bicubic swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,97.050,2.950,99.660,0.340,87.92,256,0.900,bicubic maxvit_large_tf_512.in1k,97.050,2.950,99.590,0.410,212.33,512,1.000,bicubic convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,97.040,2.960,99.670,0.330,88.59,384,1.000,bicubic caformer_m36.sail_in1k_384,97.030,2.970,99.710,0.290,56.20,384,1.000,bicubic volo_d3_448.sail_in1k,97.030,2.970,99.680,0.320,86.63,448,1.000,bicubic dm_nfnet_f5.dm_in1k,97.030,2.970,99.670,0.330,377.21,544,0.954,bicubic caformer_m36.sail_in22k_ft_in1k,97.020,2.980,99.730,0.270,56.20,224,1.000,bicubic tf_efficientnet_b6.ns_jft_in1k,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic vit_base_patch16_384.augreg_in21k_ft_in1k,97.020,2.980,99.710,0.290,86.86,384,1.000,bicubic vit_large_patch14_clip_224.laion2b_ft_in1k,97.020,2.980,99.670,0.330,304.20,224,1.000,bicubic tf_efficientnetv2_m.in21k_ft_in1k,97.000,3.000,99.630,0.370,54.14,480,1.000,bicubic convnext_small.in12k_ft_in1k_384,96.990,3.010,99.660,0.340,50.22,384,1.000,bicubic coatnet_2_rw_224.sw_in12k_ft_in1k,96.990,3.010,99.650,0.350,73.87,224,0.950,bicubic dm_nfnet_f6.dm_in1k,96.970,3.030,99.760,0.240,438.36,576,0.956,bicubic maxvit_tiny_tf_512.in1k,96.970,3.030,99.670,0.330,31.05,512,1.000,bicubic vit_large_r50_s32_384.augreg_in21k_ft_in1k,96.950,3.050,99.710,0.290,329.09,384,1.000,bicubic vit_base_patch8_224.augreg2_in21k_ft_in1k,96.950,3.050,99.640,0.360,86.58,224,0.900,bicubic dm_nfnet_f4.dm_in1k,96.950,3.050,99.630,0.370,316.07,512,0.951,bicubic tiny_vit_21m_384.dist_in22k_ft_in1k,96.950,3.050,99.610,0.390,21.23,384,1.000,bicubic swin_large_patch4_window7_224.ms_in22k_ft_in1k,96.940,3.060,99.670,0.330,196.53,224,0.900,bicubic xcit_large_24_p16_384.fb_dist_in1k,96.940,3.060,99.510,0.490,189.10,384,1.000,bicubic maxvit_large_tf_384.in1k,96.930,3.070,99.570,0.430,212.03,384,1.000,bicubic beitv2_base_patch16_224.in1k_ft_in22k_in1k,96.910,3.090,99.730,0.270,86.53,224,0.900,bicubic tiny_vit_21m_512.dist_in22k_ft_in1k,96.900,3.100,99.690,0.310,21.27,512,1.000,bicubic vit_base_patch16_clip_384.laion2b_ft_in1k,96.900,3.100,99.670,0.330,86.86,384,1.000,bicubic caformer_s36.sail_in1k_384,96.880,3.120,99.670,0.330,39.30,384,1.000,bicubic volo_d5_224.sail_in1k,96.880,3.120,99.670,0.330,295.46,224,0.960,bicubic resnetv2_152x4_bit.goog_in21k_ft_in1k,96.880,3.120,99.660,0.340,936.53,480,1.000,bilinear cait_m48_448.fb_dist_in1k,96.880,3.120,99.620,0.380,356.46,448,1.000,bicubic convformer_b36.sail_in1k_384,96.870,3.130,99.650,0.350,99.88,384,1.000,bicubic tf_efficientnet_b5.ns_jft_in1k,96.870,3.130,99.640,0.360,30.39,456,0.934,bicubic convnext_base.clip_laiona_augreg_ft_in1k_384,96.860,3.140,99.690,0.310,88.59,384,1.000,bicubic deit3_base_patch16_224.fb_in22k_ft_in1k,96.860,3.140,99.620,0.380,86.59,224,1.000,bicubic deit3_large_patch16_384.fb_in1k,96.850,3.150,99.620,0.380,304.76,384,1.000,bicubic cait_m36_384.fb_dist_in1k,96.840,3.160,99.660,0.340,271.22,384,1.000,bicubic convnextv2_large.fcmae_ft_in1k,96.830,3.170,99.760,0.240,197.96,288,1.000,bicubic regnety_160.sw_in12k_ft_in1k,96.820,3.180,99.690,0.310,83.59,288,1.000,bicubic caformer_s36.sail_in22k_ft_in1k,96.820,3.180,99.620,0.380,39.30,224,1.000,bicubic regnety_160.lion_in12k_ft_in1k,96.810,3.190,99.710,0.290,83.59,288,1.000,bicubic vit_base_patch16_clip_384.openai_ft_in1k,96.810,3.190,99.660,0.340,86.86,384,1.000,bicubic xcit_small_24_p8_384.fb_dist_in1k,96.810,3.190,99.630,0.370,47.63,384,1.000,bicubic convnext_small.fb_in22k_ft_in1k,96.810,3.190,99.510,0.490,50.22,288,1.000,bicubic convformer_s18.sail_in22k_ft_in1k_384,96.790,3.210,99.710,0.290,26.77,384,1.000,bicubic convnext_base.clip_laion2b_augreg_ft_in12k_in1k,96.790,3.210,99.680,0.320,88.59,256,1.000,bicubic regnety_320.swag_lc_in1k,96.780,3.220,99.730,0.270,145.05,224,0.965,bicubic volo_d4_224.sail_in1k,96.780,3.220,99.670,0.330,192.96,224,0.960,bicubic convformer_m36.sail_in1k_384,96.780,3.220,99.620,0.380,57.05,384,1.000,bicubic flexivit_large.1200ep_in1k,96.780,3.220,99.610,0.390,304.36,240,0.950,bicubic xcit_medium_24_p8_384.fb_dist_in1k,96.770,3.230,99.620,0.380,84.32,384,1.000,bicubic efficientnet_b5.sw_in12k_ft_in1k,96.770,3.230,99.600,0.400,30.39,448,1.000,bicubic resnext101_32x32d.fb_wsl_ig1b_ft_in1k,96.770,3.230,99.530,0.470,468.53,224,0.875,bilinear xcit_large_24_p8_384.fb_dist_in1k,96.760,3.240,99.560,0.440,188.93,384,1.000,bicubic maxvit_small_tf_384.in1k,96.750,3.250,99.600,0.400,69.02,384,1.000,bicubic beitv2_base_patch16_224.in1k_ft_in1k,96.750,3.250,99.540,0.460,86.53,224,0.900,bicubic tf_efficientnetv2_l.in1k,96.740,3.260,99.550,0.450,118.52,480,1.000,bicubic flexivit_large.600ep_in1k,96.730,3.270,99.560,0.440,304.36,240,0.950,bicubic inception_next_base.sail_in1k_384,96.720,3.280,99.610,0.390,86.67,384,1.000,bicubic tf_efficientnet_b4.ns_jft_in1k,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic volo_d2_384.sail_in1k,96.710,3.290,99.600,0.400,58.87,384,1.000,bicubic vit_large_patch16_224.augreg_in21k_ft_in1k,96.700,3.300,99.650,0.350,304.33,224,0.900,bicubic flexivit_large.300ep_in1k,96.700,3.300,99.580,0.420,304.36,240,0.950,bicubic convformer_s36.sail_in1k_384,96.700,3.300,99.570,0.430,40.01,384,1.000,bicubic tf_efficientnet_b8.ra_in1k,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic eva02_small_patch14_336.mim_in22k_ft_in1k,96.690,3.310,99.610,0.390,22.13,336,1.000,bicubic xcit_medium_24_p16_384.fb_dist_in1k,96.690,3.310,99.600,0.400,84.40,384,1.000,bicubic swin_base_patch4_window7_224.ms_in22k_ft_in1k,96.680,3.320,99.670,0.330,87.77,224,0.900,bicubic deit3_small_patch16_384.fb_in22k_ft_in1k,96.670,3.330,99.640,0.360,22.21,384,1.000,bicubic beit_base_patch16_224.in22k_ft_in22k_in1k,96.660,3.340,99.660,0.340,86.53,224,0.900,bicubic cait_s36_384.fb_dist_in1k,96.630,3.370,99.610,0.390,68.37,384,1.000,bicubic xcit_large_24_p8_224.fb_dist_in1k,96.630,3.370,99.460,0.540,188.93,224,1.000,bicubic dm_nfnet_f3.dm_in1k,96.620,3.380,99.630,0.370,254.92,416,0.940,bicubic convnextv2_tiny.fcmae_ft_in22k_in1k_384,96.620,3.380,99.580,0.420,28.64,384,1.000,bicubic vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,96.620,3.380,99.560,0.440,86.57,224,0.950,bicubic vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,96.610,3.390,99.480,0.520,88.30,384,1.000,bicubic regnetz_e8.ra3_in1k,96.600,3.400,99.620,0.380,57.70,320,1.000,bicubic convnext_small.in12k_ft_in1k,96.600,3.400,99.580,0.420,50.22,288,1.000,bicubic maxvit_tiny_tf_384.in1k,96.600,3.400,99.560,0.440,30.98,384,1.000,bicubic deit3_huge_patch14_224.fb_in1k,96.580,3.420,99.520,0.480,632.13,224,0.900,bicubic cait_s24_384.fb_dist_in1k,96.570,3.430,99.550,0.450,47.06,384,1.000,bicubic tf_efficientnet_b7.ra_in1k,96.570,3.430,99.520,0.480,66.35,600,0.949,bicubic coat_lite_medium_384.in1k,96.570,3.430,99.470,0.530,44.57,384,1.000,bicubic convnext_base.clip_laion2b_augreg_ft_in1k,96.560,3.440,99.650,0.350,88.59,256,1.000,bicubic convnext_tiny.in12k_ft_in1k_384,96.560,3.440,99.630,0.370,28.59,384,1.000,bicubic vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,96.560,3.440,99.520,0.480,88.34,448,1.000,bicubic regnety_120.sw_in12k_ft_in1k,96.550,3.450,99.680,0.320,51.82,288,1.000,bicubic xcit_small_24_p8_224.fb_dist_in1k,96.550,3.450,99.560,0.440,47.63,224,1.000,bicubic tf_efficientnet_b8.ap_in1k,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic hrnet_w48_ssld.paddle_in1k,96.540,3.460,99.640,0.360,77.47,288,1.000,bilinear caformer_s18.sail_in22k_ft_in1k_384,96.530,3.470,99.580,0.420,26.34,384,1.000,bicubic regnety_2560.seer_ft_in1k,96.530,3.470,99.520,0.480,"1,282.60",384,1.000,bicubic xcit_medium_24_p8_224.fb_dist_in1k,96.530,3.470,99.510,0.490,84.32,224,1.000,bicubic resnetv2_152x2_bit.goog_in21k_ft_in1k,96.520,3.480,99.590,0.410,236.34,448,1.000,bilinear dm_nfnet_f2.dm_in1k,96.520,3.480,99.570,0.430,193.78,352,0.920,bicubic deit_base_distilled_patch16_384.fb_in1k,96.510,3.490,99.590,0.410,87.63,384,1.000,bicubic vit_base_patch16_224.augreg2_in21k_ft_in1k,96.510,3.490,99.560,0.440,86.57,224,0.900,bicubic vit_base_patch16_clip_224.openai_ft_in12k_in1k,96.510,3.490,99.550,0.450,86.57,224,0.950,bicubic convformer_s36.sail_in22k_ft_in1k,96.500,3.500,99.630,0.370,40.01,224,1.000,bicubic caformer_b36.sail_in1k,96.500,3.500,99.460,0.540,98.75,224,1.000,bicubic vit_medium_patch16_gap_384.sw_in12k_ft_in1k,96.490,3.510,99.620,0.380,39.03,384,0.950,bicubic tf_efficientnetv2_m.in1k,96.480,3.520,99.610,0.390,54.14,480,1.000,bicubic volo_d1_384.sail_in1k,96.480,3.520,99.550,0.450,26.78,384,1.000,bicubic convnextv2_base.fcmae_ft_in1k,96.480,3.520,99.520,0.480,88.72,288,1.000,bicubic tf_efficientnetv2_s.in21k_ft_in1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic xcit_small_12_p8_384.fb_dist_in1k,96.470,3.530,99.490,0.510,26.21,384,1.000,bicubic regnety_160.swag_lc_in1k,96.450,3.550,99.750,0.250,83.59,224,0.965,bicubic vit_base_r50_s16_384.orig_in21k_ft_in1k,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic eca_nfnet_l2.ra3_in1k,96.450,3.550,99.610,0.390,56.72,384,1.000,bicubic ecaresnet269d.ra2_in1k,96.450,3.550,99.610,0.390,102.09,352,1.000,bicubic seresnextaa101d_32x8d.ah_in1k,96.440,3.560,99.510,0.490,93.59,288,1.000,bicubic volo_d3_224.sail_in1k,96.430,3.570,99.630,0.370,86.33,224,0.960,bicubic resnext101_32x16d.fb_wsl_ig1b_ft_in1k,96.430,3.570,99.540,0.460,194.03,224,0.875,bilinear volo_d2_224.sail_in1k,96.420,3.580,99.500,0.500,58.68,224,0.960,bicubic vit_base_patch32_clip_384.openai_ft_in12k_in1k,96.420,3.580,99.460,0.540,88.30,384,0.950,bicubic caformer_s18.sail_in1k_384,96.410,3.590,99.560,0.440,26.34,384,1.000,bicubic caformer_m36.sail_in1k,96.410,3.590,99.530,0.470,56.20,224,1.000,bicubic resnetrs420.tf_in1k,96.400,3.600,99.540,0.460,191.89,416,1.000,bicubic convnext_large.fb_in1k,96.400,3.600,99.530,0.470,197.77,288,1.000,bicubic mvitv2_large.fb_in1k,96.400,3.600,99.450,0.550,217.99,224,0.900,bicubic tiny_vit_21m_224.dist_in22k_ft_in1k,96.380,3.620,99.500,0.500,21.20,224,0.950,bicubic swin_base_patch4_window12_384.ms_in1k,96.380,3.620,99.420,0.580,87.90,384,1.000,bicubic tf_efficientnet_b6.ap_in1k,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic seresnext101d_32x8d.ah_in1k,96.360,3.640,99.470,0.530,93.59,288,1.000,bicubic resnetaa101d.sw_in12k_ft_in1k,96.360,3.640,99.440,0.560,44.57,288,1.000,bicubic tf_efficientnet_b7.ap_in1k,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic resnetrs200.tf_in1k,96.350,3.650,99.550,0.450,93.21,320,1.000,bicubic resmlp_big_24_224.fb_in22k_ft_in1k,96.350,3.650,99.520,0.480,129.14,224,0.875,bicubic xcit_small_24_p16_384.fb_dist_in1k,96.340,3.660,99.580,0.420,47.67,384,1.000,bicubic convnextv2_tiny.fcmae_ft_in22k_in1k,96.340,3.660,99.550,0.450,28.64,288,1.000,bicubic xcit_small_12_p16_384.fb_dist_in1k,96.340,3.660,99.490,0.510,26.25,384,1.000,bicubic maxvit_base_tf_224.in1k,96.340,3.660,99.370,0.630,119.47,224,0.950,bicubic maxvit_large_tf_224.in1k,96.330,3.670,99.410,0.590,211.79,224,0.950,bicubic vit_base_patch16_clip_224.laion2b_ft_in1k,96.320,3.680,99.540,0.460,86.57,224,1.000,bicubic regnetz_040_h.ra3_in1k,96.320,3.680,99.520,0.480,28.94,320,1.000,bicubic xcit_large_24_p16_224.fb_dist_in1k,96.320,3.680,99.500,0.500,189.10,224,1.000,bicubic vit_base_patch16_clip_224.openai_ft_in1k,96.310,3.690,99.550,0.450,86.57,224,0.900,bicubic seresnet152d.ra2_in1k,96.310,3.690,99.510,0.490,66.84,320,1.000,bicubic convnext_base.fb_in1k,96.310,3.690,99.500,0.500,88.59,288,1.000,bicubic regnety_1280.seer_ft_in1k,96.310,3.690,99.410,0.590,644.81,384,1.000,bicubic vit_base_patch16_224.augreg_in21k_ft_in1k,96.300,3.700,99.560,0.440,86.57,224,0.900,bicubic dm_nfnet_f1.dm_in1k,96.300,3.700,99.530,0.470,132.63,320,0.910,bicubic tf_efficientnet_b6.aa_in1k,96.300,3.700,99.530,0.470,43.04,528,0.942,bicubic fastvit_ma36.apple_dist_in1k,96.300,3.700,99.500,0.500,44.07,256,0.950,bicubic resnetv2_50x3_bit.goog_in21k_ft_in1k,96.270,3.730,99.630,0.370,217.32,448,1.000,bilinear efficientnetv2_rw_m.agc_in1k,96.270,3.730,99.560,0.440,53.24,416,1.000,bicubic resnext101_32x16d.fb_swsl_ig1b_ft_in1k,96.270,3.730,99.500,0.500,194.03,224,0.875,bilinear resnext101_32x8d.fb_swsl_ig1b_ft_in1k,96.250,3.750,99.590,0.410,88.79,224,0.875,bilinear resnetv2_101x3_bit.goog_in21k_ft_in1k,96.250,3.750,99.580,0.420,387.93,448,1.000,bilinear convformer_s18.sail_in1k_384,96.250,3.750,99.540,0.460,26.77,384,1.000,bicubic resnetrs350.tf_in1k,96.250,3.750,99.470,0.530,163.96,384,1.000,bicubic xcit_medium_24_p16_224.fb_dist_in1k,96.250,3.750,99.410,0.590,84.40,224,1.000,bicubic convnext_tiny.in12k_ft_in1k,96.240,3.760,99.640,0.360,28.59,288,1.000,bicubic davit_base.msft_in1k,96.240,3.760,99.410,0.590,87.95,224,0.950,bicubic convformer_b36.sail_in1k,96.240,3.760,99.290,0.710,99.88,224,1.000,bicubic xcit_tiny_24_p8_384.fb_dist_in1k,96.230,3.770,99.440,0.560,12.11,384,1.000,bicubic deit3_base_patch16_384.fb_in1k,96.230,3.770,99.400,0.600,86.88,384,1.000,bicubic maxxvit_rmlp_small_rw_256.sw_in1k,96.210,3.790,99.480,0.520,66.01,256,0.950,bicubic coatnet_rmlp_2_rw_224.sw_in1k,96.210,3.790,99.280,0.720,73.88,224,0.950,bicubic vit_base_patch16_384.orig_in21k_ft_in1k,96.200,3.800,99.530,0.470,86.86,384,1.000,bicubic edgenext_base.in21k_ft_in1k,96.200,3.800,99.470,0.530,18.51,320,1.000,bicubic maxvit_small_tf_224.in1k,96.200,3.800,99.460,0.540,68.93,224,0.950,bicubic resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,96.190,3.810,99.500,0.500,236.34,384,1.000,bicubic deit3_large_patch16_224.fb_in1k,96.190,3.810,99.300,0.700,304.37,224,0.900,bicubic vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.180,3.820,99.530,0.470,328.99,224,0.900,bicubic regnetz_040.ra3_in1k,96.180,3.820,99.510,0.490,27.12,320,1.000,bicubic convnext_tiny.fb_in22k_ft_in1k_384,96.170,3.830,99.500,0.500,28.59,384,1.000,bicubic swinv2_base_window16_256.ms_in1k,96.170,3.830,99.390,0.610,87.92,256,0.900,bicubic crossvit_18_dagger_408.in1k,96.150,3.850,99.470,0.530,44.61,408,1.000,bicubic regnetz_d8_evos.ch_in1k,96.140,3.860,99.490,0.510,23.46,320,1.000,bicubic deit3_medium_patch16_224.fb_in22k_ft_in1k,96.140,3.860,99.480,0.520,38.85,224,1.000,bicubic seresnext101_32x8d.ah_in1k,96.140,3.860,99.360,0.640,93.57,288,1.000,bicubic efficientvit_b3.r288_in1k,96.140,3.860,99.290,0.710,48.65,288,1.000,bicubic coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,96.130,3.870,99.340,0.660,41.72,224,0.950,bicubic flexivit_base.1200ep_in1k,96.120,3.880,99.410,0.590,86.59,240,0.950,bicubic resnest269e.in1k,96.110,3.890,99.520,0.480,110.93,416,0.928,bicubic resnet200d.ra2_in1k,96.110,3.890,99.460,0.540,64.69,320,1.000,bicubic convformer_s36.sail_in1k,96.110,3.890,99.310,0.690,40.01,224,1.000,bicubic convformer_s18.sail_in22k_ft_in1k,96.100,3.900,99.490,0.510,26.77,224,1.000,bicubic tf_efficientnet_b3.ns_jft_in1k,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic rexnetr_300.sw_in12k_ft_in1k,96.090,3.910,99.540,0.460,34.81,288,1.000,bicubic tf_efficientnet_b5.ap_in1k,96.090,3.910,99.540,0.460,30.39,456,0.934,bicubic xcit_large_24_p8_224.fb_in1k,96.090,3.910,99.140,0.860,188.93,224,1.000,bicubic caformer_s36.sail_in1k,96.080,3.920,99.510,0.490,39.30,224,1.000,bicubic gcvit_base.in1k,96.080,3.920,99.390,0.610,90.32,224,0.875,bicubic convformer_m36.sail_in1k,96.080,3.920,99.250,0.750,57.05,224,1.000,bicubic resnest200e.in1k,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic tf_efficientnet_b7.aa_in1k,96.070,3.930,99.450,0.550,66.35,600,0.949,bicubic swinv2_small_window16_256.ms_in1k,96.070,3.930,99.340,0.660,49.73,256,0.900,bicubic vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.060,3.940,99.550,0.450,36.47,384,1.000,bicubic regnety_640.seer_ft_in1k,96.060,3.940,99.500,0.500,281.38,384,1.000,bicubic resnetrs270.tf_in1k,96.060,3.940,99.480,0.520,129.86,352,1.000,bicubic swin_small_patch4_window7_224.ms_in22k_ft_in1k,96.060,3.940,99.480,0.520,49.61,224,0.900,bicubic swinv2_base_window8_256.ms_in1k,96.060,3.940,99.410,0.590,87.92,256,0.900,bicubic resnext101_32x4d.fb_swsl_ig1b_ft_in1k,96.040,3.960,99.540,0.460,44.18,224,0.875,bilinear maxvit_rmlp_tiny_rw_256.sw_in1k,96.040,3.960,99.410,0.590,29.15,256,0.950,bicubic swin_s3_base_224.ms_in1k,96.040,3.960,99.350,0.650,71.13,224,0.900,bicubic vit_base_patch16_224_miil.in21k_ft_in1k,96.040,3.960,99.350,0.650,86.54,224,0.875,bilinear davit_small.msft_in1k,96.030,3.970,99.400,0.600,49.75,224,0.950,bicubic volo_d1_224.sail_in1k,96.030,3.970,99.390,0.610,26.63,224,0.960,bicubic caformer_s18.sail_in22k_ft_in1k,96.010,3.990,99.550,0.450,26.34,224,1.000,bicubic regnetz_d8.ra3_in1k,96.010,3.990,99.520,0.480,23.37,320,1.000,bicubic vit_medium_patch16_gap_256.sw_in12k_ft_in1k,96.000,4.000,99.500,0.500,38.86,256,0.950,bicubic cs3se_edgenet_x.c2ns_in1k,96.000,4.000,99.430,0.570,50.72,320,1.000,bicubic cait_xs24_384.fb_dist_in1k,96.000,4.000,99.420,0.580,26.67,384,1.000,bicubic coat_lite_medium.in1k,96.000,4.000,99.350,0.650,44.57,224,0.900,bicubic repvit_m2_3.dist_450e_in1k,95.990,4.010,99.400,0.600,23.69,224,0.950,bicubic vit_small_patch16_384.augreg_in21k_ft_in1k,95.980,4.020,99.590,0.410,22.20,384,1.000,bicubic mvitv2_base.fb_in1k,95.980,4.020,99.330,0.670,51.47,224,0.900,bicubic tf_efficientnet_b5.ra_in1k,95.970,4.030,99.460,0.540,30.39,456,0.934,bicubic convnext_small.fb_in1k,95.970,4.030,99.430,0.570,50.22,288,1.000,bicubic fastvit_ma36.apple_in1k,95.970,4.030,99.360,0.640,44.07,256,0.950,bicubic xcit_small_12_p8_224.fb_dist_in1k,95.960,4.040,99.430,0.570,26.21,224,1.000,bicubic flexivit_base.600ep_in1k,95.960,4.040,99.420,0.580,86.59,240,0.950,bicubic resnetrs152.tf_in1k,95.960,4.040,99.380,0.620,86.62,320,1.000,bicubic fastvit_sa36.apple_dist_in1k,95.960,4.040,99.370,0.630,31.53,256,0.900,bicubic maxvit_rmlp_small_rw_224.sw_in1k,95.960,4.040,99.350,0.650,64.90,224,0.900,bicubic repvgg_d2se.rvgg_in1k,95.950,4.050,99.470,0.530,133.33,320,1.000,bilinear flexivit_base.300ep_in1k,95.950,4.050,99.370,0.630,86.59,240,0.950,bicubic pvt_v2_b5.in1k,95.940,4.060,99.390,0.610,81.96,224,0.900,bicubic resnext101_32x8d.fb_wsl_ig1b_ft_in1k,95.940,4.060,99.380,0.620,88.79,224,0.875,bilinear eca_nfnet_l1.ra2_in1k,95.930,4.070,99.490,0.510,41.41,320,1.000,bicubic pvt_v2_b4.in1k,95.920,4.080,99.360,0.640,62.56,224,0.900,bicubic inception_next_base.sail_in1k,95.920,4.080,99.220,0.780,86.67,224,0.950,bicubic gcvit_small.in1k,95.910,4.090,99.280,0.720,51.09,224,0.875,bicubic vit_base_patch32_384.augreg_in21k_ft_in1k,95.900,4.100,99.440,0.560,88.30,384,1.000,bicubic focalnet_base_srf.ms_in1k,95.900,4.100,99.340,0.660,88.15,224,0.900,bicubic swin_base_patch4_window7_224.ms_in1k,95.900,4.100,99.310,0.690,87.77,224,0.900,bicubic xcit_small_24_p8_224.fb_in1k,95.900,4.100,99.180,0.820,47.63,224,1.000,bicubic mvitv2_small.fb_in1k,95.890,4.110,99.360,0.640,34.87,224,0.900,bicubic tf_efficientnet_b5.aa_in1k,95.880,4.120,99.350,0.650,30.39,456,0.934,bicubic regnety_160.deit_in1k,95.870,4.130,99.560,0.440,83.59,288,1.000,bicubic sequencer2d_l.in1k,95.870,4.130,99.470,0.530,54.30,224,0.875,bicubic regnety_080.ra3_in1k,95.870,4.130,99.440,0.560,39.18,288,1.000,bicubic resmlp_big_24_224.fb_distilled_in1k,95.870,4.130,99.440,0.560,129.14,224,0.875,bicubic regnetz_d32.ra3_in1k,95.870,4.130,99.430,0.570,27.58,320,0.950,bicubic resnet152d.ra2_in1k,95.870,4.130,99.430,0.570,60.21,320,1.000,bicubic tf_efficientnet_b5.in1k,95.870,4.130,99.390,0.610,30.39,456,0.934,bicubic xcit_medium_24_p8_224.fb_in1k,95.860,4.140,99.080,0.920,84.32,224,1.000,bicubic deit3_small_patch16_224.fb_in22k_ft_in1k,95.830,4.170,99.390,0.610,22.06,224,1.000,bicubic convnextv2_tiny.fcmae_ft_in1k,95.830,4.170,99.340,0.660,28.64,288,1.000,bicubic efficientvit_b3.r256_in1k,95.830,4.170,99.220,0.780,48.65,256,1.000,bicubic swin_s3_small_224.ms_in1k,95.830,4.170,99.200,0.800,49.74,224,0.900,bicubic focalnet_base_lrf.ms_in1k,95.830,4.170,99.180,0.820,88.75,224,0.900,bicubic resnext101_64x4d.tv_in1k,95.820,4.180,99.320,0.680,83.46,224,0.875,bilinear crossvit_15_dagger_408.in1k,95.820,4.180,99.310,0.690,28.50,408,1.000,bicubic tresnet_v2_l.miil_in21k_ft_in1k,95.820,4.180,99.290,0.710,46.17,224,0.875,bilinear pit_b_distilled_224.in1k,95.820,4.180,99.210,0.790,74.79,224,0.900,bicubic maxvit_tiny_tf_224.in1k,95.810,4.190,99.260,0.740,30.92,224,0.950,bicubic edgenext_base.usi_in1k,95.790,4.210,99.570,0.430,18.51,320,1.000,bicubic regnety_320.seer_ft_in1k,95.790,4.210,99.390,0.610,145.05,384,1.000,bicubic xcit_small_24_p16_224.fb_dist_in1k,95.790,4.210,99.350,0.650,47.67,224,1.000,bicubic convnextv2_nano.fcmae_ft_in22k_in1k_384,95.790,4.210,99.300,0.700,15.62,384,1.000,bicubic regnety_064.ra3_in1k,95.790,4.210,99.290,0.710,30.58,288,1.000,bicubic regnetv_064.ra3_in1k,95.780,4.220,99.420,0.580,30.58,288,1.000,bicubic deit3_base_patch16_224.fb_in1k,95.770,4.230,99.270,0.730,86.59,224,0.900,bicubic efficientformerv2_l.snap_dist_in1k,95.760,4.240,99.370,0.630,26.32,224,0.950,bicubic resnet152.a1h_in1k,95.750,4.250,99.430,0.570,60.19,288,1.000,bicubic resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,95.750,4.250,99.430,0.570,236.34,224,0.875,bicubic deit_base_distilled_patch16_224.fb_in1k,95.750,4.250,99.280,0.720,87.34,224,0.900,bicubic resnet101d.ra2_in1k,95.740,4.260,99.440,0.560,44.57,320,1.000,bicubic focalnet_small_lrf.ms_in1k,95.740,4.260,99.210,0.790,50.34,224,0.900,bicubic maxvit_tiny_rw_224.sw_in1k,95.740,4.260,99.160,0.840,29.06,224,0.950,bicubic regnetv_040.ra3_in1k,95.730,4.270,99.380,0.620,20.64,288,1.000,bicubic swinv2_small_window8_256.ms_in1k,95.730,4.270,99.360,0.640,49.73,256,0.900,bicubic xcit_small_12_p16_224.fb_dist_in1k,95.730,4.270,99.300,0.700,26.25,224,1.000,bicubic twins_pcpvt_large.in1k,95.720,4.280,99.490,0.510,60.99,224,0.900,bicubic tf_efficientnetv2_s.in1k,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic efficientnetv2_rw_s.ra2_in1k,95.710,4.290,99.380,0.620,23.94,384,1.000,bicubic hrnet_w18_ssld.paddle_in1k,95.710,4.290,99.340,0.660,21.30,288,1.000,bilinear swin_small_patch4_window7_224.ms_in1k,95.710,4.290,99.290,0.710,49.61,224,0.900,bicubic swinv2_cr_small_ns_224.sw_in1k,95.710,4.290,99.290,0.710,49.70,224,0.900,bicubic tiny_vit_11m_224.dist_in22k_ft_in1k,95.710,4.290,99.260,0.740,11.00,224,0.950,bicubic twins_svt_large.in1k,95.700,4.300,99.370,0.630,99.27,224,0.900,bicubic dm_nfnet_f0.dm_in1k,95.690,4.310,99.350,0.650,71.49,256,0.900,bicubic xception65.ra3_in1k,95.690,4.310,99.320,0.680,39.92,299,0.940,bicubic caformer_s18.sail_in1k,95.680,4.320,99.290,0.710,26.34,224,1.000,bicubic inception_next_small.sail_in1k,95.680,4.320,99.250,0.750,49.37,224,0.875,bicubic cait_s24_224.fb_dist_in1k,95.660,4.340,99.390,0.610,46.92,224,1.000,bicubic gcvit_tiny.in1k,95.660,4.340,99.330,0.670,28.22,224,0.875,bicubic xception65p.ra3_in1k,95.660,4.340,99.270,0.730,39.82,299,0.940,bicubic tiny_vit_21m_224.in1k,95.650,4.350,99.250,0.750,21.20,224,0.950,bicubic regnetz_c16_evos.ch_in1k,95.640,4.360,99.420,0.580,13.49,320,0.950,bicubic deit_base_patch16_384.fb_in1k,95.640,4.360,99.240,0.760,86.86,384,1.000,bicubic resnext50_32x4d.fb_swsl_ig1b_ft_in1k,95.630,4.370,99.440,0.560,25.03,224,0.875,bilinear ecaresnet101d.miil_in1k,95.630,4.370,99.410,0.590,44.57,288,0.950,bicubic focalnet_small_srf.ms_in1k,95.630,4.370,99.290,0.710,49.89,224,0.900,bicubic coatnet_1_rw_224.sw_in1k,95.620,4.380,99.220,0.780,41.72,224,0.950,bicubic fastvit_sa36.apple_in1k,95.610,4.390,99.320,0.680,31.53,256,0.900,bicubic efficientformer_l7.snap_dist_in1k,95.600,4.400,99.440,0.560,82.23,224,0.950,bicubic deit3_small_patch16_384.fb_in1k,95.600,4.400,99.390,0.610,22.21,384,1.000,bicubic tf_efficientnetv2_b3.in21k_ft_in1k,95.600,4.400,99.280,0.720,14.36,300,0.900,bicubic repvit_m2_3.dist_300e_in1k,95.590,4.410,99.390,0.610,23.69,224,0.950,bicubic tf_efficientnet_b4.aa_in1k,95.590,4.410,99.330,0.670,19.34,380,0.922,bicubic sequencer2d_m.in1k,95.580,4.420,99.270,0.730,38.31,224,0.875,bicubic resnet101.a1h_in1k,95.580,4.420,99.250,0.750,44.55,288,1.000,bicubic efficientvit_b2.r288_in1k,95.580,4.420,99.220,0.780,24.33,288,1.000,bicubic resnetv2_101.a1h_in1k,95.570,4.430,99.370,0.630,44.54,288,1.000,bicubic resnest101e.in1k,95.570,4.430,99.270,0.730,48.28,256,0.875,bilinear regnety_320.tv2_in1k,95.560,4.440,99.390,0.610,145.05,224,0.965,bicubic twins_svt_base.in1k,95.560,4.440,99.230,0.770,56.07,224,0.900,bicubic fastvit_sa24.apple_dist_in1k,95.550,4.450,99.310,0.690,21.55,256,0.900,bicubic rexnet_300.nav_in1k,95.540,4.460,99.320,0.680,34.71,224,0.875,bicubic nest_base_jx.goog_in1k,95.540,4.460,99.290,0.710,67.72,224,0.875,bicubic nest_small_jx.goog_in1k,95.540,4.460,99.220,0.780,38.35,224,0.875,bicubic efficientvit_b3.r224_in1k,95.540,4.460,99.190,0.810,48.65,224,0.950,bicubic efficientnet_b4.ra2_in1k,95.530,4.470,99.400,0.600,19.34,384,1.000,bicubic resnext101_64x4d.c1_in1k,95.530,4.470,99.290,0.710,83.46,288,1.000,bicubic tf_efficientnet_b2.ns_jft_in1k,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic tresnet_xl.miil_in1k_448,95.510,4.490,99.340,0.660,78.44,448,0.875,bilinear regnety_040.ra3_in1k,95.490,4.510,99.420,0.580,20.65,288,1.000,bicubic tf_efficientnet_b4.ap_in1k,95.490,4.510,99.390,0.610,19.34,380,0.922,bicubic xcit_tiny_24_p16_384.fb_dist_in1k,95.490,4.510,99.360,0.640,12.12,384,1.000,bicubic coatnet_rmlp_1_rw_224.sw_in1k,95.490,4.510,99.250,0.750,41.69,224,0.950,bicubic tf_efficientnet_b4.in1k,95.480,4.520,99.270,0.730,19.34,380,0.922,bicubic twins_pcpvt_base.in1k,95.470,4.530,99.390,0.610,43.83,224,0.900,bicubic pvt_v2_b3.in1k,95.470,4.530,99.310,0.690,45.24,224,0.900,bicubic maxvit_nano_rw_256.sw_in1k,95.470,4.530,99.120,0.880,15.45,256,0.950,bicubic eca_nfnet_l0.ra2_in1k,95.460,4.540,99.390,0.610,24.14,288,1.000,bicubic regnety_032.ra_in1k,95.460,4.540,99.320,0.680,19.44,288,1.000,bicubic cs3edgenet_x.c2_in1k,95.460,4.540,99.280,0.720,47.82,288,1.000,bicubic sequencer2d_s.in1k,95.460,4.540,99.260,0.740,27.65,224,0.875,bicubic xcit_tiny_24_p8_224.fb_dist_in1k,95.450,4.550,99.360,0.640,12.11,224,1.000,bicubic maxvit_rmlp_nano_rw_256.sw_in1k,95.440,4.560,99.060,0.940,15.50,256,0.950,bicubic maxxvitv2_nano_rw_256.sw_in1k,95.430,4.570,99.190,0.810,23.70,256,0.950,bicubic convnextv2_nano.fcmae_ft_in22k_in1k,95.420,4.580,99.310,0.690,15.62,288,1.000,bicubic xcit_small_12_p8_224.fb_in1k,95.420,4.580,99.190,0.810,26.21,224,1.000,bicubic resnetv2_50x1_bit.goog_distilled_in1k,95.410,4.590,99.430,0.570,25.55,224,0.875,bicubic cs3sedarknet_x.c2ns_in1k,95.410,4.590,99.320,0.680,35.40,288,1.000,bicubic swinv2_cr_small_224.sw_in1k,95.410,4.590,99.060,0.940,49.70,224,0.900,bicubic resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,95.400,4.600,99.400,0.600,194.03,224,0.875,bilinear tresnet_l.miil_in1k_448,95.400,4.600,99.300,0.700,55.99,448,0.875,bilinear mvitv2_tiny.fb_in1k,95.400,4.600,99.160,0.840,24.17,224,0.900,bicubic mobilevitv2_200.cvnets_in22k_ft_in1k_384,95.390,4.610,99.280,0.720,18.45,384,1.000,bicubic nfnet_l0.ra2_in1k,95.380,4.620,99.420,0.580,35.07,288,1.000,bicubic regnetz_c16.ra3_in1k,95.380,4.620,99.350,0.650,13.46,320,1.000,bicubic deit3_medium_patch16_224.fb_in1k,95.380,4.620,99.210,0.790,38.85,224,0.900,bicubic tresnet_m.miil_in21k_ft_in1k,95.380,4.620,99.150,0.850,31.39,224,0.875,bilinear pnasnet5large.tf_in1k,95.360,4.640,99.130,0.870,86.06,331,0.911,bicubic convnext_nano.in12k_ft_in1k,95.350,4.650,99.450,0.550,15.59,288,1.000,bicubic mobilevitv2_150.cvnets_in22k_ft_in1k_384,95.350,4.650,99.120,0.880,10.59,384,1.000,bicubic xcit_tiny_12_p8_384.fb_dist_in1k,95.340,4.660,99.340,0.660,6.71,384,1.000,bicubic maxxvit_rmlp_nano_rw_256.sw_in1k,95.340,4.660,99.310,0.690,16.78,256,0.950,bicubic swinv2_tiny_window16_256.ms_in1k,95.330,4.670,99.300,0.700,28.35,256,0.900,bicubic convformer_s18.sail_in1k,95.330,4.670,99.150,0.850,26.77,224,1.000,bicubic resnetv2_101x1_bit.goog_in21k_ft_in1k,95.320,4.680,99.370,0.630,44.54,448,1.000,bilinear resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,95.320,4.680,99.320,0.680,88.79,224,0.875,bilinear rexnetr_200.sw_in12k_ft_in1k,95.310,4.690,99.470,0.530,16.52,288,1.000,bicubic resnext101_32x8d.tv2_in1k,95.300,4.700,99.360,0.640,88.79,224,0.965,bilinear regnety_080_tv.tv2_in1k,95.300,4.700,99.230,0.770,39.38,224,0.965,bicubic vit_relpos_medium_patch16_cls_224.sw_in1k,95.300,4.700,99.100,0.900,38.76,224,0.900,bicubic resnetaa50d.sw_in12k_ft_in1k,95.290,4.710,99.380,0.620,25.58,288,1.000,bicubic gc_efficientnetv2_rw_t.agc_in1k,95.290,4.710,99.220,0.780,13.68,288,1.000,bicubic fastvit_sa24.apple_in1k,95.280,4.720,99.310,0.690,21.55,256,0.900,bicubic cs3darknet_x.c2ns_in1k,95.280,4.720,99.290,0.710,35.05,288,1.000,bicubic regnetx_320.tv2_in1k,95.280,4.720,99.290,0.710,107.81,224,0.965,bicubic repvit_m1_5.dist_450e_in1k,95.280,4.720,99.230,0.770,14.64,224,0.950,bicubic mobilevitv2_175.cvnets_in22k_ft_in1k_384,95.260,4.740,99.380,0.620,14.25,384,1.000,bicubic flexivit_small.600ep_in1k,95.260,4.740,99.160,0.840,22.06,240,0.950,bicubic resnetrs101.tf_in1k,95.250,4.750,99.210,0.790,63.62,288,0.940,bicubic vit_relpos_base_patch16_clsgap_224.sw_in1k,95.250,4.750,99.200,0.800,86.43,224,0.900,bicubic convnext_tiny_hnf.a2h_in1k,95.250,4.750,98.980,1.020,28.59,288,1.000,bicubic cait_xxs36_384.fb_dist_in1k,95.240,4.760,99.320,0.680,17.37,384,1.000,bicubic vit_large_patch32_384.orig_in21k_ft_in1k,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic tiny_vit_11m_224.in1k,95.240,4.760,99.230,0.770,11.00,224,0.950,bicubic wide_resnet101_2.tv2_in1k,95.240,4.760,99.200,0.800,126.89,224,0.965,bilinear vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,95.230,4.770,99.240,0.760,88.22,224,0.900,bicubic pvt_v2_b2_li.in1k,95.220,4.780,99.260,0.740,22.55,224,0.900,bicubic efficientvit_b2.r256_in1k,95.220,4.780,99.130,0.870,24.33,256,1.000,bicubic resnetv2_50d_gn.ah_in1k,95.220,4.780,99.030,0.970,25.57,288,1.000,bicubic convnext_tiny.fb_in1k,95.210,4.790,99.310,0.690,28.59,288,1.000,bicubic efficientformer_l3.snap_dist_in1k,95.210,4.790,99.310,0.690,31.41,224,0.950,bicubic regnetx_160.tv2_in1k,95.210,4.790,99.280,0.720,54.28,224,0.965,bicubic levit_384.fb_dist_in1k,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic levit_conv_384.fb_dist_in1k,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic resnet50.fb_swsl_ig1b_ft_in1k,95.200,4.800,99.390,0.610,25.56,224,0.875,bilinear resnet51q.ra2_in1k,95.200,4.800,99.280,0.720,35.70,288,1.000,bilinear vit_base_patch16_224.orig_in21k_ft_in1k,95.200,4.800,99.230,0.770,86.57,224,0.900,bicubic coat_small.in1k,95.190,4.810,99.280,0.720,21.69,224,0.900,bicubic focalnet_tiny_lrf.ms_in1k,95.190,4.810,99.220,0.780,28.65,224,0.900,bicubic vit_relpos_medium_patch16_224.sw_in1k,95.190,4.810,99.220,0.780,38.75,224,0.900,bicubic flexivit_small.1200ep_in1k,95.190,4.810,99.180,0.820,22.06,240,0.950,bicubic poolformerv2_m48.sail_in1k,95.180,4.820,99.160,0.840,73.35,224,1.000,bicubic crossvit_18_dagger_240.in1k,95.180,4.820,99.120,0.880,44.27,240,0.875,bicubic regnety_160.tv2_in1k,95.160,4.840,99.250,0.750,83.59,224,0.965,bicubic repvit_m1_5.dist_300e_in1k,95.150,4.850,99.270,0.730,14.64,224,0.950,bicubic flexivit_small.300ep_in1k,95.150,4.850,99.150,0.850,22.06,240,0.950,bicubic nasnetalarge.tf_in1k,95.150,4.850,99.130,0.870,88.75,331,0.911,bicubic resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,95.140,4.860,99.300,0.700,44.18,224,0.875,bilinear convnextv2_nano.fcmae_ft_in1k,95.140,4.860,99.220,0.780,15.62,288,1.000,bicubic efficientnet_b3.ra2_in1k,95.140,4.860,99.210,0.790,12.23,320,1.000,bicubic vit_relpos_base_patch16_224.sw_in1k,95.130,4.870,99.290,0.710,86.43,224,0.900,bicubic wide_resnet50_2.racm_in1k,95.130,4.870,99.260,0.740,68.88,288,0.950,bicubic resnet61q.ra2_in1k,95.130,4.870,99.080,0.920,36.85,288,1.000,bicubic xcit_medium_24_p16_224.fb_in1k,95.130,4.870,98.940,1.060,84.40,224,1.000,bicubic vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.120,4.880,99.220,0.780,36.43,224,0.900,bicubic fbnetv3_g.ra2_in1k,95.120,4.880,99.200,0.800,16.62,288,0.950,bilinear tf_efficientnetv2_b3.in1k,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic cs3sedarknet_l.c2ns_in1k,95.110,4.890,99.210,0.790,21.91,288,0.950,bicubic efficientformerv2_s2.snap_dist_in1k,95.110,4.890,99.120,0.880,12.71,224,0.950,bicubic convit_base.fb_in1k,95.100,4.900,99.150,0.850,86.54,224,0.875,bicubic inception_next_tiny.sail_in1k,95.100,4.900,99.140,0.860,28.06,224,0.875,bicubic poolformer_m48.sail_in1k,95.100,4.900,99.100,0.900,73.47,224,0.950,bicubic coatnet_rmlp_nano_rw_224.sw_in1k,95.090,4.910,99.170,0.830,15.15,224,0.900,bicubic resnet152.a1_in1k,95.090,4.910,98.990,1.010,60.19,288,1.000,bicubic ecaresnet50t.ra2_in1k,95.080,4.920,99.290,0.710,25.57,320,0.950,bicubic tresnet_xl.miil_in1k,95.080,4.920,99.260,0.740,78.44,224,0.875,bilinear davit_tiny.msft_in1k,95.080,4.920,99.140,0.860,28.36,224,0.950,bicubic crossvit_18_240.in1k,95.070,4.930,99.120,0.880,43.27,240,0.875,bicubic coat_lite_small.in1k,95.070,4.930,99.030,0.970,19.84,224,0.900,bicubic crossvit_base_240.in1k,95.070,4.930,98.980,1.020,105.03,240,0.875,bicubic efficientnetv2_rw_t.ra2_in1k,95.060,4.940,99.220,0.780,13.65,288,1.000,bicubic vit_relpos_medium_patch16_rpn_224.sw_in1k,95.060,4.940,99.200,0.800,38.73,224,0.900,bicubic xcit_small_24_p16_224.fb_in1k,95.060,4.940,99.070,0.930,47.67,224,1.000,bicubic xception41p.ra3_in1k,95.050,4.950,99.160,0.840,26.91,299,0.940,bicubic poolformerv2_m36.sail_in1k,95.050,4.950,99.150,0.850,56.08,224,1.000,bicubic coatnet_nano_rw_224.sw_in1k,95.050,4.950,99.140,0.860,15.14,224,0.900,bicubic mobilevitv2_200.cvnets_in22k_ft_in1k,95.050,4.950,99.080,0.920,18.45,256,0.888,bicubic focalnet_tiny_srf.ms_in1k,95.040,4.960,99.280,0.720,28.43,224,0.900,bicubic resnet152.tv2_in1k,95.040,4.960,99.170,0.830,60.19,224,0.965,bilinear poolformer_m36.sail_in1k,95.030,4.970,99.100,0.900,56.17,224,0.950,bicubic swinv2_tiny_window8_256.ms_in1k,95.020,4.980,99.170,0.830,28.35,256,0.900,bicubic gcvit_xtiny.in1k,95.020,4.980,99.160,0.840,19.98,224,0.875,bicubic deit_base_patch16_224.fb_in1k,95.020,4.980,98.970,1.030,86.57,224,0.900,bicubic pvt_v2_b2.in1k,95.010,4.990,99.140,0.860,25.36,224,0.900,bicubic halo2botnet50ts_256.a1h_in1k,95.010,4.990,99.050,0.950,22.64,256,0.950,bicubic ecaresnet101d_pruned.miil_in1k,95.000,5.000,99.230,0.770,24.88,288,0.950,bicubic seresnext50_32x4d.racm_in1k,95.000,5.000,99.190,0.810,27.56,288,0.950,bicubic resnext50_32x4d.a1h_in1k,94.990,5.010,99.190,0.810,25.03,288,1.000,bicubic crossvit_15_dagger_240.in1k,94.990,5.010,99.160,0.840,28.21,240,0.875,bicubic coatnet_bn_0_rw_224.sw_in1k,94.980,5.020,99.230,0.770,27.44,224,0.950,bicubic visformer_small.in1k,94.970,5.030,99.210,0.790,40.22,224,0.900,bicubic convmixer_1536_20.in1k,94.970,5.030,99.170,0.830,51.63,224,0.960,bicubic tf_efficientnet_b3.ap_in1k,94.970,5.030,99.110,0.890,12.23,300,0.904,bicubic resnet152.a2_in1k,94.970,5.030,99.070,0.930,60.19,288,1.000,bicubic xcit_large_24_p16_224.fb_in1k,94.960,5.040,98.830,1.170,189.10,224,1.000,bicubic cait_xxs24_384.fb_dist_in1k,94.950,5.050,99.130,0.870,12.03,384,1.000,bicubic vit_srelpos_medium_patch16_224.sw_in1k,94.940,5.060,99.200,0.800,38.74,224,0.900,bicubic resnet101.a1_in1k,94.940,5.060,99.040,0.960,44.55,288,1.000,bicubic gernet_l.idstcv_in1k,94.930,5.070,99.200,0.800,31.08,256,0.875,bilinear resnetv2_50d_evos.ah_in1k,94.920,5.080,99.180,0.820,25.59,288,1.000,bicubic swin_s3_tiny_224.ms_in1k,94.920,5.080,99.170,0.830,28.33,224,0.900,bicubic convit_small.fb_in1k,94.920,5.080,99.100,0.900,27.78,224,0.875,bicubic nest_tiny_jx.goog_in1k,94.920,5.080,99.100,0.900,17.06,224,0.875,bicubic tf_efficientnet_b3.aa_in1k,94.910,5.090,99.110,0.890,12.23,300,0.904,bicubic xcit_tiny_24_p8_224.fb_in1k,94.900,5.100,99.190,0.810,12.11,224,1.000,bicubic tresnet_l.miil_in1k,94.900,5.100,99.030,0.970,55.99,224,0.875,bilinear coatnet_0_rw_224.sw_in1k,94.900,5.100,99.020,0.980,27.44,224,0.950,bicubic vit_small_patch16_224.augreg_in21k_ft_in1k,94.890,5.110,99.270,0.730,22.05,224,0.900,bicubic ecaresnet50t.a1_in1k,94.890,5.110,99.070,0.930,25.57,288,1.000,bicubic resnet101.a2_in1k,94.890,5.110,99.060,0.940,44.55,288,1.000,bicubic mixer_b16_224.miil_in21k_ft_in1k,94.880,5.120,99.080,0.920,59.88,224,0.875,bilinear regnety_032.tv2_in1k,94.870,5.130,99.230,0.770,19.44,224,0.965,bicubic convnext_nano.d1h_in1k,94.870,5.130,99.140,0.860,15.59,288,1.000,bicubic tf_efficientnet_lite4.in1k,94.870,5.130,99.100,0.900,13.01,380,0.920,bilinear tf_efficientnet_b1.ns_jft_in1k,94.860,5.140,99.250,0.750,7.79,240,0.882,bicubic coatnext_nano_rw_224.sw_in1k,94.850,5.150,99.200,0.800,14.70,224,0.900,bicubic resnetaa50.a1h_in1k,94.850,5.150,99.120,0.880,25.56,288,1.000,bicubic efficientvit_b2.r224_in1k,94.850,5.150,98.970,1.030,24.33,224,0.950,bicubic resnet101.tv2_in1k,94.840,5.160,99.030,0.970,44.55,224,0.965,bilinear edgenext_small.usi_in1k,94.830,5.170,99.410,0.590,5.59,320,1.000,bicubic vit_base_patch16_rpn_224.sw_in1k,94.820,5.180,99.090,0.910,86.54,224,0.900,bicubic xcit_small_12_p16_224.fb_in1k,94.820,5.180,99.060,0.940,26.25,224,1.000,bicubic wide_resnet50_2.tv2_in1k,94.810,5.190,99.260,0.740,68.88,224,0.965,bilinear resnet50d.ra2_in1k,94.810,5.190,99.230,0.770,25.58,288,0.950,bicubic lamhalobotnet50ts_256.a1h_in1k,94.810,5.190,98.980,1.020,22.57,256,0.950,bicubic pit_b_224.in1k,94.810,5.190,98.820,1.180,73.76,224,0.900,bicubic swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,94.800,5.200,99.290,0.710,28.29,224,0.900,bicubic cs3darknet_focus_l.c2ns_in1k,94.790,5.210,99.160,0.840,21.15,288,0.950,bicubic gcresnet50t.ra2_in1k,94.780,5.220,99.120,0.880,25.90,288,1.000,bicubic mobilevitv2_175.cvnets_in22k_ft_in1k,94.780,5.220,99.090,0.910,14.25,256,0.888,bicubic swinv2_cr_tiny_ns_224.sw_in1k,94.770,5.230,99.110,0.890,28.33,224,0.900,bicubic twins_svt_small.in1k,94.760,5.240,99.090,0.910,24.06,224,0.900,bicubic coat_mini.in1k,94.760,5.240,98.950,1.050,10.34,224,0.900,bicubic vit_base_patch32_clip_224.laion2b_ft_in1k,94.750,5.250,99.070,0.930,88.22,224,0.900,bicubic resnetv2_50x1_bit.goog_in21k_ft_in1k,94.740,5.260,99.180,0.820,25.55,448,1.000,bilinear seresnet50.ra2_in1k,94.740,5.260,99.110,0.890,28.09,288,0.950,bicubic legacy_senet154.in1k,94.730,5.270,99.100,0.900,115.09,224,0.875,bilinear regnetx_080.tv2_in1k,94.730,5.270,99.030,0.970,39.57,224,0.965,bicubic repvit_m3.dist_in1k,94.720,5.280,99.060,0.940,10.68,224,0.950,bicubic halonet50ts.a1h_in1k,94.720,5.280,98.830,1.170,22.73,256,0.940,bicubic resnet152s.gluon_in1k,94.710,5.290,99.060,0.940,60.32,224,0.875,bicubic resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,94.700,5.300,99.240,0.760,25.03,224,0.875,bilinear poolformerv2_s36.sail_in1k,94.700,5.300,99.230,0.770,30.79,224,1.000,bicubic resnest50d_4s2x40d.in1k,94.700,5.300,99.130,0.870,30.42,224,0.875,bicubic crossvit_15_240.in1k,94.700,5.300,99.080,0.920,27.53,240,0.875,bicubic senet154.gluon_in1k,94.700,5.300,98.970,1.030,115.09,224,0.875,bicubic xcit_tiny_12_p8_224.fb_dist_in1k,94.690,5.310,99.180,0.820,6.71,224,1.000,bicubic pit_s_distilled_224.in1k,94.690,5.310,99.150,0.850,24.04,224,0.900,bicubic deit3_small_patch16_224.fb_in1k,94.690,5.310,98.760,1.240,22.06,224,0.900,bicubic vit_relpos_small_patch16_224.sw_in1k,94.680,5.320,99.110,0.890,21.98,224,0.900,bicubic fastvit_sa12.apple_dist_in1k,94.680,5.320,99.100,0.900,11.58,256,0.900,bicubic resnetv2_50.a1h_in1k,94.680,5.320,99.090,0.910,25.55,288,1.000,bicubic mobilevitv2_150.cvnets_in22k_ft_in1k,94.680,5.320,98.920,1.080,10.59,256,0.888,bicubic ecaresnet50d.miil_in1k,94.670,5.330,99.260,0.740,25.58,288,0.950,bicubic cs3darknet_l.c2ns_in1k,94.670,5.330,99.230,0.770,21.16,288,0.950,bicubic efficientnet_el.ra_in1k,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic regnetz_b16.ra3_in1k,94.670,5.330,99.130,0.870,9.72,288,1.000,bicubic rexnet_200.nav_in1k,94.670,5.330,99.090,0.910,16.37,224,0.875,bicubic tresnet_m.miil_in1k_448,94.660,5.340,99.150,0.850,31.39,448,0.875,bilinear seresnext101_64x4d.gluon_in1k,94.650,5.350,98.970,1.030,88.23,224,0.875,bicubic tiny_vit_5m_224.dist_in22k_ft_in1k,94.630,5.370,99.140,0.860,5.39,224,0.950,bicubic resnet50_gn.a1h_in1k,94.620,5.380,99.150,0.850,25.56,288,0.950,bicubic swin_tiny_patch4_window7_224.ms_in1k,94.620,5.380,99.120,0.880,28.29,224,0.900,bicubic poolformer_s36.sail_in1k,94.620,5.380,99.050,0.950,30.86,224,0.900,bicubic vit_small_patch16_384.augreg_in1k,94.610,5.390,99.140,0.860,22.20,384,1.000,bicubic twins_pcpvt_small.in1k,94.600,5.400,99.150,0.850,24.11,224,0.900,bicubic deit_small_distilled_patch16_224.fb_in1k,94.600,5.400,99.100,0.900,22.44,224,0.900,bicubic resnet50.tv2_in1k,94.600,5.400,99.090,0.910,25.56,224,0.965,bilinear efficientnet_b3_pruned.in1k,94.600,5.400,99.070,0.930,9.86,300,0.904,bicubic resnest50d.in1k,94.600,5.400,99.030,0.970,27.48,224,0.875,bilinear vit_small_patch32_384.augreg_in21k_ft_in1k,94.590,5.410,99.140,0.860,22.92,384,1.000,bicubic pit_s_224.in1k,94.590,5.410,98.930,1.070,23.46,224,0.900,bicubic crossvit_small_240.in1k,94.580,5.420,99.120,0.880,26.86,240,0.875,bicubic convnext_nano_ols.d1h_in1k,94.580,5.420,99.050,0.950,15.65,288,1.000,bicubic ecaresnet50t.a2_in1k,94.570,5.430,99.040,0.960,25.57,288,1.000,bicubic repvgg_b3.rvgg_in1k,94.570,5.430,98.910,1.090,123.09,224,0.875,bilinear lambda_resnet50ts.a1h_in1k,94.570,5.430,98.650,1.350,21.54,256,0.950,bicubic tnt_s_patch16_224,94.560,5.440,99.170,0.830,23.76,224,0.900,bicubic resmlp_36_224.fb_distilled_in1k,94.560,5.440,99.160,0.840,44.69,224,0.875,bicubic convnextv2_pico.fcmae_ft_in1k,94.560,5.440,99.140,0.860,9.07,288,0.950,bicubic vit_srelpos_small_patch16_224.sw_in1k,94.550,5.450,99.150,0.850,21.97,224,0.900,bicubic repvit_m1_1.dist_450e_in1k,94.550,5.450,99.090,0.910,8.80,224,0.950,bicubic gernet_m.idstcv_in1k,94.540,5.460,98.920,1.080,21.14,224,0.875,bilinear ecaresnetlight.miil_in1k,94.530,5.470,99.180,0.820,30.16,288,0.950,bicubic regnety_320.pycls_in1k,94.520,5.480,99.170,0.830,145.05,224,0.875,bicubic xcit_tiny_12_p16_384.fb_dist_in1k,94.520,5.480,99.170,0.830,6.72,384,1.000,bicubic res2net101d.in1k,94.520,5.480,98.980,1.020,45.23,224,0.875,bilinear mobilevitv2_200.cvnets_in1k,94.520,5.480,98.970,1.030,18.45,256,0.888,bicubic haloregnetz_b.ra3_in1k,94.520,5.480,98.960,1.040,11.68,224,0.940,bicubic regnetx_032.tv2_in1k,94.520,5.480,98.910,1.090,15.30,224,0.965,bicubic resnet50.c1_in1k,94.510,5.490,99.070,0.930,25.56,288,1.000,bicubic resnet50.b1k_in1k,94.510,5.490,99.000,1.000,25.56,288,1.000,bicubic sehalonet33ts.ra2_in1k,94.510,5.490,98.760,1.240,13.69,256,0.940,bicubic repvgg_b3g4.rvgg_in1k,94.500,5.500,99.020,0.980,83.83,224,0.875,bilinear gcresnext50ts.ch_in1k,94.490,5.510,99.010,0.990,15.67,288,1.000,bicubic ese_vovnet39b.ra_in1k,94.480,5.520,99.060,0.940,24.57,288,0.950,bicubic poolformerv2_s24.sail_in1k,94.470,5.530,99.010,0.990,21.34,224,1.000,bicubic resnet50.d_in1k,94.470,5.530,99.000,1.000,25.56,288,1.000,bicubic resnext50_32x4d.tv2_in1k,94.460,5.540,99.030,0.970,25.03,224,0.965,bilinear resnet50d.a2_in1k,94.460,5.540,98.900,1.100,25.58,288,1.000,bicubic eva02_tiny_patch14_336.mim_in22k_ft_in1k,94.450,5.550,99.100,0.900,5.76,336,1.000,bicubic seresnet50.a2_in1k,94.450,5.550,98.890,1.110,28.09,288,1.000,bicubic vit_base_patch32_clip_224.openai_ft_in1k,94.440,5.560,99.180,0.820,88.22,224,0.900,bicubic convmixer_768_32.in1k,94.440,5.560,99.110,0.890,21.11,224,0.960,bicubic vit_base_patch16_384.augreg_in1k,94.440,5.560,99.030,0.970,86.86,384,1.000,bicubic resnet152.a3_in1k,94.440,5.560,98.880,1.120,60.19,224,0.950,bicubic seresnext101_32x4d.gluon_in1k,94.430,5.570,99.090,0.910,48.96,224,0.875,bicubic fastvit_sa12.apple_in1k,94.430,5.570,99.030,0.970,11.58,256,0.900,bicubic resnet152d.gluon_in1k,94.430,5.570,99.000,1.000,60.21,224,0.875,bicubic regnety_016.tv2_in1k,94.410,5.590,99.040,0.960,11.20,224,0.965,bicubic levit_256.fb_dist_in1k,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic levit_conv_256.fb_dist_in1k,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic vit_base_patch32_224.augreg_in21k_ft_in1k,94.400,5.600,99.060,0.940,88.22,224,0.900,bicubic resnext50d_32x4d.bt_in1k,94.400,5.600,99.050,0.950,25.05,288,0.950,bicubic repvit_m2.dist_in1k,94.400,5.600,99.040,0.960,8.80,224,0.950,bicubic resnet50d.a1_in1k,94.400,5.600,98.790,1.210,25.58,288,1.000,bicubic poolformer_s24.sail_in1k,94.390,5.610,99.060,0.940,21.39,224,0.900,bicubic nf_resnet50.ra2_in1k,94.380,5.620,99.070,0.930,25.56,288,0.940,bicubic resnest50d_1s4x24d.in1k,94.380,5.620,99.070,0.930,25.68,224,0.875,bicubic inception_v4.tf_in1k,94.380,5.620,98.820,1.180,42.68,299,0.875,bicubic resnext50_32x4d.a1_in1k,94.380,5.620,98.780,1.220,25.03,288,1.000,bicubic darknet53.c2ns_in1k,94.360,5.640,99.050,0.950,41.61,288,1.000,bicubic efficientnet_b2.ra_in1k,94.360,5.640,99.050,0.950,9.11,288,1.000,bicubic edgenext_small_rw.sw_in1k,94.360,5.640,99.040,0.960,7.83,320,1.000,bicubic inception_resnet_v2.tf_in1k,94.360,5.640,98.800,1.200,55.84,299,0.897,bicubic tf_efficientnet_el.in1k,94.350,5.650,99.100,0.900,10.59,300,0.904,bicubic xcit_tiny_12_p8_224.fb_in1k,94.350,5.650,99.070,0.930,6.71,224,1.000,bicubic gcresnet33ts.ra2_in1k,94.350,5.650,98.960,1.040,19.88,288,1.000,bicubic resnext101_64x4d.gluon_in1k,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic resmlp_24_224.fb_distilled_in1k,94.330,5.670,99.090,0.910,30.02,224,0.875,bicubic resnext50_32x4d.ra_in1k,94.330,5.670,99.030,0.970,25.03,288,0.950,bicubic resnet50.fb_ssl_yfcc100m_ft_in1k,94.310,5.690,99.150,0.850,25.56,224,0.875,bilinear sebotnet33ts_256.a1h_in1k,94.310,5.690,98.600,1.400,13.70,256,0.940,bicubic ecaresnet50d_pruned.miil_in1k,94.300,5.700,99.200,0.800,19.94,288,0.950,bicubic resnet50.b2k_in1k,94.300,5.700,98.930,1.070,25.56,288,1.000,bicubic tf_efficientnet_b3.in1k,94.290,5.710,99.100,0.900,12.23,300,0.904,bicubic rexnet_150.nav_in1k,94.280,5.720,99.090,0.910,9.73,224,0.875,bicubic fastvit_s12.apple_dist_in1k,94.280,5.720,98.980,1.020,9.47,256,0.900,bicubic res2net50d.in1k,94.280,5.720,98.860,1.140,25.72,224,0.875,bilinear repvit_m1_0.dist_450e_in1k,94.270,5.730,99.040,0.960,7.30,224,0.950,bicubic resnet50.c2_in1k,94.270,5.730,99.040,0.960,25.56,288,1.000,bicubic tf_efficientnet_b2.ap_in1k,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic resmlp_big_24_224.fb_in1k,94.270,5.730,98.820,1.180,129.14,224,0.875,bicubic regnetx_120.pycls_in1k,94.260,5.740,99.170,0.830,46.11,224,0.875,bicubic seresnet33ts.ra2_in1k,94.260,5.740,99.000,1.000,19.78,288,1.000,bicubic eca_resnet33ts.ra2_in1k,94.250,5.750,99.030,0.970,19.68,288,1.000,bicubic cspresnext50.ra_in1k,94.240,5.760,99.050,0.950,20.57,256,0.887,bilinear xcit_tiny_24_p16_224.fb_dist_in1k,94.240,5.760,98.960,1.040,12.12,224,1.000,bicubic regnetx_320.pycls_in1k,94.230,5.770,99.050,0.950,107.81,224,0.875,bicubic efficientvit_b1.r288_in1k,94.230,5.770,98.950,1.050,9.10,288,1.000,bicubic mobilevitv2_175.cvnets_in1k,94.230,5.770,98.930,1.070,14.25,256,0.888,bicubic mixnet_xl.ra_in1k,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic tf_efficientnet_b2.aa_in1k,94.220,5.780,99.040,0.960,9.11,260,0.890,bicubic resnet50.a1_in1k,94.220,5.780,98.930,1.070,25.56,288,1.000,bicubic resnext50_32x4d.a2_in1k,94.220,5.780,98.750,1.250,25.03,288,1.000,bicubic maxvit_rmlp_pico_rw_256.sw_in1k,94.210,5.790,99.000,1.000,7.52,256,0.950,bicubic darknetaa53.c2ns_in1k,94.210,5.790,98.950,1.050,36.02,288,1.000,bilinear resnet50.a1h_in1k,94.200,5.800,98.920,1.080,25.56,224,1.000,bicubic repvit_m1_1.dist_300e_in1k,94.180,5.820,99.080,0.920,8.80,224,0.950,bicubic resnet101s.gluon_in1k,94.180,5.820,99.010,0.990,44.67,224,0.875,bicubic resnet101d.gluon_in1k,94.180,5.820,98.940,1.060,44.57,224,0.875,bicubic resnetblur50.bt_in1k,94.170,5.830,99.010,0.990,25.56,288,0.950,bicubic seresnext50_32x4d.gluon_in1k,94.170,5.830,98.920,1.080,27.56,224,0.875,bicubic seresnet50.a1_in1k,94.160,5.840,98.850,1.150,28.09,288,1.000,bicubic dpn92.mx_in1k,94.150,5.850,98.950,1.050,37.67,224,0.875,bicubic regnety_064.pycls_in1k,94.130,5.870,99.030,0.970,30.58,224,0.875,bicubic regnety_160.pycls_in1k,94.130,5.870,99.020,0.980,83.59,224,0.875,bicubic resnext101_32x4d.gluon_in1k,94.130,5.870,98.940,1.060,44.18,224,0.875,bicubic legacy_seresnext101_32x4d.in1k,94.120,5.880,98.970,1.030,48.96,224,0.875,bilinear resnet50.a2_in1k,94.120,5.880,98.850,1.150,25.56,288,1.000,bicubic inception_resnet_v2.tf_ens_adv_in1k,94.120,5.880,98.790,1.210,55.84,299,0.897,bicubic cspdarknet53.ra_in1k,94.100,5.900,98.980,1.020,27.64,256,0.887,bilinear fastvit_t12.apple_dist_in1k,94.100,5.900,98.950,1.050,7.55,256,0.900,bicubic efficientnet_el_pruned.in1k,94.090,5.910,99.020,0.980,10.59,300,0.904,bicubic tf_efficientnet_lite3.in1k,94.090,5.910,98.960,1.040,8.20,300,0.904,bilinear resnet50.ra_in1k,94.090,5.910,98.840,1.160,25.56,288,0.950,bicubic tresnet_m.miil_in1k,94.080,5.920,98.830,1.170,31.39,224,0.875,bilinear tf_efficientnetv2_b2.in1k,94.060,5.940,98.930,1.070,10.10,260,0.890,bicubic gcvit_xxtiny.in1k,94.050,5.950,99.080,0.920,12.00,224,0.875,bicubic mobilevitv2_150.cvnets_in1k,94.050,5.950,98.900,1.100,10.59,256,0.888,bicubic convnext_pico.d1_in1k,94.030,5.970,99.010,0.990,9.05,288,0.950,bicubic resnetrs50.tf_in1k,94.030,5.970,98.850,1.150,35.69,224,0.910,bicubic resnet152.gluon_in1k,94.030,5.970,98.740,1.260,60.19,224,0.875,bicubic regnetx_016.tv2_in1k,94.020,5.980,98.930,1.070,9.19,224,0.965,bicubic hrnet_w48.ms_in1k,94.010,5.990,99.030,0.970,77.47,224,0.875,bilinear regnety_120.pycls_in1k,94.010,5.990,99.030,0.970,51.82,224,0.875,bicubic convnext_pico_ols.d1_in1k,94.010,5.990,98.930,1.070,9.06,288,1.000,bicubic dpn107.mx_in1k,94.010,5.990,98.820,1.180,86.92,224,0.875,bicubic resnet50.ram_in1k,94.000,6.000,98.880,1.120,25.56,288,0.950,bicubic dla102x2.in1k,93.990,6.010,99.040,0.960,41.28,224,0.875,bilinear deit_small_patch16_224.fb_in1k,93.990,6.010,98.960,1.040,22.05,224,0.900,bicubic skresnext50_32x4d.ra_in1k,93.970,6.030,98.830,1.170,27.48,224,0.875,bicubic resnet50.bt_in1k,93.960,6.040,98.930,1.070,25.56,288,0.950,bicubic efficientformer_l1.snap_dist_in1k,93.940,6.060,99.030,0.970,12.29,224,0.950,bicubic ecaresnet26t.ra2_in1k,93.940,6.060,98.930,1.070,16.01,320,0.950,bicubic dpn98.mx_in1k,93.930,6.070,98.910,1.090,61.57,224,0.875,bicubic resnet33ts.ra2_in1k,93.930,6.070,98.880,1.120,19.68,288,1.000,bicubic xception71.tf_in1k,93.920,6.080,98.950,1.050,42.34,299,0.903,bicubic regnetx_160.pycls_in1k,93.910,6.090,99.090,0.910,54.28,224,0.875,bicubic cait_xxs36_224.fb_dist_in1k,93.910,6.090,98.890,1.110,17.30,224,1.000,bicubic vit_base_patch16_224.sam_in1k,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic nf_regnet_b1.ra2_in1k,93.890,6.110,98.740,1.260,10.22,288,0.900,bicubic regnety_080.pycls_in1k,93.880,6.120,99.000,1.000,39.18,224,0.875,bicubic fbnetv3_d.ra2_in1k,93.870,6.130,98.890,1.110,10.31,256,0.950,bilinear cspresnet50.ra_in1k,93.870,6.130,98.870,1.130,21.62,256,0.887,bilinear resnet152c.gluon_in1k,93.870,6.130,98.800,1.200,60.21,224,0.875,bicubic ecaresnet50t.a3_in1k,93.860,6.140,98.850,1.150,25.57,224,0.950,bicubic resnet101.a3_in1k,93.860,6.140,98.760,1.240,44.55,224,0.950,bicubic xcit_tiny_24_p16_224.fb_in1k,93.850,6.150,98.750,1.250,12.12,224,1.000,bicubic efficientformerv2_s1.snap_dist_in1k,93.840,6.160,98.890,1.110,6.19,224,0.950,bicubic hrnet_w64.ms_in1k,93.830,6.170,98.940,1.060,128.06,224,0.875,bilinear repvgg_b2g4.rvgg_in1k,93.830,6.170,98.930,1.070,61.76,224,0.875,bilinear efficientnet_b2_pruned.in1k,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic dla169.in1k,93.800,6.200,98.860,1.140,53.39,224,0.875,bilinear tiny_vit_5m_224.in1k,93.790,6.210,98.940,1.060,5.39,224,0.950,bicubic regnetx_080.pycls_in1k,93.790,6.210,98.910,1.090,39.57,224,0.875,bicubic dpn68b.ra_in1k,93.790,6.210,98.540,1.460,12.61,288,1.000,bicubic resnext101_32x8d.tv_in1k,93.780,6.220,98.960,1.040,88.79,224,0.875,bilinear dpn131.mx_in1k,93.780,6.220,98.850,1.150,79.25,224,0.875,bicubic repvit_m1_0.dist_300e_in1k,93.760,6.240,98.920,1.080,7.30,224,0.950,bicubic resnet101.gluon_in1k,93.760,6.240,98.700,1.300,44.55,224,0.875,bicubic tf_efficientnet_b0.ns_jft_in1k,93.750,6.250,98.970,1.030,5.29,224,0.875,bicubic convnextv2_femto.fcmae_ft_in1k,93.750,6.250,98.930,1.070,5.23,288,0.950,bicubic xception65.tf_in1k,93.750,6.250,98.870,1.130,39.92,299,0.903,bicubic efficientnet_em.ra2_in1k,93.730,6.270,98.930,1.070,6.90,240,0.882,bicubic hrnet_w40.ms_in1k,93.730,6.270,98.800,1.200,57.56,224,0.875,bilinear wide_resnet101_2.tv_in1k,93.720,6.280,98.810,1.190,126.89,224,0.875,bilinear tf_efficientnet_b1.aa_in1k,93.720,6.280,98.800,1.200,7.79,240,0.882,bicubic tf_efficientnet_b2.in1k,93.710,6.290,98.930,1.070,9.11,260,0.890,bicubic tf_efficientnetv2_b1.in1k,93.710,6.290,98.820,1.180,8.14,240,0.882,bicubic efficientvit_b1.r256_in1k,93.710,6.290,98.810,1.190,9.10,256,1.000,bicubic levit_192.fb_dist_in1k,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic levit_conv_192.fb_dist_in1k,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic resnet101c.gluon_in1k,93.700,6.300,98.760,1.240,44.57,224,0.875,bicubic fastvit_s12.apple_in1k,93.700,6.300,98.720,1.280,9.47,256,0.900,bicubic regnetx_040.pycls_in1k,93.690,6.310,98.930,1.070,22.12,224,0.875,bicubic rexnet_130.nav_in1k,93.680,6.320,98.700,1.300,7.56,224,0.875,bicubic resnext50_32x4d.gluon_in1k,93.670,6.330,98.690,1.310,25.03,224,0.875,bicubic resmlp_36_224.fb_in1k,93.650,6.350,98.950,1.050,44.69,224,0.875,bicubic mobileone_s4.apple_in1k,93.650,6.350,98.650,1.350,14.95,224,0.900,bilinear regnetx_064.pycls_in1k,93.640,6.360,99.040,0.960,26.21,224,0.875,bicubic resnet50.am_in1k,93.640,6.360,98.870,1.130,25.56,224,0.875,bicubic regnety_040.pycls_in1k,93.630,6.370,98.950,1.050,20.65,224,0.875,bicubic fbnetv3_b.ra2_in1k,93.630,6.370,98.910,1.090,8.60,256,0.950,bilinear tf_efficientnet_b1.ap_in1k,93.630,6.370,98.800,1.200,7.79,240,0.882,bicubic hrnet_w44.ms_in1k,93.620,6.380,98.960,1.040,67.06,224,0.875,bilinear legacy_xception.tf_in1k,93.620,6.380,98.770,1.230,22.86,299,0.897,bicubic resnet34d.ra2_in1k,93.600,6.400,98.760,1.240,21.82,288,0.950,bicubic res2net50_26w_6s.in1k,93.590,6.410,98.740,1.260,37.05,224,0.875,bilinear resnet32ts.ra2_in1k,93.590,6.410,98.740,1.260,17.96,288,1.000,bicubic halonet26t.a1h_in1k,93.590,6.410,98.630,1.370,12.48,256,0.950,bicubic repvgg_b2.rvgg_in1k,93.580,6.420,99.070,0.930,89.02,224,0.875,bilinear dla60_res2next.in1k,93.580,6.420,98.790,1.210,17.03,224,0.875,bilinear tf_efficientnet_cc_b1_8e.in1k,93.580,6.420,98.690,1.310,39.72,240,0.882,bicubic resnet50s.gluon_in1k,93.570,6.430,98.840,1.160,25.68,224,0.875,bicubic inception_v3.gluon_in1k,93.560,6.440,98.840,1.160,23.83,299,0.875,bicubic resnext50_32x4d.a3_in1k,93.550,6.450,98.820,1.180,25.03,224,0.950,bicubic eca_halonext26ts.c1_in1k,93.550,6.450,98.680,1.320,10.76,256,0.940,bicubic repghostnet_200.in1k,93.550,6.450,98.600,1.400,9.80,224,0.875,bicubic dla102x.in1k,93.540,6.460,98.850,1.150,26.31,224,0.875,bilinear resnet50d.gluon_in1k,93.540,6.460,98.710,1.290,25.58,224,0.875,bicubic res2net101_26w_4s.in1k,93.530,6.470,98.600,1.400,45.21,224,0.875,bilinear convnext_tiny.fb_in22k_ft_in1k,93.530,6.470,98.570,1.430,28.59,288,1.000,bicubic coat_tiny.in1k,93.510,6.490,98.680,1.320,5.50,224,0.900,bicubic selecsls60b.in1k,93.500,6.500,98.840,1.160,32.77,224,0.875,bicubic gmlp_s16_224.ra3_in1k,93.500,6.500,98.780,1.220,19.42,224,0.875,bicubic pvt_v2_b1.in1k,93.490,6.510,98.860,1.140,14.01,224,0.900,bicubic fastvit_t12.apple_in1k,93.490,6.510,98.710,1.290,7.55,256,0.900,bicubic hrnet_w18.ms_aug_in1k,93.480,6.520,98.980,1.020,21.30,224,0.950,bilinear mobilevitv2_125.cvnets_in1k,93.480,6.520,98.840,1.160,7.48,256,0.888,bicubic xception41.tf_in1k,93.480,6.520,98.750,1.250,26.97,299,0.903,bicubic coat_lite_mini.in1k,93.470,6.530,98.770,1.230,11.01,224,0.900,bicubic regnety_032.pycls_in1k,93.460,6.540,98.950,1.050,19.44,224,0.875,bicubic wide_resnet50_2.tv_in1k,93.460,6.540,98.950,1.050,68.88,224,0.875,bilinear repvit_m0_9.dist_300e_in1k,93.460,6.540,98.820,1.180,5.49,224,0.950,bicubic legacy_seresnext50_32x4d.in1k,93.450,6.550,98.800,1.200,27.56,224,0.875,bilinear cait_xxs24_224.fb_dist_in1k,93.450,6.550,98.780,1.220,11.96,224,1.000,bicubic vit_small_patch16_224.augreg_in1k,93.450,6.550,98.780,1.220,22.05,224,0.900,bicubic repvit_m0_9.dist_450e_in1k,93.440,6.560,98.910,1.090,5.49,224,0.950,bicubic convnext_femto.d1_in1k,93.440,6.560,98.820,1.180,5.22,288,0.950,bicubic repvgg_b1.rvgg_in1k,93.440,6.560,98.790,1.210,57.42,224,0.875,bilinear botnet26t_256.c1_in1k,93.440,6.560,98.650,1.350,12.49,256,0.950,bicubic lambda_resnet26rpt_256.c1_in1k,93.430,6.570,98.880,1.120,10.99,256,0.940,bicubic lambda_resnet26t.c1_in1k,93.430,6.570,98.730,1.270,10.96,256,0.940,bicubic vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.420,6.580,98.830,1.170,5.79,384,1.000,bicubic resmlp_24_224.fb_in1k,93.420,6.580,98.810,1.190,30.02,224,0.875,bicubic legacy_seresnet152.in1k,93.410,6.590,98.850,1.150,66.82,224,0.875,bilinear hrnet_w30.ms_in1k,93.410,6.590,98.830,1.170,37.71,224,0.875,bilinear resnet50d.a3_in1k,93.410,6.590,98.750,1.250,25.58,224,0.950,bicubic res2net50_26w_8s.in1k,93.410,6.590,98.690,1.310,48.40,224,0.875,bilinear convnext_femto_ols.d1_in1k,93.390,6.610,98.910,1.090,5.23,288,0.950,bicubic repvit_m1.dist_in1k,93.380,6.620,98.650,1.350,5.49,224,0.950,bicubic dla60_res2net.in1k,93.370,6.630,98.840,1.160,20.85,224,0.875,bilinear xcit_tiny_12_p16_224.fb_dist_in1k,93.350,6.650,98.760,1.240,6.72,224,1.000,bicubic eca_botnext26ts_256.c1_in1k,93.350,6.650,98.690,1.310,10.59,256,0.950,bicubic seresnext26t_32x4d.bt_in1k,93.350,6.650,98.690,1.310,16.81,288,0.950,bicubic vit_base_patch16_224.augreg_in1k,93.350,6.650,98.660,1.340,86.57,224,0.900,bicubic efficientvit_b1.r224_in1k,93.330,6.670,98.570,1.430,9.10,224,0.950,bicubic xcit_nano_12_p8_384.fb_dist_in1k,93.300,6.700,98.860,1.140,3.05,384,1.000,bicubic pit_xs_distilled_224.in1k,93.290,6.710,98.790,1.210,11.00,224,0.900,bicubic cs3darknet_m.c2ns_in1k,93.280,6.720,98.720,1.280,9.31,288,0.950,bicubic dla102.in1k,93.270,6.730,98.790,1.210,33.27,224,0.875,bilinear legacy_seresnet101.in1k,93.270,6.730,98.740,1.260,49.33,224,0.875,bilinear resnet152.tv_in1k,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear regnetx_032.pycls_in1k,93.240,6.760,98.720,1.280,15.30,224,0.875,bicubic mixnet_l.ft_in1k,93.240,6.760,98.700,1.300,7.33,224,0.875,bicubic resnest26d.gluon_in1k,93.220,6.780,98.850,1.150,17.07,224,0.875,bilinear dla60x.in1k,93.190,6.810,98.720,1.280,17.35,224,0.875,bilinear tf_efficientnet_em.in1k,93.190,6.810,98.660,1.340,6.90,240,0.882,bicubic inception_v3.tf_in1k,93.190,6.810,98.490,1.510,23.83,299,0.875,bicubic res2net50_26w_4s.in1k,93.180,6.820,98.660,1.340,25.70,224,0.875,bilinear vit_base_patch32_384.augreg_in1k,93.160,6.840,98.610,1.390,88.30,384,1.000,bicubic mobilevit_s.cvnets_in1k,93.150,6.850,98.780,1.220,5.58,256,0.900,bicubic regnety_008_tv.tv2_in1k,93.150,6.850,98.680,1.320,6.43,224,0.965,bicubic res2next50.in1k,93.150,6.850,98.640,1.360,24.67,224,0.875,bilinear mobilevitv2_100.cvnets_in1k,93.140,6.860,98.760,1.240,4.90,256,0.888,bicubic vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.140,6.860,98.310,1.690,119.42,256,0.900,bicubic bat_resnext26ts.ch_in1k,93.120,6.880,98.730,1.270,10.73,256,0.900,bicubic cs3darknet_focus_m.c2ns_in1k,93.100,6.900,98.750,1.250,9.30,288,0.950,bicubic ghostnetv2_160.in1k,93.090,6.910,98.740,1.260,12.39,224,0.875,bicubic seresnext26d_32x4d.bt_in1k,93.060,6.940,98.710,1.290,16.81,288,0.950,bicubic tf_efficientnetv2_b0.in1k,93.060,6.940,98.700,1.300,7.14,224,0.875,bicubic repvgg_b1g4.rvgg_in1k,93.030,6.970,98.820,1.180,39.97,224,0.875,bilinear levit_128.fb_dist_in1k,93.030,6.970,98.710,1.290,9.21,224,0.900,bicubic levit_conv_128.fb_dist_in1k,93.030,6.970,98.700,1.300,9.21,224,0.900,bicubic res2net50_14w_8s.in1k,93.030,6.970,98.700,1.300,25.06,224,0.875,bilinear densenetblur121d.ra_in1k,93.030,6.970,98.600,1.400,8.00,288,0.950,bicubic tf_mixnet_l.in1k,93.030,6.970,98.530,1.470,7.33,224,0.875,bicubic efficientnet_b1.ft_in1k,93.020,6.980,98.710,1.290,7.79,256,1.000,bicubic selecsls60.in1k,93.010,6.990,98.820,1.180,30.67,224,0.875,bicubic regnety_016.pycls_in1k,93.010,6.990,98.670,1.330,11.20,224,0.875,bicubic inception_v3.tf_adv_in1k,93.010,6.990,98.490,1.510,23.83,299,0.875,bicubic hrnet_w18_small_v2.gluon_in1k,93.000,7.000,98.760,1.240,15.60,224,0.875,bicubic resnet34.a1_in1k,93.000,7.000,98.630,1.370,21.80,288,1.000,bicubic visformer_tiny.in1k,92.980,7.020,98.730,1.270,10.32,224,0.900,bicubic convnext_atto_ols.a2_in1k,92.980,7.020,98.670,1.330,3.70,288,0.950,bicubic mobileone_s3.apple_in1k,92.980,7.020,98.630,1.370,10.17,224,0.900,bilinear hardcorenas_f.miil_green_in1k,92.980,7.020,98.620,1.380,8.20,224,0.875,bilinear efficientnet_b1_pruned.in1k,92.980,7.020,98.540,1.460,6.33,240,0.882,bicubic hrnet_w32.ms_in1k,92.950,7.050,98.850,1.150,41.23,224,0.875,bilinear seresnext26ts.ch_in1k,92.940,7.060,98.670,1.330,10.39,288,1.000,bicubic hardcorenas_e.miil_green_in1k,92.940,7.060,98.580,1.420,8.07,224,0.875,bilinear resnet50.a3_in1k,92.940,7.060,98.510,1.490,25.56,224,0.950,bicubic tf_efficientnet_b1.in1k,92.930,7.070,98.660,1.340,7.79,240,0.882,bicubic convnextv2_atto.fcmae_ft_in1k,92.920,7.080,98.560,1.440,3.71,288,0.950,bicubic resnet50c.gluon_in1k,92.910,7.090,98.700,1.300,25.58,224,0.875,bicubic efficientnet_es.ra_in1k,92.910,7.090,98.690,1.310,5.44,224,0.875,bicubic resnet26t.ra2_in1k,92.910,7.090,98.680,1.320,16.01,320,1.000,bicubic resnext50_32x4d.tv_in1k,92.900,7.100,98.730,1.270,25.03,224,0.875,bilinear inception_v3.tv_in1k,92.900,7.100,98.320,1.680,23.83,299,0.875,bicubic densenet161.tv_in1k,92.890,7.110,98.790,1.210,28.68,224,0.875,bicubic pit_xs_224.in1k,92.890,7.110,98.780,1.220,10.62,224,0.900,bicubic poolformerv2_s12.sail_in1k,92.890,7.110,98.530,1.470,11.89,224,1.000,bicubic resnet101.tv_in1k,92.880,7.120,98.660,1.340,44.55,224,0.875,bilinear resmlp_12_224.fb_distilled_in1k,92.870,7.130,98.620,1.380,15.35,224,0.875,bicubic tf_efficientnet_cc_b0_8e.in1k,92.870,7.130,98.460,1.540,24.01,224,0.875,bicubic coat_lite_tiny.in1k,92.860,7.140,98.640,1.360,5.72,224,0.900,bicubic rexnet_100.nav_in1k,92.830,7.170,98.600,1.400,4.80,224,0.875,bicubic tf_efficientnet_cc_b0_4e.in1k,92.820,7.180,98.440,1.560,13.31,224,0.875,bicubic tinynet_a.in1k,92.810,7.190,98.560,1.440,6.19,192,0.875,bicubic dpn68b.mx_in1k,92.780,7.220,98.520,1.480,12.61,224,0.875,bicubic res2net50_48w_2s.in1k,92.780,7.220,98.470,1.530,25.29,224,0.875,bilinear hrnet_w18.ms_in1k,92.770,7.230,98.660,1.340,21.30,224,0.875,bilinear convnext_atto.d2_in1k,92.770,7.230,98.620,1.380,3.70,288,0.950,bicubic crossvit_9_dagger_240.in1k,92.770,7.230,98.490,1.510,8.78,240,0.875,bicubic ese_vovnet19b_dw.ra_in1k,92.760,7.240,98.650,1.350,6.54,288,0.950,bicubic eca_resnext26ts.ch_in1k,92.750,7.250,98.710,1.290,10.30,288,1.000,bicubic gcresnext26ts.ch_in1k,92.740,7.260,98.610,1.390,10.48,288,1.000,bicubic densenet201.tv_in1k,92.700,7.300,98.640,1.360,20.01,224,0.875,bicubic densenet121.ra_in1k,92.700,7.300,98.600,1.400,7.98,288,0.950,bicubic repvgg_a2.rvgg_in1k,92.680,7.320,98.530,1.470,28.21,224,0.875,bilinear gmixer_24_224.ra3_in1k,92.680,7.320,98.280,1.720,24.72,224,0.875,bicubic mobileone_s2.apple_in1k,92.660,7.340,98.680,1.320,7.88,224,0.900,bilinear legacy_seresnet50.in1k,92.660,7.340,98.650,1.350,28.09,224,0.875,bilinear dla60.in1k,92.650,7.350,98.630,1.370,22.04,224,0.875,bilinear tf_efficientnet_b0.ap_in1k,92.620,7.380,98.370,1.630,5.29,224,0.875,bicubic mobilenetv2_120d.ra_in1k,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic hardcorenas_d.miil_green_in1k,92.610,7.390,98.430,1.570,7.50,224,0.875,bilinear legacy_seresnext26_32x4d.in1k,92.600,7.400,98.410,1.590,16.79,224,0.875,bicubic tf_efficientnet_lite2.in1k,92.590,7.410,98.540,1.460,6.09,260,0.890,bicubic fastvit_t8.apple_dist_in1k,92.590,7.410,98.430,1.570,4.03,256,0.900,bicubic resnet34.a2_in1k,92.570,7.430,98.570,1.430,21.80,288,1.000,bicubic skresnet34.ra_in1k,92.570,7.430,98.520,1.480,22.28,224,0.875,bicubic resnet50.gluon_in1k,92.560,7.440,98.550,1.450,25.56,224,0.875,bicubic resnet26d.bt_in1k,92.550,7.450,98.650,1.350,16.01,288,0.950,bicubic regnetx_016.pycls_in1k,92.530,7.470,98.550,1.450,9.19,224,0.875,bicubic poolformer_s12.sail_in1k,92.500,7.500,98.390,1.610,11.92,224,0.900,bicubic regnetx_008.tv2_in1k,92.490,7.510,98.430,1.570,7.26,224,0.965,bicubic efficientnet_b0.ra_in1k,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic xcit_tiny_12_p16_224.fb_in1k,92.480,7.520,98.630,1.370,6.72,224,1.000,bicubic selecsls42b.in1k,92.480,7.520,98.430,1.570,32.46,224,0.875,bicubic gernet_s.idstcv_in1k,92.440,7.560,98.500,1.500,8.17,224,0.875,bilinear xcit_nano_12_p8_224.fb_dist_in1k,92.430,7.570,98.540,1.460,3.05,224,1.000,bicubic tf_efficientnet_b0.aa_in1k,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic repghostnet_150.in1k,92.380,7.620,98.530,1.470,6.58,224,0.875,bicubic resnext26ts.ra2_in1k,92.380,7.620,98.390,1.610,10.30,288,1.000,bicubic seresnet50.a3_in1k,92.360,7.640,98.330,1.670,28.09,224,0.950,bicubic hardcorenas_c.miil_green_in1k,92.350,7.650,98.340,1.660,5.52,224,0.875,bilinear convmixer_1024_20_ks9_p14.in1k,92.330,7.670,98.430,1.570,24.38,224,0.960,bicubic dpn68.mx_in1k,92.300,7.700,98.610,1.390,12.61,224,0.875,bicubic densenet169.tv_in1k,92.300,7.700,98.590,1.410,14.15,224,0.875,bicubic tf_efficientnet_lite1.in1k,92.290,7.710,98.500,1.500,5.42,240,0.882,bicubic resnet34.bt_in1k,92.280,7.720,98.600,1.400,21.80,288,0.950,bicubic tf_efficientnet_b0.in1k,92.280,7.720,98.550,1.450,5.29,224,0.875,bicubic mixnet_m.ft_in1k,92.270,7.730,98.360,1.640,5.01,224,0.875,bicubic mobilenetv3_large_100.miil_in21k_ft_in1k,92.260,7.740,98.240,1.760,5.48,224,0.875,bilinear ghostnetv2_130.in1k,92.240,7.760,98.380,1.620,8.96,224,0.875,bicubic tf_mixnet_m.in1k,92.210,7.790,98.420,1.580,5.01,224,0.875,bicubic efficientvit_m5.r224_in1k,92.150,7.850,98.520,1.480,12.47,224,0.875,bicubic vit_small_patch32_224.augreg_in21k_ft_in1k,92.140,7.860,98.520,1.480,22.88,224,0.900,bicubic xcit_nano_12_p16_384.fb_dist_in1k,92.130,7.870,98.510,1.490,3.05,384,1.000,bicubic resmlp_12_224.fb_in1k,92.120,7.880,98.570,1.430,15.35,224,0.875,bicubic resnet50.tv_in1k,92.120,7.880,98.410,1.590,25.56,224,0.875,bilinear resnet26.bt_in1k,92.110,7.890,98.550,1.450,16.00,288,0.950,bicubic tf_efficientnet_es.in1k,92.110,7.890,98.430,1.570,5.44,224,0.875,bicubic mobilenetv2_140.ra_in1k,92.050,7.950,98.250,1.750,6.11,224,0.875,bicubic mobilevitv2_075.cvnets_in1k,91.970,8.030,98.300,1.700,2.87,256,0.888,bicubic repghostnet_130.in1k,91.940,8.060,98.390,1.610,5.48,224,0.875,bicubic fastvit_t8.apple_in1k,91.930,8.070,98.380,1.620,4.03,256,0.900,bicubic hardcorenas_b.miil_green_in1k,91.920,8.080,98.410,1.590,5.18,224,0.875,bilinear vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.920,8.080,98.340,1.660,5.72,224,0.900,bicubic regnety_008.pycls_in1k,91.900,8.100,98.410,1.590,6.26,224,0.875,bicubic efficientformerv2_s0.snap_dist_in1k,91.860,8.140,98.370,1.630,3.60,224,0.950,bicubic mobileone_s1.apple_in1k,91.790,8.210,98.460,1.540,4.83,224,0.900,bilinear mixnet_s.ft_in1k,91.780,8.220,98.300,1.700,4.13,224,0.875,bicubic vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,91.730,8.270,98.430,1.570,6.36,384,1.000,bicubic efficientnet_es_pruned.in1k,91.700,8.300,98.410,1.590,5.44,224,0.875,bicubic repvgg_b0.rvgg_in1k,91.680,8.320,98.450,1.550,15.82,224,0.875,bilinear tf_mixnet_s.in1k,91.680,8.320,98.240,1.760,4.13,224,0.875,bicubic semnasnet_100.rmsp_in1k,91.670,8.330,98.290,1.710,3.89,224,0.875,bicubic ghostnetv2_100.in1k,91.630,8.370,98.290,1.710,6.16,224,0.875,bicubic regnety_004.tv2_in1k,91.620,8.380,98.280,1.720,4.34,224,0.965,bicubic hardcorenas_a.miil_green_in1k,91.620,8.380,98.170,1.830,5.26,224,0.875,bilinear edgenext_x_small.in1k,91.580,8.420,98.190,1.810,2.34,288,1.000,bicubic regnety_006.pycls_in1k,91.560,8.440,98.430,1.570,6.06,224,0.875,bicubic mobilenetv3_rw.rmsp_in1k,91.550,8.450,98.280,1.720,5.48,224,0.875,bicubic levit_128s.fb_dist_in1k,91.510,8.490,98.400,1.600,7.78,224,0.900,bicubic levit_conv_128s.fb_dist_in1k,91.500,8.500,98.400,1.600,7.78,224,0.900,bicubic legacy_seresnet34.in1k,91.490,8.510,98.200,1.800,21.96,224,0.875,bilinear mobilenetv3_large_100.ra_in1k,91.470,8.530,98.320,1.680,5.48,224,0.875,bicubic tf_mobilenetv3_large_100.in1k,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear densenet121.tv_in1k,91.400,8.600,98.250,1.750,7.98,224,0.875,bicubic mobilenetv2_110d.ra_in1k,91.330,8.670,98.190,1.810,4.52,224,0.875,bicubic tf_efficientnet_lite0.in1k,91.280,8.720,98.080,1.920,4.65,224,0.875,bicubic fbnetc_100.rmsp_in1k,91.280,8.720,97.840,2.160,5.57,224,0.875,bilinear efficientnet_lite0.ra_in1k,91.250,8.750,98.240,1.760,4.65,224,0.875,bicubic dla34.in1k,91.220,8.780,98.170,1.830,15.74,224,0.875,bilinear mobilevit_xs.cvnets_in1k,91.210,8.790,98.220,1.780,2.32,256,0.900,bicubic mnasnet_100.rmsp_in1k,91.210,8.790,98.050,1.950,4.38,224,0.875,bicubic regnetx_008.pycls_in1k,91.170,8.830,98.370,1.630,7.26,224,0.875,bicubic hrnet_w18_small_v2.ms_in1k,91.160,8.840,98.340,1.660,15.60,224,0.875,bilinear regnetx_004_tv.tv2_in1k,91.160,8.840,98.100,1.900,5.50,224,0.965,bicubic resnest14d.gluon_in1k,91.150,8.850,98.330,1.670,10.61,224,0.875,bilinear mixer_b16_224.goog_in21k_ft_in1k,91.140,8.860,97.400,2.600,59.88,224,0.875,bicubic repvgg_a1.rvgg_in1k,91.120,8.880,98.160,1.840,14.09,224,0.875,bilinear tinynet_b.in1k,91.110,8.890,98.060,1.940,3.73,188,0.875,bicubic xcit_nano_12_p8_224.fb_in1k,91.100,8.900,98.230,1.770,3.05,224,1.000,bicubic resnet18.fb_swsl_ig1b_ft_in1k,91.100,8.900,98.200,1.800,11.69,224,0.875,bilinear repghostnet_111.in1k,91.100,8.900,98.050,1.950,4.54,224,0.875,bicubic resnet34.gluon_in1k,91.090,8.910,98.180,1.820,21.80,224,0.875,bicubic deit_tiny_distilled_patch16_224.fb_in1k,91.080,8.920,98.270,1.730,5.91,224,0.900,bicubic crossvit_9_240.in1k,91.050,8.950,98.320,1.680,8.55,240,0.875,bicubic vgg19_bn.tv_in1k,90.990,9.010,98.100,1.900,143.68,224,0.875,bilinear resnet18d.ra2_in1k,90.800,9.200,98.160,1.840,11.71,288,0.950,bicubic regnetx_006.pycls_in1k,90.790,9.210,98.090,1.910,6.20,224,0.875,bicubic regnety_004.pycls_in1k,90.770,9.230,98.070,1.930,4.34,224,0.875,bicubic efficientvit_m4.r224_in1k,90.740,9.260,98.040,1.960,8.80,224,0.875,bicubic pit_ti_distilled_224.in1k,90.730,9.270,98.250,1.750,5.10,224,0.900,bicubic resnet18.fb_ssl_yfcc100m_ft_in1k,90.700,9.300,98.020,1.980,11.69,224,0.875,bilinear repghostnet_100.in1k,90.690,9.310,98.120,1.880,4.07,224,0.875,bicubic spnasnet_100.rmsp_in1k,90.590,9.410,97.950,2.050,4.42,224,0.875,bilinear vit_base_patch32_224.augreg_in1k,90.590,9.410,97.720,2.280,88.22,224,0.900,bicubic convit_tiny.fb_in1k,90.550,9.450,98.190,1.810,5.71,224,0.875,bicubic vgg16_bn.tv_in1k,90.540,9.460,97.990,2.010,138.37,224,0.875,bilinear crossvit_tiny_240.in1k,90.530,9.470,97.950,2.050,7.01,240,0.875,bicubic ghostnet_100.in1k,90.460,9.540,97.910,2.090,5.18,224,0.875,bicubic pit_ti_224.in1k,90.420,9.580,98.010,1.990,4.85,224,0.900,bicubic tf_mobilenetv3_large_075.in1k,90.330,9.670,97.870,2.130,3.99,224,0.875,bilinear hrnet_w18_small.gluon_in1k,90.310,9.690,97.750,2.250,13.19,224,0.875,bicubic resnet34.tv_in1k,90.300,9.700,97.970,2.030,21.80,224,0.875,bilinear resnet34.a3_in1k,90.240,9.760,97.880,2.120,21.80,224,0.950,bicubic semnasnet_075.rmsp_in1k,90.220,9.780,97.950,2.050,2.91,224,0.875,bicubic resnet18.a1_in1k,90.200,9.800,97.760,2.240,11.69,288,1.000,bicubic xcit_nano_12_p16_224.fb_dist_in1k,90.190,9.810,97.750,2.250,3.05,224,1.000,bicubic skresnet18.ra_in1k,90.180,9.820,97.780,2.220,11.96,224,0.875,bicubic efficientvit_m3.r224_in1k,90.000,10.000,97.830,2.170,6.90,224,0.875,bicubic hrnet_w18_small.ms_in1k,89.870,10.130,97.890,2.110,13.19,224,0.875,bilinear mobilenetv2_100.ra_in1k,89.870,10.130,97.830,2.170,3.50,224,0.875,bicubic vit_base_patch32_224.sam_in1k,89.870,10.130,97.600,2.400,88.22,224,0.900,bicubic edgenext_xx_small.in1k,89.800,10.200,97.500,2.500,1.33,288,1.000,bicubic repvgg_a0.rvgg_in1k,89.680,10.320,97.760,2.240,9.11,224,0.875,bilinear vgg19.tv_in1k,89.680,10.320,97.550,2.450,143.67,224,0.875,bilinear deit_tiny_patch16_224.fb_in1k,89.610,10.390,97.960,2.040,5.72,224,0.900,bicubic regnetx_004.pycls_in1k,89.470,10.530,97.760,2.240,5.16,224,0.875,bicubic resnet18.a2_in1k,89.470,10.530,97.630,2.370,11.69,288,1.000,bicubic repghostnet_080.in1k,89.470,10.530,97.410,2.590,3.28,224,0.875,bicubic vgg16.tv_in1k,89.370,10.630,97.520,2.480,138.36,224,0.875,bilinear vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.340,10.660,97.700,2.300,6.34,224,0.900,bicubic legacy_seresnet18.in1k,89.260,10.740,97.690,2.310,11.78,224,0.875,bicubic resnet14t.c3_in1k,89.250,10.750,97.440,2.560,10.08,224,0.950,bicubic vgg13_bn.tv_in1k,89.200,10.800,97.530,2.470,133.05,224,0.875,bilinear tf_mobilenetv3_large_minimal_100.in1k,89.160,10.840,97.310,2.690,3.92,224,0.875,bilinear mobilevitv2_050.cvnets_in1k,89.030,10.970,97.600,2.400,1.37,256,0.888,bicubic pvt_v2_b0.in1k,88.970,11.030,97.690,2.310,3.67,224,0.900,bicubic xcit_nano_12_p16_224.fb_in1k,88.970,11.030,97.410,2.590,3.05,224,1.000,bicubic efficientvit_m2.r224_in1k,88.910,11.090,97.390,2.610,4.19,224,0.875,bicubic lcnet_100.ra2_in1k,88.910,11.090,97.380,2.620,2.95,224,0.875,bicubic mobileone_s0.apple_in1k,88.810,11.190,97.220,2.780,5.29,224,0.875,bilinear resnet18.gluon_in1k,88.660,11.340,97.100,2.900,11.69,224,0.875,bicubic tinynet_c.in1k,88.400,11.600,97.260,2.740,2.46,184,0.875,bicubic vgg11_bn.tv_in1k,88.400,11.600,97.250,2.750,132.87,224,0.875,bilinear efficientvit_b0.r224_in1k,88.300,11.700,96.880,3.120,3.41,224,0.950,bicubic regnety_002.pycls_in1k,88.170,11.830,97.440,2.560,3.16,224,0.875,bicubic resnet18.tv_in1k,88.150,11.850,97.120,2.880,11.69,224,0.875,bilinear mobilevit_xxs.cvnets_in1k,87.940,12.060,97.190,2.810,1.27,256,0.900,bicubic vgg13.tv_in1k,87.570,12.430,97.110,2.890,133.05,224,0.875,bilinear regnetx_002.pycls_in1k,87.370,12.630,97.000,3.000,2.68,224,0.875,bicubic vgg11.tv_in1k,87.350,12.650,97.100,2.900,132.86,224,0.875,bilinear efficientvit_m1.r224_in1k,87.220,12.780,97.020,2.980,2.98,224,0.875,bicubic repghostnet_058.in1k,87.150,12.850,96.780,3.220,2.55,224,0.875,bicubic dla60x_c.in1k,87.110,12.890,97.140,2.860,1.32,224,0.875,bilinear resnet18.a3_in1k,87.070,12.930,96.660,3.340,11.69,224,0.950,bicubic mixer_l16_224.goog_in21k_ft_in1k,86.990,13.010,94.070,5.930,208.20,224,0.875,bicubic lcnet_075.ra2_in1k,86.960,13.040,96.550,3.450,2.36,224,0.875,bicubic resnet10t.c3_in1k,86.680,13.320,96.730,3.270,5.44,224,0.950,bicubic mobilenetv3_small_100.lamb_in1k,86.180,13.820,96.450,3.550,2.54,224,0.875,bicubic tf_mobilenetv3_small_100.in1k,85.950,14.050,96.400,3.600,2.54,224,0.875,bilinear mnasnet_small.lamb_in1k,85.480,14.520,96.000,4.000,2.03,224,0.875,bicubic repghostnet_050.in1k,85.450,14.550,96.150,3.850,2.31,224,0.875,bicubic dla46x_c.in1k,85.440,14.560,96.420,3.580,1.07,224,0.875,bilinear tinynet_d.in1k,85.440,14.560,96.020,3.980,2.34,152,0.875,bicubic mobilenetv2_050.lamb_in1k,84.990,15.010,95.620,4.380,1.97,224,0.875,bicubic dla46_c.in1k,84.700,15.300,96.210,3.790,1.30,224,0.875,bilinear tf_mobilenetv3_small_075.in1k,84.500,15.500,95.880,4.120,2.04,224,0.875,bilinear mobilenetv3_small_075.lamb_in1k,84.120,15.880,95.520,4.480,2.04,224,0.875,bicubic efficientvit_m0.r224_in1k,83.220,16.780,95.700,4.300,2.35,224,0.875,bicubic lcnet_050.ra2_in1k,83.040,16.960,95.020,4.980,1.88,224,0.875,bicubic tf_mobilenetv3_small_minimal_100.in1k,82.660,17.340,95.030,4.970,2.04,224,0.875,bilinear tinynet_e.in1k,79.810,20.190,93.970,6.030,2.04,106,0.875,bicubic mobilenetv3_small_050.lamb_in1k,78.080,21.920,93.010,6.990,1.59,224,0.875,bicubic
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt112-cu113-rtx3090.csv	model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count tinynet_e,70939.06,14.424,1024,106,0.03,0.69,2.04 mobilenetv3_small_050,53363.87,19.179,1024,224,0.03,0.92,1.59 lcnet_035,39908.29,25.648,1024,224,0.03,1.04,1.64 mobilenetv3_small_075,38048.72,26.902,1024,224,0.05,1.3,2.04 tinynet_d,35634.7,28.724,1024,152,0.05,1.42,2.34 lcnet_050,35231.0,29.055,1024,224,0.05,1.26,1.88 mobilenetv3_small_100,34913.55,29.319,1024,224,0.06,1.42,2.54 tf_mobilenetv3_small_minimal_100,31288.96,32.716,1024,224,0.06,1.41,2.04 tf_mobilenetv3_small_075,30676.85,33.368,1024,224,0.05,1.3,2.04 lcnet_075,30088.74,34.022,1024,224,0.1,1.99,2.36 tf_mobilenetv3_small_100,28547.5,35.858,1024,224,0.06,1.42,2.54 lcnet_100,23945.91,42.753,1024,224,0.16,2.52,2.95 mnasnet_small,22244.35,46.024,1024,224,0.07,2.16,2.03 levit_128s,22002.58,46.529,1024,224,0.31,1.88,7.78 mobilenetv2_035,20937.19,48.897,1024,224,0.07,2.86,1.68 mnasnet_050,18984.06,53.93,1024,224,0.11,3.07,2.22 ghostnet_050,18415.88,55.593,1024,224,0.05,1.77,2.59 tinynet_c,17846.73,57.365,1024,184,0.11,2.87,2.46 mobilenetv2_050,16928.19,60.48,1024,224,0.1,3.64,1.97 semnasnet_050,16394.61,62.449,1024,224,0.11,3.44,2.08 lcnet_150,15508.0,66.02,1024,224,0.34,3.79,4.5 gernet_s,15282.73,66.993,1024,224,0.75,2.65,8.17 levit_128,14929.05,68.581,1024,224,0.41,2.71,9.21 cs3darknet_focus_s,14654.05,69.868,1024,256,0.69,2.7,3.27 cs3darknet_s,14422.34,70.991,1024,256,0.72,2.97,3.28 mobilenetv3_large_075,14412.2,71.04,1024,224,0.16,4.0,3.99 mixer_s32_224,13576.58,75.414,1024,224,1.0,2.28,19.1 resnet10t,13509.21,75.789,1024,224,1.1,2.43,5.44 mobilenetv3_rw,13202.47,77.551,1024,224,0.23,4.41,5.48 levit_192,13174.02,77.717,1024,224,0.66,3.2,10.95 mobilenetv3_large_100,12955.77,79.027,1024,224,0.23,4.41,5.48 mobilenetv3_large_100_miil,12954.49,79.035,1024,224,0.23,4.41,5.48 vit_small_patch32_224,12913.76,79.284,1024,224,1.15,2.5,22.88 hardcorenas_a,12748.25,80.313,1024,224,0.23,4.38,5.26 mnasnet_075,12700.79,80.614,1024,224,0.23,4.77,3.17 tf_mobilenetv3_large_075,12296.64,83.262,1024,224,0.16,4.0,3.99 tinynet_b,12104.09,84.587,1024,188,0.21,4.44,3.73 tf_mobilenetv3_large_minimal_100,11915.92,85.923,1024,224,0.22,4.4,3.92 hardcorenas_b,11667.78,87.752,1024,224,0.26,5.09,5.18 hardcorenas_c,11602.35,88.247,1024,224,0.28,5.01,5.52 ese_vovnet19b_slim_dw,11450.49,89.417,1024,224,0.4,5.28,1.9 mnasnet_b1,11305.32,90.567,1024,224,0.33,5.46,4.38 mnasnet_100,11303.72,90.578,1024,224,0.33,5.46,4.38 tf_mobilenetv3_large_100,11165.87,91.695,1024,224,0.23,4.41,5.48 gluon_resnet18_v1b,11046.16,92.691,1024,224,1.82,2.48,11.69 ssl_resnet18,11027.01,92.852,1024,224,1.82,2.48,11.69 resnet18,11023.6,92.881,1024,224,1.82,2.48,11.69 swsl_resnet18,11003.86,93.048,1024,224,1.82,2.48,11.69 semnasnet_075,10953.15,93.479,1024,224,0.23,5.54,2.91 regnetx_004,10946.78,93.533,1024,224,0.4,3.14,5.16 hardcorenas_d,10898.13,93.95,1024,224,0.3,4.93,7.5 mobilenetv2_075,10779.36,94.985,1024,224,0.22,5.86,2.64 ghostnet_100,10498.38,97.527,1024,224,0.15,3.55,5.18 seresnet18,10333.85,99.081,1024,224,1.82,2.49,11.78 vit_tiny_r_s16_p8_224,10273.23,99.666,1024,224,0.44,2.06,6.34 spnasnet_100,10240.55,99.983,1024,224,0.35,6.03,4.42 legacy_seresnet18,10026.58,102.118,1024,224,1.82,2.49,11.78 mnasnet_a1,9730.7,105.223,1024,224,0.32,6.23,3.89 semnasnet_100,9728.29,105.249,1024,224,0.32,6.23,3.89 tf_efficientnetv2_b0,9714.68,105.397,1024,224,0.73,4.77,7.14 tinynet_a,9706.17,105.487,1024,192,0.35,5.41,6.19 hardcorenas_f,9654.05,106.058,1024,224,0.35,5.57,8.2 mobilenetv2_100,9591.46,106.751,1024,224,0.31,6.68,3.5 levit_256,9580.42,106.874,1024,224,1.13,4.23,18.89 regnetx_002,9551.99,107.191,1024,224,0.2,2.16,2.68 hardcorenas_e,9521.78,107.531,1024,224,0.35,5.65,8.07 efficientnet_lite0,9415.07,108.751,1024,224,0.4,6.74,4.65 regnety_002,9227.09,110.966,1024,224,0.2,2.17,3.16 fbnetc_100,9182.63,111.504,1024,224,0.4,6.51,5.57 resnet18d,9153.04,111.864,1024,224,2.06,3.29,11.71 regnety_006,9048.64,113.154,1024,224,0.61,4.33,6.06 ese_vovnet19b_slim,8822.42,116.057,1024,224,1.69,3.52,3.17 ghostnet_130,8402.81,121.852,1024,224,0.24,4.6,7.36 regnetx_006,8395.84,121.954,1024,224,0.61,3.98,6.2 levit_256d,8131.84,125.914,1024,224,1.4,4.93,26.21 tf_efficientnet_lite0,8115.07,126.174,1024,224,0.4,6.74,4.65 regnetz_005,8104.32,126.341,1024,224,0.52,5.86,7.12 efficientnet_b0,8015.36,127.743,1024,224,0.4,6.75,5.29 xcit_nano_12_p16_224_dist,7700.28,132.971,1024,224,0.56,4.17,3.05 xcit_nano_12_p16_224,7692.58,133.102,1024,224,0.56,4.17,3.05 mnasnet_140,7484.35,136.808,1024,224,0.6,7.71,7.12 rexnetr_100,7414.06,138.105,1024,224,0.43,7.72,4.88 resnet14t,7298.17,140.296,1024,224,1.69,5.8,10.08 mobilenetv2_110d,7233.3,141.556,1024,224,0.45,8.71,4.52 regnetx_008,7112.68,143.957,1024,224,0.81,5.15,7.26 tf_efficientnet_b0_ns,7058.22,145.067,1024,224,0.4,6.75,5.29 tf_efficientnet_b0_ap,7055.15,145.131,1024,224,0.4,6.75,5.29 tf_efficientnet_b0,7052.24,145.19,1024,224,0.4,6.75,5.29 edgenext_xx_small,6998.78,146.298,1024,256,0.33,4.21,1.33 dla46_c,6933.78,147.671,1024,224,0.58,4.5,1.3 deit_tiny_patch16_224,6855.38,149.36,1024,224,1.26,5.97,5.72 vit_tiny_patch16_224,6844.8,149.592,1024,224,1.26,5.97,5.72 regnety_008,6827.19,149.977,1024,224,0.81,5.25,6.26 gernet_m,6753.27,151.619,1024,224,3.02,5.24,21.14 deit_tiny_distilled_patch16_224,6720.97,152.347,1024,224,1.27,6.01,5.91 efficientnet_b1_pruned,6608.04,154.952,1024,240,0.4,6.21,6.33 hrnet_w18_small,6603.38,155.061,1024,224,1.61,5.72,13.19 gluon_resnet34_v1b,6434.28,159.136,1024,224,3.67,3.74,21.8 semnasnet_140,6428.21,159.287,1024,224,0.6,8.87,6.11 tv_resnet34,6406.04,159.838,1024,224,3.67,3.74,21.8 resnet34,6404.45,159.878,1024,224,3.67,3.74,21.8 ese_vovnet19b_dw,6353.89,161.15,1024,224,1.34,8.25,6.54 rexnet_100,6291.85,162.738,1024,224,0.41,7.44,4.8 mobilenetv2_140,6258.1,163.617,1024,224,0.6,9.57,6.11 mobilevitv2_050,6228.2,164.403,1024,256,0.48,8.04,1.37 efficientnet_lite1,6215.56,164.736,1024,240,0.62,10.14,5.42 tf_efficientnetv2_b1,6143.78,166.661,1024,240,1.21,7.34,8.14 visformer_tiny,6090.13,168.13,1024,224,1.27,5.72,10.32 fbnetv3_b,6012.29,170.307,1024,256,0.55,9.1,8.6 seresnet34,5988.65,170.978,1024,224,3.67,3.74,21.96 resnet26,5923.37,172.863,1024,224,2.36,7.35,16.0 efficientnet_es,5871.09,174.403,1024,224,1.81,8.73,5.44 efficientnet_es_pruned,5866.42,174.542,1024,224,1.81,8.73,5.44 selecsls42,5796.58,176.643,1024,224,2.94,4.62,30.35 pit_ti_distilled_224,5792.34,176.773,1024,224,0.71,6.23,5.1 legacy_seresnet34,5780.67,177.13,1024,224,3.67,3.74,21.96 selecsls42b,5766.92,177.544,1024,224,2.98,4.62,32.46 pit_ti_224,5764.0,177.643,1024,224,0.7,6.19,4.85 resnet34d,5748.0,178.138,1024,224,3.91,4.54,21.82 levit_384,5659.16,180.934,1024,224,2.36,6.26,39.13 tf_efficientnet_es,5608.15,182.58,1024,224,1.81,8.73,5.44 resnetblur18,5572.02,183.764,1024,224,2.34,3.39,11.69 tf_efficientnet_lite1,5541.93,184.761,1024,240,0.62,10.14,5.42 rexnetr_130,5487.71,186.587,1024,224,0.68,9.81,7.61 cs3darknet_m,5481.96,186.783,1024,288,2.63,6.69,9.31 mixnet_s,5402.43,189.533,1024,224,0.25,6.25,4.13 regnety_004,5382.71,190.227,1024,224,0.41,3.89,4.34 skresnet18,5371.9,190.61,1024,224,1.82,3.24,11.96 darknet17,5347.86,143.598,768,256,3.26,7.18,14.3 mobilevit_xxs,5306.36,192.964,1024,256,0.42,8.34,1.27 cs3darknet_focus_m,5289.88,193.566,1024,288,2.51,6.19,9.3 mobilenetv2_120d,5178.64,197.724,1024,224,0.69,11.97,5.83 repvgg_b0,5161.18,198.394,1024,224,3.41,6.15,15.82 xcit_tiny_12_p16_224_dist,5107.76,200.467,1024,224,1.24,6.29,6.72 xcit_tiny_12_p16_224,5104.48,200.597,1024,224,1.24,6.29,6.72 resnet26d,5093.48,201.03,1024,224,2.6,8.15,16.01 tf_mixnet_s,4981.9,205.528,1024,224,0.25,6.25,4.13 vit_base_patch32_224_sam,4925.79,207.875,1024,224,4.41,5.01,88.22 vit_base_patch32_224,4923.14,207.986,1024,224,4.41,5.01,88.22 selecsls60,4922.02,208.033,1024,224,3.59,5.52,30.67 mixer_b32_224,4909.46,208.566,1024,224,3.24,6.29,60.29 selecsls60b,4902.59,208.858,1024,224,3.63,5.52,32.77 rexnetr_150,4887.15,209.518,1024,224,0.89,11.13,9.78 nf_resnet26,4834.78,211.787,1024,224,2.41,7.35,16.0 darknet21,4804.05,159.854,768,256,3.93,7.47,20.86 resmlp_12_distilled_224,4801.62,213.251,1024,224,3.01,5.5,15.35 resmlp_12_224,4801.47,213.257,1024,224,3.01,5.5,15.35 efficientnet_lite2,4791.14,213.716,1024,260,0.89,12.9,6.09 pit_xs_224,4790.75,213.733,1024,224,1.4,7.71,10.62 fbnetv3_d,4788.83,213.819,1024,256,0.68,11.1,10.31 pit_xs_distilled_224,4740.73,215.989,1024,224,1.41,7.76,11.0 dla34,4712.5,217.283,1024,224,3.07,5.02,15.74 sedarknet21,4615.23,166.394,768,256,3.93,7.47,20.95 resnext26ts,4512.74,226.902,1024,256,2.43,10.52,10.3 tf_efficientnetv2_b2,4506.54,227.212,1024,260,1.72,9.84,10.1 mixer_s16_224,4471.0,229.021,1024,224,3.79,5.97,18.53 legacy_seresnext26_32x4d,4467.81,229.184,1024,224,2.49,9.39,16.79 edgenext_x_small,4458.42,229.664,1024,256,0.68,7.5,2.34 gernet_l,4450.89,230.055,1024,256,4.57,8.0,31.08 tf_efficientnet_b1,4403.29,232.542,1024,240,0.71,10.88,7.79 tf_efficientnet_b1_ns,4402.75,232.57,1024,240,0.71,10.88,7.79 tf_efficientnet_b1_ap,4402.24,232.597,1024,240,0.71,10.88,7.79 eca_resnext26ts,4354.84,235.13,1024,256,2.43,10.52,10.3 seresnext26ts,4350.24,235.378,1024,256,2.43,10.52,10.39 tf_efficientnet_lite2,4300.72,238.087,1024,260,0.89,12.9,6.09 gcresnext26ts,4295.78,238.361,1024,256,2.43,10.53,10.48 rexnet_130,4282.77,239.085,1024,224,0.68,9.71,7.56 efficientnet_b1,4273.32,239.615,1024,256,0.77,12.22,7.79 gmlp_ti16_224,4143.15,247.143,1024,224,1.34,7.55,5.87 efficientnet_b0_g16_evos,4127.77,248.064,1024,224,1.01,7.42,8.11 crossvit_tiny_240,4122.07,248.408,1024,240,1.57,9.08,7.01 nf_ecaresnet26,4104.24,249.486,1024,224,2.41,7.36,16.0 efficientnet_b2_pruned,4103.05,249.558,1024,260,0.73,9.13,8.31 nf_seresnet26,4102.39,249.599,1024,224,2.41,7.36,17.4 mobilevitv2_075,4066.07,251.829,1024,256,1.05,12.06,2.87 vit_small_patch32_384,4025.63,254.359,1024,384,3.45,8.25,22.92 ecaresnext50t_32x4d,4000.29,255.97,1024,224,2.7,10.09,15.41 ecaresnext26t_32x4d,3998.13,256.108,1024,224,2.7,10.09,15.41 seresnext26tn_32x4d,3995.56,256.274,1024,224,2.7,10.09,16.81 seresnext26t_32x4d,3993.92,256.378,1024,224,2.7,10.09,16.81 seresnext26d_32x4d,3982.98,257.083,1024,224,2.73,10.19,16.81 resnet26t,3922.56,261.042,1024,256,3.35,10.52,16.01 dla46x_c,3917.72,261.363,1024,224,0.54,5.66,1.07 rexnet_150,3870.49,264.554,1024,224,0.9,11.21,9.73 resnetv2_50,3868.33,264.701,1024,224,4.11,11.11,25.55 crossvit_9_240,3865.11,264.922,1024,240,1.85,9.52,8.55 convnext_nano_ols,3859.14,265.333,1024,224,2.5,8.37,15.6 nf_regnet_b0,3819.49,268.086,1024,256,0.64,5.58,8.76 ecaresnet50d_pruned,3812.92,268.549,1024,224,2.53,6.43,19.94 regnetx_016,3808.7,268.846,1024,224,1.62,7.93,9.19 crossvit_9_dagger_240,3792.18,270.018,1024,240,1.99,9.97,8.78 dla60x_c,3787.69,270.337,1024,224,0.59,6.01,1.32 convnext_nano_hnf,3786.83,270.399,1024,224,2.46,8.37,15.59 ecaresnetlight,3729.3,274.57,1024,224,4.11,8.42,30.16 poolformer_s12,3722.39,275.081,1024,224,1.82,5.53,11.92 gmixer_12_224,3686.02,277.795,1024,224,2.67,7.26,12.7 gluon_resnet50_v1b,3677.5,278.438,1024,224,4.11,11.11,25.56 resnet50,3676.35,278.526,1024,224,4.11,11.11,25.56 ssl_resnet50,3674.86,278.638,1024,224,4.11,11.11,25.56 tv_resnet50,3673.31,278.756,1024,224,4.11,11.11,25.56 swsl_resnet50,3672.81,278.794,1024,224,4.11,11.11,25.56 dpn68,3650.67,280.484,1024,224,2.35,10.47,12.61 dpn68b,3606.63,283.907,1024,224,2.35,10.47,12.61 botnet26t_256,3555.59,287.983,1024,256,3.32,11.98,12.49 regnety_016,3516.07,291.222,1024,224,1.63,8.04,11.2 repvgg_a2,3514.85,291.324,1024,224,5.7,6.26,28.21 resnetv2_50t,3513.08,291.47,1024,224,4.32,11.82,25.57 efficientnet_em,3504.92,292.149,1024,240,3.04,14.34,6.9 mixnet_m,3496.31,292.868,1024,224,0.36,8.19,5.01 resnetv2_50d,3496.21,292.876,1024,224,4.35,11.92,25.57 rexnetr_200,3492.0,219.921,768,224,1.59,15.11,16.52 halonet26t,3480.88,294.167,1024,256,3.19,11.69,12.48 gluon_resnet50_v1c,3476.97,294.498,1024,224,4.35,11.92,25.58 resnet32ts,3465.97,295.433,1024,256,4.63,11.58,17.96 bat_resnext26ts,3457.26,296.173,1024,256,2.53,12.51,10.73 resnet33ts,3415.09,299.834,1024,256,4.76,11.66,19.68 tf_efficientnet_b2_ns,3414.14,299.918,1024,260,1.02,13.83,9.11 tf_efficientnet_b2,3412.6,300.052,1024,260,1.02,13.83,9.11 tf_efficientnet_b2_ap,3412.15,300.092,1024,260,1.02,13.83,9.11 tf_efficientnet_em,3389.12,302.132,1024,240,3.04,14.34,6.9 resnet50t,3345.79,306.045,1024,224,4.32,11.82,25.57 gluon_resnet50_v1d,3337.44,306.809,1024,224,4.35,11.92,25.58 resnet50d,3336.65,306.882,1024,224,4.35,11.92,25.58 vit_tiny_r_s16_p8_384,3314.06,154.482,512,384,1.34,6.49,6.36 legacy_seresnet50,3311.17,309.245,1024,224,3.88,10.6,28.09 seresnet33ts,3304.6,309.859,1024,256,4.76,11.66,19.78 tf_mixnet_m,3303.11,309.997,1024,224,0.36,8.19,5.01 eca_resnet33ts,3297.96,310.484,1024,256,4.76,11.66,19.68 convit_tiny,3289.83,311.251,1024,224,1.26,7.94,5.71 gcresnet33ts,3263.32,313.778,1024,256,4.76,11.68,19.88 vit_small_resnet26d_224,3252.42,314.83,1024,224,5.07,11.12,63.61 vovnet39a,3229.96,317.018,1024,224,7.09,6.73,22.6 efficientnet_b2a,3221.33,317.868,1024,288,1.12,16.2,9.11 efficientnet_b2,3221.08,317.894,1024,288,1.12,16.2,9.11 efficientnet_b3_pruned,3195.08,320.48,1024,300,1.04,11.86,9.86 seresnet50,3183.22,321.675,1024,224,4.11,11.13,28.09 cs3darknet_l,3171.55,322.859,1024,288,6.16,10.83,21.16 cs3darknet_focus_l,3146.07,325.473,1024,288,5.9,10.16,21.15 res2net50_48w_2s,3141.04,325.995,1024,224,4.18,11.72,25.29 eca_vovnet39b,3123.78,327.796,1024,224,7.09,6.74,22.6 ese_vovnet39b,3117.85,328.419,1024,224,7.09,6.74,24.57 mobilevit_xs,3095.24,248.113,768,256,1.05,16.33,2.32 resnext50_32x4d,3092.67,331.094,1024,224,4.26,14.4,25.03 ssl_resnext50_32x4d,3089.61,331.422,1024,224,4.26,14.4,25.03 gluon_resnext50_32x4d,3087.43,331.656,1024,224,4.26,14.4,25.03 swsl_resnext50_32x4d,3086.28,331.779,1024,224,4.26,14.4,25.03 tv_resnext50_32x4d,3085.42,331.872,1024,224,4.26,14.4,25.03 hrnet_w18_small_v2,3075.57,332.934,1024,224,2.62,9.65,15.6 eca_botnext26ts_256,3069.01,333.646,1024,256,2.46,11.6,10.59 dla60,3049.62,335.767,1024,224,4.26,10.16,22.04 vgg11,3048.59,167.933,512,224,7.61,7.44,132.86 skresnet34,3035.0,337.386,1024,224,3.67,5.13,22.28 mobilevitv2_100,3028.13,253.611,768,256,1.84,16.08,4.9 vit_small_patch16_224,3005.65,340.679,1024,224,4.61,11.95,22.05 deit_small_patch16_224,3005.16,340.735,1024,224,4.61,11.95,22.05 eca_halonext26ts,2980.4,343.567,1024,256,2.44,11.46,10.76 resnetaa50d,2972.16,344.518,1024,224,5.39,12.44,25.58 ecaresnet101d_pruned,2963.47,345.529,1024,224,3.48,7.69,24.88 coat_lite_tiny,2957.59,346.216,1024,224,1.6,11.65,5.72 cs3sedarknet_l,2955.87,346.418,1024,288,6.16,10.83,21.91 deit_small_distilled_patch16_224,2950.21,347.082,1024,224,4.63,12.02,22.44 gluon_resnet50_v1s,2947.99,347.343,1024,224,5.47,13.52,25.68 resnetrs50,2945.06,347.688,1024,224,4.48,12.14,35.69 seresnet50t,2939.07,348.397,1024,224,4.32,11.83,28.1 densenet121,2936.15,348.744,1024,224,2.87,6.9,7.98 pit_s_224,2935.87,348.777,1024,224,2.88,11.56,23.46 ecaresnet50d,2934.67,348.92,1024,224,4.35,11.93,25.58 tv_densenet121,2927.3,349.799,1024,224,2.87,6.9,7.98 selecsls84,2927.1,349.822,1024,224,5.9,7.57,50.95 pit_s_distilled_224,2909.88,351.892,1024,224,2.9,11.64,24.04 vit_relpos_base_patch32_plus_rpn_256,2858.63,358.203,1024,256,7.68,8.01,119.42 deit3_small_patch16_224_in21ft1k,2858.2,358.255,1024,224,4.61,11.95,22.06 deit3_small_patch16_224,2853.97,358.786,1024,224,4.61,11.95,22.06 resnext50d_32x4d,2849.78,359.313,1024,224,4.5,15.2,25.05 vit_relpos_small_patch16_rpn_224,2814.18,363.86,1024,224,4.59,13.05,21.97 densenet121d,2808.28,364.624,1024,224,3.11,7.7,8.0 cspresnet50,2805.19,365.024,1024,256,4.54,11.5,21.62 vit_relpos_small_patch16_224,2800.45,365.643,1024,224,4.59,13.05,21.98 gcresnext50ts,2798.56,365.89,1024,256,3.75,15.46,15.67 vit_srelpos_small_patch16_224,2795.09,366.343,1024,224,4.59,12.16,21.97 coat_lite_mini,2774.65,369.044,1024,224,2.0,12.25,11.01 haloregnetz_b,2774.49,369.064,1024,224,1.97,11.94,11.68 vit_base_patch32_plus_256,2772.64,369.31,1024,256,7.79,7.76,119.48 rexnet_200,2762.14,278.034,768,224,1.56,14.91,16.37 res2net50_26w_4s,2757.69,371.313,1024,224,4.28,12.61,25.7 seresnext50_32x4d,2741.98,373.44,1024,224,4.26,14.42,27.56 gluon_seresnext50_32x4d,2737.7,374.024,1024,224,4.26,14.42,27.56 legacy_seresnext50_32x4d,2737.27,374.083,1024,224,4.26,14.42,27.56 xcit_tiny_24_p16_224_dist,2733.14,374.648,1024,224,2.34,11.82,12.12 xcit_tiny_24_p16_224,2731.46,374.879,1024,224,2.34,11.82,12.12 xcit_nano_12_p16_384_dist,2727.34,375.445,1024,384,1.64,12.15,3.05 dla60x,2724.01,375.903,1024,224,3.54,13.8,17.35 gcresnet50t,2718.77,376.628,1024,256,5.42,14.67,25.9 vgg11_bn,2701.91,189.486,512,224,7.62,7.44,132.87 visformer_small,2695.9,379.825,1024,224,4.88,11.43,40.22 lambda_resnet26rpt_256,2689.85,380.679,1024,256,3.16,11.87,10.99 mixnet_l,2682.98,286.237,768,224,0.58,10.84,7.33 resnetblur50,2682.32,381.746,1024,224,5.16,12.02,25.56 vovnet57a,2674.97,382.797,1024,224,8.95,7.52,36.64 efficientnet_lite3,2659.04,192.54,512,300,1.65,21.85,8.2 cspresnet50d,2649.78,386.434,1024,256,4.86,12.55,21.64 seresnetaa50d,2644.86,387.154,1024,224,5.4,12.46,28.11 efficientnetv2_rw_t,2633.28,388.856,1024,288,3.19,16.42,13.65 cspresnet50w,2624.25,390.195,1024,256,5.04,12.19,28.12 twins_svt_small,2599.22,393.951,1024,224,2.94,13.75,24.06 tf_efficientnetv2_b3,2587.08,395.8,1024,300,3.04,15.74,14.36 nf_regnet_b2,2583.08,396.413,1024,272,1.22,9.27,14.31 vit_base_resnet26d_224,2575.01,397.656,1024,224,6.97,13.16,101.4 ese_vovnet57b,2572.63,398.024,1024,224,8.95,7.52,38.61 fbnetv3_g,2570.51,398.353,1024,288,1.77,21.09,16.62 gc_efficientnetv2_rw_t,2557.73,400.342,1024,288,3.2,16.45,13.68 tf_mixnet_l,2550.98,301.047,768,224,0.58,10.84,7.33 nf_regnet_b1,2527.71,405.098,1024,288,1.02,9.2,10.22 res2net50_14w_8s,2514.56,407.217,1024,224,4.21,13.28,25.06 inception_v3,2512.32,407.579,1024,299,5.73,8.97,23.83 densenetblur121d,2509.93,407.967,1024,224,3.11,7.9,8.0 adv_inception_v3,2509.35,408.057,1024,299,5.73,8.97,23.83 tf_inception_v3,2505.31,408.714,1024,299,5.73,8.97,23.83 gluon_inception_v3,2501.93,409.271,1024,299,5.73,8.97,23.83 resnetblur50d,2498.32,409.863,1024,224,5.4,12.82,25.58 nf_ecaresnet50,2492.25,410.862,1024,224,4.21,11.13,25.56 nf_seresnet50,2488.35,411.506,1024,224,4.21,11.13,28.09 resmlp_24_224,2465.19,415.371,1024,224,5.96,10.91,30.02 resmlp_24_distilled_224,2463.93,415.584,1024,224,5.96,10.91,30.02 mobilevit_s,2450.02,313.456,768,256,2.03,19.94,5.58 regnetx_032,2449.76,417.988,1024,224,3.2,11.37,15.3 cspresnext50,2440.84,419.515,1024,256,4.05,15.86,20.57 dla60_res2net,2430.52,421.296,1024,224,4.15,12.34,20.85 resnest14d,2424.63,422.32,1024,224,2.76,7.33,10.61 densenet169,2421.68,422.835,1024,224,3.4,7.3,14.15 convnext_tiny_hnfd,2418.1,423.46,1024,224,4.47,13.44,28.59 convnext_tiny_hnf,2414.45,424.101,1024,224,4.47,13.44,28.59 tf_efficientnet_lite3,2396.12,213.668,512,300,1.65,21.85,8.2 sehalonet33ts,2392.73,427.951,1024,256,3.55,14.7,13.69 efficientnet_cc_b0_4e,2389.83,428.47,1024,224,0.41,9.42,13.31 efficientnet_cc_b0_8e,2386.88,429.0,1024,224,0.42,9.42,24.01 convnext_tiny_in22ft1k,2380.94,430.068,1024,224,4.47,13.44,28.59 convnext_tiny,2379.5,430.329,1024,224,4.47,13.44,28.59 regnetz_b16,2348.93,435.929,1024,288,2.39,16.43,9.72 resnetv2_101,2321.74,441.036,1024,224,7.83,16.23,44.54 convnext_nano,2304.31,444.37,1024,288,4.06,13.84,15.59 tf_efficientnet_cc_b0_4e,2293.39,446.488,1024,224,0.41,9.42,13.31 semobilevit_s,2279.96,336.836,768,256,2.03,19.95,5.74 gluon_resnet101_v1b,2249.95,455.108,1024,224,7.83,16.23,44.55 tv_resnet101,2246.24,455.861,1024,224,7.83,16.23,44.55 resnet101,2246.09,455.891,1024,224,7.83,16.23,44.55 mobilevitv2_125,2233.52,343.842,768,256,2.86,20.1,7.48 skresnet50,2232.97,458.569,1024,224,4.11,12.5,25.8 ecaresnet26t,2203.93,464.611,1024,320,5.24,16.44,16.01 resnetv2_101d,2180.36,469.635,1024,224,8.07,17.04,44.56 gluon_resnet101_v1c,2174.26,470.952,1024,224,8.08,17.04,44.57 twins_pcpvt_small,2160.75,473.897,1024,224,3.83,18.08,24.11 xcit_small_12_p16_224_dist,2141.07,478.253,1024,224,4.82,12.58,26.25 xcit_small_12_p16_224,2140.23,478.441,1024,224,4.82,12.58,26.25 cs3darknet_focus_x,2138.85,478.75,1024,256,8.03,10.69,35.02 edgenext_small,2120.03,482.998,1024,320,1.97,14.16,5.59 gluon_resnet101_v1d,2116.52,483.8,1024,224,8.08,17.04,44.57 tf_efficientnet_cc_b0_8e,2114.86,484.181,1024,224,0.42,9.42,24.01 vgg13,2106.04,486.207,1024,224,11.31,12.25,133.05 skresnet50d,2104.19,486.637,1024,224,4.36,13.31,25.82 xcit_nano_12_p8_224_dist,2091.47,489.594,1024,224,2.16,15.71,3.05 xcit_nano_12_p8_224,2088.8,490.222,1024,224,2.16,15.71,3.05 sebotnet33ts_256,2061.15,248.392,512,256,3.89,17.46,13.7 efficientnet_b0_gn,2058.74,373.032,768,224,0.42,6.75,5.29 wide_resnet50_2,2057.11,497.774,1024,224,11.43,14.4,68.88 dla102,2034.05,503.416,1024,224,7.19,14.18,33.27 vit_base_resnet50d_224,2032.15,503.887,1024,224,8.73,16.92,110.97 resnet51q,2003.15,511.181,1024,288,8.07,20.94,35.7 legacy_seresnet101,1997.44,512.643,1024,224,7.61,15.74,49.33 regnetx_040,1987.99,515.08,1024,224,3.99,12.2,22.12 gmlp_s16_224,1983.97,516.124,1024,224,4.42,15.1,19.42 res2net50_26w_6s,1970.9,519.546,1024,224,6.33,15.28,37.05 resnetaa101d,1964.05,521.361,1024,224,9.12,17.56,44.57 gluon_resnet101_v1s,1954.76,523.835,1024,224,9.19,18.64,44.67 seresnet101,1951.77,524.639,1024,224,7.84,16.27,49.33 repvgg_b1,1949.45,525.265,1024,224,13.16,10.64,57.42 crossvit_small_240,1948.12,525.623,1024,240,5.63,18.17,26.86 cs3sedarknet_xdw,1942.13,527.244,1024,256,5.97,17.18,21.6 resnetaa50,1934.76,529.254,1024,288,8.52,19.24,25.56 swin_tiny_patch4_window7_224,1924.88,531.966,1024,224,4.51,17.06,28.29 resnext101_32x4d,1924.17,532.166,1024,224,8.01,21.23,44.18 ssl_resnext101_32x4d,1923.99,532.215,1024,224,8.01,21.23,44.18 poolformer_s24,1923.48,532.355,1024,224,3.41,10.68,21.39 gluon_resnext101_32x4d,1922.64,532.587,1024,224,8.01,21.23,44.18 swsl_resnext101_32x4d,1922.44,532.644,1024,224,8.01,21.23,44.18 vit_relpos_medium_patch16_cls_224,1918.8,533.655,1024,224,8.03,18.24,38.76 vit_relpos_medium_patch16_rpn_224,1917.82,533.927,1024,224,7.97,17.02,38.73 vit_relpos_medium_patch16_224,1911.97,535.56,1024,224,7.97,17.02,38.75 vit_srelpos_medium_patch16_224,1908.42,536.559,1024,224,7.96,16.21,38.74 resnest50d_1s4x24d,1891.51,541.355,1024,224,4.43,13.57,25.68 darknet53,1883.2,407.805,768,288,11.78,15.68,41.61 gmixer_24_224,1881.95,544.104,1024,224,5.28,14.45,24.72 darknetaa53,1875.8,409.414,768,288,10.08,15.68,36.02 densenet201,1868.67,547.971,1024,224,4.34,7.85,20.01 halonet50ts,1867.02,548.456,1024,256,5.3,19.2,22.73 mobilevitv2_150,1865.71,274.416,512,256,4.09,24.11,10.59 mobilevitv2_150_in22ft1k,1864.94,274.529,512,256,4.09,24.11,10.59 tf_efficientnet_b3_ns,1862.24,274.927,512,300,1.87,23.83,12.23 tf_efficientnet_b3,1861.19,275.081,512,300,1.87,23.83,12.23 tf_efficientnet_b3_ap,1860.71,275.153,512,300,1.87,23.83,12.23 nf_resnet101,1854.11,552.273,1024,224,8.01,16.23,44.55 dla102x,1853.94,552.322,1024,224,5.89,19.42,26.31 ecaresnet101d,1853.67,552.405,1024,224,8.08,17.07,44.57 vgg13_bn,1850.18,276.718,512,224,11.33,12.25,133.05 cspdarknet53,1837.13,418.032,768,256,6.57,16.81,27.64 efficientnet_b3a,1829.01,279.921,512,320,2.01,26.52,12.23 efficientnet_b3,1828.85,279.946,512,320,2.01,26.52,12.23 mixnet_xl,1821.62,281.057,512,224,0.93,14.57,11.9 resnet61q,1810.56,565.559,1024,288,9.87,21.52,36.85 vit_small_r26_s32_224,1806.33,566.883,1024,224,3.56,9.85,36.43 xcit_tiny_12_p16_384_dist,1805.03,567.29,1024,384,3.64,18.26,6.72 edgenext_small_rw,1803.36,567.813,1024,320,2.46,14.85,7.83 crossvit_15_240,1792.63,571.217,1024,240,5.81,19.77,27.53 resnest26d,1781.59,574.753,1024,224,3.64,9.97,17.07 nf_resnet50,1773.35,577.425,1024,288,6.88,18.37,25.56 hrnet_w18,1766.17,579.773,1024,224,4.32,16.31,21.3 crossvit_15_dagger_240,1755.64,583.25,1024,240,6.13,20.43,28.21 swin_s3_tiny_224,1746.31,586.364,1024,224,4.64,19.13,28.33 resnetblur101d,1744.07,587.12,1024,224,9.12,17.94,44.57 res2net101_26w_4s,1715.56,596.875,1024,224,8.1,18.45,45.21 cait_xxs24_224,1707.34,599.75,1024,224,2.53,20.29,11.96 nf_regnet_b3,1702.33,601.516,1024,320,2.05,14.61,18.59 seresnext101_32x4d,1701.46,601.825,1024,224,8.02,21.26,48.96 gluon_seresnext101_32x4d,1701.17,601.927,1024,224,8.02,21.26,48.96 legacy_seresnext101_32x4d,1697.89,603.09,1024,224,8.02,21.26,48.96 resnetv2_50d_frn,1691.2,605.475,1024,224,4.33,11.92,25.59 vgg16,1690.51,605.724,1024,224,15.47,13.56,138.36 repvgg_b1g4,1670.27,613.064,1024,224,8.15,10.64,39.97 res2net50_26w_8s,1662.2,616.038,1024,224,8.37,17.95,48.4 resmlp_36_224,1656.29,618.237,1024,224,8.91,16.33,44.69 resmlp_36_distilled_224,1655.44,618.553,1024,224,8.91,16.33,44.69 regnetz_c16,1654.15,309.514,512,320,3.92,25.88,13.46 sequencer2d_s,1646.91,621.756,1024,224,4.96,11.31,27.65 efficientnet_b0_g8_gn,1636.03,469.418,768,224,0.66,6.75,6.56 botnet50ts_256,1633.5,313.424,512,256,5.54,22.23,22.74 vit_large_patch32_224,1629.91,628.244,1024,224,15.39,13.3,306.54 ese_vovnet39b_evos,1627.7,629.096,1024,224,7.07,6.74,24.58 cs3darknet_x,1627.54,629.156,1024,288,10.6,14.36,35.05 resnetv2_50d_evob,1620.95,631.713,1024,224,4.33,11.92,25.59 efficientnet_cc_b1_8e,1619.01,632.471,1024,240,0.75,15.44,39.72 resnetv2_152,1614.21,634.351,1024,224,11.55,22.56,60.19 xception41p,1587.33,322.543,512,299,9.25,39.86,26.91 regnetx_064,1586.15,484.18,768,224,6.49,16.37,26.21 coat_lite_small,1583.04,646.84,1024,224,3.96,22.09,19.84 swinv2_cr_tiny_224,1581.48,647.481,1024,224,4.66,28.45,28.33 xception,1578.2,486.619,768,299,8.4,35.83,22.86 gluon_resnet152_v1b,1573.68,650.689,1024,224,11.56,22.56,60.19 tv_resnet152,1573.22,650.883,1024,224,11.56,22.56,60.19 resnetv2_50x1_bit_distilled,1572.94,650.997,1024,224,4.23,11.11,25.55 resnet152,1571.71,651.505,1024,224,11.56,22.56,60.19 tf_efficientnet_cc_b1_8e,1564.9,654.344,1024,240,0.75,15.44,39.72 halo2botnet50ts_256,1559.2,656.732,1024,256,5.02,21.78,22.64 mixer_l32_224,1558.59,656.992,1024,224,11.27,19.86,206.94 vit_tiny_patch16_384,1557.53,657.441,1024,384,4.7,25.39,5.79 mobilevitv2_175,1554.07,329.447,512,256,5.54,28.13,14.25 swinv2_cr_tiny_ns_224,1551.84,659.847,1024,224,4.66,28.45,28.33 mobilevitv2_175_in22ft1k,1551.58,329.973,512,256,5.54,28.13,14.25 vit_base_patch32_384,1550.87,660.263,1024,384,13.06,16.5,88.3 cs3sedarknet_x,1549.02,661.048,1024,288,10.6,14.37,35.4 resnetv2_152d,1545.41,662.596,1024,224,11.8,23.36,60.2 nf_ecaresnet101,1540.66,664.639,1024,224,8.01,16.27,44.55 nf_seresnet101,1538.7,665.483,1024,224,8.02,16.27,49.33 gluon_resnet152_v1c,1536.27,666.538,1024,224,11.8,23.36,60.21 efficientnet_el,1524.59,335.818,512,300,8.0,30.7,10.59 efficientnet_el_pruned,1523.29,336.105,512,300,8.0,30.7,10.59 gluon_resnet152_v1d,1508.62,678.753,1024,224,11.8,23.36,60.21 vgg16_bn,1504.44,340.315,512,224,15.5,13.56,138.37 twins_pcpvt_base,1494.56,685.139,1024,224,6.68,25.25,43.83 tf_efficientnet_el,1481.51,345.582,512,300,8.0,30.7,10.59 cs3edgenet_x,1479.52,692.101,1024,288,14.59,16.36,47.82 vit_base_r26_s32_224,1479.31,692.202,1024,224,6.81,12.36,101.38 skresnext50_32x4d,1465.64,698.657,1024,224,4.5,17.18,27.48 convnext_small,1452.43,705.012,1024,224,8.71,21.56,50.22 convnext_small_in22ft1k,1450.42,705.987,1024,224,8.71,21.56,50.22 hrnet_w32,1444.27,708.991,1024,224,8.97,22.02,41.23 ese_vovnet99b,1442.7,709.767,1024,224,16.51,11.27,63.2 mixer_b16_224,1424.87,718.648,1024,224,12.62,14.53,59.88 gluon_resnet152_v1s,1424.84,718.665,1024,224,12.92,24.96,60.32 ecaresnet50t,1422.0,720.1,1024,320,8.82,24.13,25.57 mixer_b16_224_miil,1421.45,720.381,1024,224,12.62,14.53,59.88 vgg19,1411.57,725.42,1024,224,19.63,14.86,143.67 regnety_032,1398.62,732.137,1024,288,5.29,18.61,19.44 convit_small,1398.4,732.254,1024,224,5.76,17.87,27.78 nest_tiny,1387.46,553.516,768,224,5.83,25.48,17.06 legacy_seresnet152,1382.46,740.694,1024,224,11.33,22.08,66.82 dla169,1382.4,740.729,1024,224,11.6,20.2,53.39 xcit_tiny_12_p8_224,1374.74,744.858,1024,224,4.81,23.6,6.71 xcit_tiny_12_p8_224_dist,1374.04,745.236,1024,224,4.81,23.6,6.71 densenet161,1366.11,749.563,1024,224,7.79,11.06,28.68 jx_nest_tiny,1362.5,563.656,768,224,5.83,25.48,17.06 seresnet152,1361.36,752.174,1024,224,11.57,22.61,66.82 mobilevitv2_200_in22ft1k,1354.63,283.461,384,256,7.22,32.15,18.45 mobilevitv2_200,1354.25,283.54,384,256,7.22,32.15,18.45 xception41,1347.67,379.903,512,299,9.28,39.86,26.97 inception_v4,1323.37,773.767,1024,299,12.28,15.09,42.68 twins_svt_base,1316.07,778.059,1024,224,8.59,26.33,56.07 vit_small_resnet50d_s16_224,1305.38,784.435,1024,224,13.48,24.82,57.53 dpn92,1303.44,785.601,1024,224,6.54,18.21,37.67 tresnet_m,1297.91,788.947,1024,224,5.74,7.31,31.39 poolformer_s36,1296.72,789.674,1024,224,5.0,15.82,30.86 sequencer2d_m,1285.21,796.745,1024,224,6.55,14.26,38.31 crossvit_18_240,1273.5,804.072,1024,240,9.05,26.26,43.27 regnetx_080,1271.94,805.056,1024,224,8.02,14.06,39.57 dla102x2,1271.93,402.524,512,224,9.34,29.91,41.28 vgg19_bn,1265.83,404.467,512,224,19.66,14.86,143.68 efficientnet_lite4,1263.67,303.867,384,380,4.04,45.66,13.01 crossvit_18_dagger_240,1245.15,822.375,1024,240,9.5,27.03,44.27 res2next50,1241.53,824.775,1024,224,4.2,13.71,24.67 volo_d1_224,1235.2,829.0,1024,224,6.94,24.43,26.63 efficientnetv2_s,1221.73,838.141,1024,384,8.44,35.77,21.46 resnest50d,1214.35,843.233,1024,224,5.4,14.36,27.48 tf_efficientnetv2_s_in21ft1k,1191.03,859.741,1024,384,8.44,35.77,21.46 tf_efficientnetv2_s,1191.03,859.748,1024,384,8.44,35.77,21.46 dpn98,1188.11,861.858,1024,224,11.73,25.2,61.57 mixnet_xxl,1187.83,323.267,384,224,2.04,23.43,23.96 swin_small_patch4_window7_224,1183.48,865.227,1024,224,8.77,27.47,49.61 regnetz_d8,1180.44,867.458,1024,320,6.19,37.08,23.37 hrnet_w30,1180.28,867.576,1024,224,8.15,21.21,37.71 gluon_resnext101_64x4d,1176.11,870.653,1024,224,15.52,31.21,83.46 efficientnetv2_rw_s,1166.72,877.658,1024,384,8.72,38.03,23.94 tf_efficientnet_lite4,1164.88,329.636,384,380,4.04,45.66,13.01 swinv2_tiny_window8_256,1158.39,883.971,1024,256,5.96,24.57,28.35 wide_resnet101_2,1155.6,886.11,1024,224,22.8,21.23,126.89 repvgg_b2,1154.84,886.691,1024,224,20.45,12.9,89.02 vit_base_patch16_224_miil,1153.59,887.648,1024,224,17.58,23.9,86.54 resnet50_gn,1150.43,890.083,1024,224,4.14,11.11,25.56 resnet200,1149.46,890.84,1024,224,15.07,32.19,64.67 cait_xxs36_224,1148.13,891.873,1024,224,3.77,30.34,17.3 xception65p,1140.81,448.791,512,299,13.91,52.48,39.82 regnetz_040,1140.64,336.641,384,320,6.35,37.78,27.12 xcit_small_24_p16_224_dist,1137.7,900.049,1024,224,9.1,23.64,47.67 xcit_small_24_p16_224,1136.58,900.934,1024,224,9.1,23.64,47.67 regnetz_040h,1135.68,338.111,384,320,6.43,37.94,28.94 deit_base_patch16_224,1133.84,903.113,1024,224,17.58,23.9,86.57 vit_base_patch16_224,1133.45,903.419,1024,224,17.58,23.9,86.57 vit_base_patch16_224_sam,1132.11,904.493,1024,224,17.58,23.9,86.57 regnetz_d32,1128.13,907.679,1024,320,9.33,37.08,27.58 dla60_res2next,1127.83,907.922,1024,224,3.49,13.17,17.03 eca_nfnet_l0,1126.64,908.88,1024,288,7.12,17.29,24.14 resnetrs101,1123.2,911.667,1024,288,13.56,28.53,63.62 nfnet_l0,1123.1,911.747,1024,288,7.13,17.29,35.07 cs3se_edgenet_x,1120.46,913.898,1024,320,18.01,20.21,50.72 deit_base_distilled_patch16_224,1119.36,914.798,1024,224,17.68,24.05,87.34 vit_base_patch16_rpn_224,1111.53,921.235,1024,224,17.49,23.75,86.54 inception_resnet_v2,1108.79,923.511,1024,299,13.18,25.06,55.84 ens_adv_inception_resnet_v2,1107.31,924.747,1024,299,13.18,25.06,55.84 deit3_base_patch16_224,1093.88,936.101,1024,224,17.58,23.9,86.59 deit3_base_patch16_224_in21ft1k,1092.45,937.33,1024,224,17.58,23.9,86.59 gluon_seresnext101_64x4d,1088.94,940.353,1024,224,15.53,31.25,88.23 vit_relpos_base_patch16_clsgap_224,1081.95,946.422,1024,224,17.6,25.12,86.43 vit_relpos_base_patch16_cls_224,1081.77,946.586,1024,224,17.6,25.12,86.43 vit_relpos_base_patch16_rpn_224,1080.35,947.826,1024,224,17.51,24.97,86.41 vit_relpos_base_patch16_224,1079.52,948.56,1024,224,17.51,24.97,86.43 tnt_s_patch16_224,1078.48,949.47,1024,224,5.24,24.37,23.76 twins_pcpvt_large,1066.87,959.801,1024,224,9.84,35.82,60.99 ssl_resnext101_32x8d,1054.92,970.677,1024,224,16.48,31.21,88.79 resnext101_32x8d,1054.71,970.869,1024,224,16.48,31.21,88.79 ig_resnext101_32x8d,1054.19,971.349,1024,224,16.48,31.21,88.79 swsl_resnext101_32x8d,1053.69,971.812,1024,224,16.48,31.21,88.79 beit_base_patch16_224,1049.2,975.962,1024,224,17.58,23.9,86.53 resnest50d_4s2x40d,1042.05,982.666,1024,224,4.4,17.94,30.42 coat_tiny,1040.04,984.566,1024,224,4.35,27.2,5.5 resnet101d,1029.19,994.942,1024,320,16.48,34.77,44.57 convnext_base,1012.72,1011.124,1024,224,15.38,28.75,88.59 convnext_base_in22ft1k,1011.75,1012.088,1024,224,15.38,28.75,88.59 efficientnet_b4,993.87,386.356,384,384,4.51,50.04,19.34 pit_b_224,992.43,515.895,512,224,12.42,32.94,73.76 pit_b_distilled_224,988.79,517.791,512,224,12.5,33.07,74.79 gluon_xception65,981.26,521.764,512,299,13.96,52.48,39.92 xception65,975.37,524.918,512,299,13.96,52.48,39.92 vit_small_patch16_36x1_224,973.92,1051.404,1024,224,13.71,35.69,64.67 repvgg_b3,972.06,1053.416,1024,224,29.16,15.1,123.09 repvgg_b2g4,969.39,1056.316,1024,224,12.63,12.9,61.76 xcit_tiny_24_p16_384_dist,969.37,1056.345,1024,384,6.87,34.29,12.12 swinv2_cr_small_224,967.97,1057.869,1024,224,9.07,50.27,49.7 swinv2_cr_small_ns_224,957.86,1069.034,1024,224,9.08,50.27,49.7 vit_small_patch16_18x2_224,951.03,1076.715,1024,224,13.71,35.69,64.67 twins_svt_large,923.66,1108.616,1024,224,15.15,35.1,99.27 tf_efficientnet_b4,922.69,416.164,384,380,4.49,49.49,19.34 tf_efficientnet_b4_ap,922.5,416.247,384,380,4.49,49.49,19.34 tf_efficientnet_b4_ns,922.41,416.289,384,380,4.49,49.49,19.34 hrnet_w40,910.46,1124.691,1024,224,12.75,25.29,57.56 regnetz_b16_evos,903.54,849.98,768,288,2.36,16.43,9.74 cait_s24_224,902.24,1134.941,1024,224,9.35,40.58,46.92 nfnet_f0,901.6,1135.748,1024,256,12.62,18.05,71.49 nf_regnet_b4,900.78,1136.78,1024,384,4.7,28.61,30.21 poolformer_m36,896.53,1142.174,1024,224,8.8,22.02,56.17 nest_small,884.77,868.006,768,224,10.35,40.04,38.35 hrnet_w48,875.56,1169.527,1024,224,17.34,28.56,77.47 jx_nest_small,874.29,878.41,768,224,10.35,40.04,38.35 dpn131,866.66,1181.531,1024,224,16.09,32.97,79.25 swin_s3_small_224,854.8,898.443,768,224,9.43,37.84,49.74 regnety_040,854.48,898.782,768,288,6.61,20.3,20.65 regnetv_040,854.18,899.093,768,288,6.6,20.3,20.64 regnety_080,846.16,605.071,512,288,13.22,29.69,39.18 resnetv2_50d_evos,844.23,1212.929,1024,288,7.15,19.7,25.59 coat_mini,836.76,1223.748,1024,224,6.82,33.68,10.34 swin_base_patch4_window7_224,836.19,1224.59,1024,224,15.47,36.63,87.77 repvgg_b3g4,835.5,1225.597,1024,224,17.89,15.1,83.83 sequencer2d_l,832.91,1229.407,1024,224,9.74,22.12,54.3 dm_nfnet_f0,831.16,1232.004,1024,256,12.62,18.05,71.49 mobilevitv2_150_384_in22ft1k,826.94,309.566,256,384,9.2,54.25,10.59 gmlp_b16_224,825.63,1240.256,1024,224,15.78,30.21,73.08 convnext_tiny_384_in22ft1k,814.55,628.553,512,384,13.14,39.48,28.59 xcit_medium_24_p16_224_dist,812.18,1260.787,1024,224,16.13,31.71,84.4 xcit_medium_24_p16_224,812.16,1260.822,1024,224,16.13,31.71,84.4 regnetx_120,810.53,631.674,512,224,12.13,21.37,46.11 xcit_small_12_p16_384_dist,800.29,1279.521,1024,384,14.14,36.51,26.25 densenet264,798.84,1281.845,1024,224,12.95,12.8,72.69 hrnet_w44,790.45,1295.441,1024,224,14.94,26.92,67.06 crossvit_base_240,787.5,975.226,768,240,21.22,36.33,105.03 regnety_120,783.87,653.154,512,224,12.14,21.38,51.82 swinv2_tiny_window16_256,765.77,668.599,512,256,6.68,39.02,28.35 resnetv2_50d_gn,751.14,1022.427,768,288,7.24,19.7,25.57 xception71,749.93,682.721,512,299,18.09,69.92,42.34 vit_large_r50_s32_224,739.74,1384.252,1024,224,19.58,24.41,328.99 vit_base_patch16_plus_240,737.95,1387.618,1024,240,27.41,33.08,117.56 dpn107,736.42,1390.497,1024,224,18.38,33.46,86.92 resnet152d,736.09,1391.121,1024,320,24.08,47.67,60.21 ecaresnet200d,730.76,1401.271,1024,256,20.0,43.15,64.69 seresnet200d,728.63,1405.363,1024,256,20.01,43.15,71.86 vit_relpos_base_patch16_plus_240,727.74,1407.074,1024,240,27.3,34.33,117.38 xcit_tiny_24_p8_224,725.69,1411.06,1024,224,9.21,45.39,12.11 xcit_tiny_24_p8_224_dist,725.66,1411.108,1024,224,9.21,45.39,12.11 hrnet_w64,719.99,1422.231,1024,224,28.97,35.09,128.06 regnety_040s_gn,719.08,1068.018,768,224,4.03,12.29,20.65 xcit_nano_12_p8_384_dist,713.49,1435.191,1024,384,6.34,46.08,3.05 swinv2_small_window8_256,712.51,1437.16,1024,256,11.58,40.14,49.73 convit_base,706.35,1449.693,1024,224,17.52,31.77,86.54 resnext101_64x4d,704.57,1090.007,768,288,25.66,51.59,83.46 swin_s3_base_224,695.14,1473.064,1024,224,13.69,48.26,71.13 vit_small_patch16_384,693.62,1107.217,768,384,15.52,50.78,22.2 tnt_b_patch16_224,691.83,1480.107,1024,224,14.09,39.01,65.41 swinv2_cr_base_224,689.84,1484.394,1024,224,15.86,59.66,87.88 regnety_064,684.37,748.122,512,288,10.56,27.11,30.58 swinv2_cr_base_ns_224,683.96,1497.137,1024,224,15.86,59.66,87.88 volo_d2_224,679.94,1506.002,1024,224,14.34,41.34,58.68 mobilevitv2_175_384_in22ft1k,679.77,376.586,256,384,12.47,63.29,14.25 regnetv_064,678.0,755.149,512,288,10.55,27.11,30.58 poolformer_m48,676.06,1514.652,1024,224,11.59,29.17,73.47 deit3_small_patch16_384,669.71,1146.752,768,384,15.52,50.78,22.21 deit3_small_patch16_384_in21ft1k,669.36,1147.345,768,384,15.52,50.78,22.21 legacy_senet154,660.41,1550.529,1024,224,20.77,38.69,115.09 senet154,659.33,1553.081,1024,224,20.77,38.69,115.09 gluon_senet154,659.08,1553.657,1024,224,20.77,38.69,115.09 regnetx_160,650.99,786.486,512,224,15.99,25.52,54.28 resnetrs152,646.22,1584.595,1024,320,24.34,48.14,86.62 seresnet152d,642.19,1594.525,1024,320,24.09,47.72,66.84 regnetz_e8,633.28,1212.715,768,320,15.46,63.94,57.7 tresnet_l,630.57,1623.908,1024,224,10.88,11.9,55.99 nest_base,629.43,813.42,512,224,17.96,53.39,67.72 ese_vovnet99b_iabn,628.09,1630.325,1024,224,16.49,11.27,63.2 jx_nest_base,622.33,822.697,512,224,17.96,53.39,67.72 vit_small_r26_s32_384,613.78,834.168,512,384,10.43,29.85,36.47 vit_base_r50_s16_224,610.7,1676.739,1024,224,21.66,35.29,98.66 xcit_small_12_p8_224,609.86,1679.054,1024,224,18.69,47.21,26.21 xcit_small_12_p8_224_dist,609.57,1679.853,1024,224,18.69,47.21,26.21 efficientnetv2_m,603.09,1697.911,1024,416,18.6,67.5,54.14 mobilevitv2_200_384_in22ft1k,594.06,323.186,192,384,16.24,72.34,18.45 seresnext101_32x8d,590.8,1299.927,768,288,27.24,51.63,93.57 resnest101e,588.3,1305.448,768,256,13.38,28.66,48.28 regnetz_c16_evos,588.05,870.656,512,320,3.86,25.88,13.49 convmixer_768_32,578.91,1768.818,1024,224,19.55,25.95,21.11 seresnext101d_32x8d,575.36,1334.812,768,288,27.64,52.95,93.59 seresnet269d,570.63,1794.5,1024,256,26.59,53.6,113.67 convnext_large,561.47,1823.764,1024,224,34.4,43.13,197.77 convnext_large_in22ft1k,560.92,1825.557,1024,224,34.4,43.13,197.77 resnet200d,544.65,1880.08,1024,320,31.25,67.33,64.69 efficientnetv2_rw_m,534.87,1435.852,768,416,21.49,79.62,53.24 seresnextaa101d_32x8d,521.71,1472.057,768,288,28.51,56.44,93.59 vit_large_patch32_384,517.33,1979.373,1024,384,45.31,43.86,306.63 swinv2_base_window8_256,509.01,2011.746,1024,256,20.37,52.59,87.92 eca_nfnet_l1,497.06,2060.113,1024,320,14.92,34.42,41.41 convnext_small_384_in22ft1k,496.5,1031.206,512,384,25.58,63.37,50.22 mixer_l16_224,495.85,2065.122,1024,224,44.6,41.69,208.2 efficientnet_b5,493.19,519.054,256,456,10.46,98.86,30.39 halonet_h1,492.19,520.115,256,256,3.0,51.17,8.1 regnety_320,483.53,1058.87,512,224,32.34,30.26,145.05 swin_large_patch4_window7_224,480.21,1599.271,768,224,34.53,54.94,196.53 swinv2_small_window16_256,477.23,1072.842,512,256,12.82,66.29,49.73 volo_d3_224,474.32,2158.852,1024,224,20.78,60.09,86.33 resnetrs200,471.22,2173.072,1024,320,31.51,67.81,93.21 tf_efficientnet_b5_ns,469.35,545.419,256,456,10.46,98.86,30.39 tf_efficientnet_b5_ap,469.08,545.738,256,456,10.46,98.86,30.39 tf_efficientnet_b5,468.97,545.864,256,456,10.46,98.86,30.39 xcit_tiny_12_p8_384_dist,468.35,2186.374,1024,384,14.13,69.14,6.71 tresnet_xl,466.16,2196.648,1024,224,15.17,15.34,78.44 efficientnet_b3_gn,464.67,550.914,256,320,2.14,28.83,11.73 xcit_large_24_p16_224_dist,454.83,2251.393,1024,224,35.86,47.27,189.1 xcit_large_24_p16_224,454.8,2251.543,1024,224,35.86,47.27,189.1 tf_efficientnetv2_m_in21ft1k,444.81,1726.544,768,480,24.76,89.84,54.14 tf_efficientnetv2_m,444.79,1726.633,768,480,24.76,89.84,54.14 xcit_small_24_p16_384_dist,426.37,2401.669,1024,384,26.72,68.58,47.67 regnety_160,422.88,908.045,384,288,26.37,38.07,83.59 nf_regnet_b5,413.3,1238.797,512,456,11.7,61.95,49.74 swinv2_cr_tiny_384,408.94,625.992,256,384,15.34,161.01,28.33 swinv2_cr_large_224,406.2,1890.673,768,224,35.1,78.42,196.68 resnetv2_50x1_bitm,400.73,958.233,384,448,16.62,44.46,25.55 regnetz_d8_evos,392.85,1954.947,768,320,7.03,38.92,23.46 convmixer_1024_20_ks9_p14,390.62,2621.469,1024,224,5.55,5.51,24.38 efficientnet_b3_g8_gn,373.24,685.874,256,320,3.2,28.83,14.25 vit_large_patch16_224,370.72,2762.206,1024,224,61.6,63.52,304.33 convnext_xlarge_in22ft1k,368.8,1388.275,512,224,60.98,57.5,350.2 crossvit_15_dagger_408,368.65,694.421,256,408,21.45,95.05,28.5 vit_base_patch16_18x2_224,361.92,2829.305,1024,224,52.51,71.38,256.73 deit3_large_patch16_224_in21ft1k,358.3,2857.929,1024,224,61.6,63.52,304.37 deit3_large_patch16_224,357.82,2861.791,1024,224,61.6,63.52,304.37 swinv2_base_window16_256,346.22,1109.109,384,256,22.02,84.71,87.92 swinv2_base_window12to16_192to256_22kft1k,345.97,1109.898,384,256,22.02,84.71,87.92 nasnetalarge,345.75,1110.628,384,331,23.89,90.56,88.75 convnext_base_384_in22ft1k,345.74,1110.63,384,384,45.21,84.49,88.59 beit_large_patch16_224,343.01,2985.299,1024,224,61.6,63.52,304.43 ssl_resnext101_32x16d,338.92,1510.65,512,224,36.27,51.18,194.03 ig_resnext101_32x16d,338.75,1511.419,512,224,36.27,51.18,194.03 swsl_resnext101_32x16d,338.52,1512.441,512,224,36.27,51.18,194.03 tresnet_m_448,325.14,3149.347,1024,448,22.94,29.21,31.39 pnasnet5large,323.98,1185.25,384,331,25.04,92.89,86.06 regnetx_320,320.95,1196.437,384,224,31.81,36.3,107.81 xcit_small_24_p8_224,319.26,3207.434,1024,224,35.81,90.78,47.63 xcit_small_24_p8_224_dist,319.16,3208.44,1024,224,35.81,90.78,47.63 volo_d1_384,315.83,1621.098,512,384,22.75,108.55,26.78 nfnet_f1,306.71,3338.679,1024,320,35.97,46.77,132.63 ecaresnet269d,304.87,3358.754,1024,352,50.25,101.25,102.09 volo_d4_224,303.38,3375.331,1024,224,44.34,80.22,192.96 xcit_medium_24_p16_384_dist,297.67,2580.013,768,384,47.39,91.64,84.4 resnetrs270,296.93,3448.599,1024,352,51.13,105.48,129.86 resnetv2_152x2_bit_teacher,290.64,2642.472,768,224,46.95,45.11,236.34 vit_base_patch16_384,289.22,1327.696,384,384,55.54,101.56,86.86 deit_base_patch16_384,289.04,1328.502,384,384,55.54,101.56,86.86 deit_base_distilled_patch16_384,285.12,1346.806,384,384,55.65,101.82,87.63 dm_nfnet_f1,282.58,3623.721,1024,320,35.97,46.77,132.63 deit3_base_patch16_384,281.04,1366.341,384,384,55.54,101.56,86.88 deit3_base_patch16_384_in21ft1k,280.92,1366.918,384,384,55.54,101.56,86.88 efficientnet_b6,279.71,457.611,128,528,19.4,167.39,43.04 cait_xxs24_384,275.0,3723.573,1024,384,9.63,122.66,12.03 vit_large_patch14_224,271.58,3770.566,1024,224,81.08,88.79,304.2 crossvit_18_dagger_408,269.56,712.259,192,408,32.47,124.87,44.61 tf_efficientnet_b6_ap,267.5,478.495,128,528,19.4,167.39,43.04 tf_efficientnet_b6,267.48,478.534,128,528,19.4,167.39,43.04 tf_efficientnet_b6_ns,267.38,478.715,128,528,19.4,167.39,43.04 efficientnetv2_l,254.55,2011.402,512,480,56.4,157.99,118.52 resnetv2_101x1_bitm,252.46,1521.025,384,448,31.65,64.93,44.54 tf_efficientnetv2_l,251.41,2036.496,512,480,56.4,157.99,118.52 tf_efficientnetv2_l_in21ft1k,251.09,2039.122,512,480,56.4,157.99,118.52 swinv2_cr_small_384,250.5,1021.951,256,384,29.7,298.03,49.7 beit_base_patch16_384,248.36,1546.143,384,384,55.54,101.56,86.74 xcit_tiny_24_p8_384_dist,246.6,4152.437,1024,384,27.05,132.95,12.11 vit_large_r50_s32_384,246.12,2080.308,512,384,57.43,76.52,329.09 eca_nfnet_l2,237.09,3239.214,768,384,30.05,68.28,56.72 resmlp_big_24_224,228.25,4486.35,1024,224,100.23,87.31,129.14 resmlp_big_24_224_in22ft1k,227.96,4491.908,1024,224,100.23,87.31,129.14 resmlp_big_24_distilled_224,227.94,4492.471,1024,224,100.23,87.31,129.14 xcit_medium_24_p8_224_dist,222.27,3455.29,768,224,63.53,121.23,84.32 xcit_medium_24_p8_224,222.27,3455.239,768,224,63.53,121.23,84.32 swin_base_patch4_window12_384,221.01,868.732,192,384,47.19,134.78,87.9 swinv2_large_window12to16_192to256_22kft1k,212.32,1205.699,256,256,47.81,121.53,196.74 xcit_small_12_p8_384_dist,207.32,1852.22,384,384,54.92,138.29,26.21 resnest200e,201.89,2536.056,512,320,35.69,82.78,70.2 volo_d5_224,200.39,5110.009,1024,224,72.4,118.11,295.46 resnetrs350,194.78,3942.989,768,384,77.59,154.74,163.96 convnext_large_384_in22ft1k,191.43,1337.313,256,384,101.1,126.74,197.77 cait_xs24_384,190.0,4042.088,768,384,19.28,183.98,26.67 vit_base_patch8_224,188.23,1360.04,256,224,78.22,161.69,86.58 cait_xxs36_384,183.87,5569.221,1024,384,14.35,183.7,17.37 swinv2_cr_base_384,178.69,1432.608,256,384,50.57,333.68,87.88 vit_base_r50_s16_384,176.73,1448.509,256,384,67.43,135.03,98.95 vit_base_resnet50_384,176.72,1448.639,256,384,67.43,135.03,98.95 volo_d2_384,176.17,2179.696,384,384,46.17,184.51,58.87 swinv2_cr_huge_224,175.72,2185.293,384,224,115.97,121.08,657.83 nfnet_f2,172.68,5929.942,1024,352,63.22,79.06,193.78 xcit_large_24_p16_384_dist,168.33,3041.628,512,384,105.35,137.17,189.1 densenet264d_iabn,166.37,6155.083,1024,224,13.47,14.0,72.74 efficientnet_b7,161.46,594.574,96,600,38.33,289.94,66.35 efficientnetv2_xl,159.28,2410.894,384,512,93.85,247.32,208.12 dm_nfnet_f2,159.16,4825.445,768,352,63.22,79.06,193.78 tf_efficientnetv2_xl_in21ft1k,158.05,2429.572,384,512,93.85,247.32,208.12 tf_efficientnet_b7,155.86,615.938,96,600,38.33,289.94,66.35 tf_efficientnet_b7_ap,155.83,616.029,96,600,38.33,289.94,66.35 tf_efficientnet_b7_ns,155.78,616.248,96,600,38.33,289.94,66.35 tresnet_l_448,151.99,6737.079,1024,448,43.5,47.56,55.99 cait_s24_384,148.32,3451.871,512,384,32.17,245.31,47.06 vit_huge_patch14_224,146.38,6995.606,1024,224,167.4,139.41,632.05 ig_resnext101_32x32d,143.03,1789.868,256,224,87.29,91.12,468.53 resnetrs420,142.89,5374.778,768,416,108.45,213.79,191.89 deit3_huge_patch14_224,142.28,7197.12,1024,224,167.4,139.41,632.13 deit3_huge_patch14_224_in21ft1k,142.06,7208.264,1024,224,167.4,139.41,632.13 eca_nfnet_l3,132.47,3864.977,512,448,52.55,118.4,72.04 swin_large_patch4_window12_384,130.79,978.633,128,384,104.08,202.16,196.74 convnext_xlarge_384_in22ft1k,126.14,2029.4,256,384,179.2,168.99,350.2 xcit_large_24_p8_224,125.6,4076.305,512,224,141.23,181.56,188.93 xcit_large_24_p8_224_dist,125.51,4079.48,512,224,141.23,181.56,188.93 tresnet_xl_448,111.87,9153.659,1024,448,60.65,61.31,78.44 xcit_small_24_p8_384_dist,108.62,3535.115,384,384,105.24,265.91,47.63 swinv2_cr_large_384,108.25,1182.395,128,384,108.95,404.96,196.68 efficientnet_b8,101.79,943.075,96,672,63.48,442.89,87.41 resnetv2_50x3_bitm,101.1,1266.075,128,448,145.7,133.37,217.32 cait_s36_384,99.17,5162.791,512,384,47.99,367.4,68.37 tf_efficientnet_b8,98.82,971.416,96,672,63.48,442.89,87.41 tf_efficientnet_b8_ap,98.81,971.518,96,672,63.48,442.89,87.41 resnetv2_152x2_bit_teacher_384,98.49,2599.208,256,384,136.16,132.56,236.34 vit_large_patch16_384,97.65,2621.481,256,384,191.21,270.24,304.72 vit_giant_patch14_224,95.7,8025.142,768,224,267.18,192.64,1012.61 deit3_large_patch16_384,94.92,2697.101,256,384,191.21,270.24,304.76 deit3_large_patch16_384_in21ft1k,94.92,2697.083,256,384,191.21,270.24,304.76 swinv2_base_window12to24_192to384_22kft1k,94.52,677.087,64,384,55.25,280.36,87.92 nfnet_f3,93.84,8184.276,768,416,115.58,141.78,254.92 resnest269e,93.31,4115.183,384,416,77.69,171.98,110.93 dm_nfnet_f3,86.56,5914.801,512,416,115.58,141.78,254.92 beit_large_patch16_384,84.77,3019.757,256,384,191.21,270.24,305.0 volo_d3_448,76.33,2515.269,192,448,96.33,446.83,86.63 xcit_medium_24_p8_384_dist,75.97,3369.835,256,384,186.67,354.73,84.32 ig_resnext101_32x48d,74.47,2578.247,192,224,153.57,131.06,828.41 resnetv2_152x2_bitm,72.95,2631.902,192,448,184.99,180.43,236.34 tf_efficientnet_l2_ns_475,63.13,1013.713,64,475,172.11,609.89,480.31 resnetv2_101x3_bitm,60.06,2131.166,128,448,280.33,194.78,387.93 swinv2_large_window12to24_192to384_22kft1k,59.82,802.459,48,384,116.15,407.83,196.74 vit_gigantic_patch14_224,57.64,8883.342,512,224,483.95,275.37,1844.44 volo_d4_448,56.09,3423.322,192,448,197.13,527.35,193.41 convmixer_1536_20,53.95,18979.912,1024,224,48.68,33.03,51.63 swinv2_cr_giant_224,50.56,2531.46,128,224,483.85,309.15,2598.76 nfnet_f4,49.93,10254.329,512,512,216.26,262.26,316.07 swinv2_cr_huge_384,47.13,1357.909,64,384,352.04,583.18,657.94 dm_nfnet_f4,45.97,8353.673,384,512,216.26,262.26,316.07 xcit_large_24_p8_384_dist,42.84,4481.402,192,384,415.0,531.82,188.93 volo_d5_448,38.62,3314.317,128,448,315.06,737.92,295.91 nfnet_f5,37.03,10370.994,384,544,290.97,349.71,377.21 dm_nfnet_f5,33.93,11318.026,384,544,290.97,349.71,377.21 beit_large_patch16_512,33.87,2834.401,96,512,362.24,656.39,305.67 cait_m36_384,32.22,7945.944,256,384,173.11,734.81,271.22 nfnet_f6,28.33,13554.319,384,576,378.69,452.2,438.36 volo_d5_512,26.99,4742.789,128,512,425.09,1105.37,296.09 dm_nfnet_f6,26.14,9792.719,256,576,378.69,452.2,438.36 resnetv2_152x4_bitm,24.34,2629.892,64,480,844.84,414.26,936.53 efficientnet_l2,23.12,1037.889,24,800,479.12,1707.39,480.31 tf_efficientnet_l2_ns,22.7,1057.422,24,800,479.12,1707.39,480.31 nfnet_f7,22.34,11460.175,256,608,480.39,570.85,499.5 swinv2_cr_giant_384,14.63,2187.208,32,384,1450.71,1394.86,2598.76 cait_m48_448,13.64,9385.159,128,448,329.41,1708.23,356.46
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt111-cu113-rtx3090.csv	model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count tinynet_e,47972.76,21.335,1024,106,2.04 mobilenetv3_small_050,42473.43,24.099,1024,224,1.59 lcnet_035,39739.31,25.756,1024,224,1.64 lcnet_050,35211.0,29.071,1024,224,1.88 mobilenetv3_small_075,31410.3,32.589,1024,224,2.04 mobilenetv3_small_100,28111.39,36.416,1024,224,2.54 tf_mobilenetv3_small_minimal_100,27538.82,37.173,1024,224,2.04 tinynet_d,26670.17,38.384,1024,152,2.34 tf_mobilenetv3_small_075,26522.93,38.597,1024,224,2.04 tf_mobilenetv3_small_100,24036.65,42.591,1024,224,2.54 lcnet_075,22451.72,45.598,1024,224,2.36 levit_128s,19963.52,51.282,1024,224,7.78 mnasnet_small,19706.27,51.952,1024,224,2.03 lcnet_100,18132.59,56.461,1024,224,2.95 mobilenetv2_035,17586.23,58.217,1024,224,1.68 ghostnet_050,16726.5,61.209,1024,224,2.59 regnetx_002,16238.56,63.048,1024,224,2.68 regnety_002,15227.23,67.235,1024,224,3.16 mnasnet_050,15022.24,68.154,1024,224,2.22 tinynet_c,14089.9,72.665,1024,184,2.46 mobilenetv2_050,14032.51,72.961,1024,224,1.97 levit_128,13679.77,74.845,1024,224,9.21 semnasnet_050,13508.98,75.79,1024,224,2.08 vit_small_patch32_224,12109.88,84.548,1024,224,22.88 mixer_s32_224,11702.15,87.494,1024,224,19.1 levit_192,11695.39,87.545,1024,224,10.95 lcnet_150,11564.86,88.533,1024,224,4.5 mobilenetv3_large_075,11407.33,89.755,1024,224,3.99 gernet_s,10837.81,94.473,1024,224,8.17 vit_tiny_r_s16_p8_224,10598.14,96.609,1024,224,6.34 mobilenetv3_rw,10164.28,100.733,1024,224,5.48 tf_mobilenetv3_large_075,10125.76,101.117,1024,224,3.99 regnetx_004,10069.8,101.678,1024,224,5.16 mobilenetv3_large_100,10017.28,102.212,1024,224,5.48 mobilenetv3_large_100_miil,10014.68,102.238,1024,224,5.48 ese_vovnet19b_slim_dw,9944.69,102.957,1024,224,1.9 hardcorenas_a,9792.24,104.561,1024,224,5.26 mnasnet_075,9774.09,104.755,1024,224,3.17 tf_mobilenetv3_large_minimal_100,9771.38,104.784,1024,224,3.92 ghostnet_100,9041.17,113.248,1024,224,5.18 hardcorenas_b,9021.74,113.492,1024,224,5.18 tinynet_b,8976.69,114.061,1024,188,3.73 swsl_resnet18,8971.65,114.125,1024,224,11.69 tf_mobilenetv3_large_100,8954.66,114.343,1024,224,5.48 gluon_resnet18_v1b,8949.74,114.406,1024,224,11.69 ssl_resnet18,8947.2,114.439,1024,224,11.69 resnet18,8927.25,114.693,1024,224,11.69 hardcorenas_c,8864.36,115.506,1024,224,5.52 mobilenetv2_075,8764.66,116.82,1024,224,2.64 mnasnet_100,8646.99,118.411,1024,224,4.38 mnasnet_b1,8646.34,118.421,1024,224,4.38 semnasnet_075,8603.57,119.009,1024,224,2.91 levit_256,8528.42,120.058,1024,224,18.89 regnety_004,8497.03,120.501,1024,224,4.34 seresnet18,8461.0,121.015,1024,224,11.78 hardcorenas_d,8306.25,123.269,1024,224,7.5 legacy_seresnet18,8213.74,124.658,1024,224,11.78 regnetx_006,8055.06,127.114,1024,224,6.2 mobilenetv2_100,7900.5,129.6,1024,224,3.5 spnasnet_100,7827.4,130.811,1024,224,4.42 semnasnet_100,7701.96,132.941,1024,224,3.89 mnasnet_a1,7678.8,133.342,1024,224,3.89 resnet18d,7478.23,136.919,1024,224,11.71 levit_256d,7357.48,139.166,1024,224,26.21 ghostnet_130,7270.82,140.824,1024,224,7.36 regnety_006,7263.32,140.971,1024,224,6.06 hardcorenas_f,7222.67,141.764,1024,224,8.2 hardcorenas_e,7174.56,142.715,1024,224,8.07 efficientnet_lite0,7057.13,145.09,1024,224,4.65 ese_vovnet19b_slim,6975.46,146.789,1024,224,3.17 tinynet_a,6918.13,148.004,1024,192,6.19 fbnetc_100,6847.55,149.531,1024,224,5.57 tf_efficientnetv2_b0,6842.85,149.633,1024,224,7.14 xcit_nano_12_p16_224_dist,6769.74,151.25,1024,224,3.05 xcit_nano_12_p16_224,6760.61,151.454,1024,224,3.05 regnetx_008,6358.56,161.031,1024,224,7.26 deit_tiny_patch16_224,6350.86,161.227,1024,224,5.72 vit_tiny_patch16_224,6346.8,161.33,1024,224,5.72 tf_efficientnet_lite0,6324.64,161.895,1024,224,4.65 deit_tiny_distilled_patch16_224,6241.01,164.064,1024,224,5.91 efficientnet_b0,6183.5,165.59,1024,224,5.29 efficientnet_b1_pruned,6044.57,169.396,1024,240,6.33 rexnet_100,6031.43,169.765,1024,224,4.8 mnasnet_140,6024.95,169.948,1024,224,7.12 rexnetr_100,5992.24,170.876,1024,224,4.88 dla46_c,5989.01,170.968,1024,224,1.3 pit_ti_distilled_224,5978.67,171.264,1024,224,5.1 mobilenetv2_110d,5966.33,171.618,1024,224,4.52 pit_ti_224,5950.27,172.082,1024,224,4.85 regnety_008,5940.31,172.37,1024,224,6.26 resnetblur18,5929.76,172.677,1024,224,11.69 tf_efficientnet_b0,5620.98,182.161,1024,224,5.29 tf_efficientnet_b0_ns,5610.83,182.491,1024,224,5.29 tf_efficientnet_b0_ap,5603.36,182.734,1024,224,5.29 skresnet18,5578.53,183.549,1024,224,11.96 regnetz_005,5482.6,186.761,1024,224,7.12 semnasnet_140,5325.69,192.263,1024,224,6.11 mobilenetv2_140,5271.81,194.229,1024,224,6.11 resnet34,5239.3,195.434,1024,224,21.8 tv_resnet34,5236.24,195.548,1024,224,21.8 gluon_resnet34_v1b,5233.7,195.644,1024,224,21.8 ese_vovnet19b_dw,5190.19,197.284,1024,224,6.54 levit_384,5177.17,197.779,1024,224,39.13 mobilevit_xxs,5154.01,198.668,1024,256,1.27 visformer_tiny,5148.77,198.871,1024,224,10.32 hrnet_w18_small,5137.51,199.307,1024,224,13.19 nf_regnet_b0,5134.01,199.442,1024,256,8.76 seresnet34,4940.74,207.245,1024,224,21.96 mixnet_s,4921.18,208.067,1024,224,4.13 gernet_m,4881.9,209.742,1024,224,21.14 efficientnet_lite1,4817.11,212.565,1024,240,5.42 legacy_seresnet34,4790.34,213.752,1024,224,21.96 selecsls42,4787.26,213.889,1024,224,30.35 selecsls42b,4772.45,214.553,1024,224,32.46 dla46x_c,4717.05,217.071,1024,224,1.07 resnet34d,4707.81,217.499,1024,224,21.82 vit_base_patch32_224,4653.29,220.048,1024,224,88.22 vit_base_patch32_224_sam,4636.98,220.822,1024,224,88.22 pit_xs_224,4628.58,221.222,1024,224,10.62 tf_mixnet_s,4615.88,221.831,1024,224,4.13 fbnetv3_b,4595.16,222.83,1024,256,8.6 rexnetr_130,4587.31,223.212,1024,224,7.61 pit_xs_distilled_224,4586.36,223.258,1024,224,11.0 resmlp_12_distilled_224,4524.79,226.297,1024,224,15.35 resmlp_12_224,4522.51,226.411,1024,224,15.35 tf_efficientnetv2_b1,4515.62,226.756,1024,240,8.14 dla60x_c,4459.55,229.607,1024,224,1.32 tf_efficientnet_lite1,4427.03,231.295,1024,240,5.42 mixer_b32_224,4423.78,231.465,1024,224,60.29 rexnet_130,4423.43,231.482,1024,224,7.56 xcit_tiny_12_p16_224_dist,4363.62,234.654,1024,224,6.72 xcit_tiny_12_p16_224,4352.75,235.24,1024,224,6.72 resnet26,4300.48,238.1,1024,224,16.0 mobilenetv2_120d,4276.4,239.441,1024,224,5.83 efficientnet_es_pruned,4244.8,241.225,1024,224,5.44 efficientnet_es,4243.52,241.298,1024,224,5.44 repvgg_b0,4216.88,242.821,1024,224,15.82 selecsls60,4146.99,246.913,1024,224,30.67 selecsls60b,4134.81,247.64,1024,224,32.77 tf_efficientnet_es,4097.34,249.906,1024,224,5.44 fbnetv3_d,4060.14,252.196,1024,256,10.31 efficientnet_b2_pruned,4051.65,252.724,1024,260,8.31 efficientnet_b0_g16_evos,3982.5,257.113,1024,224,8.11 rexnetr_150,3947.19,259.414,1024,224,9.78 crossvit_tiny_240,3922.12,261.07,1024,240,7.01 mixer_s16_224,3902.13,262.409,1024,224,18.53 resnet26d,3896.6,262.781,1024,224,16.01 dla34,3859.49,265.307,1024,224,15.74 ecaresnet50d_pruned,3854.93,265.621,1024,224,19.94 rexnet_150,3827.99,267.492,1024,224,9.73 vit_small_patch32_384,3806.52,269.0,1024,384,22.92 nf_resnet26,3804.26,269.16,1024,224,16.0 gmixer_12_224,3797.94,269.608,1024,224,12.7 efficientnet_lite2,3793.99,269.889,1024,260,6.09 gmlp_ti16_224,3724.27,274.941,1024,224,5.87 crossvit_9_240,3711.74,275.869,1024,240,8.55 regnetx_016,3640.75,281.247,1024,224,9.19 efficientnet_cc_b0_4e,3606.59,283.912,1024,224,13.31 efficientnet_cc_b0_8e,3600.63,284.383,1024,224,24.01 crossvit_9_dagger_240,3560.19,287.614,1024,240,8.78 tf_efficientnet_b1_ap,3560.01,287.626,1024,240,7.79 tf_efficientnet_b1,3559.25,287.687,1024,240,7.79 tf_efficientnet_b1_ns,3553.74,288.134,1024,240,7.79 tf_efficientnet_lite2,3505.87,292.07,1024,260,6.09 efficientnet_b1,3481.01,294.155,1024,256,7.79 poolformer_s12,3480.88,294.166,1024,224,11.92 vit_tiny_r_s16_p8_384,3451.73,148.319,512,384,6.36 tf_efficientnetv2_b2,3443.76,297.337,1024,260,10.1 tf_efficientnet_cc_b0_8e,3407.59,300.493,1024,224,24.01 tf_efficientnet_cc_b0_4e,3402.61,300.934,1024,224,13.31 mixnet_m,3369.57,303.884,1024,224,5.01 regnety_016,3343.57,306.248,1024,224,11.2 nf_seresnet26,3326.57,307.813,1024,224,17.4 nf_ecaresnet26,3308.22,309.519,1024,224,16.0 repvgg_a2,3284.74,311.731,1024,224,28.21 gernet_l,3260.13,314.086,1024,256,31.08 tf_mixnet_m,3258.23,314.269,1024,224,5.01 resnest14d,3225.43,317.465,1024,224,10.61 efficientnet_b3_pruned,3214.49,318.545,1024,300,9.86 convnext_nano_hnf,3199.89,319.999,1024,224,15.59 skresnet34,3189.47,321.044,1024,224,22.28 convit_tiny,3117.16,328.49,1024,224,5.71 resnext26ts,3098.65,330.453,1024,256,10.3 legacy_seresnext26_32x4d,3086.27,331.78,1024,224,16.79 nf_regnet_b1,3049.58,335.771,1024,288,10.22 resnet26t,3040.5,336.774,1024,256,16.01 seresnext26ts,3026.32,338.35,1024,256,10.39 eca_resnext26ts,3023.05,338.719,1024,256,10.3 gcresnext26ts,2976.22,344.049,1024,256,10.48 ecaresnet101d_pruned,2954.94,346.526,1024,224,24.88 nf_regnet_b2,2933.65,349.041,1024,272,14.31 mobilevit_xs,2913.13,175.744,512,256,2.32 seresnext26tn_32x4d,2898.81,353.236,1024,224,16.81 seresnext26t_32x4d,2897.48,353.398,1024,224,16.81 ecaresnext26t_32x4d,2893.22,353.918,1024,224,15.41 ecaresnext50t_32x4d,2891.83,354.088,1024,224,15.41 seresnext26d_32x4d,2884.91,354.937,1024,224,16.81 ecaresnetlight,2878.45,355.733,1024,224,30.16 pit_s_224,2872.59,356.46,1024,224,23.46 deit_small_patch16_224,2853.43,358.855,1024,224,22.05 pit_s_distilled_224,2851.86,359.052,1024,224,24.04 vit_small_patch16_224,2845.41,359.865,1024,224,22.05 tf_efficientnet_b2_ap,2814.51,363.814,1024,260,9.11 tf_efficientnet_b2_ns,2814.31,363.839,1024,260,9.11 coat_lite_tiny,2814.08,363.872,1024,224,5.72 tf_efficientnet_b2,2813.96,363.886,1024,260,9.11 rexnetr_200,2808.62,182.283,512,224,16.52 deit_small_distilled_patch16_224,2801.73,365.478,1024,224,22.44 tresnet_m,2787.92,367.287,1024,224,31.39 resnetv2_50,2780.22,368.303,1024,224,25.55 eca_botnext26ts_256,2766.45,370.137,1024,256,10.59 rexnet_200,2763.52,185.259,512,224,16.37 vit_base2_patch32_256,2752.1,372.066,1024,256,119.46 botnet26t_256,2750.58,372.273,1024,256,12.49 halonet26t,2727.12,375.475,1024,256,12.48 eca_halonext26ts,2721.42,376.262,1024,256,10.76 swsl_resnet50,2693.51,380.159,1024,224,25.56 tv_resnet50,2687.71,380.98,1024,224,25.56 efficientnet_b0_gn,2686.21,381.194,1024,224,5.29 ssl_resnet50,2684.8,381.394,1024,224,25.56 gluon_resnet50_v1b,2682.34,381.744,1024,224,25.56 resnet50,2681.21,381.904,1024,224,25.56 vit_small_resnet26d_224,2675.44,382.728,1024,224,63.61 efficientnet_b2a,2654.03,385.816,1024,288,9.11 efficientnet_b2,2649.9,386.418,1024,288,9.11 coat_lite_mini,2646.6,386.899,1024,224,11.01 hrnet_w18_small_v2,2638.47,388.089,1024,224,15.6 resnetv2_50t,2621.75,390.565,1024,224,25.57 vovnet39a,2620.98,390.68,1024,224,22.6 resnetv2_50d,2613.09,391.86,1024,224,25.57 bat_resnext26ts,2594.52,394.665,1024,256,10.73 resnet32ts,2591.72,395.092,1024,256,17.96 efficientnet_em,2587.13,395.792,1024,240,6.9 cspresnet50,2561.78,399.709,1024,256,21.62 mixnet_l,2560.63,199.939,512,224,7.33 resnet33ts,2550.89,401.417,1024,256,19.68 dpn68b,2548.03,401.866,1024,224,12.61 gluon_resnet50_v1c,2543.04,402.654,1024,224,25.58 ese_vovnet39b,2535.01,403.931,1024,224,24.57 eca_vovnet39b,2533.56,404.162,1024,224,22.6 cspresnext50,2530.86,404.592,1024,224,20.57 legacy_seresnet50,2527.3,405.162,1024,224,28.09 vgg11_bn,2524.72,202.784,512,224,132.87 tf_efficientnet_em,2523.81,405.723,1024,240,6.9 resnet50t,2521.66,406.069,1024,224,25.57 resnet50d,2517.37,406.76,1024,224,25.58 gluon_resnet50_v1d,2514.4,407.243,1024,224,25.58 dpn68,2513.01,407.467,1024,224,12.61 selecsls84,2490.46,411.155,1024,224,50.95 seresnet33ts,2489.53,411.308,1024,256,19.78 eca_resnet33ts,2483.09,412.378,1024,256,19.68 lambda_resnet26t,2479.99,412.893,1024,256,10.96 tf_mixnet_l,2478.99,206.524,512,224,7.33 twins_svt_small,2475.31,413.674,1024,224,24.06 gcresnet33ts,2439.7,419.711,1024,256,19.88 cspresnet50w,2418.46,423.398,1024,256,28.12 seresnet50,2407.37,425.348,1024,224,28.09 cspresnet50d,2400.89,426.492,1024,256,21.64 dla60,2376.63,430.848,1024,224,22.04 densenet121,2346.76,436.333,1024,224,7.98 resnest26d,2346.08,436.46,1024,224,17.07 tv_densenet121,2345.79,436.514,1024,224,7.98 xcit_tiny_24_p16_224_dist,2334.54,438.616,1024,224,12.12 xcit_nano_12_p16_384_dist,2332.68,438.968,1024,384,3.05 xcit_tiny_24_p16_224,2328.43,439.766,1024,224,12.12 haloregnetz_b,2320.38,441.294,1024,224,11.68 resmlp_24_224,2308.85,443.498,1024,224,30.02 resmlp_24_distilled_224,2308.13,443.636,1024,224,30.02 resnetaa50d,2295.34,446.109,1024,224,25.58 seresnet50t,2282.97,448.526,1024,224,28.1 efficientnet_cc_b1_8e,2282.2,448.676,1024,240,39.72 convnext_tiny,2276.32,449.828,1024,224,28.59 res2net50_48w_2s,2265.43,451.999,1024,224,25.29 resnetblur50,2265.02,452.08,1024,224,25.56 efficientnet_lite3,2261.44,226.393,512,300,8.2 ecaresnet50d,2260.92,452.9,1024,224,25.58 efficientnet_b0_g8_gn,2259.05,453.276,1024,224,6.56 densenet121d,2243.46,456.425,1024,224,8.0 resnetrs50,2241.6,456.804,1024,224,35.69 mobilevit_s,2240.37,228.522,512,256,5.58 regnetx_032,2201.52,465.118,1024,224,15.3 visformer_small,2194.82,466.539,1024,224,40.22 gluon_resnet50_v1s,2193.42,466.838,1024,224,25.68 tf_efficientnet_cc_b1_8e,2188.26,467.941,1024,240,39.72 vit_base_resnet26d_224,2188.18,467.954,1024,224,101.4 resnetblur50d,2150.42,476.173,1024,224,25.58 gluon_inception_v3,2150.17,476.225,1024,299,23.83 adv_inception_v3,2148.48,476.602,1024,299,23.83 vovnet57a,2148.25,476.651,1024,224,36.64 tf_inception_v3,2147.17,476.894,1024,299,23.83 inception_v3,2146.78,476.978,1024,299,23.83 densenetblur121d,2133.31,479.992,1024,224,8.0 cspresnext50_iabn,2129.31,480.895,1024,256,20.57 semobilevit_s,2105.83,243.122,512,256,5.74 seresnetaa50d,2088.02,490.403,1024,224,28.11 cspdarknet53_iabn,2087.26,490.582,1024,256,27.64 swsl_resnext50_32x4d,2080.34,492.214,1024,224,25.03 convnext_tiny_hnf,2075.56,493.349,1024,224,28.59 ese_vovnet57b,2074.63,493.57,1024,224,38.61 tf_efficientnet_lite3,2074.49,246.795,512,300,8.2 resnext50_32x4d,2073.97,493.725,1024,224,25.03 ssl_resnext50_32x4d,2073.23,493.902,1024,224,25.03 gluon_resnext50_32x4d,2072.3,494.125,1024,224,25.03 tv_resnext50_32x4d,2055.26,498.221,1024,224,25.03 res2net50_26w_4s,2037.79,502.491,1024,224,25.7 twins_pcpvt_small,2036.4,502.835,1024,224,24.11 xcit_small_12_p16_224_dist,2021.26,506.599,1024,224,26.25 tf_efficientnetv2_b3,2020.91,506.688,1024,300,14.36 xcit_small_12_p16_224,2020.4,506.814,1024,224,26.25 nf_seresnet50,2015.9,507.948,1024,224,28.09 skresnet50,2014.26,508.362,1024,224,25.8 nf_ecaresnet50,2005.54,510.572,1024,224,25.56 regnetx_040,2003.23,511.16,1024,224,22.12 efficientnetv2_rw_t,1999.8,512.038,1024,288,13.65 sehalonet33ts,1991.52,257.078,512,256,13.69 fbnetv3_g,1991.44,514.187,1024,288,16.62 dla60x,1986.19,515.547,1024,224,17.35 gcresnet50t,1982.14,516.599,1024,256,25.9 resnext50d_32x4d,1976.72,518.018,1024,224,25.05 lambda_resnet26rpt_256,1950.98,262.42,512,256,10.99 gmixer_24_224,1937.61,528.474,1024,224,24.72 gc_efficientnetv2_rw_t,1928.42,530.993,1024,288,13.68 res2net50_14w_8s,1920.35,533.22,1024,224,25.06 skresnet50d,1919.46,533.468,1024,224,25.82 densenet169,1916.46,534.305,1024,224,14.15 dla60_res2net,1907.38,536.848,1024,224,20.85 gcresnext50ts,1902.33,538.276,1024,256,15.67 seresnext50_32x4d,1902.13,538.331,1024,224,27.56 gluon_seresnext50_32x4d,1901.68,538.457,1024,224,27.56 res2next50,1896.89,539.818,1024,224,24.67 legacy_seresnext50_32x4d,1896.31,539.984,1024,224,27.56 repvgg_b1g4,1884.44,543.384,1024,224,39.97 resnest50d_1s4x24d,1879.81,544.722,1024,224,25.68 crossvit_small_240,1855.05,551.992,1024,240,26.86 nf_regnet_b3,1852.1,552.873,1024,320,18.59 darknet53,1847.62,277.102,512,256,41.61 mixnet_xl,1839.34,278.346,512,224,11.9 dla60_res2next,1829.2,559.793,1024,224,17.03 swin_tiny_patch4_window7_224,1820.74,562.394,1024,224,28.29 cspdarknet53,1804.24,283.762,512,256,27.64 poolformer_s24,1803.36,567.817,1024,224,21.39 vit_small_r26_s32_224,1799.77,568.949,1024,224,36.43 xcit_nano_12_p8_224_dist,1796.05,570.128,1024,224,3.05 xcit_nano_12_p8_224,1795.49,570.304,1024,224,3.05 regnetz_b16,1791.93,571.438,1024,288,9.72 ecaresnet26t,1786.82,573.073,1024,320,16.01 convnext_tiny_hnfd,1744.82,586.868,1024,224,28.63 gmlp_s16_224,1741.6,587.951,1024,224,19.42 sebotnet33ts_256,1718.54,223.433,384,256,13.7 crossvit_15_240,1711.44,598.314,1024,240,27.53 resnetv2_101,1693.03,604.82,1024,224,44.54 vit_base_resnet50d_224,1670.98,612.798,1024,224,110.97 swin_s3_tiny_224,1660.14,616.801,1024,224,28.33 repvgg_b1,1657.85,617.654,1024,224,57.42 tv_resnet101,1656.03,618.332,1024,224,44.55 gluon_resnet101_v1b,1653.8,619.167,1024,224,44.55 resnet101,1652.45,619.673,1024,224,44.55 tf_efficientnet_b3_ap,1649.82,310.323,512,300,12.23 tf_efficientnet_b3,1649.61,310.363,512,300,12.23 tf_efficientnet_b3_ns,1649.37,310.407,512,300,12.23 crossvit_15_dagger_240,1648.34,621.218,1024,240,28.21 lambda_resnet50ts,1639.74,624.475,1024,256,21.54 resnetv2_101d,1628.19,628.906,1024,224,44.56 efficientnet_b3,1614.3,317.152,512,320,12.23 efficientnet_b3a,1613.57,317.296,512,320,12.23 gluon_resnet101_v1c,1597.11,641.145,1024,224,44.57 resnest50d,1586.43,645.46,1024,224,27.48 gluon_resnet101_v1d,1584.97,646.054,1024,224,44.57 wide_resnet50_2,1583.06,646.835,1024,224,68.88 cait_xxs24_224,1580.68,647.808,1024,224,11.96 dla102,1573.96,650.576,1024,224,33.27 resnetv2_50x1_bit_distilled,1561.81,655.635,1024,224,25.55 res2net50_26w_6s,1556.3,657.955,1024,224,37.05 vit_large_patch32_224,1551.79,659.869,1024,224,306.54 resmlp_36_224,1549.07,661.03,1024,224,44.69 regnetx_080,1548.92,661.091,1024,224,39.57 resmlp_36_distilled_224,1548.49,661.277,1024,224,44.69 halonet50ts,1542.39,663.891,1024,256,22.73 legacy_seresnet101,1528.82,669.783,1024,224,49.33 ese_vovnet39b_evos,1523.51,672.121,1024,224,24.58 coat_lite_small,1509.49,678.361,1024,224,19.84 vgg13_bn,1504.09,340.392,512,224,133.05 xcit_tiny_12_p16_384_dist,1501.95,681.763,1024,384,6.72 resnetaa101d,1496.92,684.059,1024,224,44.57 swin_v2_cr_tiny_224,1495.36,684.765,1024,224,28.33 densenet201,1488.38,687.985,1024,224,20.01 seresnet101,1484.39,689.83,1024,224,49.33 vit_tiny_patch16_384,1479.06,692.319,1024,384,5.79 lamhalobotnet50ts_256,1474.4,694.504,1024,256,22.57 swin_v2_cr_tiny_ns_224,1471.62,695.817,1024,224,28.33 vit_base_patch32_384,1470.27,696.459,1024,384,88.3 vit_base_r26_s32_224,1467.48,697.779,1024,224,101.38 gluon_resnet101_v1s,1452.66,704.901,1024,224,44.67 regnetx_064,1450.48,352.975,512,224,26.21 mixer_b16_224,1448.87,706.746,1024,224,59.88 nf_resnet101,1448.19,707.076,1024,224,44.55 mixer_b16_224_miil,1446.14,708.08,1024,224,59.88 resnetv2_50d_frn,1444.18,709.041,1024,224,25.59 resnetblur101d,1433.31,714.414,1024,224,44.57 nf_resnet50,1425.83,718.163,1024,288,25.56 mixer_l32_224,1425.39,718.388,1024,224,206.94 ecaresnet101d,1423.43,719.375,1024,224,44.57 hrnet_w18,1416.64,722.817,1024,224,21.3 convnext_small,1412.23,725.081,1024,224,50.22 tresnet_l,1401.27,730.75,1024,224,55.99 twins_pcpvt_base,1398.04,732.441,1024,224,43.83 regnety_032,1384.84,739.421,1024,288,19.44 nest_tiny,1381.77,370.526,512,224,17.06 resnet50_gn,1377.54,743.338,1024,224,25.56 resnet51q,1366.14,749.545,1024,288,35.7 resnetv2_50d_evob,1365.49,749.897,1024,224,25.59 jx_nest_tiny,1357.18,377.241,512,224,17.06 botnet50ts_256,1354.82,377.899,512,256,22.74 xception,1347.6,379.923,512,299,22.86 dla102x,1333.01,768.169,1024,224,26.31 convit_small,1329.45,770.231,1024,224,27.78 halo2botnet50ts_256,1327.73,385.607,512,256,22.64 skresnext50_32x4d,1314.86,778.776,1024,224,27.48 swsl_resnext101_32x4d,1313.2,779.762,1024,224,44.18 ssl_resnext101_32x4d,1309.86,781.751,1024,224,44.18 gluon_resnext101_32x4d,1309.44,781.999,1024,224,44.18 resnext101_32x4d,1308.3,782.684,1024,224,44.18 repvgg_b2g4,1287.88,795.093,1024,224,61.76 res2net50_26w_8s,1277.42,801.601,1024,224,48.4 res2net101_26w_4s,1275.83,802.597,1024,224,45.21 resnest50d_4s2x40d,1267.83,807.666,1024,224,30.42 nf_seresnet101,1246.67,821.372,1024,224,49.33 twins_svt_base,1242.25,824.296,1024,224,56.07 nf_ecaresnet101,1240.12,825.715,1024,224,44.55 vgg16_bn,1239.17,413.169,512,224,138.37 resnet61q,1236.58,828.077,1024,288,36.85 eca_nfnet_l0,1234.82,829.257,1024,288,24.14 nfnet_l0,1234.39,829.546,1024,288,35.07 hrnet_w32,1225.86,835.318,1024,224,41.23 xception41p,1219.47,419.841,512,299,26.91 poolformer_s36,1217.85,840.81,1024,224,30.86 ese_vovnet99b_iabn,1214.4,843.2,1024,224,63.2 crossvit_18_240,1210.54,845.892,1024,240,43.27 regnetv_040,1210.02,423.122,512,288,20.64 regnety_040,1209.84,423.186,512,288,20.65 hrnet_w30,1208.97,846.988,1024,224,37.71 dpn92,1205.79,849.224,1024,224,37.67 gluon_seresnext101_32x4d,1198.19,854.61,1024,224,48.96 seresnext101_32x4d,1197.93,854.792,1024,224,48.96 legacy_seresnext101_32x4d,1196.55,855.783,1024,224,48.96 efficientnet_el,1181.26,433.424,512,300,10.59 efficientnet_el_pruned,1179.3,434.142,512,300,10.59 ese_vovnet99b,1178.02,869.24,1024,224,63.2 resnetv2_152,1172.64,873.226,1024,224,60.19 crossvit_18_dagger_240,1169.98,875.211,1024,240,44.27 tf_efficientnet_el,1156.16,442.832,512,300,10.59 tv_resnet152,1155.28,886.354,1024,224,60.19 resnet152,1153.46,887.752,1024,224,60.19 gluon_resnet152_v1b,1153.39,887.802,1024,224,60.19 xcit_tiny_12_p8_224_dist,1148.77,891.372,1024,224,6.71 xcit_tiny_12_p8_224,1148.55,891.542,1024,224,6.71 vit_small_resnet50d_s16_224,1140.9,897.523,1024,224,57.53 resnetv2_152d,1140.85,897.561,1024,224,60.2 mixnet_xxl,1136.05,338.002,384,224,23.96 ecaresnet50t,1133.49,903.391,1024,320,25.57 regnetz_c16,1132.12,452.237,512,320,13.46 repvgg_b2,1129.63,906.478,1024,224,89.02 gluon_resnet152_v1c,1126.87,908.694,1024,224,60.21 vit_base_patch16_224_miil,1124.8,910.37,1024,224,86.54 gluon_resnet152_v1d,1122.21,912.468,1024,224,60.21 volo_d1_224,1121.56,912.997,1024,224,26.63 swin_small_patch4_window7_224,1117.13,916.62,1024,224,49.61 regnety_040s_gn,1113.45,919.647,1024,224,20.65 inception_v4,1099.65,931.189,1024,299,42.68 vit_base_patch16_224_sam,1089.03,940.269,1024,224,86.57 xception41,1089.03,470.133,512,299,26.97 vit_base_patch16_224,1089.0,940.3,1024,224,86.57 deit_base_patch16_224,1088.05,941.117,1024,224,86.57 xcit_small_24_p16_224_dist,1079.16,948.867,1024,224,47.67 xcit_small_24_p16_224,1078.82,949.17,1024,224,47.67 densenet161,1076.84,950.914,1024,224,28.68 convmixer_1024_20_ks9_p14,1075.95,951.707,1024,224,24.38 nfnet_f0,1075.06,952.487,1024,256,71.49 deit_base_distilled_patch16_224,1073.74,953.659,1024,224,87.34 dla169,1071.22,955.906,1024,224,53.39 vgg19_bn,1060.19,482.919,512,224,143.68 cait_xxs36_224,1058.79,967.13,1024,224,17.3 tnt_s_patch16_224,1056.28,969.422,1024,224,23.76 legacy_seresnet152,1054.79,970.791,1024,224,66.82 gluon_resnet152_v1s,1052.41,972.991,1024,224,60.32 regnetx_120,1048.48,488.312,512,224,46.11 seresnet152,1033.07,991.203,1024,224,66.82 tresnet_xl,1032.49,991.76,1024,224,78.44 efficientnet_lite4,1029.33,373.047,384,380,13.01 beit_base_patch16_224,1003.86,1020.053,1024,224,86.53 regnety_120,1003.65,510.126,512,224,51.82 repvgg_b3g4,997.42,1026.637,1024,224,83.83 twins_pcpvt_large,995.78,1028.325,1024,224,60.99 convnext_base,984.44,1040.164,1024,224,88.59 convnext_base_in22ft1k,984.35,1040.256,1024,224,88.59 coat_tiny,976.27,1048.875,1024,224,5.5 tf_efficientnet_lite4,967.59,396.848,384,380,13.01 pit_b_224,954.91,536.164,512,224,73.76 dm_nfnet_f0,948.25,1079.874,1024,256,71.49 pit_b_distilled_224,947.46,540.381,512,224,74.79 vit_small_patch16_36x1_224,919.98,1113.057,1024,224,64.67 wide_resnet101_2,917.07,1116.581,1024,224,126.89 swin_v2_cr_small_224,915.96,1117.937,1024,224,49.7 dla102x2,910.31,562.434,512,224,41.28 resnetv2_50d_gn,909.47,1125.915,1024,288,25.57 efficientnetv2_s,905.47,1130.894,1024,384,21.46 vit_small_patch16_18x2_224,899.06,1138.948,1024,224,64.67 tf_efficientnetv2_s_in21ft1k,889.42,1151.294,1024,384,21.46 tf_efficientnetv2_s,889.32,1151.431,1024,384,21.46 xception65p,886.31,577.66,512,299,39.82 nest_small,881.75,580.652,512,224,38.35 twins_svt_large,880.33,1163.185,1024,224,99.27 repvgg_b3,878.37,1165.779,1024,224,123.09 resnetrs101,877.57,1166.842,1024,288,63.62 jx_nest_small,871.53,587.458,512,224,38.35 efficientnetv2_rw_s,866.51,1181.735,1024,384,23.94 dpn98,862.25,1187.578,1024,224,61.57 ens_adv_inception_resnet_v2,856.37,1195.737,1024,299,55.84 inception_resnet_v2,855.57,1196.844,1024,299,55.84 nf_regnet_b4,854.46,1198.403,1024,384,30.21 regnetz_b16_evos,853.4,599.942,512,288,9.74 regnetx_160,848.43,603.455,512,224,54.28 regnetz_d8,845.88,1210.552,1024,320,23.37 cait_s24_224,834.81,1226.608,1024,224,46.92 gluon_resnext101_64x4d,834.02,1227.777,1024,224,83.46 resnet200,828.19,1236.414,1024,224,64.67 regnetz_040,826.88,464.381,384,320,27.12 regnetz_040h,823.24,466.438,384,320,28.94 efficientnet_b4,820.41,468.046,384,384,19.34 swin_s3_small_224,817.53,626.263,512,224,49.74 hrnet_w40,816.49,1254.128,1024,224,57.56 poolformer_m36,815.39,1255.826,1024,224,56.17 swsl_resnext101_32x8d,805.27,1271.611,1024,224,88.79 regnety_064,803.23,637.411,512,288,30.58 ssl_resnext101_32x8d,802.86,1275.43,1024,224,88.79 xcit_tiny_24_p16_384_dist,802.73,1275.631,1024,384,12.12 ig_resnext101_32x8d,802.17,1276.521,1024,224,88.79 resnext101_32x8d,802.06,1276.704,1024,224,88.79 regnetv_064,800.43,639.64,512,288,30.58 resnetv2_50d_evos,798.97,640.813,512,288,25.59 gluon_xception65,797.15,642.277,512,299,39.92 resnet101d,796.13,1286.203,1024,320,44.57 xception65,795.21,643.84,512,299,39.92 resnest101e,791.92,646.513,512,256,48.28 swin_base_patch4_window7_224,791.65,1293.482,1024,224,87.77 gluon_seresnext101_64x4d,787.65,1300.055,1024,224,88.23 coat_mini,785.23,1304.064,1024,224,10.34 tf_efficientnet_b4_ap,782.92,490.459,384,380,19.34 tf_efficientnet_b4,782.13,490.953,384,380,19.34 tf_efficientnet_b4_ns,782.09,490.976,384,380,19.34 regnety_080,767.29,667.271,512,288,39.18 hrnet_w44,759.24,1348.7,1024,224,67.06 crossvit_base_240,756.03,677.208,512,240,105.03 xcit_medium_24_p16_224_dist,748.3,1368.42,1024,224,84.4 xcit_medium_24_p16_224,747.93,1369.089,1024,224,84.4 gmlp_b16_224,744.43,1375.538,1024,224,73.08 hrnet_w48,733.55,1395.939,1024,224,77.47 tresnet_m_448,727.54,1407.473,1024,448,31.39 vit_large_r50_s32_224,726.03,1410.402,1024,224,328.99 regnetz_d32,721.75,1418.755,1024,320,27.58 vit_small_patch16_384,684.33,748.167,512,384,22.2 tnt_b_patch16_224,677.0,1512.541,1024,224,65.41 xcit_small_12_p16_384_dist,675.76,1515.313,1024,384,26.25 convit_base,673.78,1519.763,1024,224,86.54 swin_s3_base_224,663.17,1544.087,1024,224,71.13 swin_v2_cr_base_224,651.94,1570.69,1024,224,87.88 densenet264d_iabn,647.21,1582.161,1024,224,72.74 efficientnet_b3_gn,646.96,395.683,256,320,11.73 dpn131,635.27,1611.889,1024,224,79.25 densenet264,628.46,1629.36,1024,224,72.69 nest_base,627.45,815.992,512,224,67.72 volo_d2_224,626.17,1635.329,1024,224,58.68 jx_nest_base,620.43,825.215,512,224,67.72 poolformer_m48,615.67,1663.217,1024,224,73.47 vit_small_r26_s32_384,611.26,628.196,384,384,36.47 xcit_nano_12_p8_384_dist,610.06,1678.498,1024,384,3.05 xcit_tiny_24_p8_224,603.0,1698.15,1024,224,12.11 xcit_tiny_24_p8_224_dist,602.22,1700.353,1024,224,12.11 xception71,600.16,853.086,512,299,42.34 hrnet_w64,599.02,1709.451,1024,224,128.06 vit_base_r50_s16_224,598.25,1711.642,1024,224,98.66 legacy_senet154,582.07,1759.212,1024,224,115.09 senet154,582.01,1759.392,1024,224,115.09 gluon_senet154,581.94,1759.609,1024,224,115.09 dpn107,578.97,1768.627,1024,224,86.92 eca_nfnet_l1,575.61,1778.965,1024,320,41.41 seresnet200d,562.84,1819.333,1024,256,71.86 resnet152d,561.29,1824.359,1024,320,60.21 ecaresnet200d,559.73,1829.437,1024,256,64.69 convnext_large_in22ft1k,546.71,936.5,512,224,197.77 convnext_large,546.09,937.561,512,224,197.77 regnetz_c16_evos,539.7,948.669,512,320,13.49 regnety_320,534.72,957.496,512,224,145.05 regnety_160,523.72,733.2,384,288,83.59 efficientnet_b3_g8_gn,520.23,492.079,256,320,14.25 xcit_small_12_p8_224,514.94,1988.547,1024,224,26.21 xcit_small_12_p8_224_dist,514.63,1989.761,1024,224,26.21 halonet_h1,507.89,504.03,256,256,8.1 resnext101_64x4d,505.79,1012.259,512,288,83.46 seresnet152d,503.07,2035.474,1024,320,66.84 resnetrs152,499.61,2049.599,1024,320,86.62 vit_large_patch32_384,494.68,2070.028,1024,384,306.63 regnetx_320,468.44,819.734,384,224,107.81 mixer_l16_224,464.51,2204.436,1024,224,208.2 seresnext101_32x8d,461.67,1109.006,512,288,93.57 swin_large_patch4_window7_224,454.97,1125.326,512,224,196.53 regnetz_e8,451.54,1133.877,512,320,57.7 efficientnetv2_m,442.19,2315.716,1024,416,54.14 seresnet269d,440.45,2324.895,1024,256,113.67 volo_d3_224,438.95,2332.847,1024,224,86.33 xcit_large_24_p16_224,432.87,2365.575,1024,224,189.1 xcit_large_24_p16_224_dist,432.7,2366.505,1024,224,189.1 efficientnet_b5,411.07,622.743,256,456,30.39 efficientnetv2_rw_m,406.88,1258.338,512,416,53.24 resnet200d,404.01,2534.588,1024,320,64.69 tf_efficientnet_b5,395.61,647.082,256,456,30.39 tf_efficientnet_b5_ap,395.52,647.242,256,456,30.39 tf_efficientnet_b5_ns,395.47,647.322,256,456,30.39 resnetv2_50x1_bitm,392.67,1303.884,512,448,25.55 xcit_tiny_12_p8_384_dist,390.32,2623.487,1024,384,6.71 swin_v2_cr_large_224,385.91,1326.718,512,224,196.68 swsl_resnext101_32x16d,375.78,1362.484,512,224,194.03 ig_resnext101_32x16d,373.86,1369.478,512,224,194.03 ssl_resnext101_32x16d,373.64,1370.28,512,224,194.03 regnetz_d8_evos,373.09,1372.306,512,320,23.46 swin_v2_cr_tiny_384,364.64,702.039,256,384,28.33 tresnet_l_448,361.5,2832.633,1024,448,55.99 convnext_xlarge_in22ft1k,360.86,1418.822,512,224,350.2 xcit_small_24_p16_384_dist,360.65,2839.301,1024,384,47.67 resnetrs200,358.31,2857.856,1024,320,93.21 nfnet_f1,357.09,2867.576,1024,320,132.63 vit_large_patch16_224,356.78,2870.107,1024,224,304.33 crossvit_15_dagger_408,354.9,721.306,256,408,28.5 vit_base_patch16_18x2_224,348.19,2940.87,1024,224,256.73 tf_efficientnetv2_m_in21ft1k,347.38,1473.892,512,480,54.14 tf_efficientnetv2_m,346.86,1476.074,512,480,54.14 dm_nfnet_f1,342.33,1495.606,512,320,132.63 convnext_base_384_in22ft1k,336.37,1141.59,384,384,88.59 beit_large_patch16_224,330.46,3098.668,1024,224,304.43 volo_d1_384,293.12,1746.724,512,384,26.78 convmixer_768_32,290.99,3518.992,1024,224,21.11 eca_nfnet_l2,282.42,1812.878,512,384,56.72 volo_d4_224,281.7,3635.003,1024,224,192.96 resnetv2_152x2_bit_teacher,280.73,1823.773,512,224,236.34 vit_base_patch16_384,280.43,1369.296,384,384,86.86 deit_base_patch16_384,280.4,1369.456,384,384,86.86 deit_base_distilled_patch16_384,276.56,1388.495,384,384,87.63 xcit_small_24_p8_224,269.73,3796.43,1024,224,47.63 tresnet_xl_448,269.71,1898.302,512,448,78.44 xcit_small_24_p8_224_dist,269.67,3797.157,1024,224,47.63 resnest200e,265.14,1931.055,512,320,70.2 cait_xxs24_384,264.07,3877.737,1024,384,12.03 vit_large_patch14_224,262.13,3906.369,1024,224,304.2 crossvit_18_dagger_408,260.17,983.968,256,408,44.61 xcit_medium_24_p16_384_dist,254.44,2012.271,512,384,84.4 nasnetalarge,254.21,1510.535,384,331,88.75 pnasnet5large,251.02,1529.719,384,331,86.06 resnetv2_101x1_bitm,246.55,2076.684,512,448,44.54 beit_base_patch16_384,241.2,1592.025,384,384,86.74 vit_large_r50_s32_384,240.58,1596.142,384,384,329.09 efficientnet_b6,239.36,534.745,128,528,43.04 ecaresnet269d,233.52,4385.094,1024,352,102.09 tf_efficientnet_b6_ns,231.37,553.209,128,528,43.04 tf_efficientnet_b6,231.23,553.545,128,528,43.04 tf_efficientnet_b6_ap,231.04,553.998,128,528,43.04 resnetrs270,227.52,4500.663,1024,352,129.86 swin_v2_cr_small_384,224.2,1141.845,256,384,49.7 swin_base_patch4_window12_384,211.77,906.647,192,384,87.9 resmlp_big_24_224,209.16,4895.869,1024,224,129.14 resmlp_big_24_distilled_224,208.94,4900.863,1024,224,129.14 resmlp_big_24_224_in22ft1k,208.92,4901.346,1024,224,129.14 xcit_tiny_24_p8_384_dist,204.37,5010.586,1024,384,12.11 nfnet_f2,200.76,5100.492,1024,352,193.78 tf_efficientnetv2_l,199.73,1922.595,384,480,118.52 efficientnetv2_l,198.4,2580.63,512,480,118.52 tf_efficientnetv2_l_in21ft1k,196.98,2599.257,512,480,118.52 dm_nfnet_f2,194.75,2628.942,512,352,193.78 xcit_medium_24_p8_224,189.81,2697.371,512,224,84.32 xcit_medium_24_p8_224_dist,189.81,2697.462,512,224,84.32 volo_d5_224,187.06,5474.175,1024,224,295.46 convnext_large_384_in22ft1k,186.8,1370.399,256,384,197.77 cait_xs24_384,184.72,2771.758,512,384,26.67 vit_base_patch8_224,183.25,1396.951,256,224,86.58 cait_xxs36_384,176.62,5797.903,1024,384,17.37 vit_base_r50_s16_384,174.09,2205.77,384,384,98.95 vit_base_resnet50_384,174.05,2206.21,384,384,98.95 xcit_small_12_p8_384_dist,173.11,2957.56,512,384,26.21 swin_v2_cr_huge_224,170.28,2255.094,384,224,657.83 convmixer_1536_20,167.4,6117.1,1024,224,51.63 volo_d2_384,164.47,2334.792,384,384,58.87 swin_v2_cr_base_384,160.01,1199.906,192,384,87.88 eca_nfnet_l3,159.42,3211.652,512,448,72.04 resnetrs350,151.88,3371.146,512,384,163.96 xcit_large_24_p16_384_dist,147.05,3481.712,512,384,189.1 ig_resnext101_32x32d,146.83,1743.474,256,224,468.53 cait_s24_384,142.26,3598.927,512,384,47.06 vit_huge_patch14_224,141.6,7231.567,1024,224,632.05 efficientnet_b7,139.66,687.357,96,600,66.35 tf_efficientnet_b7,135.86,706.608,96,600,66.35 tf_efficientnet_b7_ap,135.79,706.955,96,600,66.35 tf_efficientnet_b7_ns,135.74,707.2,96,600,66.35 efficientnetv2_xl,127.77,3005.388,384,512,208.12 tf_efficientnetv2_xl_in21ft1k,127.07,3021.991,384,512,208.12 swin_large_patch4_window12_384,125.28,1021.736,128,384,196.74 resnest269e,123.52,3108.719,384,416,110.93 convnext_xlarge_384_in22ft1k,123.3,1557.136,192,384,350.2 xcit_large_24_p8_224,110.05,4652.475,512,224,188.93 xcit_large_24_p8_224_dist,109.94,4656.907,512,224,188.93 nfnet_f3,109.46,4677.461,512,416,254.92 resnetrs420,108.78,4706.738,512,416,191.89 dm_nfnet_f3,98.93,5175.208,512,416,254.92 swin_v2_cr_large_384,98.1,1304.756,128,384,196.68 resnetv2_152x2_bit_teacher_384,97.22,2633.19,256,384,236.34 cait_s36_384,95.27,5374.225,512,384,68.37 vit_large_patch16_384,94.74,2702.22,256,384,304.72 resnetv2_50x3_bitm,94.62,1352.761,128,448,217.32 vit_giant_patch14_224,92.89,5511.692,512,224,1012.61 xcit_small_24_p8_384_dist,90.84,4227.257,384,384,47.63 ig_resnext101_32x48d,87.18,2202.253,192,224,828.41 efficientnet_b8,83.91,1144.078,96,672,87.41 beit_large_patch16_384,82.64,3097.749,256,384,305.0 tf_efficientnet_b8_ap,82.27,1166.925,96,672,87.41 tf_efficientnet_b8,82.25,1167.133,96,672,87.41 volo_d3_448,72.26,2657.14,192,448,86.63 resnetv2_152x2_bitm,71.6,2681.389,192,448,236.34 xcit_medium_24_p8_384_dist,64.15,3990.849,256,384,84.32 nfnet_f4,60.24,6374.659,384,512,316.07 dm_nfnet_f4,59.09,4332.344,256,512,316.07 resnetv2_101x3_bitm,58.2,2199.411,128,448,387.93 vit_gigantic_patch14_224,56.14,9120.818,512,224,1844.44 volo_d4_448,52.76,2425.859,128,448,193.41 swin_v2_cr_giant_224,49.04,2610.23,128,224,2598.76 tf_efficientnet_l2_ns_475,46.41,1378.993,64,475,480.31 nfnet_f5,44.14,5799.172,256,544,377.21 swin_v2_cr_huge_384,43.59,1468.203,64,384,657.94 dm_nfnet_f5,39.84,6426.216,256,544,377.21 xcit_large_24_p8_384_dist,36.78,5220.859,192,384,188.93 volo_d5_448,36.57,3500.029,128,448,295.91 nfnet_f6,34.59,7401.234,256,576,438.36 beit_large_patch16_512,33.3,2882.954,96,512,305.67 cait_m36_384,31.36,8162.98,256,384,271.22 dm_nfnet_f6,31.17,8214.193,256,576,438.36 nfnet_f7,26.91,9514.003,256,608,499.5 volo_d5_512,25.64,4991.614,128,512,296.09 resnetv2_152x4_bitm,18.58,3444.83,64,480,936.53 efficientnet_l2,16.55,1450.295,24,800,480.31 tf_efficientnet_l2_ns,16.36,1467.093,24,800,480.31 swin_v2_cr_giant_384,13.63,1760.856,24,384,2598.76 cait_m48_448,13.35,9584.614,128,448,356.46
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt112-cu113-rtx3090.csv	model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count tinynet_e,49285.12,20.767,1024,106,0.03,0.69,2.04 mobilenetv3_small_050,43905.96,23.312,1024,224,0.03,0.92,1.59 lcnet_035,40961.84,24.988,1024,224,0.03,1.04,1.64 lcnet_050,36451.18,28.081,1024,224,0.05,1.26,1.88 mobilenetv3_small_075,32291.57,31.7,1024,224,0.05,1.3,2.04 mobilenetv3_small_100,28935.54,35.379,1024,224,0.06,1.42,2.54 tf_mobilenetv3_small_minimal_100,27926.5,36.657,1024,224,0.06,1.41,2.04 tinynet_d,27303.88,37.493,1024,152,0.05,1.42,2.34 tf_mobilenetv3_small_075,26850.04,38.127,1024,224,0.05,1.3,2.04 tf_mobilenetv3_small_100,24320.21,42.094,1024,224,0.06,1.42,2.54 lcnet_075,22627.19,45.245,1024,224,0.1,1.99,2.36 mnasnet_small,20150.91,50.806,1024,224,0.07,2.16,2.03 levit_128s,19458.78,52.613,1024,224,0.31,1.88,7.78 lcnet_100,18910.66,54.139,1024,224,0.16,2.52,2.95 mobilenetv2_035,18047.72,56.728,1024,224,0.07,2.86,1.68 regnetx_002,17921.55,57.126,1024,224,0.2,2.16,2.68 regnety_002,16656.92,61.462,1024,224,0.2,2.17,3.16 ghostnet_050,16494.57,62.071,1024,224,0.05,1.77,2.59 mnasnet_050,15574.97,65.736,1024,224,0.11,3.07,2.22 mobilenetv2_050,14533.98,70.445,1024,224,0.1,3.64,1.97 tinynet_c,14397.76,71.111,1024,184,0.11,2.87,2.46 semnasnet_050,14065.61,72.79,1024,224,0.11,3.44,2.08 levit_128,13348.5,76.702,1024,224,0.41,2.71,9.21 vit_small_patch32_224,12899.41,79.373,1024,224,1.15,2.5,22.88 mixer_s32_224,12823.61,79.842,1024,224,1.0,2.28,19.1 lcnet_150,12599.24,81.264,1024,224,0.34,3.79,4.5 regnetx_004,12314.46,83.141,1024,224,0.4,3.14,5.16 cs3darknet_focus_s,11852.98,86.381,1024,256,0.69,2.7,3.27 mobilenetv3_large_075,11687.27,87.605,1024,224,0.16,4.0,3.99 resnet10t,11549.51,88.651,1024,224,1.1,2.43,5.44 cs3darknet_s,11540.93,88.716,1024,256,0.72,2.97,3.28 vit_tiny_r_s16_p8_224,10917.33,93.785,1024,224,0.44,2.06,6.34 ese_vovnet19b_slim_dw,10530.7,97.229,1024,224,0.4,5.28,1.9 mobilenetv3_rw,10453.43,97.947,1024,224,0.23,4.41,5.48 hardcorenas_a,10387.47,98.569,1024,224,0.23,4.38,5.26 mobilenetv3_large_100_miil,10298.68,99.419,1024,224,0.23,4.41,5.48 mobilenetv3_large_100,10295.13,99.453,1024,224,0.23,4.41,5.48 tf_mobilenetv3_large_075,10277.2,99.627,1024,224,0.16,4.0,3.99 gernet_s,10228.24,100.105,1024,224,0.75,2.65,8.17 mnasnet_075,10209.23,100.29,1024,224,0.23,4.77,3.17 levit_192,10099.95,101.375,1024,224,0.66,3.2,10.95 tf_mobilenetv3_large_minimal_100,10021.88,102.166,1024,224,0.22,4.4,3.92 hardcorenas_b,9469.88,108.121,1024,224,0.26,5.09,5.18 regnetx_006,9309.45,109.982,1024,224,0.61,3.98,6.2 tinynet_b,9298.6,110.113,1024,188,0.21,4.44,3.73 regnety_004,9296.36,110.137,1024,224,0.41,3.89,4.34 ghostnet_100,9264.87,110.513,1024,224,0.15,3.55,5.18 hardcorenas_c,9196.31,111.338,1024,224,0.28,5.01,5.52 resnet18,9171.4,111.64,1024,224,1.82,2.48,11.69 tf_mobilenetv3_large_100,9170.64,111.649,1024,224,0.23,4.41,5.48 mobilenetv2_075,9151.72,111.88,1024,224,0.22,5.86,2.64 swsl_resnet18,9145.07,111.962,1024,224,1.82,2.48,11.69 mnasnet_100,9128.95,112.159,1024,224,0.33,5.46,4.38 mnasnet_b1,9096.68,112.558,1024,224,0.33,5.46,4.38 gluon_resnet18_v1b,9092.93,112.604,1024,224,1.82,2.48,11.69 ssl_resnet18,9043.33,113.221,1024,224,1.82,2.48,11.69 semnasnet_075,8958.16,114.297,1024,224,0.23,5.54,2.91 hardcorenas_d,8756.1,116.935,1024,224,0.3,4.93,7.5 seresnet18,8678.54,117.981,1024,224,1.82,2.49,11.78 regnety_006,8404.01,121.832,1024,224,0.61,4.33,6.06 mobilenetv2_100,8360.81,122.466,1024,224,0.31,6.68,3.5 legacy_seresnet18,8318.12,123.094,1024,224,1.82,2.49,11.78 spnasnet_100,8246.33,124.165,1024,224,0.35,6.03,4.42 semnasnet_100,8027.18,127.555,1024,224,0.32,6.23,3.89 mnasnet_a1,8013.14,127.779,1024,224,0.32,6.23,3.89 levit_256,7862.24,130.228,1024,224,1.13,4.23,18.89 resnet18d,7721.04,132.614,1024,224,2.06,3.29,11.71 hardcorenas_f,7642.68,133.973,1024,224,0.35,5.57,8.2 hardcorenas_e,7588.05,134.938,1024,224,0.35,5.65,8.07 ese_vovnet19b_slim,7530.8,135.964,1024,224,1.69,3.52,3.17 efficientnet_lite0,7530.79,135.964,1024,224,0.4,6.74,4.65 ghostnet_130,7411.84,138.146,1024,224,0.24,4.6,7.36 regnetx_008,7376.89,138.798,1024,224,0.81,5.15,7.26 tinynet_a,7260.16,141.032,1024,192,0.35,5.41,6.19 tf_efficientnetv2_b0,7117.22,143.865,1024,224,0.73,4.77,7.14 fbnetc_100,7115.49,143.899,1024,224,0.4,6.51,5.57 regnety_008,7108.36,144.037,1024,224,0.81,5.25,6.26 xcit_nano_12_p16_224_dist,7019.86,145.861,1024,224,0.56,4.17,3.05 xcit_nano_12_p16_224,7000.29,146.268,1024,224,0.56,4.17,3.05 edgenext_xx_small,6963.01,147.05,1024,256,0.33,4.21,1.33 levit_256d,6856.41,149.338,1024,224,1.4,4.93,26.21 deit_tiny_patch16_224,6794.46,150.698,1024,224,1.26,5.97,5.72 vit_tiny_patch16_224,6769.81,151.248,1024,224,1.26,5.97,5.72 tf_efficientnet_lite0,6667.82,153.562,1024,224,0.4,6.74,4.65 deit_tiny_distilled_patch16_224,6647.4,154.032,1024,224,1.27,6.01,5.91 efficientnet_b0,6576.16,155.702,1024,224,0.4,6.75,5.29 dla46_c,6538.59,156.596,1024,224,0.58,4.5,1.3 rexnetr_100,6369.79,160.748,1024,224,0.43,7.72,4.88 mnasnet_140,6297.39,162.595,1024,224,0.6,7.71,7.12 rexnet_100,6295.89,162.634,1024,224,0.41,7.44,4.8 efficientnet_b1_pruned,6269.62,163.315,1024,240,0.4,6.21,6.33 mobilenetv2_110d,6263.44,163.477,1024,224,0.45,8.71,4.52 regnetz_005,6057.27,169.042,1024,224,0.52,5.86,7.12 resnetblur18,6056.25,169.07,1024,224,2.34,3.39,11.69 pit_ti_distilled_224,6026.49,169.903,1024,224,0.71,6.23,5.1 pit_ti_224,5988.08,170.993,1024,224,0.7,6.19,4.85 nf_regnet_b0,5936.35,172.485,1024,256,0.64,5.58,8.76 mobilevitv2_050,5906.65,173.353,1024,256,0.48,8.04,1.37 tf_efficientnet_b0_ap,5894.61,173.707,1024,224,0.4,6.75,5.29 tf_efficientnet_b0,5892.32,173.774,1024,224,0.4,6.75,5.29 tf_efficientnet_b0_ns,5891.52,173.799,1024,224,0.4,6.75,5.29 visformer_tiny,5845.93,175.153,1024,224,1.27,5.72,10.32 resnet14t,5834.5,175.495,1024,224,1.69,5.8,10.08 dla46x_c,5690.98,179.922,1024,224,0.54,5.66,1.07 skresnet18,5640.2,181.543,1024,224,1.82,3.24,11.96 semnasnet_140,5544.22,184.685,1024,224,0.6,8.87,6.11 hrnet_w18_small,5451.81,187.816,1024,224,1.61,5.72,13.19 mobilenetv2_140,5399.1,189.649,1024,224,0.6,9.57,6.11 resnet34,5356.43,191.161,1024,224,3.67,3.74,21.8 dla60x_c,5292.02,193.487,1024,224,0.59,6.01,1.32 mobilevit_xxs,5275.05,194.109,1024,256,0.42,8.34,1.27 ese_vovnet19b_dw,5260.83,194.634,1024,224,1.34,8.25,6.54 gluon_resnet34_v1b,5203.76,196.769,1024,224,3.67,3.74,21.8 tv_resnet34,5193.55,197.156,1024,224,3.67,3.74,21.8 efficientnet_lite1,5144.81,199.024,1024,240,0.62,10.14,5.42 mixnet_s,5054.78,202.566,1024,224,0.25,6.25,4.13 seresnet34,5051.53,202.699,1024,224,3.67,3.74,21.96 gernet_m,5028.39,203.632,1024,224,3.02,5.24,21.14 fbnetv3_b,4982.49,205.508,1024,256,0.55,9.1,8.6 selecsls42,4945.53,207.043,1024,224,2.94,4.62,30.35 selecsls42b,4942.3,207.179,1024,224,2.98,4.62,32.46 vit_base_patch32_224_sam,4921.3,208.063,1024,224,4.41,5.01,88.22 vit_base_patch32_224,4918.17,208.197,1024,224,4.41,5.01,88.22 resnet34d,4834.03,211.82,1024,224,3.91,4.54,21.82 rexnetr_130,4789.2,213.803,1024,224,0.68,9.81,7.61 pit_xs_224,4766.56,214.816,1024,224,1.4,7.71,10.62 tf_efficientnetv2_b1,4738.51,216.09,1024,240,1.21,7.34,8.14 legacy_seresnet34,4737.15,216.152,1024,224,3.67,3.74,21.96 pit_xs_distilled_224,4722.37,216.826,1024,224,1.41,7.76,11.0 mixer_b32_224,4707.3,217.523,1024,224,3.24,6.29,60.29 tf_mixnet_s,4706.57,217.551,1024,224,0.25,6.25,4.13 tf_efficientnet_lite1,4662.36,219.62,1024,240,0.62,10.14,5.42 xcit_tiny_12_p16_224_dist,4593.26,222.924,1024,224,1.24,6.29,6.72 xcit_tiny_12_p16_224,4592.09,222.979,1024,224,1.24,6.29,6.72 rexnet_130,4578.43,223.646,1024,224,0.68,9.71,7.56 levit_384,4538.82,225.597,1024,224,2.36,6.26,39.13 mobilenetv2_120d,4530.56,226.009,1024,224,0.69,11.97,5.83 edgenext_x_small,4436.58,230.795,1024,256,0.68,7.5,2.34 cs3darknet_focus_m,4399.27,232.755,1024,288,2.51,6.19,9.3 efficientnet_b0_g16_evos,4394.3,233.017,1024,224,1.01,7.42,8.11 efficientnet_es,4389.85,233.253,1024,224,1.81,8.73,5.44 efficientnet_es_pruned,4389.6,233.266,1024,224,1.81,8.73,5.44 resnet26,4383.27,233.604,1024,224,2.36,7.35,16.0 cs3darknet_m,4330.89,236.429,1024,288,2.63,6.69,9.31 fbnetv3_d,4328.59,236.555,1024,256,0.68,11.1,10.31 repvgg_b0,4286.85,238.858,1024,224,3.41,6.15,15.82 selecsls60,4286.11,238.899,1024,224,3.59,5.52,30.67 darknet17,4270.14,179.843,768,256,3.26,7.18,14.3 selecsls60b,4265.59,240.05,1024,224,3.63,5.52,32.77 efficientnet_b2_pruned,4264.69,240.099,1024,260,0.73,9.13,8.31 tf_efficientnet_es,4239.07,241.551,1024,224,1.81,8.73,5.44 regnetx_016,4196.72,243.986,1024,224,1.62,7.93,9.19 rexnetr_150,4170.12,245.545,1024,224,0.89,11.13,9.78 crossvit_tiny_240,4122.19,248.4,1024,240,1.57,9.08,7.01 dla34,4120.09,248.525,1024,224,3.07,5.02,15.74 mixer_s16_224,4085.83,250.611,1024,224,3.79,5.97,18.53 vit_small_patch32_384,4015.79,254.982,1024,384,3.45,8.25,22.92 rexnet_150,3990.72,256.583,1024,224,0.9,11.21,9.73 resnet26d,3989.2,256.681,1024,224,2.6,8.15,16.01 ecaresnet50d_pruned,3983.23,257.066,1024,224,2.53,6.43,19.94 efficientnet_lite2,3977.91,257.41,1024,260,0.89,12.9,6.09 gmlp_ti16_224,3944.3,259.603,1024,224,1.34,7.55,5.87 mobilevitv2_075,3905.27,262.199,1024,256,1.05,12.06,2.87 crossvit_9_240,3875.46,264.215,1024,240,1.85,9.52,8.55 darknet21,3872.25,198.322,768,256,3.93,7.47,20.86 nf_resnet26,3857.21,265.465,1024,224,2.41,7.35,16.0 convnext_nano_ols,3756.44,272.585,1024,224,2.5,8.37,15.6 convnext_nano_hnf,3749.56,273.084,1024,224,2.46,8.37,15.59 sedarknet21,3744.18,205.107,768,256,3.93,7.47,20.95 efficientnet_b1,3742.67,273.59,1024,256,0.77,12.22,7.79 crossvit_9_dagger_240,3734.14,274.215,1024,240,1.99,9.97,8.78 tf_efficientnet_b1,3731.51,274.409,1024,240,0.71,10.88,7.79 tf_efficientnet_b1_ns,3731.48,274.411,1024,240,0.71,10.88,7.79 tf_efficientnet_b1_ap,3726.19,274.8,1024,240,0.71,10.88,7.79 resnest14d,3644.88,280.93,1024,224,2.76,7.33,10.61 regnety_016,3624.55,282.503,1024,224,1.63,8.04,11.2 tf_efficientnet_lite2,3624.06,282.543,1024,260,0.89,12.9,6.09 vit_tiny_r_s16_p8_384,3594.94,213.622,768,384,1.34,6.49,6.36 tf_efficientnetv2_b2,3593.98,284.91,1024,260,1.72,9.84,10.1 poolformer_s12,3483.41,293.951,1024,224,1.82,5.53,11.92 resmlp_12_224,3460.87,295.868,1024,224,3.01,5.5,15.35 resmlp_12_distilled_224,3458.54,296.067,1024,224,3.01,5.5,15.35 mixnet_m,3455.23,296.35,1024,224,0.36,8.19,5.01 gmixer_12_224,3401.29,301.051,1024,224,2.67,7.26,12.7 resnext26ts,3375.26,303.371,1024,256,2.43,10.52,10.3 nf_ecaresnet26,3365.9,304.215,1024,224,2.41,7.36,16.0 nf_seresnet26,3360.23,304.729,1024,224,2.41,7.36,17.4 gernet_l,3328.59,307.626,1024,256,4.57,8.0,31.08 repvgg_a2,3325.03,307.955,1024,224,5.7,6.26,28.21 tf_mixnet_m,3322.0,308.236,1024,224,0.36,8.19,5.01 efficientnet_b3_pruned,3297.4,310.535,1024,300,1.04,11.86,9.86 nf_regnet_b1,3293.07,310.944,1024,288,1.02,9.2,10.22 seresnext26ts,3291.26,311.115,1024,256,2.43,10.52,10.39 eca_resnext26ts,3290.56,311.182,1024,256,2.43,10.52,10.3 legacy_seresnext26_32x4d,3269.09,313.225,1024,224,2.49,9.39,16.79 skresnet34,3229.96,317.02,1024,224,3.67,5.13,22.28 gcresnext26ts,3229.79,317.037,1024,256,2.43,10.53,10.48 nf_regnet_b2,3193.49,320.64,1024,272,1.22,9.27,14.31 convit_tiny,3179.42,322.058,1024,224,1.26,7.94,5.71 resnet26t,3149.41,325.128,1024,256,3.35,10.52,16.01 rexnetr_200,3135.78,244.904,768,224,1.59,15.11,16.52 ecaresnet101d_pruned,3129.51,327.195,1024,224,3.48,7.69,24.88 seresnext26tn_32x4d,3050.2,335.704,1024,224,2.7,10.09,16.81 seresnext26t_32x4d,3050.01,335.724,1024,224,2.7,10.09,16.81 ecaresnext50t_32x4d,3049.83,335.744,1024,224,2.7,10.09,15.41 ecaresnext26t_32x4d,3048.36,335.905,1024,224,2.7,10.09,15.41 seresnext26d_32x4d,3037.9,337.063,1024,224,2.73,10.19,16.81 deit_small_patch16_224,3002.36,341.052,1024,224,4.61,11.95,22.05 rexnet_200,3001.86,255.828,768,224,1.56,14.91,16.37 vit_small_patch16_224,3000.37,341.279,1024,224,4.61,11.95,22.05 mobilevit_xs,2981.72,257.559,768,256,1.05,16.33,2.32 deit_small_distilled_patch16_224,2950.87,347.001,1024,224,4.63,12.02,22.44 pit_s_224,2945.22,347.668,1024,224,2.88,11.56,23.46 ecaresnetlight,2941.7,348.085,1024,224,4.11,8.42,30.16 coat_lite_tiny,2932.4,349.189,1024,224,1.6,11.65,5.72 eca_botnext26ts_256,2930.99,349.358,1024,256,2.46,11.6,10.59 pit_s_distilled_224,2918.75,350.821,1024,224,2.9,11.64,24.04 tf_efficientnet_b2_ns,2903.13,352.71,1024,260,1.02,13.83,9.11 tf_efficientnet_b2,2902.67,352.766,1024,260,1.02,13.83,9.11 tf_efficientnet_b2_ap,2901.98,352.851,1024,260,1.02,13.83,9.11 eca_halonext26ts,2883.09,355.163,1024,256,2.44,11.46,10.76 tresnet_m,2870.7,356.694,1024,224,5.74,7.31,31.39 botnet26t_256,2862.72,357.688,1024,256,3.32,11.98,12.49 regnetx_032,2852.1,359.019,1024,224,3.2,11.37,15.3 hrnet_w18_small_v2,2845.04,359.912,1024,224,2.62,9.65,15.6 deit3_small_patch16_224_in21ft1k,2837.48,360.868,1024,224,4.61,11.95,22.06 halonet26t,2832.73,361.477,1024,256,3.19,11.69,12.48 resnetv2_50,2829.74,361.858,1024,224,4.11,11.11,25.55 deit3_small_patch16_224,2828.59,362.004,1024,224,4.61,11.95,22.06 vgg11,2795.7,183.125,512,224,7.61,7.44,132.86 haloregnetz_b,2794.73,366.391,1024,224,1.97,11.94,11.68 bat_resnext26ts,2793.93,366.495,1024,256,2.53,12.51,10.73 vit_relpos_base_patch32_plus_rpn_256,2775.87,368.882,1024,256,7.68,8.01,119.42 vit_base_patch32_plus_256,2773.66,369.174,1024,256,7.79,7.76,119.48 dpn68b,2762.53,370.662,1024,224,2.35,10.47,12.61 vit_small_resnet26d_224,2758.05,371.264,1024,224,5.07,11.12,63.61 efficientnet_b2,2753.12,371.929,1024,288,1.12,16.2,9.11 coat_lite_mini,2752.31,372.037,1024,224,2.0,12.25,11.01 efficientnet_b2a,2752.1,372.068,1024,288,1.12,16.2,9.11 efficientnet_b0_gn,2748.63,372.536,1024,224,0.42,6.75,5.29 resnet50,2733.34,374.621,1024,224,4.11,11.11,25.56 ssl_resnet50,2732.73,374.705,1024,224,4.11,11.11,25.56 tv_resnet50,2732.0,374.804,1024,224,4.11,11.11,25.56 gluon_resnet50_v1b,2731.92,374.815,1024,224,4.11,11.11,25.56 swsl_resnet50,2730.89,374.957,1024,224,4.11,11.11,25.56 cspresnet50,2720.51,376.385,1024,256,4.54,11.5,21.62 resnet32ts,2719.03,376.593,1024,256,4.63,11.58,17.96 dpn68,2711.28,377.669,1024,224,2.35,10.47,12.61 mobilevitv2_100,2710.59,283.322,768,256,1.84,16.08,4.9 vovnet39a,2706.48,378.339,1024,224,7.09,6.73,22.6 resnetv2_50t,2687.6,380.997,1024,224,4.32,11.82,25.57 resnet33ts,2683.32,381.605,1024,256,4.76,11.66,19.68 resnetv2_50d,2678.25,382.327,1024,224,4.35,11.92,25.57 efficientnet_em,2663.14,384.496,1024,240,3.04,14.34,6.9 mixnet_l,2651.17,289.672,768,224,0.58,10.84,7.33 visformer_small,2638.82,388.04,1024,224,4.88,11.43,40.22 ese_vovnet39b,2631.4,389.135,1024,224,7.09,6.74,24.57 resnest26d,2624.21,390.2,1024,224,3.64,9.97,17.07 vit_relpos_small_patch16_224,2615.53,391.496,1024,224,4.59,13.05,21.98 seresnet33ts,2613.57,391.79,1024,256,4.76,11.66,19.78 eca_resnet33ts,2609.68,392.373,1024,256,4.76,11.66,19.68 vit_srelpos_small_patch16_224,2607.7,392.67,1024,224,4.59,12.16,21.97 eca_vovnet39b,2607.14,392.755,1024,224,7.09,6.74,22.6 gluon_resnet50_v1c,2599.91,393.848,1024,224,4.35,11.92,25.58 tf_efficientnet_em,2599.16,393.961,1024,240,3.04,14.34,6.9 cspresnet50w,2589.44,395.44,1024,256,5.04,12.19,28.12 resnet50d,2584.02,396.27,1024,224,4.35,11.92,25.58 legacy_seresnet50,2582.34,396.527,1024,224,3.88,10.6,28.09 resnet50t,2580.37,396.829,1024,224,4.32,11.82,25.57 twins_svt_small,2576.63,397.407,1024,224,2.94,13.75,24.06 gluon_resnet50_v1d,2570.89,398.293,1024,224,4.35,11.92,25.58 gcresnet33ts,2569.2,398.556,1024,256,4.76,11.68,19.88 cspresnet50d,2560.0,399.988,1024,256,4.86,12.55,21.64 lambda_resnet26t,2551.69,401.29,1024,256,3.02,11.87,10.96 tf_mixnet_l,2550.79,301.072,768,224,0.58,10.84,7.33 selecsls84,2543.3,402.613,1024,224,5.9,7.57,50.95 vgg11_bn,2541.42,201.45,512,224,7.62,7.44,132.87 dla60,2525.2,405.498,1024,224,4.26,10.16,22.04 cs3darknet_focus_l,2520.03,406.331,1024,288,5.9,10.16,21.15 res2net50_48w_2s,2502.4,409.196,1024,224,4.18,11.72,25.29 cs3darknet_l,2485.99,411.896,1024,288,6.16,10.83,21.16 densenet121,2467.71,414.945,1024,224,2.87,6.9,7.98 xcit_nano_12_p16_384_dist,2466.74,415.111,1024,384,1.64,12.15,3.05 xcit_tiny_24_p16_224_dist,2463.21,415.705,1024,224,2.34,11.82,12.12 tv_densenet121,2461.4,416.011,1024,224,2.87,6.9,7.98 xcit_tiny_24_p16_224,2457.2,416.72,1024,224,2.34,11.82,12.12 seresnet50,2438.84,419.859,1024,224,4.11,11.13,28.09 convnext_tiny_hnfd,2399.93,426.664,1024,224,4.47,13.44,28.59 convnext_tiny_hnf,2395.62,427.433,1024,224,4.47,13.44,28.59 efficientnet_lite3,2383.15,214.83,512,300,1.65,21.85,8.2 efficientnet_b0_g8_gn,2375.8,431.002,1024,224,0.66,6.75,6.56 convnext_tiny_in22ft1k,2362.17,433.485,1024,224,4.47,13.44,28.59 densenet121d,2362.07,433.503,1024,224,3.11,7.7,8.0 convnext_tiny,2359.72,433.936,1024,224,4.47,13.44,28.59 cs3sedarknet_l,2353.08,435.161,1024,288,6.16,10.83,21.91 resnetaa50d,2350.26,435.685,1024,224,5.39,12.44,25.58 efficientnet_cc_b0_4e,2334.0,438.721,1024,224,0.41,9.42,13.31 seresnet50t,2333.68,438.78,1024,224,4.32,11.83,28.1 ecaresnet50d,2316.33,442.066,1024,224,4.35,11.93,25.58 resnetblur50,2298.66,445.465,1024,224,5.16,12.02,25.56 mobilevit_s,2279.76,336.866,768,256,2.03,19.94,5.58 convnext_nano,2276.19,449.862,1024,288,4.06,13.84,15.59 resnetrs50,2276.18,449.864,1024,224,4.48,12.14,35.69 vit_base_resnet26d_224,2262.15,452.654,1024,224,6.97,13.16,101.4 gluon_resnet50_v1s,2257.16,453.655,1024,224,5.47,13.52,25.68 vovnet57a,2253.6,454.372,1024,224,8.95,7.52,36.64 adv_inception_v3,2250.27,455.041,1024,299,5.73,8.97,23.83 gluon_inception_v3,2249.35,455.229,1024,299,5.73,8.97,23.83 tf_inception_v3,2245.22,456.064,1024,299,5.73,8.97,23.83 tf_efficientnet_cc_b0_4e,2243.01,456.518,1024,224,0.41,9.42,13.31 inception_v3,2240.78,456.965,1024,299,5.73,8.97,23.83 tf_efficientnet_cc_b0_8e,2240.71,456.986,1024,224,0.42,9.42,24.01 densenetblur121d,2235.56,458.037,1024,224,3.11,7.9,8.0 resnest50d_1s4x24d,2213.57,462.589,1024,224,4.43,13.57,25.68 res2net50_26w_4s,2209.54,463.432,1024,224,4.28,12.61,25.7 ssl_resnext50_32x4d,2205.13,464.359,1024,224,4.26,14.4,25.03 swsl_resnext50_32x4d,2204.8,464.429,1024,224,4.26,14.4,25.03 gluon_resnext50_32x4d,2203.2,464.765,1024,224,4.26,14.4,25.03 resnext50_32x4d,2199.44,465.561,1024,224,4.26,14.4,25.03 tv_resnext50_32x4d,2198.23,465.818,1024,224,4.26,14.4,25.03 regnetx_040,2190.95,467.362,1024,224,3.99,12.2,22.12 cspresnext50,2182.4,469.194,1024,256,4.05,15.86,20.57 resnetblur50d,2182.09,469.263,1024,224,5.4,12.82,25.58 regnetz_b16,2180.8,469.54,1024,288,2.39,16.43,9.72 ese_vovnet57b,2171.57,471.535,1024,224,8.95,7.52,38.61 tf_efficientnet_lite3,2166.77,236.285,512,300,1.65,21.85,8.2 mobilevitv2_125,2151.63,356.926,768,256,2.86,20.1,7.48 efficientnet_cc_b0_8e,2149.58,476.36,1024,224,0.42,9.42,24.01 semobilevit_s,2143.19,358.331,768,256,2.03,19.95,5.74 twins_pcpvt_small,2142.01,478.043,1024,224,3.83,18.08,24.11 nf_regnet_b3,2133.81,479.88,1024,320,2.05,14.61,18.59 tf_efficientnetv2_b3,2121.62,482.639,1024,300,3.04,15.74,14.36 seresnetaa50d,2118.99,483.236,1024,224,5.4,12.46,28.11 efficientnetv2_rw_t,2117.33,483.616,1024,288,3.19,16.42,13.65 gcresnext50ts,2113.73,484.438,1024,256,3.75,15.46,15.67 edgenext_small,2107.47,485.876,1024,320,1.97,14.16,5.59 resnext50d_32x4d,2094.1,488.98,1024,224,4.5,15.2,25.05 dla60x,2080.97,492.062,1024,224,3.54,13.8,17.35 res2net50_14w_8s,2066.79,495.441,1024,224,4.21,13.28,25.06 gc_efficientnetv2_rw_t,2061.37,496.743,1024,288,3.2,16.45,13.68 sehalonet33ts,2057.14,373.322,768,256,3.55,14.7,13.69 gcresnet50t,2055.89,498.068,1024,256,5.42,14.67,25.9 skresnet50,2048.81,499.79,1024,224,4.11,12.5,25.8 fbnetv3_g,2047.87,500.019,1024,288,1.77,21.09,16.62 nf_ecaresnet50,2039.26,502.129,1024,224,4.21,11.13,25.56 nf_seresnet50,2037.45,502.576,1024,224,4.21,11.13,28.09 cs3darknet_focus_x,2026.01,505.415,1024,256,8.03,10.69,35.02 dla60_res2net,2015.55,508.035,1024,224,4.15,12.34,20.85 lambda_resnet26rpt_256,2015.24,190.536,384,256,3.16,11.87,10.99 seresnext50_32x4d,2010.4,509.339,1024,224,4.26,14.42,27.56 legacy_seresnext50_32x4d,2003.57,511.076,1024,224,4.26,14.42,27.56 repvgg_b1g4,2003.2,511.169,1024,224,8.15,10.64,39.97 gluon_seresnext50_32x4d,2002.45,511.358,1024,224,4.26,14.42,27.56 densenet169,1987.41,515.228,1024,224,3.4,7.3,14.15 res2next50,1967.78,520.369,1024,224,4.2,13.71,24.67 vit_relpos_small_patch16_rpn_224,1966.99,520.579,1024,224,4.59,13.05,21.97 skresnet50d,1957.14,523.201,1024,224,4.36,13.31,25.82 xcit_small_12_p16_224_dist,1952.72,524.382,1024,224,4.82,12.58,26.25 crossvit_small_240,1952.54,524.431,1024,240,5.63,18.17,26.86 xcit_small_12_p16_224,1952.1,524.55,1024,224,4.82,12.58,26.25 cs3sedarknet_xdw,1919.73,533.397,1024,256,5.97,17.18,21.6 swin_tiny_patch4_window7_224,1915.52,534.569,1024,224,4.51,17.06,28.29 vit_relpos_medium_patch16_cls_224,1909.56,536.236,1024,224,8.03,18.24,38.76 mixnet_xl,1903.31,268.993,512,224,0.93,14.57,11.9 dla60_res2next,1893.19,540.873,1024,224,3.49,13.17,17.03 xcit_nano_12_p8_224_dist,1887.0,542.649,1024,224,2.16,15.71,3.05 xcit_nano_12_p8_224,1883.21,543.74,1024,224,2.16,15.71,3.05 cspdarknet53,1881.33,408.211,768,256,6.57,16.81,27.64 gmlp_s16_224,1873.82,546.464,1024,224,4.42,15.1,19.42 edgenext_small_rw,1831.96,558.95,1024,320,2.46,14.85,7.83 ecaresnet26t,1828.87,559.898,1024,320,5.24,16.44,16.01 vit_small_r26_s32_224,1825.94,560.792,1024,224,3.56,9.85,36.43 vgg13,1819.73,281.346,512,224,11.31,12.25,133.05 poolformer_s24,1804.4,567.487,1024,224,3.41,10.68,21.39 crossvit_15_240,1799.06,569.173,1024,240,5.81,19.77,27.53 vit_relpos_medium_patch16_224,1794.09,570.75,1024,224,7.97,17.02,38.75 vit_srelpos_medium_patch16_224,1787.05,573.0,1024,224,7.96,16.21,38.74 mobilevitv2_150,1774.77,288.477,512,256,4.09,24.11,10.59 mobilevitv2_150_in22ft1k,1773.43,288.695,512,256,4.09,24.11,10.59 sebotnet33ts_256,1762.47,217.864,384,256,3.89,17.46,13.7 resmlp_24_224,1761.92,581.171,1024,224,5.96,10.91,30.02 efficientnet_b3,1761.71,290.615,512,320,2.01,26.52,12.23 efficientnet_b3a,1761.71,290.614,512,320,2.01,26.52,12.23 resmlp_24_distilled_224,1760.69,581.576,1024,224,5.96,10.91,30.02 regnetx_064,1757.88,436.877,768,224,6.49,16.37,26.21 resnest50d,1750.78,584.87,1024,224,5.4,14.36,27.48 gmixer_24_224,1741.35,588.036,1024,224,5.28,14.45,24.72 swin_s3_tiny_224,1737.42,589.369,1024,224,4.64,19.13,28.33 crossvit_15_dagger_240,1736.98,589.517,1024,240,6.13,20.43,28.21 vit_base_resnet50d_224,1722.65,594.42,1024,224,8.73,16.92,110.97 resnetv2_101,1717.18,596.314,1024,224,7.83,16.23,44.54 tf_efficientnet_b3_ap,1706.97,299.935,512,300,1.87,23.83,12.23 tf_efficientnet_b3,1705.74,300.151,512,300,1.87,23.83,12.23 tf_efficientnet_b3_ns,1705.51,300.191,512,300,1.87,23.83,12.23 lambda_resnet50ts,1694.68,604.231,1024,256,5.07,17.48,21.54 dla102,1693.75,604.56,1024,224,7.19,14.18,33.27 darknetaa53,1689.16,454.651,768,288,10.08,15.68,36.02 gluon_resnet101_v1b,1679.75,609.599,1024,224,7.83,16.23,44.55 tv_resnet101,1679.18,609.808,1024,224,7.83,16.23,44.55 resnet101,1676.67,610.719,1024,224,7.83,16.23,44.55 repvgg_b1,1663.53,615.546,1024,224,13.16,10.64,57.42 resnetv2_101d,1653.14,619.414,1024,224,8.07,17.04,44.56 gluon_resnet101_v1c,1649.02,620.96,1024,224,8.08,17.04,44.57 vgg13_bn,1642.15,311.774,512,224,11.33,12.25,133.05 cait_xxs24_224,1641.65,623.749,1024,224,2.53,20.29,11.96 res2net50_26w_6s,1639.34,624.627,1024,224,6.33,15.28,37.05 hrnet_w18,1631.65,627.569,1024,224,4.32,16.31,21.3 vit_large_patch32_224,1623.29,630.805,1024,224,15.39,13.3,306.54 wide_resnet50_2,1618.04,632.851,1024,224,11.43,14.4,68.88 gluon_resnet101_v1d,1616.88,633.307,1024,224,8.08,17.04,44.57 xcit_tiny_12_p16_384_dist,1614.06,634.414,1024,384,3.64,18.26,6.72 regnetv_040,1604.78,478.557,768,288,6.6,20.3,20.64 halonet50ts,1600.56,639.764,1024,256,5.3,19.2,22.73 regnety_040,1597.66,480.688,768,288,6.61,20.3,20.65 darknet53,1585.01,484.528,768,288,11.78,15.68,41.61 efficientnet_cc_b1_8e,1576.34,649.593,1024,240,0.75,15.44,39.72 coat_lite_small,1576.03,649.72,1024,224,3.96,22.09,19.84 regnety_032,1576.03,649.722,1024,288,5.29,18.61,19.44 resnetv2_50x1_bit_distilled,1575.9,649.775,1024,224,4.23,11.11,25.55 swinv2_cr_tiny_224,1574.62,650.304,1024,224,4.66,28.45,28.33 legacy_seresnet101,1569.43,652.454,1024,224,7.61,15.74,49.33 vit_base_patch32_384,1551.76,659.885,1024,384,13.06,16.5,88.3 ese_vovnet39b_evos,1551.37,660.05,1024,224,7.07,6.74,24.58 swinv2_cr_tiny_ns_224,1546.02,662.33,1024,224,4.66,28.45,28.33 vit_tiny_patch16_384,1542.96,663.648,1024,384,4.7,25.39,5.79 lamhalobotnet50ts_256,1533.2,667.873,1024,256,5.02,18.44,22.57 tf_efficientnet_cc_b1_8e,1527.24,670.479,1024,240,0.75,15.44,39.72 resnetaa101d,1521.49,673.009,1024,224,9.12,17.56,44.57 densenet201,1515.85,675.514,1024,224,4.34,7.85,20.01 resnetaa50,1510.7,677.817,1024,288,8.52,19.24,25.56 mixer_l32_224,1508.54,678.791,1024,224,11.27,19.86,206.94 seresnet101,1502.48,681.526,1024,224,7.84,16.27,49.33 vit_base_r26_s32_224,1492.4,686.129,1024,224,6.81,12.36,101.38 gluon_resnet101_v1s,1485.37,689.375,1024,224,9.19,18.64,44.67 twins_pcpvt_base,1484.93,689.584,1024,224,6.68,25.25,43.83 mobilevitv2_175,1472.18,347.77,512,256,5.54,28.13,14.25 mobilevitv2_175_in22ft1k,1472.06,347.8,512,256,5.54,28.13,14.25 nf_resnet101,1469.16,696.987,1024,224,8.01,16.23,44.55 resnest50d_4s2x40d,1467.36,697.84,1024,224,4.4,17.94,30.42 vgg16,1464.57,349.576,512,224,15.47,13.56,138.36 resnetv2_50d_frn,1463.93,699.474,1024,224,4.33,11.92,25.59 resnetblur101d,1458.09,702.276,1024,224,9.12,17.94,44.57 ecaresnet101d,1457.01,702.796,1024,224,8.08,17.07,44.57 sequencer2d_s,1455.29,703.627,1024,224,4.96,11.31,27.65 nf_resnet50,1445.9,708.195,1024,288,6.88,18.37,25.56 convnext_small,1445.85,708.22,1024,224,8.71,21.56,50.22 convnext_small_in22ft1k,1443.98,709.135,1024,224,8.71,21.56,50.22 regnetz_c16,1437.42,356.181,512,320,3.92,25.88,13.46 tresnet_l,1432.52,714.812,1024,224,10.88,11.9,55.99 cs3darknet_x,1429.24,716.453,1024,288,10.6,14.36,35.05 dla102x,1397.97,732.475,1024,224,5.89,19.42,26.31 ssl_resnext101_32x4d,1392.96,735.11,1024,224,8.01,21.23,44.18 swsl_resnext101_32x4d,1392.73,735.231,1024,224,8.01,21.23,44.18 resnext101_32x4d,1390.48,736.423,1024,224,8.01,21.23,44.18 botnet50ts_256,1389.99,276.247,384,256,5.54,22.23,22.74 skresnext50_32x4d,1389.9,736.732,1024,224,4.5,17.18,27.48 gluon_resnext101_32x4d,1389.41,736.987,1024,224,8.01,21.23,44.18 nest_tiny,1388.05,553.283,768,224,5.83,25.48,17.06 resnet50_gn,1386.72,738.422,1024,224,4.14,11.11,25.56 resnetv2_50d_evob,1383.3,740.244,1024,224,4.33,11.92,25.59 res2net50_26w_8s,1373.33,745.622,1024,224,8.37,17.95,48.4 halo2botnet50ts_256,1372.33,559.619,768,256,5.02,21.78,22.64 regnetx_080,1370.56,747.125,1024,224,8.02,14.06,39.57 cs3sedarknet_x,1368.84,748.067,1024,288,10.6,14.37,35.4 jx_nest_tiny,1362.62,563.605,768,224,5.83,25.48,17.06 convit_small,1355.18,755.603,1024,224,5.76,17.87,27.78 res2net101_26w_4s,1353.43,756.586,1024,224,8.1,18.45,45.21 xception,1340.72,572.814,768,299,8.4,35.83,22.86 mixer_b16_224_miil,1340.03,764.147,1024,224,12.62,14.53,59.88 repvgg_b2g4,1335.06,766.992,1024,224,12.63,12.9,61.76 vgg16_bn,1335.02,383.503,512,224,15.5,13.56,138.37 mixer_b16_224,1328.05,771.041,1024,224,12.62,14.53,59.88 twins_svt_base,1307.2,783.34,1024,224,8.59,26.33,56.07 dpn92,1299.67,787.878,1024,224,6.54,18.21,37.67 cs3edgenet_x,1289.05,794.37,1024,288,14.59,16.36,47.82 ese_vovnet99b_iabn,1282.63,798.345,1024,224,16.49,11.27,63.2 crossvit_18_240,1272.74,804.553,1024,240,9.05,26.26,43.27 regnety_040s_gn,1271.39,805.405,1024,224,4.03,12.29,20.65 eca_nfnet_l0,1271.38,805.411,1024,288,7.12,17.29,24.14 nfnet_l0,1269.37,806.681,1024,288,7.13,17.29,35.07 seresnext101_32x4d,1268.1,807.494,1024,224,8.02,21.26,48.96 legacy_seresnext101_32x4d,1267.59,807.817,1024,224,8.02,21.26,48.96 gluon_seresnext101_32x4d,1265.67,809.045,1024,224,8.02,21.26,48.96 nf_ecaresnet101,1264.2,809.986,1024,224,8.01,16.27,44.55 vit_relpos_medium_patch16_rpn_224,1263.66,810.331,1024,224,7.97,17.02,38.73 nf_seresnet101,1261.42,811.77,1024,224,8.02,16.27,49.33 mobilevitv2_200,1256.15,305.684,384,256,7.22,32.15,18.45 mobilevitv2_200_in22ft1k,1255.83,305.762,384,256,7.22,32.15,18.45 xception41p,1254.65,408.071,512,299,9.25,39.86,26.91 resnet51q,1254.6,816.185,1024,288,8.07,20.94,35.7 efficientnet_el,1254.42,408.143,512,300,8.0,30.7,10.59 efficientnet_el_pruned,1254.28,408.188,512,300,8.0,30.7,10.59 ese_vovnet99b,1240.88,825.205,1024,224,16.51,11.27,63.2 xcit_tiny_12_p8_224_dist,1237.16,827.688,1024,224,4.81,23.6,6.71 xcit_tiny_12_p8_224,1235.05,829.105,1024,224,4.81,23.6,6.71 crossvit_18_dagger_240,1235.02,829.126,1024,240,9.5,27.03,44.27 vgg19,1227.1,417.229,512,224,19.63,14.86,143.67 tf_efficientnet_el,1226.94,417.286,512,300,8.0,30.7,10.59 poolformer_s36,1217.09,841.334,1024,224,5.0,15.82,30.86 hrnet_w32,1204.83,849.897,1024,224,8.97,22.02,41.23 hrnet_w30,1202.88,851.275,1024,224,8.15,21.21,37.71 resnetv2_152,1196.21,856.023,1024,224,11.55,22.56,60.19 nfnet_f0,1193.84,857.722,1024,256,12.62,18.05,71.49 swin_small_patch4_window7_224,1179.92,867.841,1024,224,8.77,27.47,49.61 resmlp_36_224,1179.88,867.87,1024,224,8.91,16.33,44.69 vit_small_resnet50d_s16_224,1179.15,868.406,1024,224,13.48,24.82,57.53 resmlp_36_distilled_224,1179.01,868.509,1024,224,8.91,16.33,44.69 efficientnet_lite4,1178.02,325.958,384,380,4.04,45.66,13.01 tv_resnet152,1172.68,873.198,1024,224,11.56,22.56,60.19 gluon_resnet152_v1b,1172.67,873.208,1024,224,11.56,22.56,60.19 resnet152,1170.69,874.682,1024,224,11.56,22.56,60.19 mixnet_xxl,1163.99,329.888,384,224,2.04,23.43,23.96 resnetv2_152d,1163.57,880.032,1024,224,11.8,23.36,60.2 ecaresnet50t,1162.34,880.97,1024,320,8.82,24.13,25.57 resnet61q,1160.54,882.331,1024,288,9.87,21.52,36.85 vit_base_patch16_224_miil,1154.75,886.763,1024,224,17.58,23.9,86.54 repvgg_b2,1154.25,887.146,1024,224,20.45,12.9,89.02 inception_v4,1153.57,887.661,1024,299,12.28,15.09,42.68 swinv2_tiny_window8_256,1152.79,888.266,1024,256,5.96,24.57,28.35 densenet161,1147.8,892.122,1024,224,7.79,11.06,28.68 gluon_resnet152_v1c,1146.71,892.979,1024,224,11.8,23.36,60.21 gluon_resnet152_v1d,1141.31,897.204,1024,224,11.8,23.36,60.21 sequencer2d_m,1138.06,899.765,1024,224,6.55,14.26,38.31 vit_base_patch16_224_sam,1132.42,904.242,1024,224,17.58,23.9,86.57 deit_base_patch16_224,1132.42,904.245,1024,224,17.58,23.9,86.57 vit_base_patch16_224,1132.21,904.413,1024,224,17.58,23.9,86.57 dla169,1130.13,906.071,1024,224,11.6,20.2,53.39 regnetx_120,1129.55,453.263,512,224,12.13,21.37,46.11 volo_d1_224,1126.62,908.904,1024,224,6.94,24.43,26.63 vgg19_bn,1122.31,456.189,512,224,19.66,14.86,143.68 deit_base_distilled_patch16_224,1116.6,917.056,1024,224,17.68,24.05,87.34 xception41,1110.46,461.057,512,299,9.28,39.86,26.97 cait_xxs36_224,1104.66,926.97,1024,224,3.77,30.34,17.3 tf_efficientnet_lite4,1091.59,351.767,384,380,4.04,45.66,13.01 convmixer_1024_20_ks9_p14,1091.56,938.092,1024,224,5.55,5.51,24.38 deit3_base_patch16_224,1090.26,939.213,1024,224,17.58,23.9,86.59 deit3_base_patch16_224_in21ft1k,1088.57,940.667,1024,224,17.58,23.9,86.59 legacy_seresnet152,1086.41,942.544,1024,224,11.33,22.08,66.82 tnt_s_patch16_224,1079.54,948.54,1024,224,5.24,24.37,23.76 regnety_120,1077.58,475.125,512,224,12.14,21.38,51.82 repvgg_b3g4,1077.28,950.524,1024,224,17.89,15.1,83.83 vit_relpos_base_patch16_clsgap_224,1077.01,950.767,1024,224,17.6,25.12,86.43 vit_relpos_base_patch16_cls_224,1076.19,951.489,1024,224,17.6,25.12,86.43 gluon_resnet152_v1s,1074.28,953.181,1024,224,12.92,24.96,60.32 twins_pcpvt_large,1061.77,964.416,1024,224,9.84,35.82,60.99 seresnet152,1047.32,977.721,1024,224,11.57,22.61,66.82 beit_base_patch16_224,1045.12,979.774,1024,224,17.58,23.9,86.53 xcit_small_24_p16_224_dist,1038.39,986.125,1024,224,9.1,23.64,47.67 xcit_small_24_p16_224,1037.69,986.793,1024,224,9.1,23.64,47.67 coat_tiny,1036.7,987.731,1024,224,4.35,27.2,5.5 dm_nfnet_f0,1035.11,989.253,1024,256,12.62,18.05,71.49 nf_regnet_b4,1027.0,997.065,1024,384,4.7,28.61,30.21 vit_relpos_base_patch16_224,1017.61,1006.263,1024,224,17.51,24.97,86.43 convnext_base_in22ft1k,1006.85,1017.02,1024,224,15.38,28.75,88.59 convnext_base,1006.73,1017.126,1024,224,15.38,28.75,88.59 pit_b_224,993.61,515.277,512,224,12.42,32.94,73.76 pit_b_distilled_224,985.16,519.696,512,224,12.5,33.07,74.79 tresnet_xl,983.38,1041.292,1024,224,15.17,15.34,78.44 efficientnetv2_s,976.0,1049.166,1024,384,8.44,35.77,21.46 dla102x2,973.1,526.138,512,224,9.34,29.91,41.28 cs3se_edgenet_x,972.26,1053.196,1024,320,18.01,20.21,50.72 vit_small_patch16_36x1_224,972.14,1053.329,1024,224,13.71,35.69,64.67 swinv2_cr_small_224,966.28,1059.712,1024,224,9.07,50.27,49.7 swinv2_cr_small_ns_224,955.69,1071.465,1024,224,9.08,50.27,49.7 tf_efficientnetv2_s_in21ft1k,955.24,1071.964,1024,384,8.44,35.77,21.46 tf_efficientnetv2_s,955.13,1072.086,1024,384,8.44,35.77,21.46 vit_small_patch16_18x2_224,948.32,1079.793,1024,224,13.71,35.69,64.67 wide_resnet101_2,939.08,1090.412,1024,224,22.8,21.23,126.89 regnetx_160,936.53,546.684,512,224,15.99,25.52,54.28 regnety_080,933.52,548.447,512,288,13.22,29.69,39.18 regnetz_b16_evos,933.51,822.691,768,288,2.36,16.43,9.74 efficientnetv2_rw_s,931.24,1099.596,1024,384,8.72,38.03,23.94 resnetv2_50d_gn,920.9,1111.946,1024,288,7.24,19.7,25.57 twins_svt_large,918.22,1115.185,1024,224,15.15,35.1,99.27 efficientnet_b4,917.89,418.339,384,384,4.51,50.04,19.34 regnetz_040,913.72,420.249,384,320,6.35,37.78,27.12 xception65p,910.71,562.184,512,299,13.91,52.48,39.82 regnetz_040h,909.33,422.274,384,320,6.43,37.94,28.94 dpn98,906.73,1129.316,1024,224,11.73,25.2,61.57 repvgg_b3,901.67,1135.661,1024,224,29.16,15.1,123.09 resnetrs101,898.53,1139.62,1024,288,13.56,28.53,63.62 gluon_resnext101_64x4d,887.37,1153.955,1024,224,15.52,31.21,83.46 nest_small,885.28,867.51,768,224,10.35,40.04,38.35 poolformer_m36,879.83,1163.84,1024,224,8.8,22.02,56.17 regnetz_d8,877.84,1166.489,1024,320,6.19,37.08,23.37 jx_nest_small,874.11,878.596,768,224,10.35,40.04,38.35 ssl_resnext101_32x8d,874.01,1171.597,1024,224,16.48,31.21,88.79 swsl_resnext101_32x8d,873.31,1172.532,1024,224,16.48,31.21,88.79 resnext101_32x8d,873.01,1172.932,1024,224,16.48,31.21,88.79 ig_resnext101_32x8d,872.81,1173.211,1024,224,16.48,31.21,88.79 regnetz_d32,869.58,1177.564,1024,320,9.33,37.08,27.58 inception_resnet_v2,868.78,1178.653,1024,299,13.18,25.06,55.84 ens_adv_inception_resnet_v2,868.32,1179.275,1024,299,13.18,25.06,55.84 xcit_tiny_24_p16_384_dist,866.54,1181.7,1024,384,6.87,34.29,12.12 cait_s24_224,865.33,1183.354,1024,224,9.35,40.58,46.92 resnest101e,858.93,894.122,768,256,13.38,28.66,48.28 tf_efficientnet_b4,858.91,447.067,384,380,4.49,49.49,19.34 tf_efficientnet_b4_ap,858.7,447.171,384,380,4.49,49.49,19.34 tf_efficientnet_b4_ns,858.52,447.267,384,380,4.49,49.49,19.34 swin_s3_small_224,853.54,899.766,768,224,9.43,37.84,49.74 regnetv_064,852.1,600.857,512,288,10.55,27.11,30.58 regnety_064,851.33,601.396,512,288,10.56,27.11,30.58 resnet200,847.44,1208.333,1024,224,15.07,32.19,64.67 gluon_seresnext101_64x4d,834.87,1226.518,1024,224,15.53,31.25,88.23 coat_mini,833.41,1228.669,1024,224,6.82,33.68,10.34 swin_base_patch4_window7_224,832.6,1229.869,1024,224,15.47,36.63,87.77 resnet101d,816.8,1253.661,1024,320,16.48,34.77,44.57 gluon_xception65,816.5,627.052,512,299,13.96,52.48,39.92 xception65,811.16,631.185,512,299,13.96,52.48,39.92 resnetv2_50d_evos,810.51,947.543,768,288,7.15,19.7,25.59 convnext_tiny_384_in22ft1k,807.27,634.218,512,384,13.14,39.48,28.59 gmlp_b16_224,789.84,1296.449,1024,224,15.78,30.21,73.08 hrnet_w40,787.85,1299.728,1024,224,12.75,25.29,57.56 crossvit_base_240,787.17,975.639,768,240,21.22,36.33,105.03 hrnet_w44,771.15,1327.87,1024,224,14.94,26.92,67.06 swinv2_tiny_window16_256,763.4,670.672,512,256,6.68,39.02,28.35 mobilevitv2_150_384_in22ft1k,757.55,337.918,256,384,9.2,54.25,10.59 xcit_medium_24_p16_224_dist,748.7,1367.689,1024,224,16.13,31.71,84.4 xcit_medium_24_p16_224,748.18,1368.635,1024,224,16.13,31.71,84.4 tresnet_m_448,743.16,1377.885,1024,448,22.94,29.21,31.39 vit_large_r50_s32_224,742.19,1379.692,1024,224,19.58,24.41,328.99 hrnet_w48,738.63,1386.343,1024,224,17.34,28.56,77.47 vit_base_patch16_plus_240,738.11,1387.321,1024,240,27.41,33.08,117.56 sequencer2d_l,736.17,1390.978,1024,224,9.74,22.12,54.3 xcit_small_12_p16_384_dist,715.91,1430.327,1024,384,14.14,36.51,26.25 swinv2_small_window8_256,710.32,1441.594,1024,256,11.58,40.14,49.73 swin_s3_base_224,693.67,1476.198,1024,224,13.69,48.26,71.13 vit_small_patch16_384,692.4,1109.164,768,384,15.52,50.78,22.2 vit_relpos_base_patch16_plus_240,691.79,1480.194,1024,240,27.3,34.33,117.38 tnt_b_patch16_224,691.78,1480.223,1024,224,14.09,39.01,65.41 swinv2_cr_base_224,688.11,1488.125,1024,224,15.86,59.66,87.88 densenet264d_iabn,687.57,1489.287,1024,224,13.47,14.0,72.74 convit_base,685.88,1492.962,1024,224,17.52,31.77,86.54 swinv2_cr_base_ns_224,682.58,1500.17,1024,224,15.86,59.66,87.88 vit_base_patch16_rpn_224,667.73,1533.544,1024,224,17.49,23.75,86.54 densenet264,664.62,1540.716,1024,224,12.95,12.8,72.69 deit3_small_patch16_384,664.03,1156.564,768,384,15.52,50.78,22.21 poolformer_m48,663.83,1542.547,1024,224,11.59,29.17,73.47 deit3_small_patch16_384_in21ft1k,663.62,1157.274,768,384,15.52,50.78,22.21 efficientnet_b3_gn,662.87,386.187,256,320,2.14,28.83,11.73 dpn131,660.11,1551.238,1024,224,16.09,32.97,79.25 eca_nfnet_l1,655.87,1561.27,1024,320,14.92,34.42,41.41 vit_relpos_base_patch16_rpn_224,655.49,1562.186,1024,224,17.51,24.97,86.41 xcit_tiny_24_p8_224,650.45,1574.283,1024,224,9.21,45.39,12.11 xcit_tiny_24_p8_224_dist,649.22,1577.262,1024,224,9.21,45.39,12.11 xcit_nano_12_p8_384_dist,643.06,1592.369,1024,384,6.34,46.08,3.05 nest_base,629.02,813.95,512,224,17.96,53.39,67.72 volo_d2_224,627.91,1630.781,1024,224,14.34,41.34,58.68 mobilevitv2_175_384_in22ft1k,627.52,407.942,256,384,12.47,63.29,14.25 jx_nest_base,621.88,823.3,512,224,17.96,53.39,67.72 vit_small_r26_s32_384,619.54,619.804,384,384,10.43,29.85,36.47 senet154,618.82,1654.743,1024,224,20.77,38.69,115.09 gluon_senet154,618.51,1655.586,1024,224,20.77,38.69,115.09 legacy_senet154,618.16,1656.503,1024,224,20.77,38.69,115.09 xception71,616.97,829.852,512,299,18.09,69.92,42.34 vit_base_r50_s16_224,613.11,1670.152,1024,224,21.66,35.29,98.66 hrnet_w64,609.7,1679.491,1024,224,28.97,35.09,128.06 regnety_320,607.61,842.637,512,224,32.34,30.26,145.05 dpn107,606.08,1689.539,1024,224,18.38,33.46,86.92 regnetz_c16_evos,598.89,854.904,512,320,3.86,25.88,13.49 ecaresnet200d,592.5,1728.248,1024,256,20.0,43.15,64.69 seresnet200d,591.19,1732.085,1024,256,20.01,43.15,71.86 resnet152d,576.9,1774.999,1024,320,24.08,47.67,60.21 convnext_large,559.02,1831.761,1024,224,34.4,43.13,197.77 convnext_large_in22ft1k,558.96,1831.941,1024,224,34.4,43.13,197.77 regnety_160,558.21,687.896,384,288,26.37,38.07,83.59 efficientnet_b3_g8_gn,557.9,458.854,256,320,3.2,28.83,14.25 xcit_small_12_p8_224,546.6,1873.371,1024,224,18.69,47.21,26.21 xcit_small_12_p8_224_dist,546.45,1873.905,1024,224,18.69,47.21,26.21 resnext101_64x4d,541.68,1417.803,768,288,25.66,51.59,83.46 mobilevitv2_200_384_in22ft1k,527.08,364.262,192,384,16.24,72.34,18.45 halonet_h1,518.76,493.471,256,256,3.0,51.17,8.1 vit_large_patch32_384,517.18,1979.967,1024,384,45.31,43.86,306.63 seresnet152d,516.02,1984.399,1024,320,24.09,47.72,66.84 resnetrs152,512.78,1996.941,1024,320,24.34,48.14,86.62 swinv2_base_window8_256,507.32,1513.812,768,256,20.37,52.59,87.92 seresnext101_32x8d,503.19,1526.235,768,288,27.24,51.63,93.57 convnext_small_384_in22ft1k,494.64,1035.087,512,384,25.58,63.37,50.22 seresnext101d_32x8d,494.43,1553.287,768,288,27.64,52.95,93.59 swin_large_patch4_window7_224,478.67,1604.435,768,224,34.53,54.94,196.53 swinv2_small_window16_256,476.38,1074.753,512,256,12.82,66.29,49.73 regnetz_e8,474.49,1618.577,768,320,15.46,63.94,57.7 regnetx_320,471.27,814.799,384,224,31.81,36.3,107.81 ssl_resnext101_32x16d,471.02,1086.983,512,224,36.27,51.18,194.03 swsl_resnext101_32x16d,470.83,1087.428,512,224,36.27,51.18,194.03 ig_resnext101_32x16d,470.74,1087.624,512,224,36.27,51.18,194.03 mixer_l16_224,470.73,2175.315,1024,224,44.6,41.69,208.2 seresnextaa101d_32x8d,463.39,1657.351,768,288,28.51,56.44,93.59 seresnet269d,463.29,2210.273,1024,256,26.59,53.6,113.67 nf_regnet_b5,450.96,1135.344,512,456,11.7,61.95,49.74 efficientnetv2_m,449.82,2276.453,1024,416,18.6,67.5,54.14 volo_d3_224,439.99,2327.294,1024,224,20.78,60.09,86.33 efficientnet_b5,425.78,601.238,256,456,10.46,98.86,30.39 xcit_large_24_p16_224_dist,423.07,2420.403,1024,224,35.86,47.27,189.1 xcit_large_24_p16_224,422.98,2420.908,1024,224,35.86,47.27,189.1 xcit_tiny_12_p8_384_dist,419.35,2441.847,1024,384,14.13,69.14,6.71 resnet200d,417.0,2455.593,1024,320,31.25,67.33,64.69 efficientnetv2_rw_m,411.82,1864.879,768,416,21.49,79.62,53.24 tf_efficientnet_b5_ns,408.16,627.186,256,456,10.46,98.86,30.39 swinv2_cr_tiny_384,408.1,627.286,256,384,15.34,161.01,28.33 tf_efficientnet_b5,407.78,627.773,256,456,10.46,98.86,30.39 tf_efficientnet_b5_ap,407.68,627.936,256,456,10.46,98.86,30.39 swinv2_cr_large_224,405.25,1895.127,768,224,35.1,78.42,196.68 resnetv2_50x1_bitm,401.93,955.37,384,448,16.62,44.46,25.55 nfnet_f1,399.69,2561.946,1024,320,35.97,46.77,132.63 xcit_small_24_p16_384_dist,382.57,2676.633,1024,384,26.72,68.58,47.67 regnetz_d8_evos,376.87,2037.797,768,320,7.03,38.92,23.46 tresnet_l_448,371.52,2756.242,1024,448,43.5,47.56,55.99 vit_large_patch16_224,369.7,2769.802,1024,224,61.6,63.52,304.33 resnetrs200,368.58,2778.22,1024,320,31.51,67.81,93.21 convnext_xlarge_in22ft1k,368.02,1391.221,512,224,60.98,57.5,350.2 crossvit_15_dagger_408,366.37,698.731,256,408,21.45,95.05,28.5 vit_base_patch16_18x2_224,361.96,2829.064,1024,224,52.51,71.38,256.73 deit3_large_patch16_224,358.07,2859.733,1024,224,61.6,63.52,304.37 deit3_large_patch16_224_in21ft1k,357.9,2861.143,1024,224,61.6,63.52,304.37 dm_nfnet_f1,357.87,2146.026,768,320,35.97,46.77,132.63 tf_efficientnetv2_m,350.54,2190.896,768,480,24.76,89.84,54.14 tf_efficientnetv2_m_in21ft1k,350.14,2193.372,768,480,24.76,89.84,54.14 swinv2_base_window16_256,345.6,1111.087,384,256,22.02,84.71,87.92 swinv2_base_window12to16_192to256_22kft1k,345.47,1111.525,384,256,22.02,84.71,87.92 convnext_base_384_in22ft1k,344.56,1485.926,512,384,45.21,84.49,88.59 beit_large_patch16_224,342.32,2991.347,1024,224,61.6,63.52,304.43 eca_nfnet_l2,322.02,2384.947,768,384,30.05,68.28,56.72 volo_d1_384,293.04,1747.159,512,384,22.75,108.55,26.78 convmixer_768_32,292.83,3496.872,1024,224,19.55,25.95,21.11 resnetv2_152x2_bit_teacher,291.46,2634.992,768,224,46.95,45.11,236.34 deit_base_patch16_384,288.65,1330.327,384,384,55.54,101.56,86.86 vit_base_patch16_384,288.47,1331.141,384,384,55.54,101.56,86.86 resnest200e,288.19,1776.58,512,320,35.69,82.78,70.2 xcit_small_24_p8_224,286.12,3578.848,1024,224,35.81,90.78,47.63 xcit_small_24_p8_224_dist,286.06,3579.677,1024,224,35.81,90.78,47.63 deit_base_distilled_patch16_384,284.56,1349.413,384,384,55.65,101.82,87.63 volo_d4_224,282.61,3623.333,1024,224,44.34,80.22,192.96 deit3_base_patch16_384,277.81,1382.217,384,384,55.54,101.56,86.88 deit3_base_patch16_384_in21ft1k,277.78,1382.367,384,384,55.54,101.56,86.88 tresnet_xl_448,277.15,2771.052,768,448,60.65,61.31,78.44 nasnetalarge,276.88,1386.877,384,331,23.89,90.56,88.75 vit_large_patch14_224,271.51,3771.489,1024,224,81.08,88.79,304.2 cait_xxs24_384,269.82,3795.14,1024,384,9.63,122.66,12.03 crossvit_18_dagger_408,269.4,950.247,256,408,32.47,124.87,44.61 xcit_medium_24_p16_384_dist,269.2,2852.889,768,384,47.39,91.64,84.4 pnasnet5large,264.84,1449.925,384,331,25.04,92.89,86.06 resnetv2_101x1_bitm,252.59,1520.226,384,448,31.65,64.93,44.54 efficientnet_b6,252.26,507.392,128,528,19.4,167.39,43.04 swinv2_cr_small_384,250.03,1023.876,256,384,29.7,298.03,49.7 beit_base_patch16_384,247.68,1550.363,384,384,55.54,101.56,86.74 vit_large_r50_s32_384,246.17,1559.866,384,384,57.43,76.52,329.09 tf_efficientnet_b6_ns,242.42,527.986,128,528,19.4,167.39,43.04 tf_efficientnet_b6,242.34,528.179,128,528,19.4,167.39,43.04 tf_efficientnet_b6_ap,242.3,528.255,128,528,19.4,167.39,43.04 ecaresnet269d,241.69,4236.816,1024,352,50.25,101.25,102.09 resnetrs270,234.11,4373.986,1024,352,51.13,105.48,129.86 nfnet_f2,224.73,4556.614,1024,352,63.22,79.06,193.78 swin_base_patch4_window12_384,220.36,871.278,192,384,47.19,134.78,87.9 xcit_tiny_24_p8_384_dist,219.9,4656.678,1024,384,27.05,132.95,12.11 resmlp_big_24_224,218.18,4693.363,1024,224,100.23,87.31,129.14 resmlp_big_24_224_in22ft1k,217.68,4704.164,1024,224,100.23,87.31,129.14 resmlp_big_24_distilled_224,217.65,4704.831,1024,224,100.23,87.31,129.14 swinv2_large_window12to16_192to256_22kft1k,211.96,1207.756,256,256,47.81,121.53,196.74 efficientnetv2_l,206.63,2477.808,512,480,56.4,157.99,118.52 tf_efficientnetv2_l,204.52,2503.355,512,480,56.4,157.99,118.52 tf_efficientnetv2_l_in21ft1k,204.48,2503.917,512,480,56.4,157.99,118.52 ig_resnext101_32x32d,202.59,1263.594,256,224,87.29,91.12,468.53 xcit_medium_24_p8_224,202.12,5066.293,1024,224,63.53,121.23,84.32 xcit_medium_24_p8_224_dist,201.88,5072.196,1024,224,63.53,121.23,84.32 dm_nfnet_f2,200.18,3836.576,768,352,63.22,79.06,193.78 convnext_large_384_in22ft1k,190.55,1343.472,256,384,101.1,126.74,197.77 vit_base_patch8_224,188.25,1359.85,256,224,78.22,161.69,86.58 volo_d5_224,187.56,5459.662,1024,224,72.4,118.11,295.46 cait_xs24_384,186.33,4121.716,768,384,19.28,183.98,26.67 xcit_small_12_p8_384_dist,183.57,2091.823,384,384,54.92,138.29,26.21 eca_nfnet_l3,182.91,2799.141,512,448,52.55,118.4,72.04 cait_xxs36_384,180.41,5675.791,1024,384,14.35,183.7,17.37 swinv2_cr_base_384,178.38,1435.085,256,384,50.57,333.68,87.88 vit_base_resnet50_384,177.85,2159.087,384,384,67.43,135.03,98.95 vit_base_r50_s16_384,177.6,2162.196,384,384,67.43,135.03,98.95 swinv2_cr_huge_224,175.47,2188.347,384,224,115.97,121.08,657.83 convmixer_1536_20,167.1,6128.044,1024,224,48.68,33.03,51.63 volo_d2_384,164.75,1553.889,256,384,46.17,184.51,58.87 resnetrs350,156.77,4898.75,768,384,77.59,154.74,163.96 xcit_large_24_p16_384_dist,154.33,3317.602,512,384,105.35,137.17,189.1 vit_huge_patch14_224,146.32,6998.359,1024,224,167.4,139.41,632.05 efficientnet_b7,145.11,661.558,96,600,38.33,289.94,66.35 cait_s24_384,144.99,3531.336,512,384,32.17,245.31,47.06 deit3_huge_patch14_224,142.26,7197.843,1024,224,167.4,139.41,632.13 deit3_huge_patch14_224_in21ft1k,142.17,7202.758,1024,224,167.4,139.41,632.13 tf_efficientnet_b7_ns,140.64,682.566,96,600,38.33,289.94,66.35 tf_efficientnet_b7_ap,140.61,682.704,96,600,38.33,289.94,66.35 tf_efficientnet_b7,140.6,682.756,96,600,38.33,289.94,66.35 efficientnetv2_xl,139.56,2751.573,384,512,93.85,247.32,208.12 tf_efficientnetv2_xl_in21ft1k,138.42,2774.117,384,512,93.85,247.32,208.12 resnest269e,135.65,2830.833,384,416,77.69,171.98,110.93 swin_large_patch4_window12_384,130.35,981.936,128,384,104.08,202.16,196.74 convnext_xlarge_384_in22ft1k,125.25,1532.9,192,384,179.2,168.99,350.2 nfnet_f3,124.74,4104.555,512,416,115.58,141.78,254.92 ig_resnext101_32x48d,118.28,1623.193,192,224,153.57,131.06,828.41 xcit_large_24_p8_224,115.22,4443.765,512,224,141.23,181.56,188.93 xcit_large_24_p8_224_dist,115.18,4445.056,512,224,141.23,181.56,188.93 resnetrs420,112.12,6849.78,768,416,108.45,213.79,191.89 dm_nfnet_f3,110.18,4647.097,512,416,115.58,141.78,254.92 swinv2_cr_large_384,108.04,1184.75,128,384,108.95,404.96,196.68 resnetv2_50x3_bitm,102.09,1253.798,128,448,145.7,133.37,217.32 resnetv2_152x2_bit_teacher_384,98.91,2588.163,256,384,136.16,132.56,236.34 vit_large_patch16_384,97.45,2626.88,256,384,191.21,270.24,304.72 cait_s36_384,97.05,5275.469,512,384,47.99,367.4,68.37 xcit_small_24_p8_384_dist,96.34,3985.916,384,384,105.24,265.91,47.63 vit_giant_patch14_224,95.73,8022.929,768,224,267.18,192.64,1012.61 deit3_large_patch16_384,94.64,2704.996,256,384,191.21,270.24,304.76 deit3_large_patch16_384_in21ft1k,94.52,2708.314,256,384,191.21,270.24,304.76 swinv2_base_window12to24_192to384_22kft1k,94.37,678.174,64,384,55.25,280.36,87.92 efficientnet_b8,91.29,1051.594,96,672,63.48,442.89,87.41 tf_efficientnet_b8,88.95,1079.277,96,672,63.48,442.89,87.41 tf_efficientnet_b8_ap,88.84,1080.533,96,672,63.48,442.89,87.41 beit_large_patch16_384,84.67,3023.634,256,384,191.21,270.24,305.0 resnetv2_152x2_bitm,73.09,2626.956,192,448,184.99,180.43,236.34 volo_d3_448,72.41,2651.496,192,448,96.33,446.83,86.63 nfnet_f4,69.91,5493.031,384,512,216.26,262.26,316.07 xcit_medium_24_p8_384_dist,67.93,3768.466,256,384,186.67,354.73,84.32 dm_nfnet_f4,62.55,4092.528,256,512,216.26,262.26,316.07 resnetv2_101x3_bitm,61.05,2096.759,128,448,280.33,194.78,387.93 swinv2_large_window12to24_192to384_22kft1k,59.71,803.821,48,384,116.15,407.83,196.74 vit_gigantic_patch14_224,57.59,8890.782,512,224,483.95,275.37,1844.44 tf_efficientnet_l2_ns_475,56.35,1135.833,64,475,172.11,609.89,480.31 volo_d4_448,52.92,2418.622,128,448,197.13,527.35,193.41 swinv2_cr_giant_224,50.53,2532.906,128,224,483.85,309.15,2598.76 nfnet_f5,49.64,5157.064,256,544,290.97,349.71,377.21 swinv2_cr_huge_384,47.06,1360.056,64,384,352.04,583.18,657.94 dm_nfnet_f5,44.17,5795.363,256,544,290.97,349.71,377.21 xcit_large_24_p8_384_dist,38.64,4968.379,192,384,415.0,531.82,188.93 nfnet_f6,37.99,6738.223,256,576,378.69,452.2,438.36 volo_d5_448,36.49,3507.831,128,448,315.06,737.92,295.91 beit_large_patch16_512,33.88,2833.282,96,512,362.24,656.39,305.67 dm_nfnet_f6,33.83,7567.962,256,576,378.69,452.2,438.36 cait_m36_384,31.72,8071.786,256,384,173.11,734.81,271.22 nfnet_f7,30.38,8426.213,256,608,480.39,570.85,499.5 volo_d5_512,25.58,3752.221,96,512,425.09,1105.37,296.09 resnetv2_152x4_bitm,22.67,4234.474,96,480,844.84,414.26,936.53 efficientnet_l2,20.51,1169.975,24,800,479.12,1707.39,480.31 tf_efficientnet_l2_ns,20.15,1191.261,24,800,479.12,1707.39,480.31 swinv2_cr_giant_384,14.62,2188.205,32,384,1450.71,1394.86,2598.76 cait_m48_448,13.47,9503.031,128,448,329.41,1708.23,356.46
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/results-imagenet.csv	model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.052,9.948,99.048,0.952,305.08,448,1.000,bicubic eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,89.970,10.030,99.012,0.988,305.08,448,1.000,bicubic eva_giant_patch14_560.m30m_ft_in22k_in1k,89.786,10.214,98.992,1.008,"1,014.45",560,1.000,bicubic eva02_large_patch14_448.mim_in22k_ft_in1k,89.622,10.378,98.950,1.050,305.08,448,1.000,bicubic eva02_large_patch14_448.mim_m38m_ft_in1k,89.574,10.426,98.924,1.076,305.08,448,1.000,bicubic eva_giant_patch14_336.m30m_ft_in22k_in1k,89.566,10.434,98.952,1.048,"1,013.01",336,1.000,bicubic eva_giant_patch14_336.clip_ft_in1k,89.466,10.534,98.826,1.174,"1,013.01",336,1.000,bicubic eva_large_patch14_336.in22k_ft_in22k_in1k,89.206,10.794,98.854,1.146,304.53,336,1.000,bicubic eva_giant_patch14_224.clip_ft_in1k,88.880,11.120,98.680,1.320,"1,012.56",224,0.900,bicubic convnextv2_huge.fcmae_ft_in22k_in1k_512,88.858,11.142,98.748,1.252,660.29,512,1.000,bicubic eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,88.690,11.310,98.724,1.276,87.12,448,1.000,bicubic convnextv2_huge.fcmae_ft_in22k_in1k_384,88.670,11.330,98.738,1.262,660.29,384,1.000,bicubic eva_large_patch14_336.in22k_ft_in1k,88.670,11.330,98.722,1.278,304.53,336,1.000,bicubic convnext_xxlarge.clip_laion2b_soup_ft_in1k,88.604,11.396,98.708,1.292,846.47,256,1.000,bicubic beit_large_patch16_512.in22k_ft_in22k_in1k,88.596,11.404,98.656,1.344,305.67,512,1.000,bicubic vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,88.592,11.408,98.662,1.338,632.46,336,1.000,bicubic eva_large_patch14_196.in22k_ft_in22k_in1k,88.574,11.426,98.658,1.342,304.14,196,1.000,bicubic maxvit_xlarge_tf_512.in21k_ft_in1k,88.538,11.462,98.644,1.356,475.77,512,1.000,bicubic beit_large_patch16_384.in22k_ft_in22k_in1k,88.402,11.598,98.608,1.392,305.00,384,1.000,bicubic beitv2_large_patch16_224.in1k_ft_in22k_in1k,88.394,11.606,98.598,1.402,304.43,224,0.950,bicubic tf_efficientnet_l2.ns_jft_in1k,88.352,11.648,98.648,1.352,480.31,800,0.960,bicubic maxvit_xlarge_tf_384.in21k_ft_in1k,88.314,11.686,98.544,1.456,475.32,384,1.000,bicubic convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,88.306,11.694,98.582,1.418,200.13,384,1.000,bicubic vit_large_patch14_clip_336.openai_ft_in12k_in1k,88.268,11.732,98.526,1.474,304.53,336,1.000,bicubic vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,88.256,11.744,98.552,1.448,632.05,224,1.000,bicubic eva02_base_patch14_448.mim_in22k_ft_in1k,88.252,11.748,98.564,1.436,87.12,448,1.000,bicubic tf_efficientnet_l2.ns_jft_in1k_475,88.234,11.766,98.546,1.454,480.31,475,0.936,bicubic regnety_1280.swag_ft_in1k,88.230,11.770,98.686,1.314,644.81,384,1.000,bicubic maxvit_large_tf_512.in21k_ft_in1k,88.224,11.776,98.598,1.402,212.33,512,1.000,bicubic maxvit_base_tf_512.in21k_ft_in1k,88.220,11.780,98.530,1.470,119.88,512,1.000,bicubic convnextv2_large.fcmae_ft_in22k_in1k_384,88.198,11.802,98.528,1.472,197.96,384,1.000,bicubic vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,88.180,11.820,98.572,1.428,304.53,336,1.000,bicubic vit_large_patch14_clip_224.openai_ft_in12k_in1k,88.174,11.826,98.546,1.454,304.20,224,1.000,bicubic caformer_b36.sail_in22k_ft_in1k_384,88.058,11.942,98.582,1.418,98.75,384,1.000,bicubic maxvit_large_tf_384.in21k_ft_in1k,87.986,12.014,98.568,1.432,212.03,384,1.000,bicubic convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,87.958,12.042,98.476,1.524,200.13,320,1.000,bicubic eva_large_patch14_196.in22k_ft_in1k,87.932,12.068,98.498,1.502,304.14,196,1.000,bicubic maxvit_base_tf_384.in21k_ft_in1k,87.922,12.078,98.544,1.456,119.65,384,1.000,bicubic vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,87.894,12.106,98.408,1.592,304.20,224,1.000,bicubic vit_large_patch14_clip_336.laion2b_ft_in1k,87.856,12.144,98.368,1.632,304.53,336,1.000,bicubic vit_large_patch14_clip_224.openai_ft_in1k,87.854,12.146,98.426,1.574,304.20,224,1.000,bicubic convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,87.848,12.152,98.446,1.554,200.13,384,1.000,bicubic maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,87.828,12.172,98.372,1.628,116.14,384,1.000,bicubic convnext_xlarge.fb_in22k_ft_in1k_384,87.752,12.248,98.556,1.444,350.20,384,1.000,bicubic deit3_large_patch16_384.fb_in22k_ft_in1k,87.720,12.280,98.512,1.488,304.76,384,1.000,bicubic convnextv2_base.fcmae_ft_in22k_in1k_384,87.644,12.356,98.416,1.584,88.72,384,1.000,bicubic convformer_b36.sail_in22k_ft_in1k_384,87.602,12.398,98.434,1.566,99.88,384,1.000,bicubic vit_huge_patch14_clip_224.laion2b_ft_in1k,87.588,12.412,98.218,1.782,632.05,224,1.000,bicubic convnextv2_large.fcmae_ft_in22k_in1k,87.484,12.516,98.356,1.644,197.96,288,1.000,bicubic beit_large_patch16_224.in22k_ft_in22k_in1k,87.478,12.522,98.304,1.696,304.43,224,0.900,bicubic convnext_large.fb_in22k_ft_in1k_384,87.472,12.528,98.386,1.614,197.77,384,1.000,bicubic maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,87.464,12.536,98.374,1.626,116.09,384,1.000,bicubic swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,87.464,12.536,98.250,1.750,196.74,384,1.000,bicubic caformer_m36.sail_in22k_ft_in1k_384,87.446,12.554,98.308,1.692,56.20,384,1.000,bicubic caformer_b36.sail_in22k_ft_in1k,87.420,12.580,98.328,1.672,98.75,224,1.000,bicubic beitv2_large_patch16_224.in1k_ft_in1k,87.412,12.588,98.234,1.766,304.43,224,0.950,bicubic coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,87.382,12.618,98.312,1.688,73.88,384,1.000,bicubic convnext_large_mlp.clip_laion2b_augreg_ft_in1k,87.336,12.664,98.218,1.782,200.13,256,1.000,bicubic convnext_xlarge.fb_in22k_ft_in1k,87.330,12.670,98.328,1.672,350.20,288,1.000,bicubic seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,87.288,12.712,98.334,1.666,149.39,384,1.000,bicubic vit_large_patch14_clip_224.laion2b_ft_in1k,87.286,12.714,98.244,1.756,304.20,224,1.000,bicubic vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,87.206,12.794,98.034,1.966,86.86,384,1.000,bicubic deit3_huge_patch14_224.fb_in22k_ft_in1k,87.186,12.814,98.260,1.740,632.13,224,1.000,bicubic convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,87.134,12.866,98.222,1.778,88.59,384,1.000,bicubic swin_large_patch4_window12_384.ms_in22k_ft_in1k,87.132,12.868,98.234,1.766,196.74,384,1.000,bicubic swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,87.096,12.904,98.234,1.766,87.92,384,1.000,bicubic vit_large_patch16_384.augreg_in21k_ft_in1k,87.084,12.916,98.302,1.698,304.72,384,1.000,bicubic volo_d5_512.sail_in1k,87.058,12.942,97.970,2.030,296.09,512,1.150,bicubic convnext_large.fb_in22k_ft_in1k,87.026,12.974,98.204,1.796,197.77,288,1.000,bicubic vit_base_patch16_clip_384.openai_ft_in12k_in1k,87.026,12.974,98.182,1.818,86.86,384,0.950,bicubic convformer_b36.sail_in22k_ft_in1k,86.998,13.002,98.172,1.828,99.88,224,1.000,bicubic convnextv2_base.fcmae_ft_in22k_in1k,86.998,13.002,98.168,1.832,88.72,288,1.000,bicubic deit3_large_patch16_224.fb_in22k_ft_in1k,86.982,13.018,98.236,1.764,304.37,224,1.000,bicubic swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,86.952,13.048,98.106,1.894,196.74,256,0.900,bicubic volo_d5_448.sail_in1k,86.952,13.048,97.938,2.062,295.91,448,1.150,bicubic maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,86.894,13.106,98.014,1.986,116.14,224,0.950,bicubic convformer_m36.sail_in22k_ft_in1k_384,86.892,13.108,98.116,1.884,57.05,384,1.000,bicubic caformer_s36.sail_in22k_ft_in1k_384,86.858,13.142,98.212,1.788,39.30,384,1.000,bicubic tf_efficientnet_b7.ns_jft_in1k,86.840,13.160,98.092,1.908,66.35,600,0.949,bicubic regnety_320.swag_ft_in1k,86.834,13.166,98.362,1.638,145.05,384,1.000,bicubic tf_efficientnetv2_l.in21k_ft_in1k,86.802,13.198,98.136,1.864,118.52,480,1.000,bicubic beit_base_patch16_384.in22k_ft_in22k_in1k,86.800,13.200,98.136,1.864,86.74,384,1.000,bicubic convnext_base.fb_in22k_ft_in1k_384,86.796,13.204,98.264,1.736,88.59,384,1.000,bicubic volo_d4_448.sail_in1k,86.792,13.208,97.884,2.116,193.41,448,1.150,bicubic tf_efficientnetv2_xl.in21k_ft_in1k,86.748,13.252,98.014,1.986,208.12,512,1.000,bicubic deit3_base_patch16_384.fb_in22k_ft_in1k,86.740,13.260,98.116,1.884,86.88,384,1.000,bicubic seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,86.724,13.276,98.176,1.824,93.59,320,1.000,bicubic maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,86.642,13.358,98.020,1.980,116.09,224,0.950,bicubic vit_base_patch16_clip_384.laion2b_ft_in1k,86.618,13.382,98.008,1.992,86.86,384,1.000,bicubic maxvit_base_tf_512.in1k,86.602,13.398,97.918,2.082,119.88,512,1.000,bicubic caformer_m36.sail_in22k_ft_in1k,86.594,13.406,98.024,1.976,56.20,224,1.000,bicubic convnextv2_huge.fcmae_ft_in1k,86.580,13.420,97.972,2.028,660.29,288,1.000,bicubic coatnet_2_rw_224.sw_in12k_ft_in1k,86.564,13.436,97.896,2.104,73.87,224,0.950,bicubic maxvit_large_tf_512.in1k,86.526,13.474,97.880,2.120,212.33,512,1.000,bicubic coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,86.504,13.496,97.894,2.106,73.88,224,0.950,bicubic convnext_base.clip_laiona_augreg_ft_in1k_384,86.502,13.498,97.968,2.032,88.59,384,1.000,bicubic volo_d3_448.sail_in1k,86.502,13.498,97.710,2.290,86.63,448,1.000,bicubic cait_m48_448.fb_dist_in1k,86.492,13.508,97.752,2.248,356.46,448,1.000,bicubic seresnextaa101d_32x8d.sw_in12k_ft_in1k,86.484,13.516,98.030,1.970,93.59,288,1.000,bicubic beitv2_base_patch16_224.in1k_ft_in22k_in1k,86.474,13.526,98.052,1.948,86.53,224,0.900,bicubic tiny_vit_21m_512.dist_in22k_ft_in1k,86.458,13.542,97.890,2.110,21.27,512,1.000,bicubic tf_efficientnet_b6.ns_jft_in1k,86.458,13.542,97.884,2.116,43.04,528,0.942,bicubic swin_base_patch4_window12_384.ms_in22k_ft_in1k,86.438,13.562,98.066,1.934,87.90,384,1.000,bicubic caformer_b36.sail_in1k_384,86.408,13.592,97.814,2.186,98.75,384,1.000,bicubic convformer_s36.sail_in22k_ft_in1k_384,86.378,13.622,97.984,2.016,40.01,384,1.000,bicubic convnext_base.clip_laion2b_augreg_ft_in12k_in1k,86.370,13.630,97.984,2.016,88.59,256,1.000,bicubic dm_nfnet_f6.dm_in1k,86.362,13.638,97.896,2.104,438.36,576,0.956,bicubic swin_large_patch4_window7_224.ms_in22k_ft_in1k,86.312,13.688,97.902,2.098,196.53,224,0.900,bicubic maxvit_base_tf_384.in1k,86.302,13.698,97.798,2.202,119.65,384,1.000,bicubic convnext_base.fb_in22k_ft_in1k,86.274,13.726,98.092,1.908,88.59,288,1.000,bicubic swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,86.268,13.732,97.882,2.118,87.92,256,0.900,bicubic maxvit_large_tf_384.in1k,86.230,13.770,97.688,2.312,212.03,384,1.000,bicubic vit_base_patch8_224.augreg2_in21k_ft_in1k,86.218,13.782,97.832,2.168,86.58,224,0.900,bicubic vit_base_patch16_clip_384.openai_ft_in1k,86.206,13.794,97.876,2.124,86.86,384,1.000,bicubic convnext_small.in12k_ft_in1k_384,86.182,13.818,97.922,2.078,50.22,384,1.000,bicubic vit_large_r50_s32_384.augreg_in21k_ft_in1k,86.182,13.818,97.922,2.078,329.09,384,1.000,bicubic vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,86.170,13.830,97.756,2.244,86.57,224,0.950,bicubic caformer_m36.sail_in1k_384,86.166,13.834,97.820,2.180,56.20,384,1.000,bicubic convnext_base.clip_laion2b_augreg_ft_in1k,86.158,13.842,97.680,2.320,88.59,256,1.000,bicubic convformer_m36.sail_in22k_ft_in1k,86.148,13.852,97.850,2.150,57.05,224,1.000,bicubic convnextv2_large.fcmae_ft_in1k,86.118,13.882,97.822,2.178,197.96,288,1.000,bicubic tiny_vit_21m_384.dist_in22k_ft_in1k,86.108,13.892,97.710,2.290,21.23,384,1.000,bicubic dm_nfnet_f5.dm_in1k,86.100,13.900,97.688,2.312,377.21,544,0.954,bicubic tf_efficientnet_b5.ns_jft_in1k,86.088,13.912,97.756,2.244,30.39,456,0.934,bicubic maxvit_small_tf_512.in1k,86.084,13.916,97.764,2.236,69.13,512,1.000,bicubic volo_d5_224.sail_in1k,86.070,13.930,97.576,2.424,295.46,224,0.960,bicubic cait_m36_384.fb_dist_in1k,86.058,13.942,97.730,2.270,271.22,384,1.000,bicubic volo_d2_384.sail_in1k,86.042,13.958,97.574,2.426,58.87,384,1.000,bicubic regnety_160.swag_ft_in1k,86.020,13.980,98.052,1.948,83.59,384,1.000,bicubic xcit_large_24_p8_384.fb_dist_in1k,85.996,14.004,97.690,2.310,188.93,384,1.000,bicubic vit_base_patch16_384.augreg_in21k_ft_in1k,85.994,14.006,98.002,1.998,86.86,384,1.000,bicubic tf_efficientnetv2_m.in21k_ft_in1k,85.992,14.008,97.944,2.056,54.14,480,1.000,bicubic regnety_160.lion_in12k_ft_in1k,85.988,14.012,97.834,2.166,83.59,288,1.000,bicubic regnety_160.sw_in12k_ft_in1k,85.986,14.014,97.834,2.166,83.59,288,1.000,bicubic regnety_1280.swag_lc_in1k,85.982,14.018,97.850,2.150,644.81,224,0.965,bicubic vit_base_patch16_clip_224.openai_ft_in12k_in1k,85.942,14.058,97.728,2.272,86.57,224,0.950,bicubic efficientnet_b5.sw_in12k_ft_in1k,85.896,14.104,97.736,2.264,30.39,448,1.000,bicubic volo_d4_224.sail_in1k,85.872,14.128,97.472,2.528,192.96,224,0.960,bicubic vit_large_patch16_224.augreg_in21k_ft_in1k,85.836,14.164,97.818,2.182,304.33,224,0.900,bicubic dm_nfnet_f4.dm_in1k,85.836,14.164,97.664,2.336,316.07,512,0.951,bicubic xcit_medium_24_p8_384.fb_dist_in1k,85.816,14.184,97.592,2.408,84.32,384,1.000,bicubic deit3_large_patch16_384.fb_in1k,85.812,14.188,97.598,2.402,304.76,384,1.000,bicubic vit_base_patch8_224.augreg_in21k_ft_in1k,85.798,14.202,97.790,2.210,86.58,224,0.900,bicubic caformer_s36.sail_in22k_ft_in1k,85.790,14.210,97.826,2.174,39.30,224,1.000,bicubic vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,85.780,14.220,97.638,2.362,88.34,448,1.000,bicubic convnext_small.fb_in22k_ft_in1k_384,85.778,14.222,97.890,2.110,50.22,384,1.000,bicubic xcit_large_24_p16_384.fb_dist_in1k,85.754,14.246,97.538,2.462,189.10,384,1.000,bicubic caformer_s36.sail_in1k_384,85.742,14.258,97.672,2.328,39.30,384,1.000,bicubic convformer_b36.sail_in1k_384,85.740,14.260,97.524,2.476,99.88,384,1.000,bicubic eva02_small_patch14_336.mim_in22k_ft_in1k,85.718,14.282,97.634,2.366,22.13,336,1.000,bicubic deit3_base_patch16_224.fb_in22k_ft_in1k,85.700,14.300,97.746,2.254,86.59,224,1.000,bicubic dm_nfnet_f3.dm_in1k,85.686,14.314,97.570,2.430,254.92,416,0.940,bicubic maxvit_tiny_tf_512.in1k,85.664,14.336,97.584,2.416,31.05,512,1.000,bicubic tf_efficientnetv2_l.in1k,85.664,14.336,97.474,2.526,118.52,480,1.000,bicubic flexivit_large.1200ep_in1k,85.644,14.356,97.540,2.460,304.36,240,0.950,bicubic beitv2_base_patch16_224.in1k_ft_in1k,85.594,14.406,97.506,2.494,86.53,224,0.900,bicubic convformer_m36.sail_in1k_384,85.580,14.420,97.542,2.458,57.05,384,1.000,bicubic xcit_small_24_p8_384.fb_dist_in1k,85.554,14.446,97.570,2.430,47.63,384,1.000,bicubic flexivit_large.600ep_in1k,85.540,14.460,97.488,2.512,304.36,240,0.950,bicubic maxvit_small_tf_384.in1k,85.540,14.460,97.462,2.538,69.02,384,1.000,bicubic vit_medium_patch16_gap_384.sw_in12k_ft_in1k,85.530,14.470,97.636,2.364,39.03,384,0.950,bicubic caformer_b36.sail_in1k,85.504,14.496,97.310,2.690,98.75,224,1.000,bicubic convnextv2_base.fcmae_ft_in1k,85.474,14.526,97.384,2.616,88.72,288,1.000,bicubic vit_base_patch16_clip_224.laion2b_ft_in1k,85.470,14.530,97.576,2.424,86.57,224,1.000,bicubic cait_s36_384.fb_dist_in1k,85.454,14.546,97.478,2.522,68.37,384,1.000,bicubic xcit_medium_24_p16_384.fb_dist_in1k,85.424,14.576,97.406,2.594,84.40,384,1.000,bicubic deit_base_distilled_patch16_384.fb_in1k,85.424,14.576,97.330,2.670,87.63,384,1.000,bicubic caformer_s18.sail_in22k_ft_in1k_384,85.414,14.586,97.702,2.298,26.34,384,1.000,bicubic convformer_s36.sail_in22k_ft_in1k,85.414,14.586,97.568,2.432,40.01,224,1.000,bicubic volo_d3_224.sail_in1k,85.414,14.586,97.276,2.724,86.33,224,0.960,bicubic xcit_large_24_p8_224.fb_dist_in1k,85.402,14.598,97.402,2.598,188.93,224,1.000,bicubic regnety_120.sw_in12k_ft_in1k,85.400,14.600,97.582,2.418,51.82,288,1.000,bicubic convformer_s36.sail_in1k_384,85.378,14.622,97.476,2.524,40.01,384,1.000,bicubic tf_efficientnet_b8.ra_in1k,85.368,14.632,97.394,2.606,87.41,672,0.954,bicubic vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,85.366,14.634,97.660,2.340,88.30,384,1.000,bicubic tf_efficientnet_b8.ap_in1k,85.364,14.636,97.292,2.708,87.41,672,0.954,bicubic convnext_small.in12k_ft_in1k,85.330,14.670,97.546,2.454,50.22,288,1.000,bicubic vit_base_patch16_clip_224.openai_ft_in1k,85.292,14.708,97.436,2.564,86.57,224,0.900,bicubic flexivit_large.300ep_in1k,85.288,14.712,97.400,2.600,304.36,240,0.950,bicubic swin_base_patch4_window7_224.ms_in22k_ft_in1k,85.272,14.728,97.564,2.436,87.77,224,0.900,bicubic convnext_small.fb_in22k_ft_in1k,85.262,14.738,97.682,2.318,50.22,288,1.000,bicubic volo_d1_384.sail_in1k,85.244,14.756,97.214,2.786,26.78,384,1.000,bicubic mvitv2_large.fb_in1k,85.244,14.756,97.194,2.806,217.99,224,0.900,bicubic caformer_m36.sail_in1k,85.232,14.768,97.200,2.800,56.20,224,1.000,bicubic deit3_huge_patch14_224.fb_in1k,85.224,14.776,97.360,2.640,632.13,224,0.900,bicubic vit_base_patch32_clip_384.openai_ft_in12k_in1k,85.214,14.786,97.404,2.596,88.30,384,0.950,bicubic beit_base_patch16_224.in22k_ft_in22k_in1k,85.212,14.788,97.658,2.342,86.53,224,0.900,bicubic tf_efficientnetv2_m.in1k,85.204,14.796,97.364,2.636,54.14,480,1.000,bicubic inception_next_base.sail_in1k_384,85.202,14.798,97.414,2.586,86.67,384,1.000,bicubic volo_d2_224.sail_in1k,85.202,14.798,97.190,2.810,58.68,224,0.960,bicubic dm_nfnet_f2.dm_in1k,85.192,14.808,97.346,2.654,193.78,352,0.920,bicubic tf_efficientnet_b4.ns_jft_in1k,85.160,14.840,97.468,2.532,19.34,380,0.922,bicubic regnety_2560.seer_ft_in1k,85.150,14.850,97.438,2.562,"1,282.60",384,1.000,bicubic tf_efficientnet_b7.ap_in1k,85.124,14.876,97.252,2.748,66.35,600,0.949,bicubic convnext_tiny.in12k_ft_in1k_384,85.122,14.878,97.606,2.394,28.59,384,1.000,bicubic convnextv2_tiny.fcmae_ft_in22k_in1k_384,85.106,14.894,97.628,2.372,28.64,384,1.000,bicubic maxvit_tiny_tf_384.in1k,85.100,14.900,97.378,2.622,30.98,384,1.000,bicubic resnext101_32x32d.fb_wsl_ig1b_ft_in1k,85.098,14.902,97.438,2.562,468.53,224,0.875,bilinear vit_base_patch16_224.augreg2_in21k_ft_in1k,85.094,14.906,97.530,2.470,86.57,224,0.900,bicubic xcit_small_24_p16_384.fb_dist_in1k,85.090,14.910,97.312,2.688,47.67,384,1.000,bicubic tiny_vit_21m_224.dist_in22k_ft_in1k,85.086,14.914,97.366,2.634,21.20,224,0.950,bicubic xcit_small_12_p8_384.fb_dist_in1k,85.078,14.922,97.282,2.718,26.21,384,1.000,bicubic xcit_medium_24_p8_224.fb_dist_in1k,85.074,14.926,97.274,2.726,84.32,224,1.000,bicubic deit3_base_patch16_384.fb_in1k,85.074,14.926,97.250,2.750,86.88,384,1.000,bicubic cait_s24_384.fb_dist_in1k,85.048,14.952,97.346,2.654,47.06,384,1.000,bicubic regnetz_e8.ra3_in1k,85.034,14.966,97.272,2.728,57.70,320,1.000,bicubic caformer_s18.sail_in1k_384,85.026,14.974,97.358,2.642,26.34,384,1.000,bicubic resnetrs420.tf_in1k,85.004,14.996,97.124,2.876,191.89,416,1.000,bicubic convformer_s18.sail_in22k_ft_in1k_384,84.998,15.002,97.570,2.430,26.77,384,1.000,bicubic vit_base_r50_s16_384.orig_in21k_ft_in1k,84.976,15.024,97.290,2.710,98.95,384,1.000,bicubic ecaresnet269d.ra2_in1k,84.968,15.032,97.222,2.778,102.09,352,1.000,bicubic maxvit_large_tf_224.in1k,84.942,15.058,96.970,3.030,211.79,224,0.950,bicubic tf_efficientnet_b7.ra_in1k,84.932,15.068,97.208,2.792,66.35,600,0.949,bicubic resnetv2_152x4_bit.goog_in21k_ft_in1k,84.916,15.084,97.438,2.562,936.53,480,1.000,bilinear xcit_large_24_p16_224.fb_dist_in1k,84.916,15.084,97.128,2.872,189.10,224,1.000,bicubic coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,84.910,15.090,96.958,3.042,41.72,224,0.950,bicubic coat_lite_medium_384.in1k,84.878,15.122,97.372,2.628,44.57,384,1.000,bicubic xcit_small_24_p8_224.fb_dist_in1k,84.868,15.132,97.190,2.810,47.63,224,1.000,bicubic maxvit_base_tf_224.in1k,84.860,15.140,96.988,3.012,119.47,224,0.950,bicubic convnext_large.fb_in1k,84.846,15.154,97.214,2.786,197.77,288,1.000,bicubic deit3_small_patch16_384.fb_in22k_ft_in1k,84.824,15.176,97.486,2.514,22.21,384,1.000,bicubic convformer_b36.sail_in1k,84.818,15.182,96.946,3.054,99.88,224,1.000,bicubic efficientnetv2_rw_m.agc_in1k,84.810,15.190,97.152,2.848,53.24,416,1.000,bicubic tf_efficientnet_b6.ap_in1k,84.788,15.212,97.138,2.862,43.04,528,0.942,bicubic deit3_large_patch16_224.fb_in1k,84.774,15.226,97.036,2.964,304.37,224,0.900,bicubic resnetrs350.tf_in1k,84.714,15.286,96.992,3.008,163.96,384,1.000,bicubic xcit_small_12_p16_384.fb_dist_in1k,84.712,15.288,97.118,2.882,26.25,384,1.000,bicubic dm_nfnet_f1.dm_in1k,84.702,15.298,97.182,2.818,132.63,320,0.910,bicubic eca_nfnet_l2.ra3_in1k,84.700,15.300,97.266,2.734,56.72,384,1.000,bicubic flexivit_base.1200ep_in1k,84.676,15.324,96.994,3.006,86.59,240,0.950,bicubic davit_base.msft_in1k,84.642,15.358,97.020,2.980,87.95,224,0.950,bicubic maxxvit_rmlp_small_rw_256.sw_in1k,84.624,15.376,97.068,2.932,66.01,256,0.950,bicubic coatnet_rmlp_2_rw_224.sw_in1k,84.608,15.392,96.740,3.260,73.88,224,0.950,bicubic swinv2_base_window16_256.ms_in1k,84.600,15.400,97.090,2.910,87.92,256,0.900,bicubic fastvit_ma36.apple_dist_in1k,84.598,15.402,97.002,2.998,44.07,256,0.950,bicubic seresnextaa101d_32x8d.ah_in1k,84.566,15.434,97.076,2.924,93.59,288,1.000,bicubic deit3_medium_patch16_224.fb_in22k_ft_in1k,84.550,15.450,97.188,2.812,38.85,224,1.000,bicubic regnety_320.swag_lc_in1k,84.548,15.452,97.442,2.558,145.05,224,0.965,bicubic rexnetr_300.sw_in12k_ft_in1k,84.546,15.454,97.256,2.744,34.81,288,1.000,bicubic vit_base_patch16_224.augreg_in21k_ft_in1k,84.532,15.468,97.294,2.706,86.57,224,0.900,bicubic flexivit_base.600ep_in1k,84.524,15.476,96.936,3.064,86.59,240,0.950,bicubic resnetv2_152x2_bit.goog_in21k_ft_in1k,84.510,15.490,97.434,2.566,236.34,448,1.000,bilinear resnest269e.in1k,84.508,15.492,96.990,3.010,110.93,416,0.928,bicubic caformer_s36.sail_in1k,84.506,15.494,96.996,3.004,39.30,224,1.000,bicubic convformer_m36.sail_in1k,84.494,15.506,96.866,3.134,57.05,224,1.000,bicubic regnetz_040_h.ra3_in1k,84.492,15.508,97.010,2.990,28.94,320,1.000,bicubic maxvit_rmlp_small_rw_224.sw_in1k,84.492,15.508,96.758,3.242,64.90,224,0.900,bicubic hrnet_w48_ssld.paddle_in1k,84.480,15.520,97.234,2.766,77.47,288,1.000,bilinear swin_base_patch4_window12_384.ms_in1k,84.476,15.524,96.892,3.108,87.90,384,1.000,bicubic convnext_tiny.in12k_ft_in1k,84.450,15.550,97.340,2.660,28.59,288,1.000,bicubic mvitv2_base.fb_in1k,84.450,15.550,96.858,3.142,51.47,224,0.900,bicubic vit_medium_patch16_gap_256.sw_in12k_ft_in1k,84.446,15.554,97.210,2.790,38.86,256,0.950,bicubic resnetrs200.tf_in1k,84.444,15.556,97.082,2.918,93.21,320,1.000,bicubic gcvit_base.in1k,84.444,15.556,96.842,3.158,90.32,224,0.875,bicubic resnetv2_101x3_bit.goog_in21k_ft_in1k,84.438,15.562,97.382,2.618,387.93,448,1.000,bilinear regnety_1280.seer_ft_in1k,84.432,15.568,97.092,2.908,644.81,384,1.000,bicubic convnext_base.fb_in1k,84.428,15.572,96.968,3.032,88.59,288,1.000,bicubic resnetrs270.tf_in1k,84.428,15.572,96.968,3.032,129.86,352,1.000,bicubic maxvit_small_tf_224.in1k,84.426,15.574,96.824,3.176,68.93,224,0.950,bicubic vit_large_r50_s32_224.augreg_in21k_ft_in1k,84.418,15.582,97.172,2.828,328.99,224,0.900,bicubic convnextv2_tiny.fcmae_ft_in22k_in1k,84.416,15.584,97.260,2.740,28.64,288,1.000,bicubic tf_efficientnet_b7.aa_in1k,84.416,15.584,96.908,3.092,66.35,600,0.949,bicubic flexivit_base.300ep_in1k,84.406,15.594,96.884,3.116,86.59,240,0.950,bicubic convformer_s18.sail_in1k_384,84.402,15.598,97.112,2.888,26.77,384,1.000,bicubic resmlp_big_24_224.fb_in22k_ft_in1k,84.398,15.602,97.112,2.888,129.14,224,0.875,bicubic xcit_large_24_p8_224.fb_in1k,84.394,15.606,96.664,3.336,188.93,224,1.000,bicubic seresnet152d.ra2_in1k,84.360,15.640,97.040,2.960,66.84,320,1.000,bicubic seresnext101d_32x8d.ah_in1k,84.358,15.642,96.920,3.080,93.59,288,1.000,bicubic resnext101_32x8d.fb_swsl_ig1b_ft_in1k,84.302,15.698,97.176,2.824,88.79,224,0.875,bilinear tf_efficientnetv2_s.in21k_ft_in1k,84.286,15.714,97.252,2.748,21.46,384,1.000,bicubic xcit_medium_24_p16_224.fb_dist_in1k,84.286,15.714,96.932,3.068,84.40,224,1.000,bicubic vit_base_patch16_224_miil.in21k_ft_in1k,84.266,15.734,96.804,3.196,86.54,224,0.875,bilinear tf_efficientnet_b5.ap_in1k,84.258,15.742,96.974,3.026,30.39,456,0.934,bicubic davit_small.msft_in1k,84.252,15.748,96.940,3.060,49.75,224,0.950,bicubic swinv2_base_window8_256.ms_in1k,84.250,15.750,96.924,3.076,87.92,256,0.900,bicubic regnetz_040.ra3_in1k,84.240,15.760,96.932,3.068,27.12,320,1.000,bicubic xcit_small_12_p8_224.fb_dist_in1k,84.236,15.764,96.870,3.130,26.21,224,1.000,bicubic swinv2_small_window16_256.ms_in1k,84.224,15.776,96.868,3.132,49.73,256,0.900,bicubic maxvit_rmlp_tiny_rw_256.sw_in1k,84.224,15.776,96.778,3.222,29.15,256,0.950,bicubic crossvit_18_dagger_408.in1k,84.202,15.798,96.818,3.182,44.61,408,1.000,bicubic vit_base_patch16_384.orig_in21k_ft_in1k,84.200,15.800,97.218,2.782,86.86,384,1.000,bicubic seresnext101_32x8d.ah_in1k,84.186,15.814,96.874,3.126,93.57,288,1.000,bicubic resnext101_32x16d.fb_wsl_ig1b_ft_in1k,84.166,15.834,97.198,2.802,194.03,224,0.875,bilinear volo_d1_224.sail_in1k,84.162,15.838,96.776,3.224,26.63,224,0.960,bicubic efficientvit_b3.r288_in1k,84.154,15.846,96.736,3.264,48.65,288,1.000,bicubic regnetz_d8_evos.ch_in1k,84.126,15.874,97.012,2.988,23.46,320,1.000,bicubic resnetaa101d.sw_in12k_ft_in1k,84.124,15.876,97.106,2.894,44.57,288,1.000,bicubic tf_efficientnet_b6.aa_in1k,84.112,15.888,96.884,3.116,43.04,528,0.942,bicubic inception_next_base.sail_in1k,84.092,15.908,96.796,3.204,86.67,224,0.950,bicubic convnext_tiny.fb_in22k_ft_in1k_384,84.088,15.912,97.144,2.856,28.59,384,1.000,bicubic caformer_s18.sail_in22k_ft_in1k,84.074,15.926,97.198,2.802,26.34,224,1.000,bicubic cait_xs24_384.fb_dist_in1k,84.062,15.938,96.884,3.116,26.67,384,1.000,bicubic convformer_s36.sail_in1k,84.060,15.940,96.746,3.254,40.01,224,1.000,bicubic edgenext_base.in21k_ft_in1k,84.054,15.946,97.196,2.804,18.51,320,1.000,bicubic regnetz_d8.ra3_in1k,84.052,15.948,96.996,3.004,23.37,320,1.000,bicubic tf_efficientnet_b3.ns_jft_in1k,84.052,15.948,96.918,3.082,12.23,300,0.904,bicubic vit_small_r26_s32_384.augreg_in21k_ft_in1k,84.048,15.952,97.328,2.672,36.47,384,1.000,bicubic fastvit_sa36.apple_dist_in1k,84.026,15.974,96.854,3.146,31.53,256,0.900,bicubic regnetz_d32.ra3_in1k,84.022,15.978,96.868,3.132,27.58,320,0.950,bicubic resnetv2_50x3_bit.goog_in21k_ft_in1k,84.020,15.980,97.126,2.874,217.32,448,1.000,bilinear eca_nfnet_l1.ra2_in1k,84.012,15.988,97.026,2.974,41.41,320,1.000,bicubic resnet200d.ra2_in1k,83.964,16.036,96.826,3.174,64.69,320,1.000,bicubic edgenext_base.usi_in1k,83.958,16.042,96.770,3.230,18.51,320,1.000,bicubic regnety_080.ra3_in1k,83.926,16.074,96.890,3.110,39.18,288,1.000,bicubic swin_s3_base_224.ms_in1k,83.920,16.080,96.672,3.328,71.13,224,0.900,bicubic regnety_640.seer_ft_in1k,83.908,16.092,96.922,3.078,281.38,384,1.000,bicubic tf_efficientnetv2_s.in1k,83.898,16.102,96.696,3.304,21.46,384,1.000,bicubic tresnet_v2_l.miil_in21k_ft_in1k,83.894,16.106,96.490,3.510,46.17,224,0.875,bilinear gcvit_small.in1k,83.892,16.108,96.658,3.342,51.09,224,0.875,bicubic fastvit_ma36.apple_in1k,83.882,16.118,96.742,3.258,44.07,256,0.950,bicubic xcit_small_24_p16_224.fb_dist_in1k,83.874,16.126,96.736,3.264,47.67,224,1.000,bicubic swinv2_small_window8_256.ms_in1k,83.854,16.146,96.644,3.356,49.73,256,0.900,bicubic resnest200e.in1k,83.844,16.156,96.884,3.116,70.20,320,0.909,bicubic crossvit_15_dagger_408.in1k,83.840,16.160,96.778,3.222,28.50,408,1.000,bicubic focalnet_base_lrf.ms_in1k,83.838,16.162,96.608,3.392,88.75,224,0.900,bicubic resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,83.836,16.164,97.126,2.874,236.34,384,1.000,bicubic xcit_small_24_p8_224.fb_in1k,83.834,16.166,96.632,3.368,47.63,224,1.000,bicubic focalnet_base_srf.ms_in1k,83.820,16.180,96.680,3.320,88.15,224,0.900,bicubic tf_efficientnet_b5.ra_in1k,83.814,16.186,96.752,3.248,30.39,456,0.934,bicubic efficientnetv2_rw_s.ra2_in1k,83.806,16.194,96.732,3.268,23.94,384,1.000,bicubic vit_small_patch16_384.augreg_in21k_ft_in1k,83.804,16.196,97.100,2.900,22.20,384,1.000,bicubic efficientvit_b3.r256_in1k,83.802,16.198,96.516,3.484,48.65,256,1.000,bicubic deit3_base_patch16_224.fb_in1k,83.786,16.214,96.586,3.414,86.59,224,0.900,bicubic regnety_160.swag_lc_in1k,83.782,16.218,97.280,2.720,83.59,224,0.965,bicubic mvitv2_small.fb_in1k,83.770,16.230,96.576,3.424,34.87,224,0.900,bicubic pit_b_distilled_224.in1k,83.766,16.234,96.468,3.532,74.79,224,0.900,bicubic swin_s3_small_224.ms_in1k,83.756,16.244,96.452,3.548,49.74,224,0.900,bicubic xcit_tiny_24_p8_384.fb_dist_in1k,83.746,16.254,96.710,3.290,12.11,384,1.000,bicubic xcit_medium_24_p8_224.fb_in1k,83.746,16.254,96.400,3.600,84.32,224,1.000,bicubic repvit_m2_3.dist_450e_in1k,83.742,16.258,96.644,3.356,23.69,224,0.950,bicubic pvt_v2_b5.in1k,83.740,16.260,96.636,3.364,81.96,224,0.900,bicubic convformer_s18.sail_in22k_ft_in1k,83.738,16.262,97.048,2.952,26.77,224,1.000,bicubic regnety_064.ra3_in1k,83.720,16.280,96.722,3.278,30.58,288,1.000,bicubic regnetv_064.ra3_in1k,83.716,16.284,96.742,3.258,30.58,288,1.000,bicubic pvt_v2_b4.in1k,83.712,16.288,96.670,3.330,62.56,224,0.900,bicubic resnetrs152.tf_in1k,83.702,16.298,96.612,3.388,86.62,320,1.000,bicubic convnext_small.fb_in1k,83.700,16.300,96.808,3.192,50.22,288,1.000,bicubic regnety_160.deit_in1k,83.690,16.310,96.780,3.220,83.59,288,1.000,bicubic tf_efficientnet_b5.aa_in1k,83.688,16.312,96.712,3.288,30.39,456,0.934,bicubic resnet152d.ra2_in1k,83.684,16.316,96.738,3.262,60.21,320,1.000,bicubic twins_svt_large.in1k,83.678,16.322,96.588,3.412,99.27,224,0.900,bicubic caformer_s18.sail_in1k,83.654,16.346,96.518,3.482,26.34,224,1.000,bicubic efficientformerv2_l.snap_dist_in1k,83.632,16.368,96.558,3.442,26.32,224,0.950,bicubic swin_base_patch4_window7_224.ms_in1k,83.606,16.394,96.452,3.548,87.77,224,0.900,bicubic coat_lite_medium.in1k,83.600,16.400,96.728,3.272,44.57,224,0.900,bicubic coatnet_1_rw_224.sw_in1k,83.596,16.404,96.382,3.618,41.72,224,0.950,bicubic resmlp_big_24_224.fb_distilled_in1k,83.592,16.408,96.650,3.350,129.14,224,0.875,bicubic inception_next_small.sail_in1k,83.578,16.422,96.598,3.402,49.37,224,0.875,bicubic repvgg_d2se.rvgg_in1k,83.560,16.440,96.658,3.342,133.33,320,1.000,bilinear cs3se_edgenet_x.c2ns_in1k,83.546,16.454,96.670,3.330,50.72,320,1.000,bicubic nest_base_jx.goog_in1k,83.534,16.466,96.374,3.626,67.72,224,0.875,bicubic repvit_m2_3.dist_300e_in1k,83.504,16.496,96.514,3.486,23.69,224,0.950,bicubic maxvit_tiny_rw_224.sw_in1k,83.504,16.496,96.504,3.496,29.06,224,0.950,bicubic fastvit_sa36.apple_in1k,83.500,16.500,96.630,3.370,31.53,256,0.900,bicubic swinv2_cr_small_ns_224.sw_in1k,83.498,16.502,96.484,3.516,49.70,224,0.900,bicubic focalnet_small_lrf.ms_in1k,83.494,16.506,96.580,3.420,50.34,224,0.900,bicubic dm_nfnet_f0.dm_in1k,83.486,16.514,96.568,3.432,71.49,256,0.900,bicubic convnextv2_tiny.fcmae_ft_in1k,83.464,16.536,96.718,3.282,28.64,288,1.000,bicubic efficientvit_b3.r224_in1k,83.460,16.540,96.330,3.670,48.65,224,0.950,bicubic resnet152.a1h_in1k,83.450,16.550,96.538,3.462,60.19,288,1.000,bicubic cait_s24_224.fb_dist_in1k,83.442,16.558,96.574,3.426,46.92,224,1.000,bicubic deit3_small_patch16_384.fb_in1k,83.428,16.572,96.674,3.326,22.21,384,1.000,bicubic focalnet_small_srf.ms_in1k,83.416,16.584,96.438,3.562,49.89,224,0.900,bicubic efficientnet_b4.ra2_in1k,83.414,16.586,96.598,3.402,19.34,384,1.000,bicubic maxvit_tiny_tf_224.in1k,83.402,16.598,96.590,3.410,30.92,224,0.950,bicubic mobilevitv2_200.cvnets_in22k_ft_in1k_384,83.400,16.600,96.582,3.418,18.45,384,1.000,bicubic sequencer2d_l.in1k,83.394,16.606,96.496,3.504,54.30,224,0.875,bicubic deit_base_distilled_patch16_224.fb_in1k,83.390,16.610,96.488,3.512,87.34,224,0.900,bicubic gcvit_tiny.in1k,83.384,16.616,96.398,3.602,28.22,224,0.875,bicubic efficientformer_l7.snap_dist_in1k,83.382,16.618,96.536,3.464,82.23,224,0.950,bicubic convnextv2_nano.fcmae_ft_in22k_in1k_384,83.374,16.626,96.744,3.256,15.62,384,1.000,bicubic coatnet_rmlp_1_rw_224.sw_in1k,83.362,16.638,96.450,3.550,41.69,224,0.950,bicubic vit_base_patch32_384.augreg_in21k_ft_in1k,83.352,16.648,96.840,3.160,88.30,384,1.000,bicubic fastvit_sa24.apple_dist_in1k,83.342,16.658,96.552,3.448,21.55,256,0.900,bicubic resnext101_32x16d.fb_swsl_ig1b_ft_in1k,83.336,16.664,96.846,3.154,194.03,224,0.875,bilinear xcit_small_12_p8_224.fb_in1k,83.334,16.666,96.482,3.518,26.21,224,1.000,bicubic regnety_320.seer_ft_in1k,83.328,16.672,96.708,3.292,145.05,384,1.000,bicubic xcit_small_12_p16_224.fb_dist_in1k,83.328,16.672,96.416,3.584,26.25,224,1.000,bicubic swin_small_patch4_window7_224.ms_in22k_ft_in1k,83.298,16.702,96.964,3.036,49.61,224,0.900,bicubic vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,83.296,16.704,96.528,3.472,88.22,224,0.900,bicubic tiny_vit_21m_224.in1k,83.254,16.746,96.592,3.408,21.20,224,0.950,bicubic tf_efficientnet_b4.ap_in1k,83.250,16.750,96.396,3.604,19.34,380,0.922,bicubic tiny_vit_11m_224.dist_in22k_ft_in1k,83.228,16.772,96.630,3.370,11.00,224,0.950,bicubic resnext101_32x4d.fb_swsl_ig1b_ft_in1k,83.226,16.774,96.760,3.240,44.18,224,0.875,bilinear swin_small_patch4_window7_224.ms_in1k,83.208,16.792,96.316,3.684,49.61,224,0.900,bicubic regnetv_040.ra3_in1k,83.190,16.810,96.658,3.342,20.64,288,1.000,bicubic xception65.ra3_in1k,83.180,16.820,96.592,3.408,39.92,299,0.940,bicubic tf_efficientnet_b5.in1k,83.176,16.824,96.536,3.464,30.39,456,0.934,bicubic regnety_320.tv2_in1k,83.162,16.838,96.414,3.586,145.05,224,0.965,bicubic resnext101_64x4d.c1_in1k,83.156,16.844,96.374,3.626,83.46,288,1.000,bicubic rexnetr_200.sw_in12k_ft_in1k,83.138,16.862,96.636,3.364,16.52,288,1.000,bicubic swinv2_cr_small_224.sw_in1k,83.136,16.864,96.108,3.892,49.70,224,0.900,bicubic twins_pcpvt_large.in1k,83.130,16.870,96.604,3.396,60.99,224,0.900,bicubic xception65p.ra3_in1k,83.126,16.874,96.482,3.518,39.82,299,0.940,bicubic nest_small_jx.goog_in1k,83.124,16.876,96.320,3.680,38.35,224,0.875,bicubic twins_svt_base.in1k,83.120,16.880,96.414,3.586,56.07,224,0.900,bicubic pvt_v2_b3.in1k,83.118,16.882,96.556,3.444,45.24,224,0.900,bicubic maxxvitv2_nano_rw_256.sw_in1k,83.110,16.890,96.324,3.676,23.70,256,0.950,bicubic deit_base_patch16_384.fb_in1k,83.104,16.896,96.368,3.632,86.86,384,1.000,bicubic efficientvit_b2.r288_in1k,83.100,16.900,96.304,3.696,24.33,288,1.000,bicubic deit3_medium_patch16_224.fb_in1k,83.086,16.914,96.294,3.706,38.85,224,0.900,bicubic deit3_small_patch16_224.fb_in22k_ft_in1k,83.076,16.924,96.776,3.224,22.06,224,1.000,bicubic tresnet_m.miil_in21k_ft_in1k,83.070,16.930,96.110,3.890,31.39,224,0.875,bilinear tresnet_xl.miil_in1k_448,83.058,16.942,96.172,3.828,78.44,448,0.875,bilinear regnety_040.ra3_in1k,83.044,16.956,96.502,3.498,20.65,288,1.000,bicubic maxxvit_rmlp_nano_rw_256.sw_in1k,83.042,16.958,96.350,3.650,16.78,256,0.950,bicubic resnet101d.ra2_in1k,83.020,16.980,96.452,3.548,44.57,320,1.000,bicubic tf_efficientnet_b4.aa_in1k,83.018,16.982,96.300,3.700,19.34,380,0.922,bicubic resnetv2_101.a1h_in1k,83.000,17.000,96.454,3.546,44.54,288,1.000,bicubic resnext101_64x4d.tv_in1k,82.992,17.008,96.244,3.756,83.46,224,0.875,bilinear convformer_s18.sail_in1k,82.986,17.014,96.250,3.750,26.77,224,1.000,bicubic ecaresnet101d.miil_in1k,82.984,17.016,96.542,3.458,44.57,288,0.950,bicubic maxvit_rmlp_nano_rw_256.sw_in1k,82.954,17.046,96.266,3.734,15.50,256,0.950,bicubic mobilevitv2_175.cvnets_in22k_ft_in1k_384,82.938,17.062,96.426,3.574,14.25,384,1.000,bicubic maxvit_nano_rw_256.sw_in1k,82.928,17.072,96.220,3.780,15.45,256,0.950,bicubic xcit_large_24_p16_224.fb_in1k,82.902,17.098,95.884,4.116,189.10,224,1.000,bicubic resnest101e.in1k,82.884,17.116,96.322,3.678,48.28,256,0.875,bilinear resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,82.876,17.124,96.582,3.418,236.34,224,0.875,bicubic convnext_nano.in12k_ft_in1k,82.862,17.138,96.556,3.444,15.59,288,1.000,bicubic resnext101_32x8d.tv2_in1k,82.832,17.168,96.232,3.768,88.79,224,0.965,bilinear resnetv2_50x1_bit.goog_distilled_in1k,82.824,17.176,96.518,3.482,25.55,224,0.875,bicubic sequencer2d_m.in1k,82.812,17.188,96.274,3.726,38.31,224,0.875,bicubic regnetx_320.tv2_in1k,82.810,17.190,96.208,3.792,107.81,224,0.965,bicubic swinv2_tiny_window16_256.ms_in1k,82.804,17.196,96.236,3.764,28.35,256,0.900,bicubic pnasnet5large.tf_in1k,82.782,17.218,96.040,3.960,86.06,331,0.911,bicubic resnet101.a1h_in1k,82.778,17.222,96.310,3.690,44.55,288,1.000,bicubic rexnet_300.nav_in1k,82.774,17.226,96.238,3.762,34.71,224,0.875,bicubic vit_relpos_base_patch16_clsgap_224.sw_in1k,82.760,17.240,96.172,3.828,86.43,224,0.900,bicubic nfnet_l0.ra2_in1k,82.750,17.250,96.516,3.484,35.07,288,1.000,bicubic resnet152.a1_in1k,82.732,17.268,95.720,4.280,60.19,288,1.000,bicubic regnety_032.ra_in1k,82.726,17.274,96.416,3.584,19.44,288,1.000,bicubic twins_pcpvt_base.in1k,82.714,17.286,96.346,3.654,43.83,224,0.900,bicubic cs3edgenet_x.c2_in1k,82.708,17.292,96.370,3.630,47.82,288,1.000,bicubic resnext101_32x8d.fb_wsl_ig1b_ft_in1k,82.698,17.302,96.632,3.368,88.79,224,0.875,bilinear convnext_tiny.fb_in1k,82.698,17.302,96.144,3.856,28.59,288,1.000,bicubic davit_tiny.msft_in1k,82.696,17.304,96.274,3.726,28.36,224,0.950,bicubic efficientvit_b2.r256_in1k,82.690,17.310,96.094,3.906,24.33,256,1.000,bicubic fastvit_sa24.apple_in1k,82.678,17.322,96.272,3.728,21.55,256,0.900,bicubic tf_efficientnetv2_b3.in21k_ft_in1k,82.670,17.330,96.626,3.374,14.36,300,0.900,bicubic convnextv2_nano.fcmae_ft_in22k_in1k,82.664,17.336,96.520,3.480,15.62,288,1.000,bicubic cs3sedarknet_x.c2ns_in1k,82.658,17.342,96.350,3.650,35.40,288,1.000,bicubic regnety_160.tv2_in1k,82.646,17.354,96.214,3.786,83.59,224,0.965,bicubic xcit_medium_24_p16_224.fb_in1k,82.640,17.360,95.982,4.018,84.40,224,1.000,bicubic regnetz_c16_evos.ch_in1k,82.636,17.364,96.474,3.526,13.49,320,0.950,bicubic regnetz_c16.ra3_in1k,82.632,17.368,96.318,3.682,13.46,320,1.000,bicubic nasnetalarge.tf_in1k,82.626,17.374,96.042,3.958,88.75,331,0.911,bicubic poolformerv2_m48.sail_in1k,82.618,17.382,96.072,3.928,73.35,224,1.000,bicubic tf_efficientnet_b4.in1k,82.608,17.392,96.128,3.872,19.34,380,0.922,bicubic resnet152.a2_in1k,82.608,17.392,95.752,4.248,60.19,288,1.000,bicubic resnetaa50d.sw_in12k_ft_in1k,82.600,17.400,96.498,3.502,25.58,288,1.000,bicubic levit_384.fb_dist_in1k,82.596,17.404,96.018,3.982,39.13,224,0.900,bicubic regnety_080_tv.tv2_in1k,82.594,17.406,96.248,3.752,39.38,224,0.965,bicubic levit_conv_384.fb_dist_in1k,82.590,17.410,96.016,3.984,39.13,224,0.900,bicubic mobilevitv2_150.cvnets_in22k_ft_in1k_384,82.586,17.414,96.314,3.686,10.59,384,1.000,bicubic convnext_tiny_hnf.a2h_in1k,82.584,17.416,96.008,3.992,28.59,288,1.000,bicubic vit_base_patch32_clip_224.laion2b_ft_in1k,82.582,17.418,96.200,3.800,88.22,224,0.900,bicubic eca_nfnet_l0.ra2_in1k,82.578,17.422,96.492,3.508,24.14,288,1.000,bicubic xcit_small_24_p16_224.fb_in1k,82.576,17.424,96.012,3.988,47.67,224,1.000,bicubic vit_relpos_medium_patch16_cls_224.sw_in1k,82.572,17.428,96.068,3.932,38.76,224,0.900,bicubic xcit_tiny_24_p16_384.fb_dist_in1k,82.570,17.430,96.276,3.724,12.12,384,1.000,bicubic xcit_tiny_24_p8_224.fb_dist_in1k,82.566,17.434,96.172,3.828,12.11,224,1.000,bicubic regnetx_160.tv2_in1k,82.566,17.434,96.058,3.942,54.28,224,0.965,bicubic efficientformer_l3.snap_dist_in1k,82.548,17.452,96.250,3.750,31.41,224,0.950,bicubic flexivit_small.1200ep_in1k,82.526,17.474,96.126,3.874,22.06,240,0.950,bicubic resnet61q.ra2_in1k,82.524,17.476,96.130,3.870,36.85,288,1.000,bicubic crossvit_18_dagger_240.in1k,82.518,17.482,96.068,3.932,44.27,240,0.875,bicubic repvit_m1_5.dist_450e_in1k,82.512,17.488,96.112,3.888,14.64,224,0.950,bicubic wide_resnet101_2.tv2_in1k,82.502,17.498,96.016,3.984,126.89,224,0.965,bilinear vit_relpos_base_patch16_224.sw_in1k,82.496,17.504,96.138,3.862,86.43,224,0.900,bicubic convnextv2_nano.fcmae_ft_in1k,82.486,17.514,96.226,3.774,15.62,288,1.000,bicubic poolformer_m48.sail_in1k,82.482,17.518,95.966,4.034,73.47,224,0.950,bicubic inception_next_tiny.sail_in1k,82.478,17.522,96.022,3.978,28.06,224,0.875,bicubic vit_relpos_medium_patch16_224.sw_in1k,82.462,17.538,96.082,3.918,38.75,224,0.900,bicubic gc_efficientnetv2_rw_t.agc_in1k,82.456,17.544,96.296,3.704,13.68,288,1.000,bicubic pit_b_224.in1k,82.438,17.562,95.714,4.286,73.76,224,0.900,bicubic mvitv2_tiny.fb_in1k,82.410,17.590,96.152,3.848,24.17,224,0.900,bicubic coatnet_bn_0_rw_224.sw_in1k,82.400,17.600,96.186,3.814,27.44,224,0.950,bicubic crossvit_18_240.in1k,82.400,17.600,96.060,3.940,43.27,240,0.875,bicubic coatnet_0_rw_224.sw_in1k,82.390,17.610,95.836,4.164,27.44,224,0.950,bicubic xcit_tiny_12_p8_384.fb_dist_in1k,82.388,17.612,96.220,3.780,6.71,384,1.000,bicubic tf_efficientnet_b2.ns_jft_in1k,82.378,17.622,96.254,3.746,9.11,260,0.890,bicubic repvit_m1_5.dist_300e_in1k,82.376,17.624,96.030,3.970,14.64,224,0.950,bicubic coat_small.in1k,82.362,17.638,96.208,3.792,21.69,224,0.900,bicubic flexivit_small.600ep_in1k,82.362,17.638,96.084,3.916,22.06,240,0.950,bicubic resnet51q.ra2_in1k,82.360,17.640,96.186,3.814,35.70,288,1.000,bilinear ecaresnet50t.ra2_in1k,82.352,17.648,96.140,3.860,25.57,320,0.950,bicubic efficientnetv2_rw_t.ra2_in1k,82.350,17.650,96.192,3.808,13.65,288,1.000,bicubic resnetv2_101x1_bit.goog_in21k_ft_in1k,82.342,17.658,96.520,3.480,44.54,448,1.000,bilinear sequencer2d_s.in1k,82.340,17.660,96.028,3.972,27.65,224,0.875,bicubic mobilevitv2_200.cvnets_in22k_ft_in1k,82.332,17.668,95.942,4.058,18.45,256,0.888,bicubic crossvit_15_dagger_240.in1k,82.330,17.670,95.956,4.044,28.21,240,0.875,bicubic resnet101.a1_in1k,82.322,17.678,95.632,4.368,44.55,288,1.000,bicubic coat_lite_small.in1k,82.312,17.688,95.850,4.150,19.84,224,0.900,bicubic vit_relpos_medium_patch16_rpn_224.sw_in1k,82.310,17.690,95.972,4.028,38.73,224,0.900,bicubic mixer_b16_224.miil_in21k_ft_in1k,82.306,17.694,95.720,4.280,59.88,224,0.875,bilinear convit_base.fb_in1k,82.290,17.710,95.936,4.064,86.54,224,0.875,bicubic resnet152.tv2_in1k,82.286,17.714,96.004,3.996,60.19,224,0.965,bilinear resnetrs101.tf_in1k,82.284,17.716,96.014,3.986,63.62,288,0.940,bicubic wide_resnet50_2.racm_in1k,82.280,17.720,96.064,3.936,68.88,288,0.950,bicubic tresnet_l.miil_in1k_448,82.276,17.724,95.978,4.022,55.99,448,0.875,bilinear efficientnet_b3.ra2_in1k,82.246,17.754,96.118,3.882,12.23,320,1.000,bicubic vit_srelpos_medium_patch16_224.sw_in1k,82.240,17.760,95.942,4.058,38.74,224,0.900,bicubic resnet101.a2_in1k,82.236,17.764,95.730,4.270,44.55,288,1.000,bicubic cs3darknet_x.c2ns_in1k,82.222,17.778,96.230,3.770,35.05,288,1.000,bicubic poolformerv2_m36.sail_in1k,82.216,17.784,95.924,4.076,56.08,224,1.000,bicubic crossvit_base_240.in1k,82.214,17.786,95.834,4.166,105.03,240,0.875,bicubic cait_xxs36_384.fb_dist_in1k,82.204,17.796,96.144,3.856,17.37,384,1.000,bicubic vit_base_patch16_rpn_224.sw_in1k,82.202,17.798,95.996,4.004,86.54,224,0.900,bicubic seresnext50_32x4d.racm_in1k,82.196,17.804,96.148,3.852,27.56,288,0.950,bicubic pvt_v2_b2_li.in1k,82.194,17.806,96.092,3.908,22.55,224,0.900,bicubic flexivit_small.300ep_in1k,82.178,17.822,96.038,3.962,22.06,240,0.950,bicubic resnext50_32x4d.fb_swsl_ig1b_ft_in1k,82.172,17.828,96.224,3.776,25.03,224,0.875,bilinear efficientformerv2_s2.snap_dist_in1k,82.166,17.834,95.910,4.090,12.71,224,0.950,bicubic focalnet_tiny_lrf.ms_in1k,82.154,17.846,95.948,4.052,28.65,224,0.900,bicubic efficientvit_b2.r224_in1k,82.148,17.852,95.706,4.294,24.33,224,0.950,bicubic swin_s3_tiny_224.ms_in1k,82.144,17.856,95.954,4.046,28.33,224,0.900,bicubic focalnet_tiny_srf.ms_in1k,82.138,17.862,95.968,4.032,28.43,224,0.900,bicubic ecaresnet50t.a1_in1k,82.128,17.872,95.642,4.358,25.57,288,1.000,bicubic visformer_small.in1k,82.106,17.894,95.878,4.122,40.22,224,0.900,bicubic poolformer_m36.sail_in1k,82.102,17.898,95.698,4.302,56.17,224,0.950,bicubic pvt_v2_b2.in1k,82.084,17.916,95.956,4.044,25.36,224,0.900,bicubic tresnet_xl.miil_in1k,82.074,17.926,95.928,4.072,78.44,224,0.875,bilinear halo2botnet50ts_256.a1h_in1k,82.060,17.940,95.634,4.366,22.64,256,0.950,bicubic coatnet_rmlp_nano_rw_224.sw_in1k,82.050,17.950,95.878,4.122,15.15,224,0.900,bicubic hrnet_w18_ssld.paddle_in1k,82.048,17.952,96.250,3.750,21.30,288,1.000,bilinear fbnetv3_g.ra2_in1k,82.040,17.960,96.060,3.940,16.62,288,0.950,bilinear resnext50_32x4d.a1h_in1k,82.014,17.986,95.934,4.066,25.03,288,1.000,bicubic resnetv2_50d_evos.ah_in1k,82.002,17.998,95.900,4.100,25.59,288,1.000,bicubic ecaresnet101d_pruned.miil_in1k,81.998,18.002,96.160,3.840,24.88,288,0.950,bicubic deit_base_patch16_224.fb_in1k,81.992,18.008,95.736,4.264,86.57,224,0.900,bicubic xception41p.ra3_in1k,81.972,18.028,95.802,4.198,26.91,299,0.940,bicubic tf_efficientnetv2_b3.in1k,81.972,18.028,95.784,4.216,14.36,300,0.904,bicubic xcit_small_12_p16_224.fb_in1k,81.970,18.030,95.812,4.188,26.25,224,1.000,bicubic resnetv2_50d_gn.ah_in1k,81.958,18.042,95.928,4.072,25.57,288,1.000,bicubic gcvit_xtiny.in1k,81.954,18.046,95.966,4.034,19.98,224,0.875,bicubic coatnext_nano_rw_224.sw_in1k,81.942,18.058,95.916,4.084,14.70,224,0.900,bicubic mobilevitv2_175.cvnets_in22k_ft_in1k,81.938,18.062,95.790,4.210,14.25,256,0.888,bicubic vit_base_patch32_clip_224.openai_ft_in1k,81.930,18.070,95.966,4.034,88.22,224,0.900,bicubic xcit_tiny_24_p8_224.fb_in1k,81.892,18.108,95.970,4.030,12.11,224,1.000,bicubic resnet101.tv2_in1k,81.888,18.112,95.768,4.232,44.55,224,0.965,bilinear vit_small_r26_s32_224.augreg_in21k_ft_in1k,81.864,18.136,96.022,3.978,36.43,224,0.900,bicubic fastvit_sa12.apple_dist_in1k,81.854,18.146,95.710,4.290,11.58,256,0.900,bicubic resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,81.838,18.162,96.092,3.908,194.03,224,0.875,bilinear swinv2_tiny_window8_256.ms_in1k,81.820,18.180,95.994,4.006,28.35,256,0.900,bicubic tf_efficientnet_b3.ap_in1k,81.820,18.180,95.626,4.374,12.23,300,0.904,bicubic pit_s_distilled_224.in1k,81.814,18.186,95.730,4.270,24.04,224,0.900,bicubic swinv2_cr_tiny_ns_224.sw_in1k,81.802,18.198,95.818,4.182,28.33,224,0.900,bicubic vit_base_patch16_224.orig_in21k_ft_in1k,81.790,18.210,96.126,3.874,86.57,224,0.900,bicubic cs3sedarknet_l.c2ns_in1k,81.784,18.216,95.964,4.036,21.91,288,0.950,bicubic regnety_032.tv2_in1k,81.756,18.244,95.844,4.156,19.44,224,0.965,bicubic tresnet_m.miil_in1k_448,81.710,18.290,95.574,4.426,31.39,448,0.875,bilinear coatnet_nano_rw_224.sw_in1k,81.696,18.304,95.646,4.354,15.14,224,0.900,bicubic twins_svt_small.in1k,81.676,18.324,95.658,4.342,24.06,224,0.900,bicubic halonet50ts.a1h_in1k,81.662,18.338,95.610,4.390,22.73,256,0.940,bicubic ecaresnet50t.a2_in1k,81.658,18.342,95.550,4.450,25.57,288,1.000,bicubic ecaresnet50d.miil_in1k,81.650,18.350,95.882,4.118,25.58,288,0.950,bicubic tf_efficientnet_b3.aa_in1k,81.640,18.360,95.722,4.278,12.23,300,0.904,bicubic rexnet_200.nav_in1k,81.636,18.364,95.666,4.334,16.37,224,0.875,bicubic resnetaa50.a1h_in1k,81.614,18.386,95.802,4.198,25.56,288,1.000,bicubic resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,81.606,18.394,96.040,3.960,88.79,224,0.875,bilinear wide_resnet50_2.tv2_in1k,81.606,18.394,95.760,4.240,68.88,224,0.965,bilinear convnext_nano_ols.d1h_in1k,81.600,18.400,95.636,4.364,15.65,288,1.000,bicubic poolformerv2_s36.sail_in1k,81.566,18.434,95.690,4.310,30.79,224,1.000,bicubic edgenext_small.usi_in1k,81.564,18.436,95.712,4.288,5.59,320,1.000,bicubic lamhalobotnet50ts_256.a1h_in1k,81.552,18.448,95.492,4.508,22.57,256,0.950,bicubic regnetx_080.tv2_in1k,81.540,18.460,95.542,4.458,39.57,224,0.965,bicubic tnt_s_patch16_224,81.536,18.464,95.736,4.264,23.76,224,0.900,bicubic crossvit_15_240.in1k,81.536,18.464,95.690,4.310,27.53,240,0.875,bicubic tf_efficientnet_lite4.in1k,81.530,18.470,95.664,4.336,13.01,380,0.920,bilinear levit_256.fb_dist_in1k,81.524,18.476,95.494,4.506,18.89,224,0.900,bicubic levit_conv_256.fb_dist_in1k,81.522,18.478,95.490,4.510,18.89,224,0.900,bicubic vit_large_patch32_384.orig_in21k_ft_in1k,81.510,18.490,96.090,3.910,306.63,384,1.000,bicubic repvit_m3.dist_in1k,81.502,18.498,95.568,4.432,10.68,224,0.950,bicubic tiny_vit_11m_224.in1k,81.492,18.508,95.862,4.138,11.00,224,0.950,bicubic mobilevitv2_150.cvnets_in22k_ft_in1k,81.488,18.512,95.668,4.332,10.59,256,0.888,bicubic convnext_nano.d1h_in1k,81.482,18.518,95.658,4.342,15.59,288,1.000,bicubic tresnet_l.miil_in1k,81.480,18.520,95.624,4.376,55.99,224,0.875,bilinear resnext50_32x4d.a1_in1k,81.466,18.534,95.174,4.826,25.03,288,1.000,bicubic vit_relpos_small_patch16_224.sw_in1k,81.462,18.538,95.820,4.180,21.98,224,0.900,bicubic gcresnet50t.ra2_in1k,81.456,18.544,95.718,4.282,25.90,288,1.000,bicubic resnet50d.a1_in1k,81.450,18.550,95.218,4.782,25.58,288,1.000,bicubic poolformer_s36.sail_in1k,81.430,18.570,95.444,4.556,30.86,224,0.900,bicubic nest_tiny_jx.goog_in1k,81.426,18.574,95.618,4.382,17.06,224,0.875,bicubic convit_small.fb_in1k,81.420,18.580,95.744,4.256,27.78,224,0.875,bicubic ecaresnetlight.miil_in1k,81.408,18.592,95.816,4.184,30.16,288,0.950,bicubic resnetv2_50.a1h_in1k,81.398,18.602,95.726,4.274,25.55,288,1.000,bicubic tf_efficientnet_b1.ns_jft_in1k,81.388,18.612,95.738,4.262,7.79,240,0.882,bicubic vit_small_patch16_224.augreg_in21k_ft_in1k,81.386,18.614,96.136,3.864,22.05,224,0.900,bicubic swin_tiny_patch4_window7_224.ms_in1k,81.376,18.624,95.544,4.456,28.29,224,0.900,bicubic deit3_small_patch16_224.fb_in1k,81.370,18.630,95.456,4.544,22.06,224,0.900,bicubic convmixer_1536_20.in1k,81.362,18.638,95.614,4.386,51.63,224,0.960,bicubic resnet50d.ra2_in1k,81.356,18.644,95.738,4.262,25.58,288,0.950,bicubic gernet_l.idstcv_in1k,81.354,18.646,95.530,4.470,31.08,256,0.875,bilinear repvit_m1_1.dist_450e_in1k,81.312,18.688,95.560,4.440,8.80,224,0.950,bicubic efficientnet_el.ra_in1k,81.312,18.688,95.536,4.464,10.59,300,0.904,bicubic legacy_senet154.in1k,81.312,18.688,95.490,4.510,115.09,224,0.875,bilinear resnext50_32x4d.a2_in1k,81.304,18.696,95.096,4.904,25.03,288,1.000,bicubic seresnet50.ra2_in1k,81.284,18.716,95.652,4.348,28.09,288,0.950,bicubic coat_mini.in1k,81.270,18.730,95.382,4.618,10.34,224,0.900,bicubic gcresnext50ts.ch_in1k,81.230,18.770,95.542,4.458,15.67,288,1.000,bicubic senet154.gluon_in1k,81.226,18.774,95.358,4.642,115.09,224,0.875,bicubic res2net101d.in1k,81.218,18.782,95.350,4.650,45.23,224,0.875,bilinear resnet50_gn.a1h_in1k,81.216,18.784,95.624,4.376,25.56,288,0.950,bicubic deit_small_distilled_patch16_224.fb_in1k,81.216,18.784,95.384,4.616,22.44,224,0.900,bicubic resnet50.a1_in1k,81.214,18.786,95.102,4.898,25.56,288,1.000,bicubic xcit_tiny_12_p8_224.fb_dist_in1k,81.212,18.788,95.602,4.398,6.71,224,1.000,bicubic resnext50_32x4d.tv2_in1k,81.182,18.818,95.340,4.660,25.03,224,0.965,bilinear resnet50.fb_swsl_ig1b_ft_in1k,81.172,18.828,95.986,4.014,25.56,224,0.875,bilinear sebotnet33ts_256.a1h_in1k,81.168,18.832,95.168,4.832,13.70,256,0.940,bicubic resnet50d.a2_in1k,81.164,18.836,95.080,4.920,25.58,288,1.000,bicubic lambda_resnet50ts.a1h_in1k,81.158,18.842,95.098,4.902,21.54,256,0.950,bicubic resmlp_36_224.fb_distilled_in1k,81.148,18.852,95.478,4.522,44.69,224,0.875,bicubic mobilevitv2_200.cvnets_in1k,81.134,18.866,95.362,4.638,18.45,256,0.888,bicubic resnest50d_4s2x40d.in1k,81.120,18.880,95.560,4.440,30.42,224,0.875,bicubic vit_small_patch16_384.augreg_in1k,81.116,18.884,95.574,4.426,22.20,384,1.000,bicubic seresnet50.a2_in1k,81.106,18.894,95.222,4.778,28.09,288,1.000,bicubic vit_base_patch16_384.augreg_in1k,81.102,18.898,95.328,4.672,86.86,384,1.000,bicubic seresnet50.a1_in1k,81.102,18.898,95.120,4.880,28.09,288,1.000,bicubic twins_pcpvt_small.in1k,81.092,18.908,95.648,4.352,24.11,224,0.900,bicubic vit_srelpos_small_patch16_224.sw_in1k,81.092,18.908,95.570,4.430,21.97,224,0.900,bicubic convnextv2_pico.fcmae_ft_in1k,81.086,18.914,95.480,4.520,9.07,288,0.950,bicubic pit_s_224.in1k,81.086,18.914,95.330,4.670,23.46,224,0.900,bicubic fastvit_s12.apple_dist_in1k,81.070,18.930,95.284,4.716,9.47,256,0.900,bicubic haloregnetz_b.ra3_in1k,81.046,18.954,95.200,4.800,11.68,224,0.940,bicubic resmlp_big_24_224.fb_in1k,81.036,18.964,95.018,4.982,129.14,224,0.875,bicubic crossvit_small_240.in1k,81.018,18.982,95.456,4.544,26.86,240,0.875,bicubic resnet152s.gluon_in1k,81.008,18.992,95.416,4.584,60.32,224,0.875,bicubic resnest50d_1s4x24d.in1k,80.988,19.012,95.326,4.674,25.68,224,0.875,bicubic cait_xxs24_384.fb_dist_in1k,80.972,19.028,95.640,4.360,12.03,384,1.000,bicubic resnet50.d_in1k,80.972,19.028,95.430,4.570,25.56,288,1.000,bicubic swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,80.968,19.032,96.014,3.986,28.29,224,0.900,bicubic resnest50d.in1k,80.960,19.040,95.382,4.618,27.48,224,0.875,bilinear sehalonet33ts.ra2_in1k,80.958,19.042,95.272,4.728,13.69,256,0.940,bicubic xcit_tiny_12_p16_384.fb_dist_in1k,80.938,19.062,95.414,4.586,6.72,384,1.000,bicubic regnetx_032.tv2_in1k,80.926,19.074,95.278,4.722,15.30,224,0.965,bicubic resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,80.924,19.076,95.734,4.266,44.18,224,0.875,bilinear resnet50.c1_in1k,80.912,19.088,95.552,4.448,25.56,288,1.000,bicubic cs3darknet_l.c2ns_in1k,80.896,19.104,95.662,4.338,21.16,288,0.950,bicubic seresnext101_64x4d.gluon_in1k,80.894,19.106,95.296,4.704,88.23,224,0.875,bicubic seresnext101_32x4d.gluon_in1k,80.892,19.108,95.296,4.704,48.96,224,0.875,bicubic cs3darknet_focus_l.c2ns_in1k,80.876,19.124,95.682,4.318,21.15,288,0.950,bicubic tiny_vit_5m_224.dist_in22k_ft_in1k,80.876,19.124,95.664,4.336,5.39,224,0.950,bicubic tf_efficientnet_b3.in1k,80.874,19.126,95.300,4.700,12.23,300,0.904,bicubic resnet50.c2_in1k,80.870,19.130,95.534,4.466,25.56,288,1.000,bicubic mobilevitv2_175.cvnets_in1k,80.860,19.140,95.256,4.744,14.25,256,0.888,bicubic efficientnet_b3_pruned.in1k,80.852,19.148,95.244,4.756,9.86,300,0.904,bicubic resnet50.tv2_in1k,80.848,19.152,95.434,4.566,25.56,224,0.965,bilinear fastvit_sa12.apple_in1k,80.844,19.156,95.340,4.660,11.58,256,0.900,bicubic repvit_m1_1.dist_300e_in1k,80.826,19.174,95.170,4.830,8.80,224,0.950,bicubic regnety_320.pycls_in1k,80.810,19.190,95.238,4.762,145.05,224,0.875,bicubic tresnet_m.miil_in1k,80.798,19.202,94.856,5.144,31.39,224,0.875,bilinear ecaresnet50d_pruned.miil_in1k,80.790,19.210,95.570,4.430,19.94,288,0.950,bicubic seresnet33ts.ra2_in1k,80.784,19.216,95.362,4.638,19.78,288,1.000,bicubic resnet50.a2_in1k,80.772,19.228,94.988,5.012,25.56,288,1.000,bicubic resmlp_24_224.fb_distilled_in1k,80.756,19.244,95.224,4.776,30.02,224,0.875,bicubic poolformerv2_s24.sail_in1k,80.748,19.252,95.310,4.690,21.34,224,1.000,bicubic gernet_m.idstcv_in1k,80.736,19.264,95.190,4.810,21.14,224,0.875,bilinear regnetz_b16.ra3_in1k,80.728,19.272,95.518,4.482,9.72,288,1.000,bicubic vit_base_patch32_224.augreg_in21k_ft_in1k,80.716,19.284,95.566,4.434,88.22,224,0.900,bicubic resnet50.b1k_in1k,80.706,19.294,95.432,4.568,25.56,288,1.000,bicubic resnext50_32x4d.ra_in1k,80.698,19.302,95.392,4.608,25.03,288,0.950,bicubic resnet50.a1h_in1k,80.678,19.322,95.306,4.694,25.56,224,1.000,bicubic eca_resnet33ts.ra2_in1k,80.672,19.328,95.364,4.636,19.68,288,1.000,bicubic regnety_016.tv2_in1k,80.666,19.334,95.330,4.670,11.20,224,0.965,bicubic resnext50d_32x4d.bt_in1k,80.664,19.336,95.420,4.580,25.05,288,0.950,bicubic nf_resnet50.ra2_in1k,80.640,19.360,95.334,4.666,25.56,288,0.940,bicubic eva02_tiny_patch14_336.mim_in22k_ft_in1k,80.630,19.370,95.526,4.474,5.76,336,1.000,bicubic efficientnet_b2.ra_in1k,80.610,19.390,95.314,4.686,9.11,288,1.000,bicubic gcresnet33ts.ra2_in1k,80.600,19.400,95.322,4.678,19.88,288,1.000,bicubic resnext101_64x4d.gluon_in1k,80.600,19.400,94.992,5.008,83.46,224,0.875,bicubic cspresnext50.ra_in1k,80.554,19.446,95.326,4.674,20.57,256,0.887,bilinear resnet152.a3_in1k,80.546,19.454,95.000,5.000,60.19,224,0.950,bicubic darknet53.c2ns_in1k,80.532,19.468,95.432,4.568,41.61,288,1.000,bicubic maxvit_rmlp_pico_rw_256.sw_in1k,80.514,19.486,95.214,4.786,7.52,256,0.950,bicubic darknetaa53.c2ns_in1k,80.506,19.494,95.322,4.678,36.02,288,1.000,bilinear repvgg_b3.rvgg_in1k,80.506,19.494,95.254,4.746,123.09,224,0.875,bilinear efficientformer_l1.snap_dist_in1k,80.498,19.502,94.988,5.012,12.29,224,0.950,bicubic vit_small_patch32_384.augreg_in21k_ft_in1k,80.486,19.514,95.600,4.400,22.92,384,1.000,bicubic mixnet_xl.ra_in1k,80.482,19.518,94.936,5.064,11.90,224,0.875,bicubic resnet152d.gluon_in1k,80.476,19.524,95.202,4.798,60.21,224,0.875,bicubic convnext_pico_ols.d1_in1k,80.462,19.538,95.252,4.748,9.06,288,1.000,bicubic repvit_m2.dist_in1k,80.460,19.540,95.168,4.832,8.80,224,0.950,bicubic inception_resnet_v2.tf_in1k,80.458,19.542,95.308,4.692,55.84,299,0.897,bicubic edgenext_small_rw.sw_in1k,80.458,19.542,95.190,4.810,7.83,320,1.000,bicubic resnet50.b2k_in1k,80.454,19.546,95.318,4.682,25.56,288,1.000,bicubic xcit_tiny_24_p16_224.fb_dist_in1k,80.454,19.546,95.218,4.782,12.12,224,1.000,bicubic repvit_m1_0.dist_450e_in1k,80.434,19.566,94.918,5.082,7.30,224,0.950,bicubic resnet101d.gluon_in1k,80.426,19.574,95.024,4.976,44.57,224,0.875,bicubic convnext_pico.d1_in1k,80.416,19.584,95.048,4.952,9.05,288,0.950,bicubic regnety_120.pycls_in1k,80.380,19.620,95.126,4.874,51.82,224,0.875,bicubic mobilevitv2_150.cvnets_in1k,80.370,19.630,95.074,4.926,10.59,256,0.888,bicubic fastvit_t12.apple_dist_in1k,80.352,19.648,95.042,4.958,7.55,256,0.900,bicubic ese_vovnet39b.ra_in1k,80.350,19.650,95.366,4.634,24.57,288,0.950,bicubic resnetv2_50x1_bit.goog_in21k_ft_in1k,80.342,19.658,95.682,4.318,25.55,448,1.000,bilinear resnext101_32x4d.gluon_in1k,80.340,19.660,94.930,5.070,44.18,224,0.875,bicubic resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,80.334,19.666,95.400,4.600,25.03,224,0.875,bilinear rexnet_150.nav_in1k,80.324,19.676,95.176,4.824,9.73,224,0.875,bicubic efficientvit_b1.r288_in1k,80.324,19.676,94.990,5.010,9.10,288,1.000,bicubic tf_efficientnet_b2.ap_in1k,80.310,19.690,95.026,4.974,9.11,260,0.890,bicubic resnet101s.gluon_in1k,80.304,19.696,95.152,4.848,44.67,224,0.875,bicubic efficientnet_el_pruned.in1k,80.298,19.702,95.222,4.778,10.59,300,0.904,bicubic regnety_160.pycls_in1k,80.298,19.702,94.964,5.036,83.59,224,0.875,bicubic poolformer_s24.sail_in1k,80.294,19.706,95.074,4.926,21.39,224,0.900,bicubic res2net50d.in1k,80.254,19.746,95.036,4.964,25.72,224,0.875,bilinear tf_efficientnet_el.in1k,80.248,19.752,95.120,4.880,10.59,300,0.904,bicubic regnetx_320.pycls_in1k,80.246,19.754,95.022,4.978,107.81,224,0.875,bicubic vit_base_patch16_224.sam_in1k,80.238,19.762,94.756,5.244,86.57,224,0.900,bicubic resnetblur50.bt_in1k,80.234,19.766,95.234,4.766,25.56,288,0.950,bicubic legacy_seresnext101_32x4d.in1k,80.232,19.768,95.020,4.980,48.96,224,0.875,bilinear repvgg_b3g4.rvgg_in1k,80.216,19.784,95.092,4.908,83.83,224,0.875,bilinear tf_efficientnetv2_b2.in1k,80.196,19.804,95.042,4.958,10.10,260,0.890,bicubic dpn107.mx_in1k,80.170,19.830,94.942,5.058,86.92,224,0.875,bicubic convmixer_768_32.in1k,80.168,19.832,95.074,4.926,21.11,224,0.960,bicubic skresnext50_32x4d.ra_in1k,80.164,19.836,94.640,5.360,27.48,224,0.875,bicubic inception_v4.tf_in1k,80.156,19.844,94.970,5.030,42.68,299,0.875,bicubic repvit_m1_0.dist_300e_in1k,80.126,19.874,94.744,5.256,7.30,224,0.950,bicubic tf_efficientnet_b2.aa_in1k,80.084,19.916,94.906,5.094,9.11,260,0.890,bicubic cspdarknet53.ra_in1k,80.068,19.932,95.078,4.922,27.64,256,0.887,bilinear dpn92.mx_in1k,80.038,19.962,94.860,5.140,37.67,224,0.875,bicubic inception_resnet_v2.tf_ens_adv_in1k,79.978,20.022,94.948,5.052,55.84,299,0.897,bicubic resnet50.ram_in1k,79.976,20.024,95.052,4.948,25.56,288,0.950,bicubic fastvit_s12.apple_in1k,79.942,20.058,94.794,5.206,9.47,256,0.900,bicubic seresnext50_32x4d.gluon_in1k,79.924,20.076,94.824,5.176,27.56,224,0.875,bicubic efficientnet_b2_pruned.in1k,79.920,20.080,94.852,5.148,8.31,260,0.890,bicubic resnet152c.gluon_in1k,79.912,20.088,94.846,5.154,60.21,224,0.875,bicubic resnetrs50.tf_in1k,79.894,20.106,94.974,5.026,35.69,224,0.910,bicubic xception71.tf_in1k,79.874,20.126,94.928,5.072,42.34,299,0.903,bicubic regnety_080.pycls_in1k,79.868,20.132,94.832,5.168,39.18,224,0.875,bicubic regnetx_160.pycls_in1k,79.866,20.134,94.828,5.172,54.28,224,0.875,bicubic ecaresnet26t.ra2_in1k,79.850,20.150,95.090,4.910,16.01,320,0.950,bicubic deit_small_patch16_224.fb_in1k,79.848,20.152,95.044,4.956,22.05,224,0.900,bicubic levit_conv_192.fb_dist_in1k,79.838,20.162,94.784,5.216,10.95,224,0.900,bicubic levit_192.fb_dist_in1k,79.838,20.162,94.778,5.222,10.95,224,0.900,bicubic resnet50.ra_in1k,79.836,20.164,94.966,5.034,25.56,288,0.950,bicubic dpn131.mx_in1k,79.814,20.186,94.700,5.300,79.25,224,0.875,bicubic resnet101.a3_in1k,79.814,20.186,94.614,5.386,44.55,224,0.950,bicubic tf_efficientnet_lite3.in1k,79.806,20.194,94.914,5.086,8.20,300,0.904,bilinear resmlp_36_224.fb_in1k,79.772,20.228,94.884,5.116,44.69,224,0.875,bicubic cait_xxs36_224.fb_dist_in1k,79.746,20.254,94.874,5.126,17.30,224,1.000,bicubic efficientvit_b1.r256_in1k,79.734,20.266,94.780,5.220,9.10,256,1.000,bicubic gcvit_xxtiny.in1k,79.726,20.274,95.080,4.920,12.00,224,0.875,bicubic resnet33ts.ra2_in1k,79.726,20.274,94.828,5.172,19.68,288,1.000,bicubic regnety_064.pycls_in1k,79.716,20.284,94.766,5.234,30.58,224,0.875,bicubic resnet152.gluon_in1k,79.696,20.304,94.730,5.270,60.19,224,0.875,bicubic efficientformerv2_s1.snap_dist_in1k,79.692,20.308,94.716,5.284,6.19,224,0.950,bicubic xcit_tiny_12_p8_224.fb_in1k,79.688,20.312,95.054,4.946,6.71,224,1.000,bicubic fbnetv3_d.ra2_in1k,79.682,20.318,94.944,5.056,10.31,256,0.950,bilinear mobilevitv2_125.cvnets_in1k,79.680,20.320,94.858,5.142,7.48,256,0.888,bicubic dpn98.mx_in1k,79.670,20.330,94.654,5.346,61.57,224,0.875,bicubic gmlp_s16_224.ra3_in1k,79.644,20.356,94.622,5.378,19.42,224,0.875,bicubic resnet50.bt_in1k,79.640,20.360,94.892,5.108,25.56,288,0.950,bicubic tf_efficientnet_b2.in1k,79.608,20.392,94.714,5.286,9.11,260,0.890,bicubic regnetx_120.pycls_in1k,79.588,20.412,94.742,5.258,46.11,224,0.875,bicubic cspresnet50.ra_in1k,79.582,20.418,94.710,5.290,21.62,256,0.887,bilinear xception65.tf_in1k,79.556,20.444,94.658,5.342,39.92,299,0.903,bicubic ecaresnet50t.a3_in1k,79.552,20.448,94.694,5.306,25.57,224,0.950,bicubic resnet101c.gluon_in1k,79.538,20.462,94.584,5.416,44.57,224,0.875,bicubic rexnet_130.nav_in1k,79.506,20.494,94.678,5.322,7.56,224,0.875,bicubic eca_halonext26ts.c1_in1k,79.486,20.514,94.600,5.400,10.76,256,0.940,bicubic vit_relpos_base_patch32_plus_rpn_256.sw_in1k,79.484,20.516,94.138,5.862,119.42,256,0.900,bicubic hrnet_w64.ms_in1k,79.476,20.524,94.652,5.348,128.06,224,0.875,bilinear tf_efficientnetv2_b1.in1k,79.460,20.540,94.722,5.278,8.14,240,0.882,bicubic xcit_tiny_24_p16_224.fb_in1k,79.448,20.552,94.878,5.122,12.12,224,1.000,bicubic dla102x2.in1k,79.446,20.554,94.632,5.368,41.28,224,0.875,bilinear regnetx_016.tv2_in1k,79.436,20.564,94.768,5.232,9.19,224,0.965,bicubic mobileone_s4.apple_in1k,79.426,20.574,94.480,5.520,14.95,224,0.900,bilinear resnet32ts.ra2_in1k,79.388,20.612,94.652,5.348,17.96,288,1.000,bicubic repvgg_b2g4.rvgg_in1k,79.382,20.618,94.676,5.324,61.76,224,0.875,bilinear resmlp_24_224.fb_in1k,79.374,20.626,94.546,5.454,30.02,224,0.875,bicubic dpn68b.ra_in1k,79.360,20.640,94.436,5.564,12.61,288,1.000,bicubic resnext50_32x4d.gluon_in1k,79.360,20.640,94.430,5.570,25.03,224,0.875,bicubic convnextv2_femto.fcmae_ft_in1k,79.338,20.662,94.560,5.440,5.23,288,0.950,bicubic resnet101.gluon_in1k,79.310,20.690,94.522,5.478,44.55,224,0.875,bicubic resnext101_32x8d.tv_in1k,79.310,20.690,94.520,5.480,88.79,224,0.875,bilinear nf_regnet_b1.ra2_in1k,79.308,20.692,94.740,5.260,10.22,288,0.900,bicubic hrnet_w48.ms_in1k,79.306,20.694,94.516,5.484,77.47,224,0.875,bilinear tf_efficientnet_cc_b1_8e.in1k,79.302,20.698,94.374,5.626,39.72,240,0.882,bicubic tf_efficientnet_b1.ap_in1k,79.276,20.724,94.312,5.688,7.79,240,0.882,bicubic eca_botnext26ts_256.c1_in1k,79.268,20.732,94.606,5.394,10.59,256,0.950,bicubic resnext50_32x4d.a3_in1k,79.268,20.732,94.306,5.694,25.03,224,0.950,bicubic fastvit_t12.apple_in1k,79.264,20.736,94.562,5.438,7.55,256,0.900,bicubic botnet26t_256.c1_in1k,79.258,20.742,94.532,5.468,12.49,256,0.950,bicubic efficientvit_b1.r224_in1k,79.252,20.748,94.304,5.696,9.10,224,0.950,bicubic efficientnet_em.ra2_in1k,79.244,20.756,94.794,5.206,6.90,240,0.882,bicubic resnet50.fb_ssl_yfcc100m_ft_in1k,79.230,20.770,94.826,5.174,25.56,224,0.875,bilinear regnety_040.pycls_in1k,79.220,20.780,94.656,5.344,20.65,224,0.875,bicubic res2net101_26w_4s.in1k,79.200,20.800,94.436,5.564,45.21,224,0.875,bilinear regnetx_080.pycls_in1k,79.198,20.802,94.554,5.446,39.57,224,0.875,bicubic pit_xs_distilled_224.in1k,79.180,20.820,94.366,5.634,11.00,224,0.900,bicubic tiny_vit_5m_224.in1k,79.170,20.830,94.794,5.206,5.39,224,0.950,bicubic vit_base_patch16_224.augreg_in1k,79.154,20.846,94.090,5.910,86.57,224,0.900,bicubic fbnetv3_b.ra2_in1k,79.146,20.854,94.744,5.256,8.60,256,0.950,bilinear halonet26t.a1h_in1k,79.106,20.894,94.306,5.694,12.48,256,0.950,bicubic coat_lite_mini.in1k,79.102,20.898,94.608,5.392,11.01,224,0.900,bicubic lambda_resnet26t.c1_in1k,79.088,20.912,94.590,5.410,10.96,256,0.940,bicubic resnet50d.gluon_in1k,79.078,20.922,94.466,5.534,25.58,224,0.875,bicubic legacy_seresnext50_32x4d.in1k,79.076,20.924,94.432,5.568,27.56,224,0.875,bilinear regnetx_064.pycls_in1k,79.066,20.934,94.460,5.540,26.21,224,0.875,bicubic repvit_m0_9.dist_450e_in1k,79.066,20.934,94.380,5.620,5.49,224,0.950,bicubic legacy_xception.tf_in1k,79.040,20.960,94.382,5.618,22.86,299,0.897,bicubic resnet50.am_in1k,79.002,20.998,94.398,5.602,25.56,224,0.875,bicubic mixnet_l.ft_in1k,78.966,21.034,94.182,5.818,7.33,224,0.875,bicubic lambda_resnet26rpt_256.c1_in1k,78.964,21.036,94.436,5.564,10.99,256,0.940,bicubic res2net50_26w_8s.in1k,78.942,21.058,94.294,5.706,48.40,224,0.875,bilinear hrnet_w40.ms_in1k,78.932,21.068,94.464,5.536,57.56,224,0.875,bilinear convnext_femto_ols.d1_in1k,78.924,21.076,94.526,5.474,5.23,288,0.950,bicubic convnext_tiny.fb_in22k_ft_in1k,78.898,21.102,94.674,5.326,28.59,288,1.000,bicubic hrnet_w44.ms_in1k,78.894,21.106,94.364,5.636,67.06,224,0.875,bilinear regnety_032.pycls_in1k,78.876,21.124,94.408,5.592,19.44,224,0.875,bicubic vit_small_patch16_224.augreg_in1k,78.848,21.152,94.288,5.712,22.05,224,0.900,bicubic wide_resnet101_2.tv_in1k,78.842,21.158,94.282,5.718,126.89,224,0.875,bilinear tf_efficientnet_b1.aa_in1k,78.828,21.172,94.200,5.800,7.79,240,0.882,bicubic seresnext26d_32x4d.bt_in1k,78.814,21.186,94.240,5.760,16.81,288,0.950,bicubic repghostnet_200.in1k,78.806,21.194,94.330,5.670,9.80,224,0.875,bicubic inception_v3.gluon_in1k,78.802,21.198,94.376,5.624,23.83,299,0.875,bicubic efficientnet_b1.ft_in1k,78.800,21.200,94.342,5.658,7.79,256,1.000,bicubic repvgg_b2.rvgg_in1k,78.792,21.208,94.420,5.580,89.02,224,0.875,bilinear tf_mixnet_l.in1k,78.776,21.224,94.002,5.998,7.33,224,0.875,bicubic vit_base_patch32_384.augreg_in1k,78.756,21.244,94.226,5.774,88.30,384,1.000,bicubic seresnext26t_32x4d.bt_in1k,78.744,21.256,94.312,5.688,16.81,288,0.950,bicubic resnet50d.a3_in1k,78.720,21.280,94.232,5.768,25.58,224,0.950,bicubic convnext_femto.d1_in1k,78.716,21.284,94.430,5.570,5.22,288,0.950,bicubic resnet50s.gluon_in1k,78.714,21.286,94.242,5.758,25.68,224,0.875,bicubic dla169.in1k,78.708,21.292,94.344,5.656,53.39,224,0.875,bilinear pvt_v2_b1.in1k,78.704,21.296,94.502,5.498,14.01,224,0.900,bicubic tf_efficientnet_b0.ns_jft_in1k,78.668,21.332,94.372,5.628,5.29,224,0.875,bicubic regnety_008_tv.tv2_in1k,78.666,21.334,94.390,5.610,6.43,224,0.965,bicubic legacy_seresnet152.in1k,78.660,21.340,94.370,5.630,66.82,224,0.875,bilinear repvit_m0_9.dist_300e_in1k,78.658,21.342,94.116,5.884,5.49,224,0.950,bicubic xcit_tiny_12_p16_224.fb_dist_in1k,78.574,21.426,94.198,5.802,6.72,224,1.000,bicubic res2net50_26w_6s.in1k,78.568,21.432,94.122,5.878,37.05,224,0.875,bilinear tf_efficientnet_b1.in1k,78.562,21.438,94.094,5.906,7.79,240,0.882,bicubic repvit_m1.dist_in1k,78.538,21.462,94.070,5.930,5.49,224,0.950,bicubic dla102x.in1k,78.512,21.488,94.236,5.764,26.31,224,0.875,bilinear xception41.tf_in1k,78.504,21.496,94.276,5.724,26.97,299,0.903,bicubic levit_conv_128.fb_dist_in1k,78.494,21.506,94.008,5.992,9.21,224,0.900,bicubic regnetx_040.pycls_in1k,78.492,21.508,94.242,5.758,22.12,224,0.875,bicubic levit_128.fb_dist_in1k,78.490,21.510,94.012,5.988,9.21,224,0.900,bicubic resnest26d.gluon_in1k,78.482,21.518,94.294,5.706,17.07,224,0.875,bilinear wide_resnet50_2.tv_in1k,78.476,21.524,94.088,5.912,68.88,224,0.875,bilinear dla60_res2net.in1k,78.464,21.536,94.198,5.802,20.85,224,0.875,bilinear hrnet_w32.ms_in1k,78.442,21.558,94.190,5.810,41.23,224,0.875,bilinear dla60_res2next.in1k,78.440,21.560,94.144,5.856,17.03,224,0.875,bilinear resnet34d.ra2_in1k,78.436,21.564,94.344,5.656,21.82,288,0.950,bicubic coat_tiny.in1k,78.426,21.574,94.048,5.952,5.50,224,0.900,bicubic vit_tiny_patch16_384.augreg_in21k_ft_in1k,78.424,21.576,94.542,5.458,5.79,384,1.000,bicubic gcresnext26ts.ch_in1k,78.414,21.586,94.036,5.964,10.48,288,1.000,bicubic selecsls60b.in1k,78.412,21.588,94.168,5.832,32.77,224,0.875,bicubic legacy_seresnet101.in1k,78.386,21.614,94.262,5.738,49.33,224,0.875,bilinear cait_xxs24_224.fb_dist_in1k,78.384,21.616,94.316,5.684,11.96,224,1.000,bicubic repvgg_b1.rvgg_in1k,78.368,21.632,94.096,5.904,57.42,224,0.875,bilinear tf_efficientnetv2_b0.in1k,78.358,21.642,94.014,5.986,7.14,224,0.875,bicubic resnet26t.ra2_in1k,78.328,21.672,94.124,5.876,16.01,320,1.000,bicubic resnet152.tv_in1k,78.322,21.678,94.046,5.954,60.19,224,0.875,bilinear mobilevit_s.cvnets_in1k,78.312,21.688,94.148,5.852,5.58,256,0.900,bicubic seresnext26ts.ch_in1k,78.270,21.730,94.092,5.908,10.39,288,1.000,bicubic bat_resnext26ts.ch_in1k,78.252,21.748,94.098,5.902,10.73,256,0.900,bicubic res2next50.in1k,78.242,21.758,93.892,6.108,24.67,224,0.875,bilinear efficientnet_b1_pruned.in1k,78.240,21.760,93.834,6.166,6.33,240,0.882,bicubic dla60x.in1k,78.236,21.764,94.026,5.974,17.35,224,0.875,bilinear hrnet_w30.ms_in1k,78.196,21.804,94.222,5.778,37.71,224,0.875,bilinear hrnet_w18_small_v2.gluon_in1k,78.190,21.810,93.902,6.098,15.60,224,0.875,bicubic pit_xs_224.in1k,78.176,21.824,94.162,5.838,10.62,224,0.900,bicubic regnetx_032.pycls_in1k,78.168,21.832,94.082,5.918,15.30,224,0.875,bicubic visformer_tiny.in1k,78.160,21.840,94.166,5.834,10.32,224,0.900,bicubic res2net50_14w_8s.in1k,78.158,21.842,93.846,6.154,25.06,224,0.875,bilinear tf_efficientnet_em.in1k,78.126,21.874,94.048,5.952,6.90,240,0.882,bicubic hrnet_w18.ms_aug_in1k,78.122,21.878,94.054,5.946,21.30,224,0.950,bilinear hardcorenas_f.miil_green_in1k,78.096,21.904,93.802,6.198,8.20,224,0.875,bilinear mobilevitv2_100.cvnets_in1k,78.080,21.920,94.170,5.830,4.90,256,0.888,bicubic efficientnet_es.ra_in1k,78.058,21.942,93.926,6.074,5.44,224,0.875,bicubic resnet50.a3_in1k,78.048,21.952,93.780,6.220,25.56,224,0.950,bicubic gmixer_24_224.ra3_in1k,78.026,21.974,93.668,6.332,24.72,224,0.875,bicubic dla102.in1k,78.024,21.976,93.934,6.066,33.27,224,0.875,bilinear resnet50c.gluon_in1k,78.006,21.994,93.992,6.008,25.58,224,0.875,bicubic poolformerv2_s12.sail_in1k,78.002,21.998,93.864,6.136,11.89,224,1.000,bicubic eca_resnext26ts.ch_in1k,78.000,22.000,93.926,6.074,10.30,288,1.000,bicubic mobileone_s3.apple_in1k,77.992,22.008,93.914,6.086,10.17,224,0.900,bilinear selecsls60.in1k,77.988,22.012,93.830,6.170,30.67,224,0.875,bicubic resmlp_12_224.fb_distilled_in1k,77.954,22.046,93.560,6.440,15.35,224,0.875,bicubic res2net50_26w_4s.in1k,77.950,22.050,93.852,6.148,25.70,224,0.875,bilinear mobilenetv3_large_100.miil_in21k_ft_in1k,77.920,22.080,92.914,7.086,5.48,224,0.875,bilinear resnet34.a1_in1k,77.918,22.082,93.764,6.236,21.80,288,1.000,bicubic tf_efficientnet_cc_b0_8e.in1k,77.904,22.096,93.662,6.338,24.01,224,0.875,bicubic regnety_016.pycls_in1k,77.868,22.132,93.718,6.282,11.20,224,0.875,bicubic rexnet_100.nav_in1k,77.856,22.144,93.866,6.134,4.80,224,0.875,bicubic inception_v3.tf_in1k,77.856,22.144,93.640,6.360,23.83,299,0.875,bicubic ghostnetv2_160.in1k,77.832,22.168,93.940,6.060,12.39,224,0.875,bicubic xcit_nano_12_p8_384.fb_dist_in1k,77.820,22.180,94.040,5.960,3.05,384,1.000,bicubic hardcorenas_e.miil_green_in1k,77.790,22.210,93.700,6.300,8.07,224,0.875,bilinear convnextv2_atto.fcmae_ft_in1k,77.760,22.240,93.726,6.274,3.71,288,0.950,bicubic ese_vovnet19b_dw.ra_in1k,77.744,22.256,93.784,6.216,6.54,288,0.950,bicubic efficientnet_b0.ra_in1k,77.694,22.306,93.532,6.468,5.29,224,0.875,bicubic tinynet_a.in1k,77.648,22.352,93.540,6.460,6.19,192,0.875,bicubic legacy_seresnet50.in1k,77.644,22.356,93.758,6.242,28.09,224,0.875,bilinear cs3darknet_m.c2ns_in1k,77.634,22.366,94.016,5.984,9.31,288,0.950,bicubic resnext50_32x4d.tv_in1k,77.622,22.378,93.696,6.304,25.03,224,0.875,bilinear inception_v3.tf_adv_in1k,77.592,22.408,93.730,6.270,23.83,299,0.875,bicubic repvgg_b1g4.rvgg_in1k,77.588,22.412,93.836,6.164,39.97,224,0.875,bilinear resnet50.gluon_in1k,77.582,22.418,93.720,6.280,25.56,224,0.875,bicubic coat_lite_tiny.in1k,77.520,22.480,93.922,6.078,5.72,224,0.900,bicubic dpn68b.mx_in1k,77.518,22.482,93.852,6.148,12.61,224,0.875,bicubic mobileone_s2.apple_in1k,77.516,22.484,93.668,6.332,7.88,224,0.900,bilinear res2net50_48w_2s.in1k,77.514,22.486,93.550,6.450,25.29,224,0.875,bilinear tf_efficientnet_lite2.in1k,77.462,22.538,93.752,6.248,6.09,260,0.890,bicubic repghostnet_150.in1k,77.460,22.540,93.510,6.490,6.58,224,0.875,bicubic hardcorenas_d.miil_green_in1k,77.434,22.566,93.490,6.510,7.50,224,0.875,bilinear inception_v3.tv_in1k,77.434,22.566,93.474,6.526,23.83,299,0.875,bicubic resnet26d.bt_in1k,77.408,22.592,93.638,6.362,16.01,288,0.950,bicubic resnet101.tv_in1k,77.380,22.620,93.546,6.454,44.55,224,0.875,bilinear densenet161.tv_in1k,77.358,22.642,93.642,6.358,28.68,224,0.875,bicubic densenetblur121d.ra_in1k,77.322,22.678,93.788,6.212,8.00,288,0.950,bicubic mobilenetv2_120d.ra_in1k,77.308,22.692,93.502,6.498,5.83,224,0.875,bicubic regnetx_008.tv2_in1k,77.306,22.694,93.664,6.336,7.26,224,0.965,bicubic tf_efficientnet_cc_b0_4e.in1k,77.302,22.698,93.336,6.664,13.31,224,0.875,bicubic densenet201.tv_in1k,77.286,22.714,93.480,6.520,20.01,224,0.875,bicubic cs3darknet_focus_m.c2ns_in1k,77.284,22.716,93.966,6.034,9.30,288,0.950,bicubic mixnet_m.ft_in1k,77.260,22.740,93.418,6.582,5.01,224,0.875,bicubic poolformer_s12.sail_in1k,77.240,22.760,93.532,6.468,11.92,224,0.900,bicubic convnext_atto_ols.a2_in1k,77.216,22.784,93.676,6.324,3.70,288,0.950,bicubic resnext26ts.ra2_in1k,77.178,22.822,93.464,6.536,10.30,288,1.000,bicubic fastvit_t8.apple_dist_in1k,77.176,22.824,93.298,6.702,4.03,256,0.900,bicubic selecsls42b.in1k,77.170,22.830,93.392,6.608,32.46,224,0.875,bicubic resnet34.a2_in1k,77.158,22.842,93.274,6.726,21.80,288,1.000,bicubic xcit_tiny_12_p16_224.fb_in1k,77.140,22.860,93.716,6.284,6.72,224,1.000,bicubic legacy_seresnext26_32x4d.in1k,77.108,22.892,93.314,6.686,16.79,224,0.875,bicubic tf_efficientnet_b0.ap_in1k,77.090,22.910,93.262,6.738,5.29,224,0.875,bicubic hardcorenas_c.miil_green_in1k,77.066,22.934,93.162,6.838,5.52,224,0.875,bilinear efficientvit_m5.r224_in1k,77.058,22.942,93.184,6.816,12.47,224,0.875,bicubic dla60.in1k,77.046,22.954,93.318,6.682,22.04,224,0.875,bilinear seresnet50.a3_in1k,77.026,22.974,93.072,6.928,28.09,224,0.950,bicubic convnext_atto.d2_in1k,77.008,22.992,93.702,6.298,3.70,288,0.950,bicubic crossvit_9_dagger_240.in1k,76.978,23.022,93.618,6.382,8.78,240,0.875,bicubic tf_mixnet_m.in1k,76.954,23.046,93.154,6.846,5.01,224,0.875,bicubic convmixer_1024_20_ks9_p14.in1k,76.936,23.064,93.350,6.650,24.38,224,0.960,bicubic regnetx_016.pycls_in1k,76.924,23.076,93.416,6.584,9.19,224,0.875,bicubic skresnet34.ra_in1k,76.910,23.090,93.316,6.684,22.28,224,0.875,bicubic gernet_s.idstcv_in1k,76.910,23.090,93.144,6.856,8.17,224,0.875,bilinear tf_efficientnet_b0.aa_in1k,76.844,23.156,93.218,6.782,5.29,224,0.875,bicubic ghostnetv2_130.in1k,76.756,23.244,93.362,6.638,8.96,224,0.875,bicubic hrnet_w18.ms_in1k,76.752,23.248,93.444,6.556,21.30,224,0.875,bilinear resmlp_12_224.fb_in1k,76.648,23.352,93.178,6.822,15.35,224,0.875,bicubic tf_efficientnet_lite1.in1k,76.644,23.356,93.224,6.776,5.42,240,0.882,bicubic mixer_b16_224.goog_in21k_ft_in1k,76.602,23.398,92.224,7.776,59.88,224,0.875,bicubic tf_efficientnet_es.in1k,76.598,23.402,93.202,6.798,5.44,224,0.875,bicubic hardcorenas_b.miil_green_in1k,76.548,23.452,92.762,7.238,5.18,224,0.875,bilinear tf_efficientnet_b0.in1k,76.530,23.470,93.008,6.992,5.29,224,0.875,bicubic levit_128s.fb_dist_in1k,76.526,23.474,92.872,7.128,7.78,224,0.900,bicubic levit_conv_128s.fb_dist_in1k,76.520,23.480,92.866,7.134,7.78,224,0.900,bicubic mobilenetv2_140.ra_in1k,76.516,23.484,92.988,7.012,6.11,224,0.875,bicubic densenet121.ra_in1k,76.500,23.500,93.368,6.632,7.98,288,0.950,bicubic resnet34.bt_in1k,76.480,23.520,93.354,6.646,21.80,288,0.950,bicubic repvgg_a2.rvgg_in1k,76.458,23.542,93.002,6.998,28.21,224,0.875,bilinear repghostnet_130.in1k,76.376,23.624,92.892,7.108,5.48,224,0.875,bicubic resnet26.bt_in1k,76.366,23.634,93.180,6.820,16.00,288,0.950,bicubic dpn68.mx_in1k,76.346,23.654,93.008,6.992,12.61,224,0.875,bicubic xcit_nano_12_p8_224.fb_dist_in1k,76.332,23.668,93.098,6.902,3.05,224,1.000,bicubic regnety_008.pycls_in1k,76.302,23.698,93.062,6.938,6.26,224,0.875,bicubic fastvit_t8.apple_in1k,76.174,23.826,93.052,6.948,4.03,256,0.900,bicubic resnet50.tv_in1k,76.128,23.872,92.858,7.142,25.56,224,0.875,bilinear efficientformerv2_s0.snap_dist_in1k,76.114,23.886,92.858,7.142,3.60,224,0.950,bicubic vit_small_patch32_224.augreg_in21k_ft_in1k,75.994,24.006,93.270,6.730,22.88,224,0.900,bicubic mixnet_s.ft_in1k,75.994,24.006,92.800,7.200,4.13,224,0.875,bicubic vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,75.960,24.040,93.262,6.738,6.36,384,1.000,bicubic hardcorenas_a.miil_green_in1k,75.938,24.062,92.508,7.492,5.26,224,0.875,bilinear densenet169.tv_in1k,75.900,24.100,93.028,6.972,14.15,224,0.875,bicubic mobileone_s1.apple_in1k,75.786,24.214,92.792,7.208,4.83,224,0.900,bilinear mobilenetv3_large_100.ra_in1k,75.766,24.234,92.538,7.462,5.48,224,0.875,bicubic edgenext_x_small.in1k,75.688,24.312,92.766,7.234,2.34,288,1.000,bicubic tf_mixnet_s.in1k,75.652,24.348,92.640,7.360,4.13,224,0.875,bicubic mobilenetv3_rw.rmsp_in1k,75.620,24.380,92.704,7.296,5.48,224,0.875,bicubic mobilevitv2_075.cvnets_in1k,75.608,24.392,92.744,7.256,2.87,256,0.888,bicubic regnety_004.tv2_in1k,75.594,24.406,92.700,7.300,4.34,224,0.965,bicubic tf_mobilenetv3_large_100.in1k,75.516,24.484,92.594,7.406,5.48,224,0.875,bilinear resnest14d.gluon_in1k,75.508,24.492,92.508,7.492,10.61,224,0.875,bilinear efficientnet_lite0.ra_in1k,75.482,24.518,92.520,7.480,4.65,224,0.875,bicubic vit_tiny_patch16_224.augreg_in21k_ft_in1k,75.462,24.538,92.844,7.156,5.72,224,0.900,bicubic xcit_nano_12_p16_384.fb_dist_in1k,75.458,24.542,92.698,7.302,3.05,384,1.000,bicubic semnasnet_100.rmsp_in1k,75.450,24.550,92.598,7.402,3.89,224,0.875,bicubic regnety_006.pycls_in1k,75.268,24.732,92.526,7.474,6.06,224,0.875,bicubic ghostnetv2_100.in1k,75.166,24.834,92.354,7.646,6.16,224,0.875,bicubic repvgg_b0.rvgg_in1k,75.144,24.856,92.416,7.584,15.82,224,0.875,bilinear fbnetc_100.rmsp_in1k,75.130,24.870,92.388,7.612,5.57,224,0.875,bilinear hrnet_w18_small_v2.ms_in1k,75.110,24.890,92.416,7.584,15.60,224,0.875,bilinear repghostnet_111.in1k,75.056,24.944,92.192,7.808,4.54,224,0.875,bicubic mobilenetv2_110d.ra_in1k,75.054,24.946,92.184,7.816,4.52,224,0.875,bicubic regnetx_008.pycls_in1k,75.028,24.972,92.338,7.662,7.26,224,0.875,bicubic efficientnet_es_pruned.in1k,75.006,24.994,92.444,7.556,5.44,224,0.875,bicubic tinynet_b.in1k,74.978,25.022,92.186,7.814,3.73,188,0.875,bicubic vit_base_patch32_224.augreg_in1k,74.894,25.106,91.778,8.222,88.22,224,0.900,bicubic tf_efficientnet_lite0.in1k,74.832,25.168,92.170,7.830,4.65,224,0.875,bicubic legacy_seresnet34.in1k,74.802,25.198,92.126,7.874,21.96,224,0.875,bilinear densenet121.tv_in1k,74.764,25.236,92.154,7.846,7.98,224,0.875,bicubic mnasnet_100.rmsp_in1k,74.652,25.348,92.122,7.878,4.38,224,0.875,bicubic dla34.in1k,74.640,25.360,92.066,7.934,15.74,224,0.875,bilinear mobilevit_xs.cvnets_in1k,74.634,25.366,92.348,7.652,2.32,256,0.900,bicubic regnetx_004_tv.tv2_in1k,74.600,25.400,92.170,7.830,5.50,224,0.965,bicubic resnet34.gluon_in1k,74.580,25.420,91.982,8.018,21.80,224,0.875,bicubic deit_tiny_distilled_patch16_224.fb_in1k,74.504,25.496,91.890,8.110,5.91,224,0.900,bicubic repvgg_a1.rvgg_in1k,74.462,25.538,91.856,8.144,14.09,224,0.875,bilinear efficientvit_m4.r224_in1k,74.368,25.632,91.980,8.020,8.80,224,0.875,bicubic pit_ti_distilled_224.in1k,74.256,25.744,91.952,8.048,5.10,224,0.900,bicubic vgg19_bn.tv_in1k,74.216,25.784,91.844,8.156,143.68,224,0.875,bilinear repghostnet_100.in1k,74.206,25.794,91.542,8.458,4.07,224,0.875,bicubic spnasnet_100.rmsp_in1k,74.094,25.906,91.820,8.180,4.42,224,0.875,bilinear regnety_004.pycls_in1k,74.026,25.974,91.748,8.252,4.34,224,0.875,bicubic crossvit_9_240.in1k,73.960,26.040,91.962,8.038,8.55,240,0.875,bicubic ghostnet_100.in1k,73.958,26.042,91.532,8.468,5.18,224,0.875,bicubic hrnet_w18_small.gluon_in1k,73.920,26.080,91.194,8.806,13.19,224,0.875,bicubic xcit_nano_12_p8_224.fb_in1k,73.910,26.090,92.168,7.832,3.05,224,1.000,bicubic regnetx_006.pycls_in1k,73.868,26.132,91.678,8.322,6.20,224,0.875,bicubic resnet18d.ra2_in1k,73.794,26.206,91.838,8.162,11.71,288,0.950,bicubic vit_base_patch32_224.sam_in1k,73.694,26.306,91.014,8.986,88.22,224,0.900,bicubic tf_mobilenetv3_large_075.in1k,73.430,26.570,91.352,8.648,3.99,224,0.875,bilinear efficientvit_m3.r224_in1k,73.374,26.626,91.348,8.652,6.90,224,0.875,bicubic vgg16_bn.tv_in1k,73.370,26.630,91.514,8.486,138.37,224,0.875,bilinear crossvit_tiny_240.in1k,73.340,26.660,91.908,8.092,7.01,240,0.875,bicubic resnet34.tv_in1k,73.306,26.694,91.420,8.580,21.80,224,0.875,bilinear resnet18.fb_swsl_ig1b_ft_in1k,73.288,26.712,91.730,8.270,11.69,224,0.875,bilinear resnet18.a1_in1k,73.158,26.842,91.026,8.974,11.69,288,1.000,bicubic convit_tiny.fb_in1k,73.112,26.888,91.712,8.288,5.71,224,0.875,bicubic skresnet18.ra_in1k,73.034,26.966,91.172,8.828,11.96,224,0.875,bicubic semnasnet_075.rmsp_in1k,73.004,26.996,91.140,8.860,2.91,224,0.875,bicubic resnet34.a3_in1k,72.970,27.030,91.106,8.894,21.80,224,0.950,bicubic mobilenetv2_100.ra_in1k,72.968,27.032,91.016,8.984,3.50,224,0.875,bicubic pit_ti_224.in1k,72.910,27.090,91.404,8.596,4.85,224,0.900,bicubic resnet18.fb_ssl_yfcc100m_ft_in1k,72.598,27.402,91.416,8.584,11.69,224,0.875,bilinear repvgg_a0.rvgg_in1k,72.408,27.592,90.492,9.508,9.11,224,0.875,bilinear regnetx_004.pycls_in1k,72.402,27.598,90.826,9.174,5.16,224,0.875,bicubic vgg19.tv_in1k,72.378,27.622,90.874,9.126,143.67,224,0.875,bilinear resnet18.a2_in1k,72.372,27.628,90.596,9.404,11.69,288,1.000,bicubic hrnet_w18_small.ms_in1k,72.336,27.664,90.680,9.320,13.19,224,0.875,bilinear xcit_nano_12_p16_224.fb_dist_in1k,72.310,27.690,90.860,9.140,3.05,224,1.000,bicubic tf_mobilenetv3_large_minimal_100.in1k,72.264,27.736,90.640,9.360,3.92,224,0.875,bilinear resnet14t.c3_in1k,72.254,27.746,90.306,9.694,10.08,224,0.950,bicubic repghostnet_080.in1k,72.212,27.788,90.484,9.516,3.28,224,0.875,bicubic deit_tiny_patch16_224.fb_in1k,72.170,27.830,91.116,8.884,5.72,224,0.900,bicubic lcnet_100.ra2_in1k,72.102,27.898,90.354,9.646,2.95,224,0.875,bicubic mixer_l16_224.goog_in21k_ft_in1k,72.054,27.946,87.674,12.326,208.20,224,0.875,bicubic edgenext_xx_small.in1k,71.878,28.122,90.552,9.448,1.33,288,1.000,bicubic vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,71.798,28.202,90.824,9.176,6.34,224,0.900,bicubic legacy_seresnet18.in1k,71.760,28.240,90.332,9.668,11.78,224,0.875,bicubic vgg16.tv_in1k,71.592,28.408,90.384,9.616,138.36,224,0.875,bilinear vgg13_bn.tv_in1k,71.588,28.412,90.378,9.622,133.05,224,0.875,bilinear mobileone_s0.apple_in1k,71.402,28.598,89.842,10.158,5.29,224,0.875,bilinear efficientvit_b0.r224_in1k,71.398,28.602,89.428,10.572,3.41,224,0.950,bicubic tinynet_c.in1k,71.242,28.758,89.732,10.268,2.46,184,0.875,bicubic resnet18.gluon_in1k,70.834,29.166,89.756,10.244,11.69,224,0.875,bicubic efficientvit_m2.r224_in1k,70.814,29.186,90.142,9.858,4.19,224,0.875,bicubic pvt_v2_b0.in1k,70.660,29.340,90.196,9.804,3.67,224,0.900,bicubic vgg11_bn.tv_in1k,70.382,29.618,89.808,10.192,132.87,224,0.875,bilinear regnety_002.pycls_in1k,70.280,29.720,89.530,10.470,3.16,224,0.875,bicubic mobilevitv2_050.cvnets_in1k,70.148,29.852,89.918,10.082,1.37,256,0.888,bicubic xcit_nano_12_p16_224.fb_in1k,69.962,30.038,89.762,10.238,3.05,224,1.000,bicubic vgg13.tv_in1k,69.932,30.068,89.250,10.750,133.05,224,0.875,bilinear resnet18.tv_in1k,69.760,30.240,89.070,10.930,11.69,224,0.875,bilinear vgg11.tv_in1k,69.022,30.978,88.624,11.376,132.86,224,0.875,bilinear mobilevit_xxs.cvnets_in1k,68.918,31.082,88.946,11.054,1.27,256,0.900,bicubic repghostnet_058.in1k,68.914,31.086,88.420,11.580,2.55,224,0.875,bicubic lcnet_075.ra2_in1k,68.782,31.218,88.360,11.640,2.36,224,0.875,bicubic regnetx_002.pycls_in1k,68.752,31.248,88.542,11.458,2.68,224,0.875,bicubic resnet10t.c3_in1k,68.364,31.636,88.036,11.964,5.44,224,0.950,bicubic efficientvit_m1.r224_in1k,68.306,31.694,88.670,11.330,2.98,224,0.875,bicubic resnet18.a3_in1k,68.252,31.748,88.172,11.828,11.69,224,0.950,bicubic tf_mobilenetv3_small_100.in1k,67.922,32.078,87.672,12.328,2.54,224,0.875,bilinear dla60x_c.in1k,67.912,32.088,88.432,11.568,1.32,224,0.875,bilinear mobilenetv3_small_100.lamb_in1k,67.658,32.342,87.636,12.364,2.54,224,0.875,bicubic tinynet_d.in1k,66.972,33.028,87.066,12.934,2.34,152,0.875,bicubic repghostnet_050.in1k,66.966,33.034,86.920,13.080,2.31,224,0.875,bicubic mnasnet_small.lamb_in1k,66.196,33.804,86.504,13.496,2.03,224,0.875,bicubic dla46x_c.in1k,65.992,34.008,86.974,13.026,1.07,224,0.875,bilinear mobilenetv2_050.lamb_in1k,65.948,34.052,86.084,13.916,1.97,224,0.875,bicubic tf_mobilenetv3_small_075.in1k,65.726,34.274,86.132,13.868,2.04,224,0.875,bilinear mobilenetv3_small_075.lamb_in1k,65.236,34.764,85.446,14.554,2.04,224,0.875,bicubic dla46_c.in1k,64.872,35.128,86.298,13.702,1.30,224,0.875,bilinear efficientvit_m0.r224_in1k,63.270,36.730,85.176,14.824,2.35,224,0.875,bicubic lcnet_050.ra2_in1k,63.138,36.862,84.382,15.618,1.88,224,0.875,bicubic tf_mobilenetv3_small_minimal_100.in1k,62.894,37.106,84.238,15.762,2.04,224,0.875,bilinear tinynet_e.in1k,59.866,40.134,81.762,18.238,2.04,106,0.875,bicubic mobilenetv3_small_050.lamb_in1k,57.916,42.084,80.180,19.820,1.59,224,0.875,bicubic
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt210-cu121-rtx3090.csv	model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count tinynet_e,106,1024.0,50604.03,20.225,0.03,0.69,2.04 mobilenetv3_small_050,224,1024.0,46069.42,22.217,0.03,0.92,1.59 lcnet_035,224,1024.0,41190.64,24.85,0.03,1.04,1.64 lcnet_050,224,1024.0,37663.82,27.178,0.05,1.26,1.88 mobilenetv3_small_075,224,1024.0,33398.64,30.649,0.05,1.3,2.04 efficientvit_m0,224,1024.0,32179.13,31.812,0.08,0.91,2.35 mobilenetv3_small_100,224,1024.0,29653.41,34.522,0.06,1.42,2.54 tf_mobilenetv3_small_minimal_100,224,1024.0,28352.57,36.106,0.06,1.41,2.04 tinynet_d,152,1024.0,27612.87,37.074,0.05,1.42,2.34 tf_mobilenetv3_small_075,224,1024.0,27505.95,37.218,0.05,1.3,2.04 tf_mobilenetv3_small_100,224,1024.0,24859.95,41.18,0.06,1.42,2.54 efficientvit_m1,224,1024.0,24836.87,41.219,0.17,1.33,2.98 lcnet_075,224,1024.0,24184.78,42.33,0.1,1.99,2.36 efficientvit_m2,224,1024.0,21907.95,46.731,0.2,1.47,4.19 mnasnet_small,224,1024.0,20764.95,49.303,0.07,2.16,2.03 levit_128s,224,1024.0,20669.44,49.531,0.31,1.88,7.78 lcnet_100,224,1024.0,19774.93,51.772,0.16,2.52,2.95 regnetx_002,224,1024.0,18945.55,54.04,0.2,2.16,2.68 resnet10t,176,1024.0,18840.28,54.342,0.7,1.51,5.44 efficientvit_m3,224,1024.0,18627.14,54.963,0.27,1.62,6.9 mobilenetv2_035,224,1024.0,18464.78,55.447,0.07,2.86,1.68 ghostnet_050,224,1024.0,17741.46,57.707,0.05,1.77,2.59 resnet18,160,1024.0,17592.15,58.198,0.93,1.27,11.69 regnety_002,224,1024.0,17571.32,58.267,0.2,2.17,3.16 levit_conv_128s,224,1024.0,17529.9,58.404,0.31,1.88,7.78 efficientvit_m4,224,1024.0,17446.52,58.683,0.3,1.7,8.8 repghostnet_050,224,1024.0,17090.91,59.904,0.05,2.02,2.31 efficientvit_b0,224,1024.0,16784.26,60.999,0.1,2.87,3.41 vit_tiny_r_s16_p8_224,224,1024.0,16479.31,62.128,0.43,1.85,6.34 vit_small_patch32_224,224,1024.0,15974.78,64.091,1.12,2.09,22.88 mnasnet_050,224,1024.0,15859.35,64.557,0.11,3.07,2.22 mobilenetv2_050,224,1024.0,14885.11,68.783,0.1,3.64,1.97 tinynet_c,184,1024.0,14726.2,69.525,0.11,2.87,2.46 pit_ti_224,224,1024.0,14628.51,69.989,0.5,2.75,4.85 pit_ti_distilled_224,224,1024.0,14546.3,70.385,0.51,2.77,5.1 semnasnet_050,224,1024.0,14351.42,71.341,0.11,3.44,2.08 levit_128,224,1024.0,14192.78,72.139,0.41,2.71,9.21 repghostnet_058,224,1024.0,13482.93,75.937,0.07,2.59,2.55 mixer_s32_224,224,1024.0,13082.53,78.262,1.0,2.28,19.1 cs3darknet_focus_s,256,1024.0,12838.86,79.748,0.69,2.7,3.27 regnetx_004,224,1024.0,12620.59,81.127,0.4,3.14,5.16 levit_conv_128,224,1024.0,12584.5,81.359,0.41,2.71,9.21 cs3darknet_s,256,1024.0,12531.56,81.703,0.72,2.97,3.28 lcnet_150,224,1024.0,12510.06,81.844,0.34,3.79,4.5 regnetx_004_tv,224,1024.0,12294.91,83.276,0.42,3.17,5.5 efficientvit_m5,224,1024.0,12067.16,84.847,0.53,2.41,12.47 mobilenetv3_large_075,224,1024.0,12041.45,85.029,0.16,4.0,3.99 levit_192,224,1024.0,11986.94,85.416,0.66,3.2,10.95 resnet10t,224,1024.0,11963.05,85.587,1.1,2.43,5.44 gernet_s,224,1024.0,11809.29,86.701,0.75,2.65,8.17 ese_vovnet19b_slim_dw,224,1024.0,11618.32,88.126,0.4,5.28,1.9 vit_tiny_patch16_224,224,1024.0,11270.42,90.846,1.08,4.12,5.72 deit_tiny_patch16_224,224,1024.0,11259.37,90.936,1.08,4.12,5.72 deit_tiny_distilled_patch16_224,224,1024.0,11217.54,91.275,1.09,4.15,5.91 repghostnet_080,224,1024.0,11079.58,92.412,0.1,3.22,3.28 mobilenetv3_rw,224,1024.0,10908.78,93.859,0.23,4.41,5.48 levit_conv_192,224,1024.0,10768.96,95.077,0.66,3.2,10.95 mobilenetv3_large_100,224,1024.0,10731.24,95.412,0.23,4.41,5.48 hardcorenas_a,224,1024.0,10620.31,96.408,0.23,4.38,5.26 tf_mobilenetv3_large_075,224,1024.0,10495.83,97.552,0.16,4.0,3.99 resnet14t,176,1024.0,10451.45,97.965,1.07,3.61,10.08 mnasnet_075,224,1024.0,10423.24,98.231,0.23,4.77,3.17 tf_mobilenetv3_large_minimal_100,224,1024.0,10369.07,98.745,0.22,4.4,3.92 resnet34,160,1024.0,10330.89,99.109,1.87,1.91,21.8 regnety_004,224,1024.0,9931.33,103.097,0.41,3.89,4.34 nf_regnet_b0,192,1024.0,9884.05,103.59,0.37,3.15,8.76 regnetx_006,224,1024.0,9823.29,104.232,0.61,3.98,6.2 hardcorenas_b,224,1024.0,9755.67,104.953,0.26,5.09,5.18 hardcorenas_c,224,1024.0,9572.88,106.958,0.28,5.01,5.52 ghostnet_100,224,1024.0,9528.83,107.453,0.15,3.55,5.18 tf_mobilenetv3_large_100,224,1024.0,9484.05,107.96,0.23,4.41,5.48 tinynet_b,188,1024.0,9358.37,109.409,0.21,4.44,3.73 mnasnet_100,224,1024.0,9357.9,109.416,0.33,5.46,4.38 tf_efficientnetv2_b0,192,1024.0,9316.15,109.906,0.54,3.51,7.14 repghostnet_100,224,1024.0,9303.14,110.06,0.15,3.98,4.07 mobilenetv2_075,224,1024.0,9280.78,110.325,0.22,5.86,2.64 resnet18,224,1024.0,9222.44,111.023,1.82,2.48,11.69 pit_xs_distilled_224,224,1024.0,9172.76,111.624,1.11,4.15,11.0 semnasnet_075,224,1024.0,9145.4,111.959,0.23,5.54,2.91 pit_xs_224,224,1024.0,9134.12,112.096,1.1,4.12,10.62 regnety_006,224,1024.0,9106.78,112.433,0.61,4.33,6.06 convnext_atto,224,1024.0,8993.29,113.851,0.55,3.81,3.7 hardcorenas_d,224,1024.0,8915.53,114.845,0.3,4.93,7.5 levit_256,224,1024.0,8893.96,115.124,1.13,4.23,18.89 seresnet18,224,1024.0,8718.39,117.442,1.82,2.49,11.78 convnext_atto_ols,224,1024.0,8549.03,119.769,0.58,4.11,3.7 mobilenetv2_100,224,1024.0,8479.08,120.757,0.31,6.68,3.5 legacy_seresnet18,224,1024.0,8452.0,121.144,1.82,2.49,11.78 spnasnet_100,224,1024.0,8438.72,121.334,0.35,6.03,4.42 repghostnet_111,224,1024.0,8382.7,122.146,0.18,4.38,4.54 semnasnet_100,224,1024.0,8351.88,122.597,0.32,6.23,3.89 dla46_c,224,1024.0,8209.51,124.721,0.58,4.5,1.3 repvgg_a0,224,1024.0,8124.8,126.024,1.52,3.59,9.11 levit_conv_256,224,1024.0,7997.32,128.032,1.13,4.23,18.89 edgenext_xx_small,256,1024.0,7955.06,128.711,0.26,3.33,1.33 regnetx_008,224,1024.0,7889.15,129.787,0.81,5.15,7.26 resnet18d,224,1024.0,7873.83,130.041,2.06,3.29,11.71 convnext_femto,224,1024.0,7867.13,130.151,0.79,4.57,5.22 ese_vovnet19b_slim,224,1024.0,7834.56,130.693,1.69,3.52,3.17 mobilevit_xxs,256,1024.0,7818.95,130.953,0.34,5.74,1.27 hardcorenas_f,224,1024.0,7811.68,131.075,0.35,5.57,8.2 hardcorenas_e,224,1024.0,7751.65,132.09,0.35,5.65,8.07 efficientnet_lite0,224,1024.0,7716.09,132.699,0.4,6.74,4.65 xcit_nano_12_p16_224,224,1024.0,7711.63,132.776,0.56,4.17,3.05 ghostnet_130,224,1024.0,7680.26,133.318,0.24,4.6,7.36 levit_256d,224,1024.0,7643.23,133.964,1.4,4.93,26.21 tf_efficientnetv2_b0,224,1024.0,7637.19,134.07,0.73,4.77,7.14 repghostnet_130,224,1024.0,7550.55,135.609,0.25,5.24,5.48 convnext_femto_ols,224,1024.0,7514.81,136.254,0.82,4.87,5.23 regnety_008,224,1024.0,7508.88,136.361,0.81,5.25,6.26 tinynet_a,192,1024.0,7458.0,137.291,0.35,5.41,6.19 fbnetc_100,224,1024.0,7362.02,139.082,0.4,6.51,5.57 tf_efficientnetv2_b1,192,1024.0,7241.64,141.394,0.76,4.59,8.14 crossvit_tiny_240,240,1024.0,7093.57,144.345,1.3,5.67,7.01 regnety_008_tv,224,1024.0,7067.28,144.882,0.84,5.42,6.43 mobilevitv2_050,256,1024.0,7057.9,145.075,0.48,8.04,1.37 crossvit_9_240,240,1024.0,6964.15,147.028,1.55,5.59,8.55 dla46x_c,224,1024.0,6837.04,149.761,0.54,5.66,1.07 tf_efficientnet_lite0,224,1024.0,6819.73,150.142,0.4,6.74,4.65 efficientnet_b0,224,1024.0,6721.47,152.337,0.4,6.75,5.29 rexnet_100,224,1024.0,6689.15,153.073,0.41,7.44,4.8 rexnetr_100,224,1024.0,6646.85,154.047,0.43,7.72,4.88 levit_conv_256d,224,1024.0,6618.0,154.719,1.4,4.93,26.21 repvit_m1,224,1024.0,6591.52,155.339,0.83,7.45,5.49 efficientnet_b1_pruned,240,1024.0,6583.2,155.537,0.4,6.21,6.33 repghostnet_150,224,1024.0,6564.41,155.982,0.32,6.0,6.58 mnasnet_140,224,1024.0,6559.1,156.108,0.6,7.71,7.12 efficientvit_b1,224,1024.0,6458.82,158.532,0.53,7.25,9.1 visformer_tiny,224,1024.0,6456.3,158.594,1.27,5.72,10.32 crossvit_9_dagger_240,240,1024.0,6436.13,159.091,1.68,6.03,8.78 resnet14t,224,1024.0,6404.13,159.886,1.69,5.8,10.08 dla60x_c,224,1024.0,6404.11,159.885,0.59,6.01,1.32 mobilenetv2_110d,224,1024.0,6387.15,160.311,0.45,8.71,4.52 ghostnetv2_100,224,1024.0,6375.73,160.599,0.18,4.55,6.16 regnetz_005,224,1024.0,6372.66,160.676,0.52,5.86,7.12 repvit_m0_9,224,1024.0,6295.33,162.649,0.83,7.45,5.49 edgenext_xx_small,288,1024.0,6241.41,164.053,0.33,4.21,1.33 fbnetv3_b,224,1024.0,6166.1,166.058,0.42,6.97,8.6 convnext_pico,224,1024.0,6145.95,166.603,1.37,6.1,9.05 cs3darknet_focus_m,256,1024.0,6145.46,166.616,1.98,4.89,9.3 pvt_v2_b0,224,1024.0,6126.38,167.135,0.53,7.01,3.67 tf_efficientnet_b0,224,1024.0,6026.91,169.894,0.4,6.75,5.29 nf_regnet_b0,256,1024.0,5970.36,171.503,0.64,5.58,8.76 resnetblur18,224,1024.0,5963.74,171.694,2.34,3.39,11.69 ese_vovnet19b_dw,224,1024.0,5956.2,171.911,1.34,8.25,6.54 hrnet_w18_small,224,1024.0,5950.21,172.083,1.61,5.72,13.19 resnet50,160,1024.0,5943.32,172.284,2.1,5.67,25.56 repvgg_a1,224,1024.0,5891.09,173.812,2.64,4.74,14.09 cs3darknet_m,256,1024.0,5871.36,174.395,2.08,5.28,9.31 convnext_pico_ols,224,1024.0,5852.38,174.961,1.43,6.5,9.06 vit_base_patch32_clip_224,224,1024.0,5768.1,177.517,4.37,4.19,88.22 tf_efficientnetv2_b2,208,1024.0,5753.76,177.96,1.06,6.0,10.1 vit_base_patch32_224,224,1024.0,5748.7,178.117,4.37,4.19,88.22 semnasnet_140,224,1024.0,5744.77,178.239,0.6,8.87,6.11 skresnet18,224,1024.0,5740.29,178.378,1.82,3.24,11.96 vit_tiny_r_s16_p8_384,384,1024.0,5663.72,180.79,1.25,5.39,6.36 resnet50d,160,1024.0,5651.35,181.185,2.22,6.08,25.58 resnet18,288,1024.0,5636.85,181.651,3.01,4.11,11.69 mobilenetv2_140,224,1024.0,5629.57,181.886,0.6,9.57,6.11 vit_small_patch32_384,384,1024.0,5499.31,186.195,3.26,6.07,22.92 convnext_atto,288,1024.0,5487.38,186.599,0.91,6.3,3.7 efficientnet_b0_gn,224,1024.0,5481.83,186.788,0.42,6.75,5.29 selecsls42,224,1024.0,5458.22,187.596,2.94,4.62,30.35 efficientnet_lite1,240,1024.0,5452.84,187.782,0.62,10.14,5.42 fbnetv3_d,224,1024.0,5449.6,187.893,0.52,8.5,10.31 pit_s_224,224,1024.0,5438.08,188.291,2.42,6.18,23.46 selecsls42b,224,1024.0,5414.81,189.1,2.98,4.62,32.46 resnet34,224,1024.0,5413.46,189.147,3.67,3.74,21.8 pit_s_distilled_224,224,1024.0,5407.14,189.368,2.45,6.22,24.04 efficientvit_b1,256,1024.0,5391.26,189.926,0.69,9.46,9.1 seresnet18,288,1024.0,5348.84,191.432,3.01,4.11,11.78 tf_efficientnetv2_b1,240,1024.0,5293.37,193.439,1.21,7.34,8.14 levit_384,224,1024.0,5286.23,193.7,2.36,6.26,39.13 convnextv2_atto,224,1024.0,5265.85,194.45,0.55,3.81,3.71 repvit_m1_0,224,1024.0,5259.32,194.683,1.13,8.69,7.3 seresnet50,160,1024.0,5236.4,195.543,2.1,5.69,28.09 convnext_atto_ols,288,1024.0,5201.4,196.86,0.96,6.8,3.7 gernet_m,224,1024.0,5195.05,197.1,3.02,5.24,21.14 fbnetv3_b,256,1024.0,5178.49,197.729,0.55,9.1,8.6 mixnet_s,224,1024.0,5129.76,199.608,0.25,6.25,4.13 repghostnet_200,224,1024.0,5125.91,199.759,0.54,7.96,9.8 vit_base_patch32_clip_quickgelu_224,224,1024.0,5125.16,199.787,4.37,4.19,87.85 seresnet34,224,1024.0,5104.13,200.612,3.67,3.74,21.96 repvit_m2,224,1024.0,5098.16,200.845,1.36,9.43,8.8 rexnetr_130,224,1024.0,5082.35,201.471,0.68,9.81,7.61 efficientnet_b0_g16_evos,224,1024.0,5016.04,204.134,1.01,7.42,8.11 ghostnetv2_130,224,1024.0,5011.79,204.307,0.28,5.9,8.96 edgenext_x_small,256,1024.0,4992.08,205.112,0.54,5.93,2.34 ecaresnet50t,160,1024.0,4989.39,205.225,2.21,6.04,25.57 tiny_vit_5m_224,224,1024.0,4963.53,206.293,1.18,9.32,12.08 rexnet_130,224,1024.0,4939.41,207.301,0.68,9.71,7.56 legacy_seresnet34,224,1024.0,4938.49,207.34,3.67,3.74,21.96 eva02_tiny_patch14_224,224,1024.0,4931.19,207.646,1.4,6.17,5.5 resnet34d,224,1024.0,4924.89,207.912,3.91,4.54,21.82 tf_efficientnet_lite1,240,1024.0,4918.8,208.17,0.62,10.14,5.42 mixer_b32_224,224,1024.0,4917.45,208.227,3.24,6.29,60.29 resnet50,176,1024.0,4914.58,208.348,2.62,6.92,25.56 resnetrs50,160,1024.0,4904.24,208.788,2.29,6.2,35.69 xcit_tiny_12_p16_224,224,1024.0,4900.19,208.961,1.24,6.29,6.72 repvit_m1_1,224,1024.0,4858.32,210.759,1.36,9.43,8.8 levit_conv_384,224,1024.0,4851.29,211.066,2.36,6.26,39.13 efficientnet_es_pruned,224,1024.0,4832.02,211.909,1.81,8.73,5.44 efficientnet_es,224,1024.0,4828.47,212.065,1.81,8.73,5.44 dla34,224,1024.0,4823.61,212.277,3.07,5.02,15.74 resnet26,224,1024.0,4806.46,213.036,2.36,7.35,16.0 resnet18d,288,1024.0,4806.17,213.049,3.41,5.43,11.71 resnext50_32x4d,160,1024.0,4797.48,213.435,2.17,7.35,25.03 tf_mixnet_s,224,1024.0,4783.68,214.05,0.25,6.25,4.13 convnext_femto,288,1024.0,4774.19,214.475,1.3,7.56,5.22 efficientnet_b1,224,1024.0,4707.45,217.516,0.59,9.36,7.79 gmlp_ti16_224,224,1024.0,4694.71,218.108,1.34,7.55,5.87 cs3darknet_focus_m,288,1024.0,4686.36,218.495,2.51,6.19,9.3 mobilenetv2_120d,224,1024.0,4673.25,219.108,0.69,11.97,5.83 selecsls60,224,1024.0,4656.74,219.885,3.59,5.52,30.67 selecsls60b,224,1024.0,4628.67,221.219,3.63,5.52,32.77 tf_efficientnet_es,224,1024.0,4617.85,221.737,1.81,8.73,5.44 resmlp_12_224,224,1024.0,4607.73,222.224,3.01,5.5,15.35 vit_small_patch16_224,224,1024.0,4586.65,223.246,4.25,8.25,22.05 deit_small_patch16_224,224,1024.0,4584.29,223.359,4.25,8.25,22.05 fbnetv3_d,256,1024.0,4567.33,224.19,0.68,11.1,10.31 gmixer_12_224,224,1024.0,4565.4,224.285,2.67,7.26,12.7 deit_small_distilled_patch16_224,224,1024.0,4564.97,224.306,4.27,8.29,22.44 convnext_femto_ols,288,1024.0,4561.96,224.454,1.35,8.06,5.23 efficientnet_b0_g8_gn,224,1024.0,4561.27,224.488,0.66,6.75,6.56 efficientnet_cc_b0_8e,224,1024.0,4542.29,225.426,0.42,9.42,24.01 efficientnet_cc_b0_4e,224,1024.0,4540.5,225.515,0.41,9.42,13.31 repvgg_b0,224,1024.0,4526.99,226.188,3.41,6.15,15.82 mixer_s16_224,224,1024.0,4518.8,226.598,3.79,5.97,18.53 cs3darknet_m,288,1024.0,4513.42,226.868,2.63,6.69,9.31 convnextv2_femto,224,1024.0,4509.16,227.082,0.79,4.57,5.23 regnetx_016,224,1024.0,4476.6,228.734,1.62,7.93,9.19 nf_regnet_b1,256,1024.0,4444.68,230.377,0.82,7.27,10.22 vit_base_patch32_clip_256,256,1024.0,4442.76,230.476,5.68,5.44,87.86 mobilevitv2_075,256,1024.0,4419.22,231.704,1.05,12.06,2.87 rexnetr_150,224,1024.0,4415.72,231.888,0.89,11.13,9.78 darknet17,256,1024.0,4402.14,232.603,3.26,7.18,14.3 resnet26d,224,1024.0,4396.77,232.887,2.6,8.15,16.01 resnetaa34d,224,1024.0,4381.9,233.677,4.43,5.07,21.82 efficientnet_b2_pruned,260,1024.0,4356.91,235.018,0.73,9.13,8.31 convnext_nano,224,1024.0,4340.39,235.913,2.46,8.37,15.59 ecaresnet50d_pruned,224,1024.0,4337.48,236.07,2.53,6.43,19.94 efficientformer_l1,224,1024.0,4271.29,239.728,1.3,5.53,12.29 nf_resnet26,224,1024.0,4216.31,242.856,2.41,7.35,16.0 deit3_small_patch16_224,224,1024.0,4203.29,243.607,4.25,8.25,22.06 nf_regnet_b2,240,1024.0,4197.9,243.92,0.97,7.23,14.31 tf_efficientnet_cc_b0_4e,224,1024.0,4196.5,244.002,0.41,9.42,13.31 tf_efficientnet_cc_b0_8e,224,1024.0,4190.23,244.367,0.42,9.42,24.01 regnety_016,224,1024.0,4161.97,246.026,1.63,8.04,11.2 rexnet_150,224,1024.0,4147.2,246.903,0.9,11.21,9.73 ghostnetv2_160,224,1024.0,4116.92,248.718,0.42,7.23,12.39 tiny_vit_11m_224,224,1024.0,4086.56,250.566,1.9,10.73,20.35 poolformer_s12,224,1024.0,4071.24,251.51,1.82,5.53,11.92 regnetz_005,288,1024.0,4056.8,252.404,0.86,9.68,7.12 efficientnet_lite2,260,1024.0,4046.71,253.034,0.89,12.9,6.09 darknet21,256,1024.0,4001.6,255.887,3.93,7.47,20.86 efficientvit_b1,288,1024.0,3997.55,256.145,0.87,11.96,9.1 resnext50_32x4d,176,1024.0,3992.51,256.47,2.71,8.97,25.03 edgenext_x_small,288,1024.0,3965.96,258.184,0.68,7.5,2.34 efficientnet_b1,256,1024.0,3961.36,258.486,0.77,12.22,7.79 convnext_nano_ols,224,1024.0,3944.64,259.582,2.65,9.38,15.65 resnest14d,224,1024.0,3932.19,260.404,2.76,7.33,10.61 tf_efficientnet_b1,240,1024.0,3922.37,261.055,0.71,10.88,7.79 flexivit_small,240,1024.0,3913.54,261.645,4.88,9.46,22.06 mobilevit_xs,256,768.0,3904.8,196.672,0.93,13.62,2.32 regnetz_b16,224,1024.0,3893.58,262.986,1.45,9.95,9.72 sedarknet21,256,1024.0,3874.2,264.302,3.93,7.47,20.95 resnext26ts,256,1024.0,3832.52,267.176,2.43,10.52,10.3 mobileone_s1,224,1024.0,3826.99,267.562,0.86,9.67,4.83 tf_efficientnetv2_b2,260,1024.0,3817.93,268.197,1.72,9.84,10.1 edgenext_small,256,1024.0,3770.23,271.588,1.26,9.07,5.59 convnext_pico,288,1024.0,3731.48,274.411,2.27,10.08,9.05 gernet_l,256,1024.0,3727.69,274.69,4.57,8.0,31.08 seresnext26ts,256,1024.0,3724.62,274.916,2.43,10.52,10.39 eca_resnext26ts,256,1024.0,3723.07,275.031,2.43,10.52,10.3 dpn48b,224,1024.0,3716.75,275.497,1.69,8.92,9.13 tf_efficientnet_lite2,260,1024.0,3695.32,277.096,0.89,12.9,6.09 gcresnext26ts,256,1024.0,3691.17,277.409,2.43,10.53,10.48 efficientnet_b2,256,1024.0,3671.26,278.912,0.89,12.81,9.11 nf_ecaresnet26,224,1024.0,3640.87,281.24,2.41,7.36,16.0 resnetblur18,288,1024.0,3639.91,281.314,3.87,5.6,11.69 nf_seresnet26,224,1024.0,3637.43,281.506,2.41,7.36,17.4 resnet101,160,1024.0,3616.15,283.164,4.0,8.28,44.55 vit_relpos_small_patch16_224,224,1024.0,3590.52,285.183,4.24,9.38,21.98 resnet26t,256,1024.0,3578.9,286.111,3.35,10.52,16.01 vit_srelpos_small_patch16_224,224,1024.0,3572.97,286.585,4.23,8.49,21.97 convnext_pico_ols,288,1024.0,3558.03,287.789,2.37,10.74,9.06 cs3darknet_focus_l,256,1024.0,3544.69,288.872,4.66,8.03,21.15 tf_efficientnetv2_b3,240,1024.0,3543.38,288.978,1.93,9.95,14.36 legacy_seresnext26_32x4d,224,1024.0,3516.72,291.169,2.49,9.39,16.79 pvt_v2_b1,224,1024.0,3507.87,291.903,2.04,14.01,14.01 repvit_m3,224,1024.0,3501.61,292.425,1.89,13.94,10.68 repvgg_a2,224,1024.0,3495.75,292.916,5.7,6.26,28.21 efficientnetv2_rw_t,224,1024.0,3486.59,293.686,1.93,9.94,13.65 ecaresnet101d_pruned,224,1024.0,3483.13,293.977,3.48,7.69,24.88 ese_vovnet19b_dw,288,1024.0,3478.51,294.369,2.22,13.63,6.54 mixnet_m,224,1024.0,3474.22,294.731,0.36,8.19,5.01 edgenext_small_rw,256,1024.0,3458.08,296.106,1.58,9.51,7.83 convnextv2_pico,224,1024.0,3458.0,296.113,1.37,6.1,9.07 gc_efficientnetv2_rw_t,224,1024.0,3445.15,297.218,1.94,9.97,13.68 cs3darknet_l,256,1024.0,3414.99,299.845,4.86,8.55,21.16 efficientnet_b3_pruned,300,1024.0,3412.19,300.09,1.04,11.86,9.86 nf_regnet_b1,288,1024.0,3373.08,303.57,1.02,9.2,10.22 tf_mixnet_m,224,1024.0,3353.29,305.361,0.36,8.19,5.01 convit_tiny,224,1024.0,3342.83,306.316,1.26,7.94,5.71 eca_botnext26ts_256,256,1024.0,3341.38,306.449,2.46,11.6,10.59 ecaresnext50t_32x4d,224,1024.0,3327.77,307.703,2.7,10.09,15.41 ecaresnext26t_32x4d,224,1024.0,3321.66,308.269,2.7,10.09,15.41 resnet34,288,1024.0,3320.08,308.416,6.07,6.18,21.8 seresnext26t_32x4d,224,1024.0,3319.26,308.491,2.7,10.09,16.81 vit_tiny_patch16_384,384,1024.0,3311.59,309.206,3.16,12.08,5.79 vit_base_patch32_plus_256,256,1024.0,3301.22,310.177,7.7,6.35,119.48 seresnext26d_32x4d,224,1024.0,3300.83,310.214,2.73,10.19,16.81 skresnet34,224,1024.0,3294.57,310.803,3.67,5.13,22.28 mobilevitv2_100,256,768.0,3290.58,233.384,1.84,16.08,4.9 vit_relpos_small_patch16_rpn_224,224,1024.0,3279.29,312.245,4.24,9.38,21.97 eca_halonext26ts,256,1024.0,3270.39,313.1,2.44,11.46,10.76 coatnet_pico_rw_224,224,1024.0,3250.74,314.993,1.96,12.91,10.85 rexnetr_200,224,768.0,3238.38,237.146,1.59,15.11,16.52 ecaresnet26t,256,1024.0,3228.23,317.19,3.35,10.53,16.01 ecaresnetlight,224,1024.0,3222.96,317.708,4.11,8.42,30.16 coatnext_nano_rw_224,224,1024.0,3218.47,318.153,2.36,10.68,14.7 cs3sedarknet_l,256,1024.0,3218.11,318.188,4.86,8.56,21.91 coat_lite_tiny,224,1024.0,3216.35,318.362,1.6,11.65,5.72 nf_regnet_b2,272,1024.0,3205.43,319.447,1.22,9.27,14.31 convnextv2_atto,288,1024.0,3199.9,319.999,0.91,6.3,3.71 vit_small_r26_s32_224,224,1024.0,3174.89,322.52,3.54,9.44,36.43 botnet26t_256,256,1024.0,3173.81,322.63,3.32,11.98,12.49 resnetv2_50,224,1024.0,3170.95,322.919,4.11,11.11,25.55 fastvit_t8,256,1024.0,3164.9,323.538,0.7,8.63,4.03 crossvit_small_240,240,1024.0,3164.86,323.541,5.09,11.34,26.86 bat_resnext26ts,256,1024.0,3139.26,326.18,2.53,12.51,10.73 seresnet34,288,1024.0,3136.77,326.439,6.07,6.18,21.96 halonet26t,256,1024.0,3132.55,326.879,3.19,11.69,12.48 lambda_resnet26t,256,1024.0,3123.88,327.786,3.02,11.87,10.96 rexnet_200,224,768.0,3120.89,246.073,1.56,14.91,16.37 vit_small_resnet26d_224,224,1024.0,3106.26,329.645,5.04,10.65,63.61 hrnet_w18_small_v2,224,1024.0,3095.42,330.8,2.62,9.65,15.6 mobileone_s2,224,1024.0,3085.91,331.82,1.34,11.55,7.88 vit_relpos_base_patch32_plus_rpn_256,256,1024.0,3081.88,332.247,7.59,6.63,119.42 tresnet_m,224,1024.0,3073.78,333.129,5.75,7.31,31.39 resnet32ts,256,1024.0,3072.91,333.224,4.63,11.58,17.96 coatnet_nano_cc_224,224,1024.0,3066.72,333.896,2.13,13.1,13.76 resnet101,176,1024.0,3047.24,336.031,4.92,10.08,44.55 resnet33ts,256,1024.0,3032.6,337.653,4.76,11.66,19.68 efficientvit_b2,224,1024.0,3030.14,337.927,1.6,14.62,24.33 resnet50,224,1024.0,3021.24,338.922,4.11,11.11,25.56 coat_lite_mini,224,1024.0,3021.22,338.925,2.0,12.25,11.01 resnet34d,288,1024.0,3013.98,339.739,6.47,7.51,21.82 cspresnet50,256,1024.0,3012.57,339.898,4.54,11.5,21.62 resnetv2_50t,224,1024.0,3011.73,339.991,4.32,11.82,25.57 dpn68b,224,1024.0,3008.58,340.347,2.35,10.47,12.61 coatnet_nano_rw_224,224,1024.0,3001.39,341.165,2.29,13.29,15.14 dpn68,224,1024.0,3001.33,341.17,2.35,10.47,12.61 resnetv2_50d,224,1024.0,2992.98,342.12,4.35,11.92,25.57 convnext_tiny,224,1024.0,2986.71,342.841,4.47,13.44,28.59 levit_512,224,1024.0,2974.0,344.305,5.64,10.22,95.17 dla60,224,1024.0,2959.44,345.999,4.26,10.16,22.04 fbnetv3_g,240,1024.0,2957.87,346.184,1.28,14.87,16.62 tf_efficientnet_b2,260,1024.0,2957.04,346.28,1.02,13.83,9.11 efficientnet_em,240,1024.0,2948.76,347.254,3.04,14.34,6.9 crossvit_15_240,240,1024.0,2948.65,347.266,5.17,12.01,27.53 eca_resnet33ts,256,1024.0,2945.18,347.676,4.76,11.66,19.68 seresnet33ts,256,1024.0,2940.4,348.24,4.76,11.66,19.78 regnetx_032,224,1024.0,2932.49,349.18,3.2,11.37,15.3 gcresnet33ts,256,1024.0,2919.42,350.744,4.76,11.68,19.88 mobileone_s0,224,1024.0,2911.68,351.675,1.09,15.48,5.29 resnet50t,224,1024.0,2893.61,353.872,4.32,11.82,25.57 resnet50c,224,1024.0,2893.38,353.9,4.35,11.92,25.58 repvit_m1_5,224,1024.0,2891.53,354.126,2.31,15.7,14.64 selecsls84,224,1024.0,2891.52,354.128,5.9,7.57,50.95 efficientnet_cc_b1_8e,240,1024.0,2883.89,355.064,0.75,15.44,39.72 haloregnetz_b,224,1024.0,2883.33,355.134,1.97,11.94,11.68 vgg11,224,1024.0,2881.16,355.4,7.61,7.44,132.86 resnet50d,224,1024.0,2872.03,356.53,4.35,11.92,25.58 resnest26d,224,1024.0,2863.53,357.59,3.64,9.97,17.07 tf_efficientnet_em,240,1024.0,2860.98,357.908,3.04,14.34,6.9 visformer_small,224,1024.0,2837.73,360.841,4.88,11.43,40.22 cspresnet50w,256,1024.0,2834.78,361.216,5.04,12.19,28.12 vovnet39a,224,1024.0,2834.5,361.252,7.09,6.73,22.6 wide_resnet50_2,176,1024.0,2833.12,361.428,7.29,8.97,68.88 cspresnet50d,256,1024.0,2828.94,361.963,4.86,12.55,21.64 resnet26,288,1024.0,2826.83,362.233,3.9,12.15,16.0 resnext26ts,288,1024.0,2826.2,362.312,3.07,13.31,10.3 efficientnet_b2,288,1024.0,2822.88,362.739,1.12,16.2,9.11 regnetv_040,224,1024.0,2785.35,367.627,4.0,12.29,20.64 levit_512d,224,1024.0,2784.75,367.707,5.85,11.3,92.5 levit_conv_512,224,1024.0,2781.3,368.162,5.64,10.22,95.17 deit3_medium_patch16_224,224,1024.0,2780.75,368.235,7.53,10.99,38.85 crossvit_15_dagger_240,240,1024.0,2776.34,368.82,5.5,12.68,28.21 regnety_040,224,1024.0,2768.62,369.849,4.0,12.29,20.65 legacy_seresnet50,224,1024.0,2766.98,370.066,3.88,10.6,28.09 eca_resnext26ts,288,1024.0,2756.51,371.473,3.07,13.32,10.3 seresnext26ts,288,1024.0,2751.54,372.144,3.07,13.32,10.39 regnety_032,224,1024.0,2744.75,373.065,3.2,11.26,19.44 convnext_tiny_hnf,224,1024.0,2744.61,373.082,4.47,13.44,28.59 convnextv2_femto,288,1024.0,2744.25,373.131,1.3,7.56,5.23 eca_vovnet39b,224,1024.0,2742.23,373.408,7.09,6.74,22.6 resnetv2_50x1_bit,224,1024.0,2741.57,373.497,4.23,11.11,25.55 gcresnext26ts,288,1024.0,2728.39,375.302,3.07,13.33,10.48 resnetaa50,224,1024.0,2728.16,375.334,5.15,11.64,25.56 densenet121,224,1024.0,2725.3,375.726,2.87,6.9,7.98 ese_vovnet39b,224,1024.0,2723.97,375.912,7.09,6.74,24.57 mixnet_l,224,1024.0,2712.93,377.44,0.58,10.84,7.33 tf_efficientnet_cc_b1_8e,240,1024.0,2710.75,377.745,0.75,15.44,39.72 mobilevit_s,256,768.0,2698.84,284.557,1.86,17.03,5.58 cs3darknet_focus_l,288,1024.0,2695.52,379.878,5.9,10.16,21.15 seresnet50,224,1024.0,2693.22,380.203,4.11,11.13,28.09 xcit_nano_12_p16_384,384,1024.0,2679.82,382.104,1.64,12.14,3.05 resnetaa34d,288,1024.0,2675.02,382.79,7.33,8.38,21.82 twins_svt_small,224,1024.0,2670.35,383.458,2.82,10.7,24.06 ecaresnet50d_pruned,288,1024.0,2662.19,384.634,4.19,10.61,19.94 convnext_nano,288,1024.0,2634.79,388.635,4.06,13.84,15.59 resnet50_gn,224,1024.0,2631.91,389.06,4.14,11.11,25.56 resnetv2_50d_gn,224,1024.0,2623.43,390.317,4.38,11.92,25.57 xcit_tiny_24_p16_224,224,1024.0,2616.39,391.368,2.34,11.82,12.12 tf_mixnet_l,224,1024.0,2615.89,391.443,0.58,10.84,7.33 res2net50_48w_2s,224,1024.0,2611.06,392.166,4.18,11.72,25.29 gcvit_xxtiny,224,1024.0,2608.34,392.574,2.14,15.36,12.0 cs3darknet_l,288,1024.0,2607.33,392.728,6.16,10.83,21.16 resnetaa50d,224,1024.0,2596.72,394.332,5.39,12.44,25.58 vgg11_bn,224,1024.0,2590.27,395.315,7.62,7.44,132.87 vit_base_resnet26d_224,224,1024.0,2580.41,396.822,6.93,12.34,101.4 vit_relpos_medium_patch16_cls_224,224,1024.0,2579.62,396.946,7.55,13.3,38.76 ecaresnet50t,224,1024.0,2579.62,396.946,4.32,11.83,25.57 coatnet_rmlp_nano_rw_224,224,1024.0,2579.38,396.984,2.51,18.21,15.15 davit_tiny,224,1024.0,2578.68,397.091,4.47,17.08,28.36 seresnet50t,224,1024.0,2574.91,397.672,4.32,11.83,28.1 resnet26d,288,1024.0,2569.96,398.438,4.29,13.48,16.01 mobilevitv2_125,256,768.0,2568.23,299.03,2.86,20.1,7.48 nf_regnet_b3,288,1024.0,2563.17,399.494,1.67,11.84,18.59 ecaresnet50d,224,1024.0,2560.76,399.87,4.35,11.93,25.58 levit_conv_512d,224,1024.0,2557.63,400.359,5.85,11.3,92.5 resnet152,160,1024.0,2531.48,404.495,5.9,11.51,60.19 efficientvit_b2,256,1024.0,2531.18,404.544,2.09,19.03,24.33 mobileone_s3,224,1024.0,2513.71,407.355,1.94,13.85,10.17 resnetrs50,224,1024.0,2512.05,407.624,4.48,12.14,35.69 twins_pcpvt_small,224,1024.0,2506.77,408.482,3.68,15.51,24.11 resnetblur50,224,1024.0,2495.43,410.338,5.16,12.02,25.56 poolformerv2_s12,224,1024.0,2489.38,411.337,1.83,5.53,11.89 convnextv2_nano,224,1024.0,2480.83,412.755,2.46,8.37,15.62 regnetx_040,224,1024.0,2478.03,413.222,3.99,12.2,22.12 eca_nfnet_l0,224,1024.0,2476.91,413.407,4.35,10.47,24.14 gcresnext50ts,256,1024.0,2473.39,413.995,3.75,15.46,15.67 nfnet_l0,224,1024.0,2472.84,414.088,4.36,10.47,35.07 tiny_vit_21m_224,224,1024.0,2468.7,414.781,4.08,15.96,33.22 cs3sedarknet_l,288,1024.0,2463.79,415.609,6.16,10.83,21.91 resnet50s,224,1024.0,2456.52,416.838,5.47,13.52,25.68 dla60x,224,1024.0,2437.95,420.012,3.54,13.8,17.35 densenetblur121d,224,1024.0,2433.6,420.765,3.11,7.9,8.0 edgenext_small,320,1024.0,2424.08,422.414,1.97,14.16,5.59 resnext50_32x4d,224,1024.0,2410.12,424.862,4.26,14.4,25.03 inception_next_tiny,224,1024.0,2404.04,425.937,4.19,11.98,28.06 convnext_nano_ols,288,1024.0,2397.01,427.188,4.38,15.5,15.65 vit_relpos_medium_patch16_224,224,1024.0,2394.54,427.629,7.5,12.13,38.75 efficientnet_lite3,300,512.0,2392.78,213.967,1.65,21.85,8.2 vit_srelpos_medium_patch16_224,224,1024.0,2386.54,429.062,7.49,11.32,38.74 regnetz_c16,256,1024.0,2383.36,429.635,2.51,16.57,13.46 resnetblur50d,224,1024.0,2382.64,429.765,5.4,12.82,25.58 vit_base_r26_s32_224,224,1024.0,2381.88,429.901,6.76,11.54,101.38 gcresnet50t,256,1024.0,2372.96,431.518,5.42,14.67,25.9 regnety_040_sgn,224,1024.0,2371.57,431.77,4.03,12.29,20.65 res2net50_26w_4s,224,1024.0,2359.62,433.957,4.28,12.61,25.7 vovnet57a,224,1024.0,2357.12,434.416,8.95,7.52,36.64 resmlp_24_224,224,1024.0,2350.19,435.697,5.96,10.91,30.02 maxvit_pico_rw_256,256,768.0,2346.84,327.238,1.68,18.77,7.46 inception_v3,299,1024.0,2346.46,436.391,5.73,8.97,23.83 maxvit_rmlp_pico_rw_256,256,768.0,2343.0,327.774,1.69,21.32,7.52 seresnetaa50d,224,1024.0,2333.21,438.87,5.4,12.46,28.11 focalnet_tiny_srf,224,1024.0,2331.81,439.132,4.42,16.32,28.43 cspresnext50,256,1024.0,2330.62,439.358,4.05,15.86,20.57 res2net50_14w_8s,224,1024.0,2327.89,439.871,4.21,13.28,25.06 dla60_res2net,224,1024.0,2327.26,439.99,4.15,12.34,20.85 coatnet_0_rw_224,224,1024.0,2319.62,441.438,4.23,15.1,27.44 regnetz_b16,288,1024.0,2318.51,441.651,2.39,16.43,9.72 gmixer_24_224,224,1024.0,2315.73,442.182,5.28,14.45,24.72 resnext50d_32x4d,224,1024.0,2305.65,444.116,4.5,15.2,25.05 lambda_resnet26rpt_256,256,768.0,2282.36,336.484,3.16,11.87,10.99 ese_vovnet57b,224,1024.0,2279.9,449.132,8.95,7.52,38.61 resnest50d_1s4x24d,224,1024.0,2278.75,449.357,4.43,13.57,25.68 dla60_res2next,224,1024.0,2268.77,451.333,3.49,13.17,17.03 sehalonet33ts,256,1024.0,2262.52,452.582,3.55,14.7,13.69 res2net50d,224,1024.0,2256.17,453.855,4.52,13.41,25.72 vit_medium_patch16_gap_240,240,1024.0,2253.27,454.439,8.6,12.57,44.4 res2next50,224,1024.0,2251.4,454.817,4.2,13.71,24.67 resnet32ts,288,1024.0,2244.87,456.139,5.86,14.65,17.96 edgenext_base,256,1024.0,2239.63,457.204,3.85,15.58,18.51 efficientvit_l1,224,1024.0,2235.54,458.043,5.27,15.85,52.65 skresnet50,224,1024.0,2226.66,459.87,4.11,12.5,25.8 nfnet_f0,192,1024.0,2226.44,459.916,7.21,10.16,71.49 tf_efficientnetv2_b3,300,1024.0,2226.35,459.935,3.04,15.74,14.36 efficientnetv2_rw_t,288,1024.0,2225.5,460.11,3.19,16.42,13.65 nf_ecaresnet50,224,1024.0,2219.3,461.395,4.21,11.13,25.56 darknetaa53,256,1024.0,2219.0,461.459,7.97,12.39,36.02 densenet169,224,1024.0,2218.3,461.604,3.4,7.3,14.15 nf_seresnet50,224,1024.0,2217.49,461.772,4.21,11.13,28.09 edgenext_small_rw,320,1024.0,2214.15,462.468,2.46,14.85,7.83 resnet33ts,288,1024.0,2214.09,462.482,6.02,14.75,19.68 xcit_small_12_p16_224,224,1024.0,2207.67,463.826,4.82,12.57,26.25 focalnet_tiny_lrf,224,1024.0,2205.41,464.301,4.49,17.76,28.65 resnet51q,256,1024.0,2195.84,466.325,6.38,16.55,35.7 repvgg_b1g4,224,1024.0,2195.75,466.344,8.15,10.64,39.97 seresnext50_32x4d,224,1024.0,2188.04,467.986,4.26,14.42,27.56 vit_relpos_medium_patch16_rpn_224,224,1024.0,2187.29,468.147,7.5,12.13,38.73 cs3darknet_focus_x,256,1024.0,2185.7,468.489,8.03,10.69,35.02 legacy_seresnext50_32x4d,224,1024.0,2184.4,468.766,4.26,14.42,27.56 tf_efficientnet_lite3,300,512.0,2178.27,235.039,1.65,21.85,8.2 resnet26t,320,1024.0,2173.03,471.22,5.24,16.44,16.01 gc_efficientnetv2_rw_t,288,1024.0,2170.84,471.696,3.2,16.45,13.68 gmlp_s16_224,224,1024.0,2161.42,473.752,4.42,15.1,19.42 seresnet33ts,288,1024.0,2156.33,474.868,6.02,14.76,19.78 eca_resnet33ts,288,1024.0,2152.27,475.765,6.02,14.76,19.68 fastvit_t12,256,1024.0,2151.9,475.846,1.42,12.42,7.55 nf_regnet_b3,320,1024.0,2148.66,476.564,2.05,14.61,18.59 eva02_small_patch14_224,224,1024.0,2144.78,477.426,5.53,12.34,21.62 resnet152,176,1024.0,2139.0,478.716,7.22,13.99,60.19 vit_medium_patch16_reg4_gap_256,256,1024.0,2137.51,479.051,9.93,14.51,38.87 gcresnet33ts,288,1024.0,2134.49,479.728,6.02,14.78,19.88 skresnet50d,224,1024.0,2133.34,479.986,4.36,13.31,25.82 ecaresnet101d_pruned,288,1024.0,2128.45,481.09,5.75,12.71,24.88 fbnetv3_g,288,1024.0,2127.74,481.25,1.77,21.09,16.62 vit_medium_patch16_reg4_256,256,1024.0,2119.83,483.047,9.97,14.56,38.87 eva02_tiny_patch14_336,336,1024.0,2106.54,486.094,3.14,13.85,5.76 convnextv2_pico,288,1024.0,2101.04,487.367,2.27,10.08,9.07 nf_resnet50,256,1024.0,2100.31,487.536,5.46,14.52,25.56 resnetrs101,192,1024.0,2100.21,487.558,6.04,12.7,63.62 poolformer_s24,224,1024.0,2099.97,487.615,3.41,10.68,21.39 pvt_v2_b2,224,1024.0,2099.92,487.626,3.9,24.96,25.36 efficientnet_b3,288,512.0,2089.91,244.977,1.63,21.49,12.23 cs3sedarknet_xdw,256,1024.0,2078.01,492.768,5.97,17.18,21.6 darknet53,256,1024.0,2077.03,493.0,9.31,12.39,41.61 ecaresnet50t,256,1024.0,2076.41,493.149,5.64,15.45,25.57 cs3darknet_x,256,1024.0,2060.02,497.071,8.38,11.35,35.05 xcit_nano_12_p8_224,224,1024.0,2059.06,497.302,2.16,15.71,3.05 mobilevitv2_150,256,512.0,2058.61,248.702,4.09,24.11,10.59 rexnetr_300,224,1024.0,2042.01,501.455,3.39,22.16,34.81 lambda_resnet50ts,256,1024.0,2041.61,501.552,5.07,17.48,21.54 fastvit_s12,256,1024.0,2028.81,504.718,1.82,13.67,9.47 coatnet_rmlp_0_rw_224,224,1024.0,2024.25,505.855,4.52,21.26,27.45 gcvit_xtiny,224,1024.0,2023.42,506.063,2.93,20.26,19.98 fastvit_sa12,256,1024.0,2022.28,506.347,1.96,13.83,11.58 crossvit_18_240,240,1024.0,2014.44,508.318,8.21,16.14,43.27 vit_medium_patch16_gap_256,256,1024.0,1996.45,512.899,9.78,14.29,38.86 resnet61q,256,1024.0,1996.22,512.958,7.8,17.01,36.85 coatnet_bn_0_rw_224,224,1024.0,1985.64,515.69,4.48,18.41,27.44 vit_base_patch32_384,384,1024.0,1984.44,516.005,12.67,12.14,88.3 vit_base_patch32_clip_384,384,1024.0,1981.44,516.784,12.67,12.14,88.3 cspdarknet53,256,1024.0,1981.04,516.888,6.57,16.81,27.64 sebotnet33ts_256,256,512.0,1977.98,258.841,3.89,17.46,13.7 ecaresnet26t,320,1024.0,1973.79,518.786,5.24,16.44,16.01 vit_base_resnet50d_224,224,1024.0,1971.35,519.428,8.68,16.1,110.97 cs3sedarknet_x,256,1024.0,1962.3,521.825,8.38,11.35,35.4 regnetx_080,224,1024.0,1962.04,521.894,8.02,14.06,39.57 seresnext26t_32x4d,288,1024.0,1950.77,524.91,4.46,16.68,16.81 mixnet_xl,224,1024.0,1948.29,525.576,0.93,14.57,11.9 resnest50d,224,1024.0,1945.36,526.368,5.4,14.36,27.48 seresnext26d_32x4d,288,1024.0,1940.04,527.813,4.51,16.85,16.81 coatnet_0_224,224,512.0,1939.29,264.004,4.43,21.14,25.04 swin_tiny_patch4_window7_224,224,1024.0,1938.74,528.165,4.51,17.06,28.29 resnetv2_101,224,1024.0,1935.15,529.146,7.83,16.23,44.54 regnetx_064,224,1024.0,1933.12,529.703,6.49,16.37,26.21 dla102,224,1024.0,1924.77,531.998,7.19,14.18,33.27 crossvit_18_dagger_240,240,1024.0,1921.19,532.991,8.65,16.91,44.27 rexnetr_200,288,512.0,1914.7,267.396,2.62,24.96,16.52 rexnet_300,224,1024.0,1911.46,535.706,3.44,22.4,34.71 nest_tiny,224,1024.0,1908.27,536.601,5.24,14.75,17.06 dm_nfnet_f0,192,1024.0,1907.3,536.873,7.21,10.16,71.49 ecaresnetlight,288,1024.0,1897.75,539.574,6.79,13.91,30.16 maxxvit_rmlp_nano_rw_256,256,768.0,1897.05,404.83,4.17,21.53,16.78 resnet101,224,1024.0,1885.15,543.183,7.83,16.23,44.55 nest_tiny_jx,224,1024.0,1884.26,543.437,5.24,14.75,17.06 pvt_v2_b2_li,224,1024.0,1882.78,543.863,3.77,25.04,22.55 vit_large_patch32_224,224,1024.0,1869.82,547.632,15.27,11.11,305.51 vgg13,224,1024.0,1868.34,548.068,11.31,12.25,133.05 resnetv2_101d,224,1024.0,1865.75,548.827,8.07,17.04,44.56 efficientformer_l3,224,1024.0,1865.63,548.865,3.93,12.01,31.41 resnetv2_50,288,1024.0,1863.99,549.347,6.79,18.37,25.55 mobileone_s4,224,1024.0,1856.33,551.615,3.04,17.74,14.95 res2net50_26w_6s,224,1024.0,1853.01,552.603,6.33,15.28,37.05 efficientvit_b2,288,1024.0,1851.14,553.16,2.64,24.03,24.33 lamhalobotnet50ts_256,256,1024.0,1841.89,555.938,5.02,18.44,22.57 maxvit_nano_rw_256,256,768.0,1833.65,418.827,4.26,25.76,15.45 maxvit_rmlp_nano_rw_256,256,768.0,1832.13,419.175,4.28,27.4,15.5 convnext_small,224,1024.0,1829.72,559.636,8.71,21.56,50.22 resnet101c,224,1024.0,1824.57,561.217,8.08,17.04,44.57 convnext_tiny,288,1024.0,1817.02,563.549,7.39,22.21,28.59 resnet101d,224,1024.0,1816.61,563.677,8.08,17.04,44.57 gcresnext50ts,288,1024.0,1802.21,568.181,4.75,19.57,15.67 efficientnetv2_s,288,1024.0,1800.9,568.595,4.75,20.13,21.46 pit_b_distilled_224,224,1024.0,1798.47,569.363,10.63,16.67,74.79 resnet50,288,1024.0,1790.94,571.757,6.8,18.37,25.56 twins_pcpvt_base,224,1024.0,1774.55,577.037,6.46,21.35,43.83 halonet50ts,256,1024.0,1772.89,577.576,5.3,19.2,22.73 dpn68b,288,1024.0,1770.85,578.24,3.89,17.3,12.61 pit_b_224,224,1024.0,1769.93,578.542,10.56,16.6,73.76 hrnet_w18_ssld,224,1024.0,1769.77,578.594,4.32,16.31,21.3 swin_s3_tiny_224,224,1024.0,1768.18,579.114,4.64,19.13,28.33 efficientvit_l2,224,1024.0,1765.89,579.866,6.97,19.58,63.71 hrnet_w18,224,1024.0,1763.75,580.57,4.32,16.31,21.3 coat_lite_small,224,1024.0,1746.27,586.38,3.96,22.09,19.84 repvgg_b1,224,1024.0,1745.5,586.64,13.16,10.64,57.42 wide_resnet50_2,224,1024.0,1744.59,586.947,11.43,14.4,68.88 efficientnet_b3,320,512.0,1740.17,294.213,2.01,26.52,12.23 gcresnet50t,288,1024.0,1734.6,590.328,6.86,18.57,25.9 densenet201,224,1024.0,1731.46,591.397,4.34,7.85,20.01 tresnet_v2_l,224,1024.0,1730.52,591.717,8.85,16.34,46.17 tf_efficientnet_b3,300,512.0,1724.68,296.856,1.87,23.83,12.23 efficientnetv2_rw_s,288,1024.0,1722.48,594.481,4.91,21.41,23.94 darknetaa53,288,1024.0,1719.51,595.509,10.08,15.68,36.02 maxxvitv2_nano_rw_256,256,768.0,1706.28,450.091,6.12,19.66,23.7 resnetaa101d,224,1024.0,1701.55,601.792,9.12,17.56,44.57 xcit_tiny_12_p16_384,384,1024.0,1700.55,602.144,3.64,18.25,6.72 cait_xxs24_224,224,1024.0,1698.66,602.815,2.53,20.29,11.96 resnet50t,288,1024.0,1694.77,604.2,7.14,19.53,25.57 legacy_seresnet101,224,1024.0,1693.62,604.611,7.61,15.74,49.33 cs3edgenet_x,256,1024.0,1692.79,604.907,11.53,12.92,47.82 resnet50d,288,1024.0,1684.01,608.061,7.19,19.7,25.58 mobilevitv2_175,256,512.0,1675.38,305.592,5.54,28.13,14.25 regnetv_064,224,1024.0,1674.09,611.663,6.39,16.41,30.58 resnetv2_101x1_bit,224,1024.0,1672.61,612.204,8.04,16.23,44.54 efficientnet_b3_gn,288,512.0,1669.75,306.623,1.74,23.35,11.73 ese_vovnet39b,288,768.0,1667.87,460.459,11.71,11.13,24.57 regnety_032,288,1024.0,1666.89,614.307,5.29,18.61,19.44 seresnet101,224,1024.0,1666.33,614.509,7.84,16.27,49.33 regnety_064,224,1024.0,1666.11,614.593,6.39,16.41,30.58 convnext_tiny_hnf,288,1024.0,1663.94,615.393,7.39,22.21,28.59 regnetv_040,288,1024.0,1658.56,617.391,6.6,20.3,20.64 regnety_040,288,1024.0,1648.75,621.064,6.61,20.3,20.65 regnety_080,224,1024.0,1645.74,622.202,8.0,17.97,39.18 resnet101s,224,1024.0,1640.53,624.176,9.19,18.64,44.67 mixer_b16_224,224,1024.0,1627.76,629.075,12.62,14.53,59.88 dla102x,224,1024.0,1623.56,630.698,5.89,19.42,26.31 nf_resnet101,224,1024.0,1622.48,631.12,8.01,16.23,44.55 swinv2_cr_tiny_224,224,1024.0,1621.28,631.59,4.66,28.45,28.33 ecaresnet101d,224,1024.0,1619.0,632.477,8.08,17.07,44.57 convnextv2_tiny,224,1024.0,1618.49,632.676,4.47,13.44,28.64 darknet53,288,1024.0,1615.64,633.795,11.78,15.68,41.61 wide_resnet101_2,176,1024.0,1615.25,633.945,14.31,13.18,126.89 repvit_m2_3,224,1024.0,1614.73,634.149,4.57,26.21,23.69 resnetaa50,288,1024.0,1610.23,635.923,8.52,19.24,25.56 resnetblur101d,224,1024.0,1609.76,636.109,9.12,17.94,44.57 efficientvit_b3,224,1024.0,1609.54,636.196,3.99,26.9,48.65 regnetz_d32,256,1024.0,1603.03,638.779,5.98,23.74,27.58 regnetz_b16_evos,224,1024.0,1602.47,639.001,1.43,9.95,9.74 ese_vovnet39b_evos,224,1024.0,1599.88,640.036,7.07,6.74,24.58 davit_small,224,1024.0,1599.81,640.066,8.69,27.54,49.75 seresnet50,288,1024.0,1595.89,641.637,6.8,18.39,28.09 cs3se_edgenet_x,256,1024.0,1593.53,642.587,11.53,12.94,50.72 nf_regnet_b4,320,1024.0,1592.57,642.975,3.29,19.88,30.21 swinv2_cr_tiny_ns_224,224,1024.0,1590.7,643.731,4.66,28.45,28.33 sequencer2d_s,224,1024.0,1586.65,645.372,4.96,11.31,27.65 tf_efficientnetv2_s,300,1024.0,1583.75,646.555,5.35,22.73,21.46 densenet121,288,1024.0,1581.16,647.615,4.74,11.41,7.98 resnet51q,288,1024.0,1581.05,647.659,8.07,20.94,35.7 regnetz_d8,256,1024.0,1580.57,647.855,3.97,23.74,23.37 resmlp_36_224,224,1024.0,1577.5,649.116,8.91,16.33,44.69 mixer_l32_224,224,1024.0,1577.26,649.215,11.27,19.86,206.94 regnetz_040,256,1024.0,1574.58,650.32,4.06,24.19,27.12 vit_base_patch16_224_miil,224,1024.0,1574.06,650.535,16.88,16.5,94.4 botnet50ts_256,256,512.0,1573.5,325.38,5.54,22.23,22.74 resnet50_gn,288,1024.0,1570.23,652.122,6.85,18.37,25.56 vit_base_patch16_clip_224,224,1024.0,1569.93,652.248,16.87,16.49,86.57 cs3darknet_x,288,1024.0,1569.68,652.352,10.6,14.36,35.05 deit_base_distilled_patch16_224,224,1024.0,1568.26,652.942,16.95,16.58,87.34 vit_base_patch16_224,224,1024.0,1568.03,653.038,16.87,16.49,86.57 deit_base_patch16_224,224,1024.0,1567.8,653.131,16.87,16.49,86.57 regnetz_040_h,256,1024.0,1564.2,654.638,4.12,24.29,28.94 resnetv2_50d_gn,288,1024.0,1555.81,658.164,7.24,19.7,25.57 resnetv2_50d_frn,224,1024.0,1553.07,659.326,4.33,11.92,25.59 tresnet_l,224,1024.0,1528.92,669.739,10.9,11.9,55.99 regnety_080_tv,224,1024.0,1528.54,669.91,8.51,19.73,39.38 resnetaa50d,288,1024.0,1524.48,671.692,8.92,20.57,25.58 nf_resnet50,288,1024.0,1524.41,671.724,6.88,18.37,25.56 caformer_s18,224,1024.0,1522.76,672.449,3.9,15.18,26.34 resnext101_32x8d,176,1024.0,1521.82,672.868,10.33,19.37,88.79 seresnet50t,288,1024.0,1518.59,674.299,7.14,19.55,28.1 ecaresnet50t,288,1024.0,1518.21,674.465,7.14,19.55,25.57 mvitv2_tiny,224,1024.0,1518.01,674.556,4.7,21.16,24.17 resnet101d,256,1024.0,1517.18,674.926,10.55,22.25,44.57 pvt_v2_b3,224,1024.0,1516.27,675.326,6.71,33.8,45.24 maxvit_tiny_rw_224,224,768.0,1513.7,507.357,4.93,28.54,29.06 ecaresnet50d,288,1024.0,1510.36,677.975,7.19,19.72,25.58 convnextv2_nano,288,768.0,1503.98,510.637,4.06,13.84,15.62 halo2botnet50ts_256,256,1024.0,1499.3,682.975,5.02,21.78,22.64 cs3sedarknet_x,288,1024.0,1498.9,683.158,10.6,14.37,35.4 res2net50_26w_8s,224,1024.0,1498.8,683.201,8.37,17.95,48.4 resnext101_32x4d,224,1024.0,1496.35,684.32,8.01,21.23,44.18 deit3_base_patch16_224,224,1024.0,1488.08,688.122,16.87,16.49,86.59 regnetz_c16,320,1024.0,1478.43,692.615,3.92,25.88,13.46 resnest50d_4s2x40d,224,1024.0,1478.06,692.785,4.4,17.94,30.42 resnetblur50,288,1024.0,1477.0,693.285,8.52,19.87,25.56 skresnext50_32x4d,224,1024.0,1470.18,696.502,4.5,17.18,27.48 efficientvit_l2,256,1024.0,1466.16,698.41,9.09,25.49,63.71 eca_nfnet_l0,288,1024.0,1463.28,699.787,7.12,17.29,24.14 mobilevitv2_200,256,768.0,1462.66,525.062,7.22,32.15,18.45 nfnet_l0,288,1024.0,1461.21,700.775,7.13,17.29,35.07 resnet61q,288,1024.0,1460.17,701.277,9.87,21.52,36.85 vit_base_patch32_clip_448,448,1024.0,1456.81,702.892,17.21,16.49,88.34 vit_small_patch16_36x1_224,224,1024.0,1454.45,704.036,12.63,24.59,64.67 vit_small_resnet50d_s16_224,224,1024.0,1451.55,705.439,13.0,21.12,57.53 beit_base_patch16_224,224,1024.0,1443.54,709.354,16.87,16.49,86.53 res2net101_26w_4s,224,1024.0,1442.54,709.848,8.1,18.45,45.21 vit_base_patch16_siglip_224,224,1024.0,1439.5,711.343,17.02,16.71,92.88 vit_base_patch16_gap_224,224,1024.0,1436.45,712.857,16.78,16.41,86.57 regnety_040_sgn,288,1024.0,1436.16,712.999,6.67,20.3,20.65 beitv2_base_patch16_224,224,1024.0,1436.01,713.075,16.87,16.49,86.53 convit_small,224,1024.0,1431.38,715.383,5.76,17.87,27.78 edgenext_base,320,1024.0,1423.6,719.289,6.01,24.32,18.51 convformer_s18,224,1024.0,1421.81,720.197,3.96,15.82,26.77 focalnet_small_srf,224,1024.0,1419.82,721.204,8.62,26.26,49.89 densenetblur121d,288,1024.0,1416.47,722.914,5.14,13.06,8.0 poolformer_s36,224,1024.0,1415.39,723.463,5.0,15.82,30.86 resnetv2_50d_evos,224,1024.0,1415.09,723.614,4.33,11.92,25.59 coatnet_rmlp_1_rw_224,224,1024.0,1413.05,724.664,7.44,28.08,41.69 res2net101d,224,1024.0,1406.68,727.943,8.35,19.25,45.23 legacy_xception,299,1024.0,1405.99,728.302,8.4,35.83,22.86 vit_small_patch16_18x2_224,224,1024.0,1405.24,728.689,12.63,24.59,64.67 resnetblur50d,288,1024.0,1403.3,729.695,8.92,21.19,25.58 resnext50_32x4d,288,1024.0,1402.5,730.115,7.04,23.81,25.03 inception_next_small,224,1024.0,1397.1,732.931,8.36,19.27,49.37 repvgg_b2g4,224,1024.0,1392.83,735.183,12.63,12.9,61.76 gcvit_tiny,224,1024.0,1390.57,736.376,4.79,29.82,28.22 vit_relpos_base_patch16_clsgap_224,224,1024.0,1386.7,738.433,16.88,17.72,86.43 vit_base_patch16_clip_quickgelu_224,224,1024.0,1384.47,739.621,16.87,16.49,86.19 vit_relpos_base_patch16_cls_224,224,1024.0,1384.18,739.775,16.88,17.72,86.43 dpn92,224,1024.0,1380.04,741.995,6.54,18.21,37.67 seresnetaa50d,288,1024.0,1379.8,742.125,8.92,20.59,28.11 vit_small_patch16_384,384,1024.0,1379.23,742.429,12.45,24.15,22.2 nf_ecaresnet101,224,1024.0,1375.27,744.569,8.01,16.27,44.55 nf_seresnet101,224,1024.0,1370.83,746.983,8.02,16.27,49.33 efficientnet_b3_gn,320,384.0,1366.12,281.077,2.14,28.83,11.73 vgg16_bn,224,1024.0,1361.56,752.067,15.5,13.56,138.37 flexivit_base,240,1024.0,1360.19,752.822,19.35,18.92,86.59 efficientformerv2_s0,224,1024.0,1357.83,754.133,0.41,5.3,3.6 resnetv2_152,224,1024.0,1356.74,754.735,11.55,22.56,60.19 seresnext101_32x4d,224,1024.0,1356.08,755.105,8.02,21.26,48.96 legacy_seresnext101_32x4d,224,1024.0,1355.29,755.543,8.02,21.26,48.96 efficientnet_b3_g8_gn,288,768.0,1342.01,572.264,2.59,23.35,14.25 efficientvit_b3,256,768.0,1340.35,572.972,5.2,35.01,48.65 efficientnet_b4,320,512.0,1338.46,382.52,3.13,34.76,19.34 nfnet_f0,256,1024.0,1336.25,766.311,12.62,18.05,71.49 resnext50d_32x4d,288,1024.0,1335.71,766.62,7.44,25.13,25.05 focalnet_small_lrf,224,1024.0,1333.55,767.863,8.74,28.61,50.34 resnet152,224,1024.0,1331.42,769.094,11.56,22.56,60.19 ese_vovnet99b,224,1024.0,1328.91,770.544,16.51,11.27,63.2 resnetv2_152d,224,1024.0,1322.45,774.307,11.8,23.36,60.2 regnetx_120,224,1024.0,1317.68,777.11,12.13,21.37,46.11 hrnet_w32,224,1024.0,1308.75,782.414,8.97,22.02,41.23 xception41p,299,512.0,1308.08,391.403,9.25,39.86,26.91 vit_relpos_base_patch16_224,224,1024.0,1306.59,783.71,16.8,17.63,86.43 xcit_tiny_12_p8_224,224,1024.0,1306.3,783.883,4.81,23.6,6.71 coatnet_1_rw_224,224,1024.0,1303.02,785.857,7.63,27.22,41.72 resnet152c,224,1024.0,1301.97,786.489,11.8,23.36,60.21 coatnet_rmlp_1_rw2_224,224,1024.0,1300.63,787.299,7.71,32.74,41.72 twins_pcpvt_large,224,1024.0,1297.56,789.162,9.53,30.21,60.99 maxvit_tiny_tf_224,224,768.0,1297.26,592.007,5.42,31.21,30.92 resnet152d,224,1024.0,1296.94,789.538,11.8,23.36,60.21 cs3edgenet_x,288,1024.0,1296.8,789.626,14.59,16.36,47.82 vit_base_patch16_xp_224,224,1024.0,1295.7,790.295,16.85,16.49,86.51 poolformerv2_s24,224,1024.0,1287.82,795.129,3.42,10.68,21.34 dla169,224,1024.0,1280.41,799.732,11.6,20.2,53.39 efficientnet_el_pruned,300,1024.0,1280.32,799.789,8.0,30.7,10.59 efficientnet_el,300,1024.0,1279.02,800.603,8.0,30.7,10.59 seresnext50_32x4d,288,1024.0,1276.82,801.978,7.04,23.82,27.56 hrnet_w30,224,1024.0,1276.63,802.098,8.15,21.21,37.71 deit3_small_patch16_384,384,1024.0,1274.41,803.494,12.45,24.15,22.21 ecaresnet50t,320,1024.0,1274.01,803.751,8.82,24.13,25.57 maxxvit_rmlp_tiny_rw_256,256,768.0,1269.37,605.011,6.36,32.69,29.64 volo_d1_224,224,1024.0,1269.05,806.894,6.94,24.43,26.63 vgg19,224,1024.0,1264.63,809.714,19.63,14.86,143.67 convnext_base,224,1024.0,1259.04,813.306,15.38,28.75,88.59 rexnetr_300,288,512.0,1257.05,407.293,5.59,36.61,34.81 vit_base_patch16_rpn_224,224,1024.0,1255.24,815.771,16.78,16.41,86.54 densenet161,224,1024.0,1254.96,815.95,7.79,11.06,28.68 efficientformerv2_s1,224,1024.0,1251.09,818.477,0.67,7.66,6.19 regnety_120,224,1024.0,1250.69,818.739,12.14,21.38,51.82 twins_svt_base,224,1024.0,1249.89,819.258,8.36,20.42,56.07 tf_efficientnet_el,300,1024.0,1249.79,819.323,8.0,30.7,10.59 sequencer2d_m,224,1024.0,1238.3,826.927,6.55,14.26,38.31 nest_small,224,1024.0,1229.99,832.512,9.41,22.88,38.35 maxvit_tiny_rw_256,256,768.0,1229.06,624.855,6.44,37.27,29.07 maxvit_rmlp_tiny_rw_256,256,768.0,1228.3,625.245,6.47,39.84,29.15 repvgg_b2,224,1024.0,1219.54,839.651,20.45,12.9,89.02 nest_small_jx,224,1024.0,1219.36,839.775,9.41,22.88,38.35 mixnet_xxl,224,768.0,1211.88,633.716,2.04,23.43,23.96 resnet152s,224,1024.0,1205.05,849.747,12.92,24.96,60.32 swin_small_patch4_window7_224,224,1024.0,1202.25,851.724,8.77,27.47,49.61 inception_v4,299,1024.0,1191.21,859.617,12.28,15.09,42.68 swinv2_tiny_window8_256,256,1024.0,1191.2,859.622,5.96,24.57,28.35 legacy_seresnet152,224,1024.0,1187.19,862.527,11.33,22.08,66.82 coatnet_1_224,224,512.0,1184.08,432.392,8.28,31.3,42.23 xcit_small_24_p16_224,224,1024.0,1178.16,869.138,9.1,23.63,47.67 vit_relpos_base_patch16_rpn_224,224,1024.0,1177.44,869.665,16.8,17.63,86.41 eca_nfnet_l1,256,1024.0,1175.13,871.38,9.62,22.04,41.41 seresnet152,224,1024.0,1173.43,872.64,11.57,22.61,66.82 maxvit_tiny_pm_256,256,768.0,1169.83,656.496,6.31,40.82,30.09 crossvit_base_240,240,1024.0,1165.77,878.374,20.13,22.67,105.03 efficientnet_lite4,380,384.0,1155.38,332.349,4.04,45.66,13.01 xception41,299,512.0,1153.48,443.864,9.28,39.86,26.97 regnetx_160,224,1024.0,1153.37,887.82,15.99,25.52,54.28 vgg19_bn,224,1024.0,1151.34,889.391,19.66,14.86,143.68 cait_xxs36_224,224,1024.0,1139.1,898.942,3.77,30.34,17.3 tresnet_xl,224,1024.0,1138.98,899.04,15.2,15.34,78.44 tnt_s_patch16_224,224,1024.0,1134.46,902.62,5.24,24.37,23.76 davit_base,224,1024.0,1133.31,903.534,15.36,36.72,87.95 dm_nfnet_f0,256,1024.0,1132.28,904.361,12.62,18.05,71.49 resnetv2_101,288,1024.0,1131.44,905.029,12.94,26.83,44.54 mvitv2_small_cls,224,1024.0,1129.19,906.833,7.04,28.17,34.87 mvitv2_small,224,1024.0,1128.19,907.64,7.0,28.08,34.87 coat_tiny,224,1024.0,1126.07,909.345,4.35,27.2,5.5 convmixer_1024_20_ks9_p14,224,1024.0,1123.31,911.577,5.55,5.51,24.38 vit_base_patch16_reg8_gap_256,256,1024.0,1115.77,917.744,22.6,22.09,86.62 fastvit_sa24,256,1024.0,1114.43,918.841,3.79,23.92,21.55 repvgg_b3g4,224,1024.0,1113.37,919.717,17.89,15.1,83.83 convnext_small,288,1024.0,1110.94,921.731,14.39,35.65,50.22 vit_base_patch16_siglip_256,256,1024.0,1108.01,924.168,22.23,21.83,92.93 resnet101,288,1024.0,1104.31,927.267,12.95,26.83,44.55 dla102x2,224,1024.0,1104.21,927.342,9.34,29.91,41.28 pvt_v2_b4,224,1024.0,1101.67,929.481,9.83,48.14,62.56 vit_large_r50_s32_224,224,1024.0,1091.33,938.289,19.45,22.22,328.99 eva02_base_patch16_clip_224,224,1024.0,1090.31,939.167,16.9,18.91,86.26 vgg13_bn,224,1024.0,1090.15,939.306,11.33,12.25,133.05 resnet152d,256,1024.0,1089.57,939.806,15.41,30.51,60.21 nf_regnet_b4,384,1024.0,1089.51,939.86,4.7,28.61,30.21 efficientnet_b3_g8_gn,320,768.0,1085.43,707.541,3.2,28.83,14.25 vit_small_r26_s32_384,384,1024.0,1083.82,944.797,10.24,27.67,36.47 efficientvit_l2,288,1024.0,1083.69,944.906,11.51,32.19,63.71 efficientnetv2_s,384,1024.0,1081.44,946.869,8.44,35.77,21.46 tf_efficientnet_lite4,380,384.0,1073.72,357.628,4.04,45.66,13.01 pvt_v2_b5,224,1024.0,1068.28,958.536,11.39,44.23,81.96 hrnet_w18_ssld,288,1024.0,1066.01,960.575,7.14,26.96,21.3 tf_efficientnetv2_s,384,1024.0,1054.1,971.431,8.44,35.77,21.46 regnety_160,224,1024.0,1046.76,978.242,15.96,23.04,83.59 samvit_base_patch16_224,224,1024.0,1027.37,996.713,16.83,17.2,86.46 convnext_tiny,384,768.0,1026.31,748.299,13.14,39.48,28.59 wide_resnet50_2,288,1024.0,1025.91,998.129,18.89,23.81,68.88 efficientnetv2_rw_s,384,1024.0,1024.66,999.343,8.72,38.03,23.94 vgg16,224,1024.0,1020.44,1003.475,15.47,13.56,138.36 cs3se_edgenet_x,320,1024.0,1009.45,1014.397,18.01,20.21,50.72 vit_base_patch16_plus_240,240,1024.0,1002.7,1021.234,26.31,22.07,117.56 swinv2_cr_small_224,224,1024.0,1001.72,1022.232,9.07,50.27,49.7 dpn98,224,1024.0,998.61,1025.406,11.73,25.2,61.57 efficientvit_b3,288,768.0,996.43,770.744,6.58,44.2,48.65 resnetaa101d,288,1024.0,996.18,1027.911,15.07,29.03,44.57 wide_resnet101_2,224,1024.0,994.0,1030.164,22.8,21.23,126.89 regnetz_d32,320,1024.0,994.0,1030.165,9.33,37.08,27.58 swinv2_cr_small_ns_224,224,1024.0,991.13,1033.149,9.08,50.27,49.7 focalnet_base_srf,224,1024.0,990.91,1033.385,15.28,35.01,88.15 convnextv2_small,224,1024.0,989.67,1034.674,8.71,21.56,50.32 resnet200,224,1024.0,987.28,1037.18,15.07,32.19,64.67 convnextv2_tiny,288,768.0,983.87,780.578,7.39,22.21,28.64 seresnet101,288,1024.0,983.64,1041.016,12.95,26.87,49.33 vit_small_patch8_224,224,1024.0,981.8,1042.968,16.76,32.86,21.67 regnetz_d8,320,1024.0,980.9,1043.922,6.19,37.08,23.37 regnety_080,288,1024.0,977.86,1047.177,13.22,29.69,39.18 inception_next_base,224,1024.0,977.1,1047.988,14.85,25.69,86.67 vit_base_r50_s16_224,224,1024.0,974.47,1050.816,20.94,27.88,97.89 resnest101e,256,1024.0,968.0,1057.838,13.38,28.66,48.28 convnext_base,256,1024.0,965.93,1060.101,20.09,37.55,88.59 regnetz_c16_evos,256,768.0,965.5,795.429,2.48,16.57,13.49 regnetz_040,320,512.0,964.02,531.096,6.35,37.78,27.12 poolformer_m36,224,1024.0,963.9,1062.337,8.8,22.02,56.17 regnetz_b16_evos,288,768.0,961.28,798.923,2.36,16.43,9.74 inception_resnet_v2,299,1024.0,958.82,1067.962,13.18,25.06,55.84 regnetz_040_h,320,512.0,958.46,534.182,6.43,37.94,28.94 seresnet152d,256,1024.0,956.44,1070.629,15.42,30.56,66.84 ecaresnet101d,288,1024.0,951.62,1076.05,13.35,28.19,44.57 regnety_064,288,1024.0,949.24,1078.741,10.56,27.11,30.58 resnetrs152,256,1024.0,948.32,1079.798,15.59,30.83,86.62 resnext101_64x4d,224,1024.0,947.79,1080.397,15.52,31.21,83.46 regnetv_064,288,1024.0,947.23,1081.038,10.55,27.11,30.58 xception65p,299,512.0,944.43,542.118,13.91,52.48,39.82 resnetblur101d,288,1024.0,942.52,1086.438,15.07,29.65,44.57 resnetrs101,288,1024.0,941.79,1087.277,13.56,28.53,63.62 focalnet_base_lrf,224,1024.0,941.31,1087.831,15.43,38.13,88.75 resnext101_32x8d,224,1024.0,939.44,1090.002,16.48,31.21,88.79 repvgg_b3,224,1024.0,933.91,1096.448,29.16,15.1,123.09 hrnet_w40,224,1024.0,931.96,1098.75,12.75,25.29,57.56 nfnet_f1,224,1024.0,924.88,1107.159,17.87,22.94,132.63 eva02_small_patch14_336,336,1024.0,923.99,1108.223,12.41,27.7,22.13 resnet101d,320,1024.0,923.18,1109.193,16.48,34.77,44.57 xcit_tiny_24_p16_384,384,1024.0,910.96,1124.082,6.87,34.29,12.12 efficientnet_b4,384,384.0,908.88,422.486,4.51,50.04,19.34 cait_s24_224,224,1024.0,904.24,1132.424,9.35,40.58,46.92 mobilevitv2_150,384,256.0,899.17,284.697,9.2,54.25,10.59 maxvit_rmlp_small_rw_224,224,768.0,898.81,854.449,10.48,42.44,64.9 coat_mini,224,1024.0,894.78,1144.406,6.82,33.68,10.34 coat_lite_medium,224,1024.0,892.4,1147.459,9.81,40.06,44.57 efficientnetv2_m,320,1024.0,889.26,1151.505,11.01,39.97,54.14 seresnext101_64x4d,224,1024.0,888.73,1152.196,15.53,31.25,88.23 gmlp_b16_224,224,1024.0,884.5,1157.706,15.78,30.21,73.08 seresnext101_32x8d,224,1024.0,883.56,1158.934,16.48,31.25,93.57 swin_s3_small_224,224,768.0,879.87,872.841,9.43,37.84,49.74 vit_relpos_base_patch16_plus_240,240,1024.0,875.04,1170.215,26.21,23.41,117.38 efficientformer_l7,224,1024.0,873.11,1172.808,10.17,24.45,82.23 nest_base,224,1024.0,870.02,1176.974,16.71,30.51,67.72 poolformerv2_s36,224,1024.0,869.16,1178.141,5.01,15.82,30.79 maxvit_small_tf_224,224,512.0,868.0,589.85,11.39,46.31,68.93 seresnext101d_32x8d,224,1024.0,866.35,1181.949,16.72,32.05,93.59 nest_base_jx,224,1024.0,862.67,1187.001,16.71,30.51,67.72 levit_384_s8,224,512.0,854.68,599.045,9.98,35.86,39.12 regnetz_e8,256,1024.0,853.36,1199.952,9.91,40.94,57.7 swin_base_patch4_window7_224,224,1024.0,852.78,1200.762,15.47,36.63,87.77 coatnet_2_rw_224,224,512.0,852.23,600.767,14.55,39.37,73.87 tf_efficientnet_b4,380,384.0,851.5,450.956,4.49,49.49,19.34 gcvit_small,224,1024.0,841.82,1216.401,8.57,41.61,51.09 convnextv2_nano,384,512.0,841.68,608.3,7.22,24.61,15.62 resnetv2_50d_evos,288,1024.0,840.21,1218.735,7.15,19.7,25.59 levit_conv_384_s8,224,512.0,839.77,609.68,9.98,35.86,39.12 xception65,299,512.0,839.39,609.953,13.96,52.48,39.92 hrnet_w44,224,1024.0,835.38,1225.779,14.94,26.92,67.06 crossvit_15_dagger_408,408,1024.0,833.7,1228.252,16.07,37.0,28.5 tiny_vit_21m_384,384,512.0,827.46,618.747,11.94,46.84,21.23 twins_svt_large,224,1024.0,824.23,1242.353,14.84,27.23,99.27 seresnextaa101d_32x8d,224,1024.0,820.77,1247.602,17.25,34.16,93.59 xcit_medium_24_p16_224,224,1024.0,820.51,1247.988,16.13,31.71,84.4 eva02_base_patch14_224,224,1024.0,819.51,1249.51,22.0,24.67,85.76 coatnet_rmlp_2_rw_224,224,512.0,814.13,628.885,14.64,44.94,73.88 hrnet_w48_ssld,224,1024.0,812.33,1260.551,17.34,28.56,77.47 hrnet_w48,224,1024.0,811.26,1262.228,17.34,28.56,77.47 caformer_s36,224,1024.0,810.13,1263.986,7.55,29.29,39.3 tresnet_m,448,1024.0,809.9,1264.343,22.99,29.21,31.39 resnet200d,256,1024.0,803.17,1274.938,20.0,43.09,64.69 sequencer2d_l,224,1024.0,802.78,1275.557,9.74,22.12,54.3 maxxvit_rmlp_small_rw_256,256,768.0,801.57,958.106,14.21,47.76,66.01 swinv2_base_window12_192,192,1024.0,799.54,1280.724,11.9,39.72,109.28 dm_nfnet_f1,224,1024.0,798.67,1282.118,17.87,22.94,132.63 coatnet_2_224,224,512.0,796.89,642.486,15.94,42.41,74.68 vit_medium_patch16_gap_384,384,1024.0,795.07,1287.922,22.01,32.15,39.03 mvitv2_base_cls,224,1024.0,791.15,1294.298,10.23,40.65,65.44 mvitv2_base,224,1024.0,785.87,1303.007,10.16,40.5,51.47 efficientnetv2_rw_m,320,1024.0,785.27,1303.997,12.72,47.14,53.24 resnet152,288,1024.0,781.77,1309.827,19.11,37.28,60.19 swinv2_tiny_window16_256,256,512.0,775.64,660.087,6.68,39.02,28.35 fastvit_sa36,256,1024.0,768.44,1332.545,5.62,34.02,31.53 xcit_small_12_p16_384,384,1024.0,764.7,1339.074,14.14,36.5,26.25 convnext_base,288,1024.0,763.36,1341.427,25.43,47.53,88.59 convformer_s36,224,1024.0,754.92,1356.424,7.67,30.5,40.01 regnety_120,288,768.0,738.36,1040.13,20.06,35.34,51.82 swinv2_small_window8_256,256,1024.0,737.99,1387.548,11.58,40.14,49.73 dpn131,224,1024.0,732.6,1397.744,16.09,32.97,79.25 swinv2_cr_small_ns_256,256,1024.0,731.79,1399.291,12.07,76.21,49.7 mobilevitv2_175,384,256.0,731.75,349.838,12.47,63.29,14.25 convit_base,224,1024.0,730.43,1401.91,17.52,31.77,86.54 resnetv2_50x1_bit,448,512.0,729.61,701.734,16.62,44.46,25.55 poolformer_m48,224,1024.0,727.01,1408.491,11.59,29.17,73.47 maxvit_rmlp_small_rw_256,256,768.0,724.69,1059.745,13.69,55.48,64.9 tnt_b_patch16_224,224,1024.0,721.67,1418.912,14.09,39.01,65.41 eca_nfnet_l1,320,1024.0,720.22,1421.77,14.92,34.42,41.41 swinv2_cr_base_224,224,1024.0,716.89,1428.383,15.86,59.66,87.88 swin_s3_base_224,224,1024.0,715.81,1430.534,13.69,48.26,71.13 volo_d2_224,224,1024.0,711.4,1439.408,14.34,41.34,58.68 swinv2_cr_base_ns_224,224,1024.0,711.07,1440.068,15.86,59.66,87.88 convnextv2_base,224,768.0,708.71,1083.64,15.38,28.75,88.72 densenet264d,224,1024.0,697.85,1467.348,13.57,14.0,72.74 ecaresnet200d,256,1024.0,697.3,1468.506,20.0,43.15,64.69 seresnet200d,256,1024.0,696.92,1469.301,20.01,43.15,71.86 nf_regnet_b5,384,1024.0,694.76,1473.879,7.95,42.9,49.74 seresnet152,288,1024.0,693.47,1476.616,19.11,37.34,66.82 resnetrs200,256,1024.0,693.26,1477.057,20.18,43.42,93.21 coat_small,224,1024.0,689.68,1484.732,12.61,44.25,21.69 convnext_large,224,1024.0,686.69,1491.207,34.4,43.13,197.77 xcit_tiny_24_p8_224,224,1024.0,684.2,1496.615,9.21,45.38,12.11 efficientvit_l3,224,1024.0,667.4,1534.307,27.62,39.16,246.04 dpn107,224,1024.0,666.43,1536.527,18.38,33.46,86.92 resnet152d,320,1024.0,664.6,1540.768,24.08,47.67,60.21 senet154,224,1024.0,664.59,1540.791,20.77,38.69,115.09 legacy_senet154,224,1024.0,663.62,1543.045,20.77,38.69,115.09 efficientformerv2_s2,224,1024.0,658.11,1555.962,1.27,11.77,12.71 maxxvitv2_rmlp_base_rw_224,224,768.0,650.48,1180.654,23.88,54.39,116.09 xcit_nano_12_p8_384,384,1024.0,649.92,1575.56,6.34,46.06,3.05 xception71,299,512.0,649.47,788.325,18.09,69.92,42.34 vit_large_patch32_384,384,1024.0,643.51,1591.268,44.28,32.22,306.63 mobilevitv2_200,384,256.0,640.82,399.48,16.24,72.34,18.45 davit_large,224,1024.0,630.01,1625.361,34.37,55.08,196.81 hrnet_w64,224,1024.0,629.26,1627.299,28.97,35.09,128.06 convnext_small,384,768.0,628.81,1221.341,25.58,63.37,50.22 regnetz_d8_evos,256,1024.0,626.83,1633.604,4.5,24.92,23.46 regnety_160,288,768.0,626.54,1225.759,26.37,38.07,83.59 convnext_base,320,768.0,617.04,1244.641,31.39,58.68,88.59 fastvit_ma36,256,1024.0,615.75,1662.995,7.85,40.39,44.07 tf_efficientnetv2_m,384,1024.0,614.24,1667.09,15.85,57.52,54.14 gcvit_base,224,1024.0,612.92,1670.669,14.87,55.48,90.32 regnety_320,224,1024.0,612.34,1672.272,32.34,30.26,145.05 efficientvit_l2,384,768.0,610.03,1258.949,20.45,57.01,63.71 poolformerv2_m36,224,1024.0,609.2,1680.886,8.81,22.02,56.08 regnetz_c16_evos,320,512.0,608.23,841.78,3.86,25.88,13.49 resnetv2_50x3_bit,224,768.0,585.49,1311.719,37.06,33.34,217.32 seresnet152d,320,1024.0,585.32,1749.453,24.09,47.72,66.84 xcit_small_12_p8_224,224,1024.0,584.75,1751.159,18.69,47.19,26.21 resnet200,288,1024.0,584.49,1751.952,24.91,53.21,64.67 resnetrs152,320,1024.0,580.71,1763.336,24.34,48.14,86.62 caformer_m36,224,1024.0,580.7,1763.373,12.75,40.61,56.2 resnext101_64x4d,288,1024.0,579.65,1766.578,25.66,51.59,83.46 levit_conv_512_s8,224,256.0,579.33,441.879,21.82,52.28,74.05 crossvit_18_dagger_408,408,1024.0,578.67,1769.56,25.31,49.38,44.61 levit_512_s8,224,256.0,564.15,453.77,21.82,52.28,74.05 convnextv2_tiny,384,384.0,553.95,693.189,13.14,39.48,28.64 convformer_m36,224,1024.0,546.86,1872.507,12.89,42.05,57.05 efficientnet_b5,416,256.0,546.68,468.268,8.27,80.68,30.39 seresnet269d,256,1024.0,545.35,1877.679,26.59,53.6,113.67 efficientvit_l3,256,768.0,542.99,1414.373,36.06,50.98,246.04 seresnext101_32x8d,288,1024.0,537.9,1903.669,27.24,51.63,93.57 efficientnetv2_m,416,1024.0,531.24,1927.549,18.6,67.5,54.14 resnetrs270,256,1024.0,529.33,1934.515,27.06,55.84,129.86 maxvit_rmlp_base_rw_224,224,768.0,529.1,1451.502,22.63,79.3,116.14 swinv2_base_window8_256,256,1024.0,528.71,1936.775,20.37,52.59,87.92 regnetz_e8,320,768.0,528.46,1453.264,15.46,63.94,57.7 seresnext101d_32x8d,288,1024.0,527.36,1941.726,27.64,52.95,93.59 convnext_large_mlp,256,768.0,525.72,1460.834,44.94,56.33,200.13 nfnet_f2,256,1024.0,524.14,1953.657,33.76,41.85,193.78 halonet_h1,256,256.0,522.84,489.621,3.0,51.17,8.1 regnetx_320,224,1024.0,522.6,1959.408,31.81,36.3,107.81 mixer_l16_224,224,1024.0,520.22,1968.376,44.6,41.69,208.2 resnext101_32x16d,224,1024.0,519.8,1969.975,36.27,51.18,194.03 eca_nfnet_l2,320,1024.0,509.51,2009.758,20.95,47.43,56.72 ecaresnet200d,288,1024.0,503.74,2032.793,25.31,54.59,64.69 seresnet200d,288,1024.0,503.36,2034.329,25.32,54.6,71.86 caformer_s18,384,512.0,501.38,1021.162,11.45,44.61,26.34 volo_d3_224,224,1024.0,497.87,2056.757,20.78,60.09,86.33 resnet200d,320,1024.0,493.82,2073.621,31.25,67.33,64.69 swin_large_patch4_window7_224,224,768.0,492.35,1559.852,34.53,54.94,196.53 vit_base_patch16_18x2_224,224,1024.0,492.32,2079.918,50.37,49.17,256.73 deit_base_patch16_384,384,1024.0,491.82,2082.046,49.4,48.3,86.86 vit_base_patch16_clip_384,384,1024.0,491.74,2082.405,49.41,48.3,86.86 vit_base_patch16_384,384,1024.0,491.42,2083.727,49.4,48.3,86.86 deit_base_distilled_patch16_384,384,1024.0,491.32,2084.164,49.49,48.39,87.63 hrnet_w48_ssld,288,1024.0,490.92,2085.876,28.66,47.21,77.47 eva_large_patch14_196,196,1024.0,490.45,2087.863,59.66,43.77,304.14 maxvit_base_tf_224,224,512.0,488.88,1047.285,23.52,81.67,119.47 efficientnet_b5,448,256.0,488.83,523.691,9.59,93.56,30.39 vit_large_patch16_224,224,1024.0,488.5,2096.219,59.7,43.77,304.33 swinv2_small_window16_256,256,512.0,486.59,1052.215,12.82,66.29,49.73 swinv2_large_window12_192,192,768.0,485.58,1581.6,26.17,56.53,228.77 convformer_s18,384,512.0,484.08,1057.663,11.63,46.49,26.77 seresnextaa101d_32x8d,288,1024.0,479.96,2133.497,28.51,56.44,93.59 coatnet_3_rw_224,224,256.0,478.44,535.067,32.63,59.07,181.81 coatnet_rmlp_3_rw_224,224,256.0,477.75,535.833,32.75,64.7,165.15 xcit_large_24_p16_224,224,1024.0,472.07,2169.166,35.86,47.26,189.1 vit_small_patch14_dinov2,518,1024.0,469.29,2181.987,29.46,57.34,22.06 deit3_base_patch16_384,384,1024.0,466.88,2193.286,49.4,48.3,86.88 deit3_large_patch16_224,224,1024.0,466.56,2194.777,59.7,43.77,304.37 efficientnetv2_rw_m,416,768.0,466.5,1646.281,21.49,79.62,53.24 nfnet_f1,320,1024.0,466.35,2195.774,35.97,46.77,132.63 nf_regnet_b5,456,768.0,464.5,1653.385,11.7,61.95,49.74 coatnet_3_224,224,256.0,464.1,551.594,35.72,63.61,166.97 vit_small_patch14_reg4_dinov2,518,1024.0,460.4,2224.119,29.55,57.51,22.06 poolformerv2_m48,224,1024.0,459.37,2229.113,11.59,29.17,73.35 beitv2_large_patch16_224,224,1024.0,452.16,2264.697,59.7,43.77,304.43 beit_large_patch16_224,224,1024.0,452.15,2264.716,59.7,43.77,304.43 resnetv2_101x1_bit,448,512.0,451.35,1134.365,31.65,64.93,44.54 dm_nfnet_f2,256,1024.0,451.22,2269.395,33.76,41.85,193.78 vit_base_patch16_siglip_384,384,1024.0,448.34,2283.991,50.0,49.11,93.18 resnetv2_152x2_bit,224,1024.0,441.5,2319.35,46.95,45.11,236.34 convnext_xlarge,224,768.0,435.62,1762.988,60.98,57.5,350.2 maxvit_tiny_tf_384,384,256.0,434.99,588.503,16.0,94.22,30.98 efficientformerv2_l,224,1024.0,431.02,2375.769,2.59,18.54,26.32 convnext_base,384,512.0,430.72,1188.698,45.21,84.49,88.59 convnextv2_base,288,512.0,429.59,1191.832,25.43,47.53,88.72 resnetrs200,320,1024.0,428.05,2392.217,31.51,67.81,93.21 flexivit_large,240,1024.0,424.67,2411.279,68.48,50.22,304.36 convnextv2_large,224,512.0,423.49,1208.977,34.4,43.13,197.96 xcit_tiny_12_p8_384,384,1024.0,423.2,2419.661,14.12,69.12,6.71 swinv2_cr_large_224,224,768.0,422.05,1819.675,35.1,78.42,196.68 caformer_b36,224,768.0,419.19,1832.111,22.5,54.14,98.75 swinv2_cr_tiny_384,384,256.0,419.04,610.909,15.34,161.01,28.33 tf_efficientnet_b5,456,256.0,418.1,612.278,10.46,98.86,30.39 convnext_large,288,512.0,415.42,1232.482,56.87,71.29,197.77 davit_huge,224,512.0,410.45,1247.402,60.93,73.44,348.92 maxxvitv2_rmlp_large_rw_224,224,768.0,409.41,1875.861,43.69,75.4,215.42 tiny_vit_21m_512,512,384.0,408.26,940.575,21.23,83.26,21.27 xcit_small_24_p16_384,384,1024.0,408.08,2509.308,26.72,68.57,47.67 tf_efficientnetv2_m,480,768.0,405.02,1896.185,24.76,89.84,54.14 tresnet_l,448,1024.0,403.56,2537.407,43.59,47.56,55.99 beit_base_patch16_384,384,1024.0,401.76,2548.786,49.4,48.3,86.74 convformer_b36,224,768.0,396.81,1935.431,22.69,56.06,99.88 regnetz_d8_evos,320,768.0,395.82,1940.285,7.03,38.92,23.46 seresnextaa101d_32x8d,320,1024.0,395.0,2592.386,35.19,69.67,93.59 seresnet269d,288,1024.0,393.84,2600.059,33.65,67.81,113.67 dm_nfnet_f1,320,1024.0,393.6,2601.642,35.97,46.77,132.63 regnety_160,384,384.0,378.47,1014.589,46.87,67.67,83.59 vit_large_r50_s32_384,384,1024.0,372.96,2745.589,56.4,64.88,329.09 regnety_640,224,768.0,362.45,2118.906,64.16,42.5,281.38 eca_nfnet_l2,384,768.0,361.66,2123.504,30.05,68.28,56.72 vit_large_patch14_224,224,1024.0,359.79,2846.069,77.83,57.11,304.2 vit_large_patch14_clip_224,224,1024.0,359.08,2851.744,77.83,57.11,304.2 swinv2_base_window12to16_192to256,256,384.0,358.35,1071.569,22.02,84.71,87.92 swinv2_base_window16_256,256,384.0,358.25,1071.869,22.02,84.71,87.92 vit_large_patch16_siglip_256,256,1024.0,351.53,2912.942,78.12,57.42,315.96 vit_base_patch8_224,224,1024.0,350.95,2917.813,66.87,65.71,86.58 efficientvit_l3,320,512.0,346.1,1479.341,56.32,79.34,246.04 efficientnetv2_l,384,1024.0,342.83,2986.92,36.1,101.16,118.52 tf_efficientnetv2_l,384,1024.0,338.97,3020.897,36.1,101.16,118.52 ecaresnet269d,320,1024.0,337.13,3037.39,41.53,83.69,102.09 resnest200e,320,1024.0,336.33,3044.627,35.69,82.78,70.2 maxvit_large_tf_224,224,384.0,336.26,1141.954,42.99,109.57,211.79 convnext_large_mlp,320,512.0,336.03,1523.669,70.21,88.02,200.13 inception_next_base,384,512.0,335.9,1524.27,43.64,75.48,86.67 resnetv2_101x3_bit,224,768.0,334.56,2295.509,71.23,48.7,387.93 eca_nfnet_l3,352,768.0,328.62,2337.043,32.57,73.12,72.04 vit_large_patch14_clip_quickgelu_224,224,1024.0,324.15,3159.023,77.83,57.11,303.97 repvgg_d2se,320,1024.0,320.2,3197.943,74.57,46.82,133.33 vit_base_r50_s16_384,384,1024.0,317.01,3230.175,61.29,81.77,98.95 volo_d4_224,224,1024.0,317.0,3230.22,44.34,80.22,192.96 volo_d1_384,384,512.0,314.1,1630.023,22.75,108.55,26.78 vit_large_patch14_xp_224,224,1024.0,309.84,3304.92,77.77,57.11,304.06 convmixer_768_32,224,1024.0,308.6,3318.227,19.55,25.95,21.11 xcit_small_24_p8_224,224,1024.0,305.72,3349.464,35.81,90.77,47.63 resnetrs350,288,1024.0,304.48,3363.098,43.67,87.09,163.96 nasnetalarge,331,384.0,300.79,1276.642,23.89,90.56,88.75 coat_lite_medium_384,384,512.0,299.62,1708.831,28.73,116.7,44.57 tresnet_xl,448,768.0,296.15,2593.304,60.77,61.31,78.44 maxvit_small_tf_384,384,192.0,288.16,666.295,33.58,139.86,69.02 pnasnet5large,331,384.0,287.26,1336.778,25.04,92.89,86.06 xcit_medium_24_p16_384,384,1024.0,282.76,3621.451,47.39,91.63,84.4 ecaresnet269d,352,1024.0,281.17,3641.867,50.25,101.25,102.09 coatnet_4_224,224,256.0,280.04,914.128,60.81,98.85,275.43 cait_xxs24_384,384,1024.0,277.04,3696.16,9.63,122.65,12.03 coatnet_rmlp_2_rw_384,384,192.0,273.87,701.059,43.04,132.57,73.88 resnetrs270,352,1024.0,271.91,3765.914,51.13,105.48,129.86 nfnet_f2,352,768.0,270.88,2835.244,63.22,79.06,193.78 caformer_s36,384,512.0,266.29,1922.686,22.2,86.08,39.3 convnext_xlarge,288,512.0,263.75,1941.25,100.8,95.05,350.2 swinv2_cr_small_384,384,256.0,258.42,990.618,29.7,298.03,49.7 efficientnet_b6,528,128.0,257.57,496.944,19.4,167.39,43.04 convformer_s36,384,512.0,257.36,1989.401,22.54,89.62,40.01 convnextv2_large,288,256.0,256.91,996.448,56.87,71.29,197.96 eva02_large_patch14_224,224,1024.0,256.79,3987.739,77.9,65.52,303.27 eva02_large_patch14_clip_224,224,1024.0,253.51,4039.312,77.93,65.52,304.11 resnext101_32x32d,224,512.0,253.0,2023.672,87.29,91.12,468.53 maxvit_tiny_tf_512,512,192.0,249.39,769.864,28.66,172.66,31.05 tf_efficientnet_b6,528,128.0,247.44,517.29,19.4,167.39,43.04 nfnet_f3,320,1024.0,247.37,4139.575,68.77,83.93,254.92 mvitv2_large_cls,224,768.0,246.55,3114.926,42.17,111.69,234.58 vit_so400m_patch14_siglip_224,224,1024.0,246.49,4154.292,106.18,70.45,427.68 efficientnetv2_xl,384,1024.0,244.46,4188.739,52.81,139.2,208.12 mvitv2_large,224,512.0,242.6,2110.485,43.87,112.02,217.99 convnextv2_base,384,256.0,242.26,1056.699,45.21,84.49,88.72 vit_base_patch16_siglip_512,512,512.0,241.2,2122.705,88.89,87.3,93.52 convnext_large,384,384.0,234.69,1636.209,101.1,126.74,197.77 convnext_large_mlp,384,384.0,234.65,1636.476,101.11,126.74,200.13 dm_nfnet_f2,352,768.0,234.38,3276.685,63.22,79.06,193.78 tf_efficientnetv2_xl,384,1024.0,230.18,4448.679,52.81,139.2,208.12 efficientnetv2_l,480,512.0,229.94,2226.68,56.4,157.99,118.52 tf_efficientnetv2_l,480,512.0,227.38,2251.742,56.4,157.99,118.52 swin_base_patch4_window12_384,384,256.0,226.65,1129.483,47.19,134.78,87.9 regnety_320,384,384.0,225.95,1699.504,95.0,88.87,145.05 resnetrs420,320,1024.0,221.8,4616.729,64.2,126.56,191.89 xcit_tiny_24_p8_384,384,1024.0,221.03,4632.753,27.05,132.94,12.11 efficientvit_l3,384,384.0,220.15,1744.25,81.08,114.02,246.04 swinv2_large_window12to16_192to256,256,256.0,218.91,1169.41,47.81,121.53,196.74 maxxvitv2_rmlp_base_rw_384,384,384.0,215.87,1778.825,70.18,160.22,116.09 resmlp_big_24_224,224,1024.0,214.65,4770.604,100.23,87.31,129.14 dm_nfnet_f3,320,1024.0,212.33,4822.62,68.77,83.93,254.92 volo_d5_224,224,1024.0,212.3,4823.349,72.4,118.11,295.46 xcit_medium_24_p8_224,224,1024.0,210.35,4868.038,63.52,121.22,84.32 seresnextaa201d_32x8d,320,1024.0,207.05,4945.752,70.22,138.71,149.39 eca_nfnet_l3,448,512.0,204.74,2500.737,52.55,118.4,72.04 xcit_small_12_p8_384,384,512.0,195.78,2615.134,54.92,138.25,26.21 cait_xs24_384,384,768.0,193.45,3970.037,19.28,183.98,26.67 caformer_m36,384,256.0,191.51,1336.728,37.45,119.33,56.2 focalnet_huge_fl3,224,384.0,190.45,2016.221,118.26,104.8,745.28 eva02_base_patch14_448,448,512.0,189.13,2707.053,87.74,98.4,87.12 maxvit_xlarge_tf_224,224,256.0,188.97,1354.682,96.49,164.37,506.99 convformer_m36,384,384.0,186.96,2053.847,37.87,123.56,57.05 cait_xxs36_384,384,1024.0,185.14,5531.038,14.35,183.7,17.37 swinv2_cr_base_384,384,256.0,184.66,1386.338,50.57,333.68,87.88 resnetrs350,384,1024.0,184.39,5553.562,77.59,154.74,163.96 regnety_1280,224,512.0,182.89,2799.45,127.66,71.58,644.81 swinv2_cr_huge_224,224,384.0,181.27,2118.357,115.97,121.08,657.83 vit_huge_patch14_clip_224,224,1024.0,179.25,5712.71,161.99,95.07,632.05 vit_huge_patch14_224,224,1024.0,179.24,5713.082,161.99,95.07,630.76 volo_d2_384,384,384.0,177.67,2161.247,46.17,184.51,58.87 maxvit_rmlp_base_rw_384,384,384.0,177.21,2166.875,66.51,233.79,116.14 vit_base_patch14_dinov2,518,512.0,175.93,2910.275,117.11,114.68,86.58 vit_huge_patch14_gap_224,224,1024.0,175.35,5839.715,161.36,94.7,630.76 vit_base_patch14_reg4_dinov2,518,512.0,175.34,2920.066,117.45,115.02,86.58 convnextv2_huge,224,256.0,174.19,1469.676,115.0,79.07,660.29 deit3_huge_patch14_224,224,1024.0,172.49,5936.531,161.99,95.07,632.13 convmixer_1536_20,224,1024.0,172.27,5944.074,48.68,33.03,51.63 vit_huge_patch14_clip_quickgelu_224,224,1024.0,165.12,6201.386,161.99,95.07,632.08 maxvit_small_tf_512,512,96.0,163.95,585.546,60.02,256.36,69.13 maxvit_base_tf_384,384,192.0,162.75,1179.72,69.34,247.75,119.65 xcit_large_24_p16_384,384,1024.0,162.01,6320.659,105.34,137.15,189.1 resnetv2_152x2_bit,384,384.0,160.06,2399.153,136.16,132.56,236.34 vit_huge_patch14_xp_224,224,1024.0,159.21,6431.544,161.88,95.07,631.8 resnest269e,416,512.0,159.04,3219.278,77.69,171.98,110.93 eva_large_patch14_336,336,768.0,155.41,4941.906,174.74,128.21,304.53 vit_large_patch14_clip_336,336,768.0,155.09,4951.819,174.74,128.21,304.53 vit_large_patch16_384,384,768.0,154.94,4956.737,174.85,128.21,304.72 convnext_xxlarge,256,384.0,152.35,2520.42,198.09,124.45,846.47 davit_giant,224,384.0,151.56,2533.626,192.34,138.2,1406.47 resnetv2_50x3_bit,448,192.0,150.44,1276.251,145.7,133.37,217.32 coatnet_5_224,224,192.0,149.61,1283.336,142.72,143.69,687.47 efficientnetv2_xl,512,512.0,149.15,3432.877,93.85,247.32,208.12 cait_s24_384,384,512.0,148.91,3438.219,32.17,245.3,47.06 convnext_xlarge,384,256.0,148.61,1722.573,179.2,168.99,350.2 tf_efficientnetv2_xl,512,512.0,148.0,3459.525,93.85,247.32,208.12 efficientnet_b7,600,96.0,147.91,649.053,38.33,289.94,66.35 deit3_large_patch16_384,384,1024.0,147.79,6928.856,174.85,128.21,304.76 seresnextaa201d_32x8d,384,768.0,147.05,5222.537,101.11,199.72,149.39 nfnet_f3,416,512.0,146.71,3489.974,115.58,141.78,254.92 vit_giant_patch16_gap_224,224,1024.0,145.38,7043.632,198.14,103.64,1011.37 convnextv2_large,384,192.0,144.92,1324.86,101.1,126.74,197.96 resnetv2_152x4_bit,224,512.0,144.91,3533.266,186.9,90.22,936.53 vit_large_patch16_siglip_384,384,768.0,144.23,5324.878,175.76,129.18,316.28 tf_efficientnet_b7,600,96.0,143.48,669.058,38.33,289.94,66.35 nfnet_f4,384,768.0,142.67,5383.101,122.14,147.57,316.07 vit_large_patch14_clip_quickgelu_336,336,768.0,140.95,5448.604,174.74,128.21,304.29 caformer_b36,384,256.0,138.42,1849.458,66.12,159.11,98.75 swin_large_patch4_window12_384,384,128.0,135.49,944.717,104.08,202.16,196.74 convformer_b36,384,256.0,135.29,1892.221,66.67,164.75,99.88 resnetrs420,416,1024.0,130.11,7870.213,108.45,213.79,191.89 beit_large_patch16_384,384,768.0,129.31,5939.365,174.84,128.21,305.0 dm_nfnet_f3,416,512.0,127.57,4013.328,115.58,141.78,254.92 regnety_640,384,256.0,126.8,2018.836,188.47,124.83,281.38 dm_nfnet_f4,384,768.0,123.05,6241.189,122.14,147.57,316.07 focalnet_huge_fl4,224,512.0,122.81,4169.023,118.9,113.34,686.46 xcit_large_24_p8_224,224,512.0,120.1,4263.036,141.22,181.53,188.93 resnetv2_152x2_bit,448,256.0,117.91,2171.109,184.99,180.43,236.34 eva_giant_patch14_224,224,1024.0,116.71,8773.739,259.74,135.89,1012.56 eva_giant_patch14_clip_224,224,1024.0,116.64,8779.464,259.74,135.89,1012.59 vit_giant_patch14_224,224,1024.0,114.18,8968.21,259.74,135.89,1012.61 vit_giant_patch14_clip_224,224,1024.0,114.09,8975.383,259.74,135.89,1012.65 swinv2_cr_large_384,384,128.0,112.81,1134.666,108.96,404.96,196.68 maxvit_large_tf_384,384,128.0,111.17,1151.411,126.61,332.3,212.03 eva02_large_patch14_clip_336,336,1024.0,110.28,9285.405,174.97,147.1,304.43 mvitv2_huge_cls,224,384.0,107.61,3568.518,120.67,243.63,694.8 convnextv2_huge,288,128.0,105.35,1214.957,190.1,130.7,660.29 xcit_small_24_p8_384,384,512.0,102.73,4983.926,105.23,265.87,47.63 nfnet_f5,416,512.0,100.11,5114.164,170.71,204.56,377.21 cait_s36_384,384,512.0,99.61,5140.29,47.99,367.39,68.37 swinv2_base_window12to24_192to384,384,96.0,96.35,996.364,55.25,280.36,87.92 efficientnet_b8,672,96.0,95.78,1002.248,63.48,442.89,87.41 focalnet_large_fl3,384,384.0,94.47,4064.948,105.06,168.04,239.13 tf_efficientnet_b8,672,96.0,93.18,1030.252,63.48,442.89,87.41 maxvit_base_tf_512,512,96.0,92.2,1041.169,123.93,456.26,119.88 focalnet_large_fl4,384,256.0,90.17,2839.222,105.2,181.78,239.32 resnetv2_101x3_bit,448,192.0,87.88,2184.819,280.33,194.78,387.93 dm_nfnet_f5,416,512.0,86.64,5909.833,170.71,204.56,377.21 nfnet_f4,512,384.0,81.51,4711.211,216.26,262.26,316.07 volo_d3_448,448,192.0,76.74,2501.831,96.33,446.83,86.63 vit_so400m_patch14_siglip_384,384,512.0,75.92,6743.556,302.34,200.62,428.23 nfnet_f6,448,512.0,75.59,6773.482,229.7,273.62,438.36 vit_huge_patch14_clip_336,336,768.0,75.49,10173.683,363.7,213.44,632.46 xcit_medium_24_p8_384,384,384.0,71.15,5396.903,186.67,354.69,84.32 dm_nfnet_f4,512,384.0,69.56,5520.408,216.26,262.26,316.07 vit_gigantic_patch14_224,224,512.0,66.18,7736.423,473.4,204.12,1844.44 vit_gigantic_patch14_clip_224,224,512.0,66.18,7735.92,473.41,204.12,1844.91 focalnet_xlarge_fl3,384,256.0,66.07,3874.786,185.61,223.99,408.79 dm_nfnet_f6,448,512.0,65.28,7842.994,229.7,273.62,438.36 maxvit_large_tf_512,512,64.0,63.68,1005.087,225.96,611.85,212.33 focalnet_xlarge_fl4,384,192.0,63.39,3028.979,185.79,242.31,409.03 maxvit_xlarge_tf_384,384,96.0,63.2,1518.995,283.86,498.45,475.32 regnety_1280,384,128.0,62.14,2059.919,374.99,210.2,644.81 beit_large_patch16_512,512,256.0,61.47,4164.41,310.6,227.76,305.67 convnextv2_huge,384,96.0,60.73,1580.79,337.96,232.35,660.29 swinv2_large_window12to24_192to384,384,48.0,60.6,792.119,116.15,407.83,196.74 eva02_large_patch14_448,448,512.0,59.6,8591.147,310.69,261.32,305.08 tf_efficientnet_l2,475,128.0,59.14,2164.439,172.11,609.89,480.31 nfnet_f5,544,384.0,58.55,6558.595,290.97,349.71,377.21 vit_huge_patch14_clip_378,378,512.0,58.17,8801.788,460.13,270.04,632.68 volo_d4_448,448,192.0,57.2,3356.883,197.13,527.35,193.41 nfnet_f7,480,384.0,57.05,6730.663,300.08,355.86,499.5 vit_large_patch14_dinov2,518,384.0,56.81,6759.458,414.89,304.42,304.37 vit_large_patch14_reg4_dinov2,518,384.0,56.51,6795.142,416.1,305.31,304.37 vit_huge_patch14_clip_quickgelu_378,378,384.0,53.9,7123.722,460.13,270.04,632.68 swinv2_cr_giant_224,224,192.0,52.42,3662.593,483.85,309.15,2598.76 dm_nfnet_f5,544,384.0,50.82,7555.977,290.97,349.71,377.21 eva_giant_patch14_336,336,512.0,49.6,10322.486,583.14,305.1,1013.01 swinv2_cr_huge_384,384,64.0,48.85,1310.056,352.04,583.18,657.94 nfnet_f6,576,256.0,45.99,5566.397,378.69,452.2,438.36 xcit_large_24_p8_384,384,256.0,40.54,6315.135,415.0,531.74,188.93 volo_d5_448,448,192.0,39.97,4803.918,315.06,737.92,295.91 dm_nfnet_f6,576,256.0,39.68,6452.4,378.69,452.2,438.36 nfnet_f7,608,256.0,35.92,7127.91,480.39,570.85,499.5 maxvit_xlarge_tf_512,512,48.0,35.73,1343.449,505.95,917.77,475.77 regnety_2560,384,96.0,35.19,2728.299,747.83,296.49,1282.6 convnextv2_huge,512,48.0,34.07,1408.989,600.81,413.07,660.29 cait_m36_384,384,256.0,32.53,7868.895,173.11,734.79,271.22 resnetv2_152x4_bit,480,128.0,32.31,3961.512,844.84,414.26,936.53 volo_d5_512,512,96.0,27.94,3435.72,425.09,1105.37,296.09 samvit_base_patch16,1024,12.0,23.01,521.487,371.55,403.08,89.67 efficientnet_l2,800,32.0,22.53,1420.616,479.12,1707.39,480.31 tf_efficientnet_l2,800,32.0,22.12,1446.454,479.12,1707.39,480.31 vit_giant_patch14_dinov2,518,192.0,17.14,11200.639,1553.56,871.89,1136.48 vit_giant_patch14_reg4_dinov2,518,128.0,17.05,7505.847,1558.09,874.43,1136.48 swinv2_cr_giant_384,384,32.0,15.01,2131.256,1450.71,1394.86,2598.76 eva_giant_patch14_560,560,192.0,15.01,12792.976,1618.04,846.56,1014.45 cait_m48_448,448,128.0,13.76,9299.464,329.4,1708.21,356.46 samvit_large_patch16,1024,8.0,10.25,780.237,1317.08,1055.58,308.28 samvit_huge_patch16,1024,6.0,6.31,950.475,2741.59,1727.57,637.03 eva02_enormous_patch14_clip_224,224,,,,1132.46,497.58,4350.56 vit_huge_patch16_gap_448,448,,,,544.7,636.83,631.67
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/results-sketch.csv	model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff eva_giant_patch14_336.clip_ft_in1k,71.177,28.823,90.299,9.701,"1,013.01",336,1.000,bicubic,-18.289,-8.527,+6 eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,70.662,29.338,89.856,10.144,305.08,448,1.000,bicubic,-19.390,-9.192,-1 eva02_large_patch14_448.mim_m38m_ft_in1k,70.546,29.454,89.843,10.157,305.08,448,1.000,bicubic,-19.028,-9.081,+2 convnext_xxlarge.clip_laion2b_soup_ft_in1k,70.039,29.961,90.334,9.666,846.47,256,1.000,bicubic,-18.565,-8.374,+10 eva_giant_patch14_224.clip_ft_in1k,70.021,29.979,89.768,10.232,"1,012.56",224,0.900,bicubic,-18.859,-8.912,+4 eva_giant_patch14_336.m30m_ft_in22k_in1k,68.052,31.948,87.819,12.181,"1,013.01",336,1.000,bicubic,-21.514,-11.133,0 eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,67.533,32.467,87.506,12.494,305.08,448,1.000,bicubic,-22.437,-11.506,-5 eva_giant_patch14_560.m30m_ft_in22k_in1k,67.486,32.514,87.473,12.527,"1,014.45",560,1.000,bicubic,-22.300,-11.519,-5 vit_huge_patch14_clip_224.laion2b_ft_in1k,67.396,32.604,87.882,12.118,632.05,224,1.000,bicubic,-20.192,-10.337,+39 eva02_large_patch14_448.mim_in22k_ft_in1k,66.987,33.013,87.443,12.557,305.08,448,1.000,bicubic,-22.635,-11.507,-6 convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,66.435,33.565,87.349,12.651,200.13,384,1.000,bicubic,-21.413,-11.097,+31 vit_large_patch14_clip_336.laion2b_ft_in1k,65.741,34.259,86.909,13.091,304.53,336,1.000,bicubic,-22.115,-11.459,+28 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,65.731,34.269,87.017,12.983,200.13,384,1.000,bicubic,-22.575,-11.565,+10 vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,65.323,34.677,86.826,13.174,632.05,224,1.000,bicubic,-22.933,-11.726,+11 vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,65.260,34.740,86.757,13.242,632.46,336,1.000,bicubic,-23.332,-11.905,+1 convnext_large_mlp.clip_laion2b_augreg_ft_in1k,65.091,34.909,86.284,13.716,200.13,256,1.000,bicubic,-22.245,-11.934,+42 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,64.918,35.082,86.649,13.351,200.13,320,1.000,bicubic,-23.040,-11.827,+19 vit_large_patch14_clip_224.laion2b_ft_in1k,64.820,35.181,86.575,13.425,304.20,224,1.000,bicubic,-22.466,-11.669,+43 regnety_1280.swag_ft_in1k,64.106,35.894,86.034,13.966,644.81,384,1.000,bicubic,-24.124,-12.652,+9 vit_large_patch14_clip_336.openai_ft_in12k_in1k,64.065,35.935,85.912,14.088,304.53,336,1.000,bicubic,-24.203,-12.614,+4 eva_large_patch14_336.in22k_ft_in1k,63.096,36.904,84.382,15.618,304.53,336,1.000,bicubic,-25.574,-14.340,-8 vit_large_patch14_clip_224.openai_ft_in1k,62.629,37.371,85.109,14.891,304.20,224,1.000,bicubic,-25.225,-13.317,+19 vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,62.065,37.935,84.313,15.687,304.20,224,1.000,bicubic,-25.830,-14.095,+16 vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,61.611,38.389,83.651,16.349,304.53,336,1.000,bicubic,-26.569,-14.921,+8 vit_large_patch14_clip_224.openai_ft_in12k_in1k,61.402,38.598,83.362,16.638,304.20,224,1.000,bicubic,-26.772,-15.184,+8 eva_large_patch14_196.in22k_ft_in1k,61.113,38.887,82.776,17.224,304.14,196,1.000,bicubic,-26.819,-15.722,+11 eva_large_patch14_336.in22k_ft_in22k_in1k,60.944,39.056,82.136,17.864,304.53,336,1.000,bicubic,-28.262,-16.718,-19 convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,60.795,39.205,83.220,16.780,88.59,384,1.000,bicubic,-26.339,-15.002,+36 convnext_base.clip_laion2b_augreg_ft_in1k,60.276,39.724,82.678,17.322,88.59,256,1.000,bicubic,-25.882,-15.002,+90 regnety_1280.swag_lc_in1k,59.913,40.087,83.161,16.839,644.81,224,0.965,bicubic,-26.069,-14.689,+105 eva_large_patch14_196.in22k_ft_in22k_in1k,59.883,40.117,81.143,18.857,304.14,196,1.000,bicubic,-28.691,-17.515,-14 convnext_base.clip_laion2b_augreg_ft_in12k_in1k,59.836,40.164,82.810,17.190,88.59,256,1.000,bicubic,-26.534,-15.174,+74 convnext_base.clip_laiona_augreg_ft_in1k_384,59.392,40.608,82.242,17.758,88.59,384,1.000,bicubic,-27.110,-15.726,+63 eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,58.512,41.488,80.797,19.203,87.12,448,1.000,bicubic,-30.178,-17.927,-23 resnext101_32x32d.fb_wsl_ig1b_ft_in1k,58.376,41.624,80.398,19.602,468.53,224,0.875,bilinear,-26.722,-17.040,+163 beitv2_large_patch16_224.in1k_ft_in22k_in1k,58.366,41.634,80.226,19.774,304.43,224,0.950,bicubic,-30.028,-18.372,-16 eva02_base_patch14_448.mim_in22k_ft_in1k,58.036,41.964,80.768,19.232,87.12,448,1.000,bicubic,-30.216,-17.796,-11 regnety_320.swag_ft_in1k,57.906,42.094,81.456,18.544,145.05,384,1.000,bicubic,-28.928,-16.906,+42 convnextv2_huge.fcmae_ft_in22k_in1k_384,57.865,42.135,79.671,20.329,660.29,384,1.000,bicubic,-30.805,-19.067,-27 convnextv2_huge.fcmae_ft_in22k_in1k_512,57.851,42.149,79.497,20.503,660.29,512,1.000,bicubic,-31.007,-19.251,-30 resnext101_32x16d.fb_wsl_ig1b_ft_in1k,57.696,42.304,79.907,20.093,194.03,224,0.875,bilinear,-26.470,-17.291,+242 resnext101_32x16d.fb_swsl_ig1b_ft_in1k,57.478,42.522,80.373,19.627,194.03,224,0.875,bilinear,-25.858,-16.473,+333 vit_base_patch16_clip_384.laion2b_ft_in1k,56.879,43.121,80.004,19.997,86.86,384,1.000,bicubic,-29.739,-18.004,+45 beit_large_patch16_384.in22k_ft_in22k_in1k,56.879,43.121,79.221,20.779,305.00,384,1.000,bicubic,-31.523,-19.387,-24 beit_large_patch16_512.in22k_ft_in22k_in1k,56.757,43.243,78.911,21.089,305.67,512,1.000,bicubic,-31.839,-19.745,-30 resnext101_32x8d.fb_swsl_ig1b_ft_in1k,56.438,43.562,78.931,21.069,88.79,224,0.875,bilinear,-27.864,-18.245,+223 maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,56.327,43.673,77.305,22.695,116.14,384,1.000,bicubic,-31.502,-21.067,-4 maxvit_xlarge_tf_384.in21k_ft_in1k,56.212,43.788,78.742,21.258,475.32,384,1.000,bicubic,-32.101,-19.802,-26 maxvit_xlarge_tf_512.in21k_ft_in1k,56.156,43.844,78.636,21.364,475.77,512,1.000,bicubic,-32.382,-20.008,-31 maxvit_base_tf_512.in21k_ft_in1k,56.083,43.917,78.606,21.394,119.88,512,1.000,bicubic,-32.137,-19.924,-20 deit3_huge_patch14_224.fb_in22k_ft_in1k,55.767,44.233,77.626,22.374,632.13,224,1.000,bicubic,-31.420,-20.634,+12 maxvit_base_tf_384.in21k_ft_in1k,55.639,44.361,78.078,21.922,119.65,384,1.000,bicubic,-32.283,-20.466,-14 vit_base_patch16_clip_224.laion2b_ft_in1k,55.405,44.595,79.050,20.950,86.57,224,1.000,bicubic,-30.065,-18.525,+111 regnety_320.swag_lc_in1k,55.354,44.646,79.703,20.297,145.05,224,0.965,bicubic,-29.194,-17.739,+184 regnety_160.swag_ft_in1k,55.177,44.823,79.316,20.684,83.59,384,1.000,bicubic,-30.843,-18.736,+74 maxvit_large_tf_512.in21k_ft_in1k,55.171,44.829,77.276,22.724,212.33,512,1.000,bicubic,-33.053,-21.322,-27 maxvit_large_tf_384.in21k_ft_in1k,55.077,44.923,77.142,22.858,212.03,384,1.000,bicubic,-32.909,-21.426,-22 beit_large_patch16_224.in22k_ft_in22k_in1k,54.965,45.035,77.608,22.392,304.43,224,0.900,bicubic,-32.513,-20.696,-8 convnext_xlarge.fb_in22k_ft_in1k_384,54.961,45.039,76.824,23.176,350.20,384,1.000,bicubic,-32.791,-21.732,-15 resnext101_32x8d.fb_wsl_ig1b_ft_in1k,54.908,45.092,77.541,22.459,88.79,224,0.875,bilinear,-27.790,-18.603,+374 deit3_large_patch16_384.fb_in22k_ft_in1k,54.886,45.114,77.372,22.628,304.76,384,1.000,bicubic,-32.834,-21.140,-16 maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,54.747,45.253,76.848,23.152,116.09,384,1.000,bicubic,-32.717,-21.526,-10 caformer_b36.sail_in22k_ft_in1k_384,54.440,45.560,76.830,23.170,98.75,384,1.000,bicubic,-33.618,-21.752,-29 deit3_large_patch16_224.fb_in22k_ft_in1k,54.359,45.641,76.563,23.437,304.37,224,1.000,bicubic,-32.623,-21.673,+9 beitv2_large_patch16_224.in1k_ft_in1k,54.161,45.839,75.562,24.438,304.43,224,0.950,bicubic,-33.251,-22.671,-9 convnextv2_large.fcmae_ft_in22k_in1k_384,53.947,46.053,76.007,23.993,197.96,384,1.000,bicubic,-34.251,-22.521,-35 maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,53.731,46.269,75.140,24.860,116.14,224,0.950,bicubic,-33.163,-22.874,+9 resnext101_32x4d.fb_swsl_ig1b_ft_in1k,53.587,46.413,76.337,23.663,44.18,224,0.875,bilinear,-29.639,-20.423,+316 vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,53.493,46.507,75.653,24.347,86.86,384,1.000,bicubic,-33.713,-22.381,-7 vit_base_patch16_clip_384.openai_ft_in1k,53.074,46.926,76.655,23.345,86.86,384,1.000,bicubic,-33.132,-21.221,+44 regnety_160.swag_lc_in1k,53.043,46.957,78.090,21.910,83.59,224,0.965,bicubic,-30.739,-19.190,+253 convnextv2_base.fcmae_ft_in22k_in1k_384,52.907,47.093,75.083,24.917,88.72,384,1.000,bicubic,-34.737,-23.333,-26 convformer_b36.sail_in22k_ft_in1k_384,52.874,47.126,74.979,25.021,99.88,384,1.000,bicubic,-34.728,-23.455,-26 convnext_large.fb_in22k_ft_in1k_384,52.774,47.226,74.700,25.300,197.77,384,1.000,bicubic,-34.698,-23.686,-23 vit_large_patch16_384.augreg_in21k_ft_in1k,52.760,47.240,74.706,25.294,304.72,384,1.000,bicubic,-34.324,-23.596,-8 caformer_b36.sail_in22k_ft_in1k,52.756,47.244,75.309,24.691,98.75,224,1.000,bicubic,-34.664,-23.019,-21 convformer_b36.sail_in22k_ft_in1k,52.746,47.254,74.896,25.104,99.88,224,1.000,bicubic,-34.252,-23.276,-6 coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,52.389,47.611,73.802,26.198,73.88,384,1.000,bicubic,-34.994,-24.510,-21 swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,52.304,47.696,74.415,25.585,196.74,384,1.000,bicubic,-35.160,-23.835,-26 convnext_xlarge.fb_in22k_ft_in1k,52.216,47.784,73.955,26.045,350.20,288,1.000,bicubic,-35.114,-24.373,-21 vit_large_r50_s32_384.augreg_in21k_ft_in1k,52.041,47.959,73.570,26.430,329.09,384,1.000,bicubic,-34.141,-24.352,+35 vit_large_patch16_224.augreg_in21k_ft_in1k,51.819,48.181,73.690,26.310,304.33,224,0.900,bicubic,-34.017,-23.974,+57 vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,51.785,48.215,74.637,25.363,86.57,224,0.950,bicubic,-34.385,-23.119,+34 convnext_base.fb_in22k_ft_in1k_384,51.565,48.435,74.543,25.457,88.59,384,1.000,bicubic,-35.231,-23.721,-1 tf_efficientnet_l2.ns_jft_in1k_475,51.496,48.504,73.931,26.069,480.31,475,0.936,bicubic,-36.738,-24.615,-58 maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,51.190,48.810,73.126,26.874,116.09,224,0.950,bicubic,-35.452,-24.894,+2 vit_base_patch16_clip_384.openai_ft_in12k_in1k,51.153,48.847,74.323,25.677,86.86,384,0.950,bicubic,-35.873,-23.859,-17 caformer_m36.sail_in22k_ft_in1k_384,51.048,48.952,73.442,26.558,56.20,384,1.000,bicubic,-36.398,-24.866,-34 swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,50.976,49.024,73.295,26.705,87.92,384,1.000,bicubic,-36.120,-24.939,-23 vit_base_patch16_clip_224.openai_ft_in1k,50.936,49.064,74.841,25.159,86.57,224,0.900,bicubic,-34.356,-22.595,+88 seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,50.710,49.290,73.666,26.334,149.39,384,1.000,bicubic,-36.578,-24.668,-31 resnext50_32x4d.fb_swsl_ig1b_ft_in1k,50.465,49.535,73.366,26.634,25.03,224,0.875,bilinear,-31.707,-22.858,+420 swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,50.433,49.567,72.735,27.265,196.74,256,0.900,bicubic,-36.519,-25.371,-19 swin_large_patch4_window12_384.ms_in22k_ft_in1k,50.394,49.606,72.538,27.462,196.74,384,1.000,bicubic,-36.738,-25.696,-29 convnextv2_large.fcmae_ft_in22k_in1k,50.160,49.840,72.399,27.601,197.96,288,1.000,bicubic,-37.324,-25.957,-46 convnext_large.fb_in22k_ft_in1k,49.999,50.001,72.267,27.733,197.77,288,1.000,bicubic,-37.027,-25.937,-27 tf_efficientnetv2_xl.in21k_ft_in1k,49.722,50.278,72.124,27.876,208.12,512,1.000,bicubic,-37.026,-25.890,-12 vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.700,50.300,72.868,27.132,86.57,224,0.950,bicubic,-36.242,-24.860,+37 caformer_m36.sail_in22k_ft_in1k,49.700,50.300,72.141,27.859,56.20,224,1.000,bicubic,-36.894,-25.883,-7 resnet50.fb_swsl_ig1b_ft_in1k,49.531,50.469,72.338,27.662,25.56,224,0.875,bilinear,-31.641,-23.648,+508 beitv2_base_patch16_224.in1k_ft_in22k_in1k,49.516,50.484,72.391,27.609,86.53,224,0.900,bicubic,-36.958,-25.661,-1 coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,49.394,50.606,70.848,29.152,73.88,224,0.950,bicubic,-37.110,-27.046,-7 convnextv2_base.fcmae_ft_in22k_in1k,49.142,50.858,71.230,28.770,88.72,288,1.000,bicubic,-37.856,-26.939,-31 convformer_m36.sail_in22k_ft_in1k_384,49.132,50.868,71.387,28.613,57.05,384,1.000,bicubic,-37.760,-26.729,-27 convformer_m36.sail_in22k_ft_in1k,49.091,50.909,71.471,28.529,57.05,224,1.000,bicubic,-37.057,-26.379,+15 vit_base_patch32_clip_224.laion2b_ft_in1k,49.062,50.938,72.584,27.416,88.22,224,0.900,bicubic,-33.520,-23.617,+350 swin_large_patch4_window7_224.ms_in22k_ft_in1k,48.993,51.007,71.387,28.613,196.53,224,0.900,bicubic,-37.319,-26.515,+1 convnext_base.fb_in22k_ft_in1k,48.938,51.062,71.748,28.252,88.59,288,1.000,bicubic,-37.336,-26.344,+2 swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,48.781,51.219,71.410,28.590,87.92,256,0.900,bicubic,-37.487,-26.472,+2 tf_efficientnetv2_l.in21k_ft_in1k,48.739,51.261,71.992,28.008,118.52,480,1.000,bicubic,-38.063,-26.144,-29 coatnet_2_rw_224.sw_in12k_ft_in1k,48.678,51.322,70.123,29.877,73.87,224,0.950,bicubic,-37.885,-27.773,-18 beit_base_patch16_384.in22k_ft_in22k_in1k,48.669,51.331,72.102,27.898,86.74,384,1.000,bicubic,-38.131,-26.034,-30 swin_base_patch4_window12_384.ms_in22k_ft_in1k,48.545,51.455,71.819,28.181,87.90,384,1.000,bicubic,-37.893,-26.247,-10 caformer_s36.sail_in22k_ft_in1k_384,48.486,51.514,71.518,28.482,39.30,384,1.000,bicubic,-38.372,-26.694,-36 maxvit_base_tf_512.in1k,48.240,51.760,70.793,29.207,119.88,512,1.000,bicubic,-38.362,-27.125,-25 vit_large_r50_s32_224.augreg_in21k_ft_in1k,48.185,51.815,70.866,29.134,328.99,224,0.900,bicubic,-36.233,-26.306,+144 vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.934,52.066,70.923,29.077,88.30,384,1.000,bicubic,-37.432,-26.737,+58 tf_efficientnet_b7.ns_jft_in1k,47.798,52.202,69.638,30.362,66.35,600,0.949,bicubic,-39.042,-28.454,-39 tf_efficientnet_b6.ns_jft_in1k,47.761,52.239,69.956,30.044,43.04,528,0.942,bicubic,-38.697,-27.934,-17 vit_base_patch8_224.augreg_in21k_ft_in1k,47.727,52.273,70.933,29.067,86.58,224,0.900,bicubic,-38.071,-26.857,+23 deit3_base_patch16_384.fb_in22k_ft_in1k,47.676,52.324,69.752,30.248,86.88,384,1.000,bicubic,-39.064,-28.364,-35 tf_efficientnet_l2.ns_jft_in1k,47.574,52.426,70.019,29.981,480.31,800,0.960,bicubic,-40.778,-28.629,-101 vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,47.570,52.430,70.047,29.953,88.34,448,1.000,bicubic,-38.210,-27.591,+22 vit_base_patch8_224.augreg2_in21k_ft_in1k,47.507,52.493,70.326,29.674,86.58,224,0.900,bicubic,-38.711,-27.506,-11 tf_efficientnetv2_m.in21k_ft_in1k,47.456,52.544,70.945,29.055,54.14,480,1.000,bicubic,-38.536,-26.999,+7 deit3_base_patch16_224.fb_in22k_ft_in1k,47.378,52.622,69.769,30.230,86.59,224,1.000,bicubic,-38.322,-27.977,+25 tiny_vit_21m_512.dist_in22k_ft_in1k,47.254,52.746,70.062,29.938,21.27,512,1.000,bicubic,-39.204,-27.822,-26 convformer_s36.sail_in22k_ft_in1k_384,47.152,52.848,69.498,30.502,40.01,384,1.000,bicubic,-39.226,-28.486,-23 maxvit_large_tf_512.in1k,47.022,52.978,69.506,30.494,212.33,512,1.000,bicubic,-39.504,-28.374,-35 convnext_small.fb_in22k_ft_in1k_384,46.882,53.118,69.528,30.472,50.22,384,1.000,bicubic,-38.896,-28.362,+16 convnextv2_huge.fcmae_ft_in1k,46.880,53.120,67.785,32.215,660.29,288,1.000,bicubic,-39.700,-30.187,-39 convformer_s36.sail_in22k_ft_in1k,46.863,53.137,69.528,30.472,40.01,224,1.000,bicubic,-38.551,-28.040,+37 caformer_s36.sail_in22k_ft_in1k,46.708,53.292,69.744,30.256,39.30,224,1.000,bicubic,-39.083,-28.082,+11 tiny_vit_21m_384.dist_in22k_ft_in1k,46.256,53.744,69.231,30.769,21.23,384,1.000,bicubic,-39.852,-28.479,-12 beit_base_patch16_224.in22k_ft_in22k_in1k,46.254,53.746,69.885,30.115,86.53,224,0.900,bicubic,-38.958,-27.773,+52 vit_base_patch32_clip_384.openai_ft_in12k_in1k,46.234,53.766,69.288,30.712,88.30,384,0.950,bicubic,-38.980,-28.116,+49 maxvit_base_tf_384.in1k,46.234,53.766,68.531,31.468,119.65,384,1.000,bicubic,-40.068,-29.267,-27 hrnet_w48_ssld.paddle_in1k,46.177,53.823,68.056,31.944,77.47,288,1.000,bilinear,-38.303,-29.178,+110 beitv2_base_patch16_224.in1k_ft_in1k,46.002,53.998,67.859,32.141,86.53,224,0.900,bicubic,-39.592,-29.647,+17 vit_base_patch16_384.augreg_in21k_ft_in1k,45.894,54.106,68.557,31.443,86.86,384,1.000,bicubic,-40.100,-29.445,-9 seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,45.863,54.137,68.608,31.392,93.59,320,1.000,bicubic,-40.861,-29.568,-54 tf_efficientnet_b8.ap_in1k,45.780,54.220,67.907,32.093,87.41,672,0.954,bicubic,-39.584,-29.385,+34 maxvit_large_tf_384.in1k,45.760,54.240,68.160,31.840,212.03,384,1.000,bicubic,-40.470,-29.528,-31 vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,45.758,54.242,68.881,31.119,88.22,224,0.900,bicubic,-37.538,-27.647,+236 convnext_small.in12k_ft_in1k_384,45.721,54.279,67.818,32.182,50.22,384,1.000,bicubic,-40.461,-30.104,-30 tf_efficientnet_b5.ns_jft_in1k,45.611,54.389,67.850,32.150,30.39,456,0.934,bicubic,-40.477,-29.906,-22 swin_base_patch4_window7_224.ms_in22k_ft_in1k,45.532,54.468,68.504,31.496,87.77,224,0.900,bicubic,-39.740,-29.060,+33 mvitv2_large.fb_in1k,45.277,54.723,65.183,34.817,217.99,224,0.900,bicubic,-39.967,-32.031,+35 vit_base_patch16_224.augreg2_in21k_ft_in1k,45.114,54.886,67.394,32.606,86.57,224,0.900,bicubic,-39.980,-30.136,+50 vit_base_patch32_clip_224.openai_ft_in1k,45.031,54.969,68.453,31.547,88.22,224,0.900,bicubic,-36.899,-27.513,+388 tiny_vit_21m_224.dist_in22k_ft_in1k,44.851,55.149,67.590,32.410,21.20,224,0.950,bicubic,-40.235,-29.776,+50 seresnextaa101d_32x8d.sw_in12k_ft_in1k,44.801,55.199,67.372,32.628,93.59,288,1.000,bicubic,-41.683,-30.658,-53 convnextv2_large.fcmae_ft_in1k,44.797,55.203,65.853,34.147,197.96,288,1.000,bicubic,-41.320,-31.969,-32 volo_d5_512.sail_in1k,44.577,55.423,65.765,34.235,296.09,512,1.150,bicubic,-42.481,-32.205,-86 convnextv2_tiny.fcmae_ft_in22k_in1k_384,44.322,55.678,66.655,33.345,28.64,384,1.000,bicubic,-40.784,-30.973,+41 cait_m48_448.fb_dist_in1k,44.212,55.788,64.674,35.326,356.46,448,1.000,bicubic,-42.280,-33.078,-58 deit3_large_patch16_384.fb_in1k,44.182,55.818,64.845,35.155,304.76,384,1.000,bicubic,-41.630,-32.753,-15 volo_d5_448.sail_in1k,44.100,55.900,65.065,34.935,295.91,448,1.150,bicubic,-42.852,-32.873,-83 eva02_small_patch14_336.mim_in22k_ft_in1k,43.960,56.040,65.942,34.058,22.13,336,1.000,bicubic,-41.758,-31.692,-9 deit3_huge_patch14_224.fb_in1k,43.807,56.193,64.350,35.650,632.13,224,0.900,bicubic,-41.417,-33.010,+25 convnext_small.fb_in22k_ft_in1k,43.620,56.380,66.464,33.536,50.22,288,1.000,bicubic,-41.642,-31.218,+20 deit3_large_patch16_224.fb_in1k,43.516,56.484,63.572,36.428,304.37,224,0.900,bicubic,-41.258,-33.464,+63 vit_base_r50_s16_384.orig_in21k_ft_in1k,43.501,56.499,66.781,33.219,98.95,384,1.000,bicubic,-41.475,-30.509,+47 tf_efficientnet_b4.ns_jft_in1k,43.447,56.553,65.513,34.487,19.34,380,0.922,bicubic,-41.712,-31.955,+28 deit3_medium_patch16_224.fb_in22k_ft_in1k,43.276,56.724,64.888,35.112,38.85,224,1.000,bicubic,-41.273,-32.300,+72 volo_d5_224.sail_in1k,43.243,56.757,64.079,35.921,295.46,224,0.960,bicubic,-42.827,-33.497,-40 vit_base_patch16_224.augreg_in21k_ft_in1k,43.221,56.779,65.722,34.279,86.57,224,0.900,bicubic,-41.310,-31.572,+73 volo_d4_448.sail_in1k,43.133,56.867,64.108,35.892,193.41,448,1.150,bicubic,-43.659,-33.776,-84 efficientnet_b5.sw_in12k_ft_in1k,42.872,57.128,65.415,34.585,30.39,448,1.000,bicubic,-43.024,-32.321,-32 xcit_large_24_p8_384.fb_dist_in1k,42.838,57.162,63.418,36.582,188.93,384,1.000,bicubic,-43.158,-34.272,-40 regnety_160.lion_in12k_ft_in1k,42.748,57.252,64.203,35.797,83.59,288,1.000,bicubic,-43.240,-33.632,-38 regnety_160.sw_in12k_ft_in1k,42.683,57.317,64.338,35.662,83.59,288,1.000,bicubic,-43.303,-33.496,-38 maxvit_small_tf_512.in1k,42.681,57.319,64.537,35.464,69.13,512,1.000,bicubic,-43.403,-33.227,-48 convnext_small.in12k_ft_in1k,42.669,57.331,64.342,35.658,50.22,288,1.000,bicubic,-42.661,-33.204,+3 xcit_large_24_p8_224.fb_dist_in1k,42.557,57.443,63.098,36.902,188.93,224,1.000,bicubic,-42.845,-34.304,-4 tf_efficientnet_b8.ra_in1k,42.498,57.502,64.873,35.127,87.41,672,0.954,bicubic,-42.870,-32.521,-2 caformer_b36.sail_in1k,42.465,57.535,62.849,37.151,98.75,224,1.000,bicubic,-43.039,-34.461,-15 caformer_b36.sail_in1k_384,42.457,57.543,62.856,37.144,98.75,384,1.000,bicubic,-43.951,-34.958,-74 maxvit_large_tf_224.in1k,42.414,57.586,63.399,36.601,211.79,224,0.950,bicubic,-42.528,-33.571,+33 cait_m36_384.fb_dist_in1k,42.410,57.590,63.324,36.676,271.22,384,1.000,bicubic,-43.648,-34.406,-53 volo_d4_224.sail_in1k,42.304,57.696,63.002,36.998,192.96,224,0.960,bicubic,-43.568,-34.470,-43 caformer_s18.sail_in22k_ft_in1k_384,42.054,57.946,64.774,35.226,26.34,384,1.000,bicubic,-43.360,-32.928,-14 deit3_small_patch16_384.fb_in22k_ft_in1k,41.954,58.046,64.564,35.436,22.21,384,1.000,bicubic,-42.870,-32.922,+38 vit_medium_patch16_gap_384.sw_in12k_ft_in1k,41.891,58.109,63.701,36.299,39.03,384,0.950,bicubic,-43.639,-33.935,-23 maxvit_tiny_tf_512.in1k,41.842,58.158,63.576,36.424,31.05,512,1.000,bicubic,-43.822,-34.008,-32 caformer_s36.sail_in1k_384,41.738,58.262,62.762,37.238,39.30,384,1.000,bicubic,-44.004,-34.910,-38 swin_small_patch4_window7_224.ms_in22k_ft_in1k,41.590,58.410,64.542,35.458,49.61,224,0.900,bicubic,-41.708,-32.422,+192 convformer_s18.sail_in22k_ft_in1k_384,41.573,58.427,63.348,36.652,26.77,384,1.000,bicubic,-43.425,-34.222,+21 regnety_2560.seer_ft_in1k,41.524,58.476,64.896,35.104,"1,282.60",384,1.000,bicubic,-43.626,-32.542,+4 caformer_m36.sail_in1k_384,41.498,58.502,61.524,38.476,56.20,384,1.000,bicubic,-44.668,-36.296,-72 coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,41.486,58.514,61.485,38.515,41.72,224,0.950,bicubic,-43.424,-35.473,+25 tf_efficientnet_b7.ra_in1k,41.437,58.563,63.027,36.973,66.35,600,0.949,bicubic,-43.495,-34.181,+21 tf_efficientnet_b7.ap_in1k,41.433,58.567,62.880,37.120,66.35,600,0.949,bicubic,-43.691,-34.372,+1 tf_efficientnet_b5.ap_in1k,41.418,58.582,62.074,37.926,30.39,456,0.934,bicubic,-42.840,-34.900,+79 regnety_120.sw_in12k_ft_in1k,41.331,58.669,63.187,36.813,51.82,288,1.000,bicubic,-44.069,-34.395,-23 dm_nfnet_f3.dm_in1k,41.323,58.677,62.110,37.890,254.92,416,0.940,bicubic,-44.363,-35.460,-44 resnetv2_152x4_bit.goog_in21k_ft_in1k,41.306,58.694,64.311,35.689,936.53,480,1.000,bilinear,-43.610,-33.127,+17 dm_nfnet_f5.dm_in1k,41.290,58.710,62.013,37.987,377.21,544,0.954,bicubic,-44.810,-35.675,-75 caformer_s18.sail_in22k_ft_in1k,41.217,58.783,63.831,36.169,26.34,224,1.000,bicubic,-42.857,-33.367,+92 dm_nfnet_f6.dm_in1k,41.170,58.830,62.843,37.157,438.36,576,0.956,bicubic,-45.192,-35.053,-93 convnext_tiny.in12k_ft_in1k_384,41.113,58.887,62.825,37.175,28.59,384,1.000,bicubic,-44.009,-34.781,-6 tf_efficientnet_b6.ap_in1k,41.095,58.905,62.365,37.635,43.04,528,0.942,bicubic,-43.693,-34.773,+22 xcit_large_24_p16_384.fb_dist_in1k,41.036,58.964,61.237,38.763,189.10,384,1.000,bicubic,-44.718,-36.301,-56 xcit_large_24_p16_224.fb_dist_in1k,40.942,59.058,61.326,38.674,189.10,224,1.000,bicubic,-43.974,-35.802,+11 tf_efficientnetv2_l.in1k,40.928,59.072,62.011,37.989,118.52,480,1.000,bicubic,-44.736,-35.463,-51 tf_efficientnetv2_s.in21k_ft_in1k,40.922,59.078,63.849,36.151,21.46,384,1.000,bicubic,-43.364,-33.403,+64 maxvit_small_tf_384.in1k,40.844,59.156,61.972,38.028,69.02,384,1.000,bicubic,-44.696,-35.490,-47 edgenext_base.in21k_ft_in1k,40.836,59.164,61.776,38.224,18.51,320,1.000,bicubic,-43.218,-35.420,+86 maxvit_base_tf_224.in1k,40.789,59.211,61.196,38.804,119.47,224,0.950,bicubic,-44.071,-35.792,+10 xcit_medium_24_p8_224.fb_dist_in1k,40.498,59.502,60.502,39.498,84.32,224,1.000,bicubic,-44.576,-36.748,-7 vit_small_r26_s32_384.augreg_in21k_ft_in1k,40.474,59.526,62.731,37.269,36.47,384,1.000,bicubic,-43.574,-34.597,+85 tf_efficientnet_b4.ap_in1k,40.474,59.526,61.713,38.287,19.34,380,0.922,bicubic,-42.776,-34.683,+171 deit3_base_patch16_224.fb_in1k,40.382,59.618,60.164,39.836,86.59,224,0.900,bicubic,-43.404,-36.422,+110 inception_next_base.sail_in1k_384,40.333,59.667,60.781,39.219,86.67,384,1.000,bicubic,-44.869,-36.633,-25 convformer_s18.sail_in22k_ft_in1k,40.301,59.699,61.719,38.281,26.77,224,1.000,bicubic,-43.437,-35.329,+117 vit_medium_patch16_gap_256.sw_in12k_ft_in1k,40.278,59.722,61.664,38.336,38.86,256,0.950,bicubic,-44.168,-35.546,+36 flexivit_large.600ep_in1k,40.268,59.732,60.365,39.635,304.36,240,0.950,bicubic,-45.272,-37.123,-58 vit_base_patch16_224_miil.in21k_ft_in1k,40.164,59.836,60.883,39.117,86.54,224,0.875,bilinear,-44.102,-35.921,+54 deit3_small_patch16_224.fb_in22k_ft_in1k,40.152,59.848,61.864,38.136,22.06,224,1.000,bicubic,-42.924,-34.912,+183 regnetz_e8.ra3_in1k,40.136,59.864,61.316,38.684,57.70,320,1.000,bicubic,-44.898,-35.956,-14 maxvit_rmlp_small_rw_224.sw_in1k,40.105,59.895,59.514,40.486,64.90,224,0.900,bicubic,-44.387,-37.496,+26 flexivit_large.1200ep_in1k,40.097,59.903,60.650,39.350,304.36,240,0.950,bicubic,-45.547,-36.890,-67 xcit_medium_24_p8_384.fb_dist_in1k,40.050,59.950,60.451,39.549,84.32,384,1.000,bicubic,-45.766,-37.141,-82 flexivit_large.300ep_in1k,39.991,60.009,59.987,40.013,304.36,240,0.950,bicubic,-45.297,-37.413,-45 maxvit_tiny_tf_384.in1k,39.971,60.029,60.909,39.091,30.98,384,1.000,bicubic,-45.129,-36.469,-28 convnextv2_tiny.fcmae_ft_in22k_in1k,39.938,60.062,61.835,38.165,28.64,288,1.000,bicubic,-44.478,-35.425,+35 dm_nfnet_f4.dm_in1k,39.926,60.074,60.449,39.551,316.07,512,0.951,bicubic,-45.910,-37.369,-87 xcit_medium_24_p16_384.fb_dist_in1k,39.897,60.103,60.097,39.903,84.40,384,1.000,bicubic,-45.527,-37.233,-62 convnext_tiny.fb_in22k_ft_in1k_384,39.787,60.213,61.536,38.464,28.59,384,1.000,bicubic,-44.301,-35.608,+61 cait_s36_384.fb_dist_in1k,39.767,60.233,60.469,39.531,68.37,384,1.000,bicubic,-45.687,-37.009,-65 convnextv2_base.fcmae_ft_in1k,39.755,60.245,59.875,40.125,88.72,288,1.000,bicubic,-45.719,-37.509,-68 volo_d3_448.sail_in1k,39.702,60.298,59.760,40.240,86.63,448,1.000,bicubic,-46.800,-37.950,-135 efficientnetv2_rw_m.agc_in1k,39.675,60.325,59.679,40.321,53.24,416,1.000,bicubic,-45.135,-37.473,-10 xception65.ra3_in1k,39.623,60.377,60.919,39.081,39.92,299,0.940,bicubic,-43.557,-35.673,+153 ecaresnet269d.ra2_in1k,39.604,60.396,60.345,39.655,102.09,352,1.000,bicubic,-45.364,-36.877,-24 tf_efficientnet_b3.ns_jft_in1k,39.586,60.414,61.475,38.525,12.23,300,0.904,bicubic,-44.466,-35.443,+60 caformer_m36.sail_in1k,39.576,60.424,58.692,41.308,56.20,224,1.000,bicubic,-45.656,-38.508,-53 convformer_b36.sail_in1k,39.525,60.475,58.081,41.919,99.88,224,1.000,bicubic,-45.293,-38.865,-16 caformer_s36.sail_in1k,39.519,60.481,59.760,40.240,39.30,224,1.000,bicubic,-44.987,-37.236,+5 volo_d3_224.sail_in1k,39.482,60.518,59.858,40.142,86.33,224,0.960,bicubic,-45.932,-37.418,-70 convnext_large.fb_in1k,39.460,60.540,59.188,40.812,197.77,288,1.000,bicubic,-45.386,-38.026,-21 deit3_base_patch16_384.fb_in1k,39.409,60.591,58.930,41.070,86.88,384,1.000,bicubic,-45.665,-38.344,-38 xcit_small_24_p8_224.fb_dist_in1k,39.327,60.673,59.402,40.598,47.63,224,1.000,bicubic,-45.541,-37.788,-25 inception_next_base.sail_in1k,39.295,60.705,59.245,40.755,86.67,224,0.950,bicubic,-44.797,-37.551,+45 xcit_medium_24_p16_224.fb_dist_in1k,39.270,60.730,59.461,40.539,84.40,224,1.000,bicubic,-45.016,-37.471,+26 convformer_m36.sail_in1k,39.234,60.766,57.631,42.369,57.05,224,1.000,bicubic,-45.260,-39.235,-1 coat_lite_medium_384.in1k,39.175,60.825,59.280,40.720,44.57,384,1.000,bicubic,-45.703,-38.092,-30 tiny_vit_11m_224.dist_in22k_ft_in1k,39.124,60.876,61.035,38.965,11.00,224,0.950,bicubic,-44.104,-35.595,+135 efficientnet_b4.ra2_in1k,39.087,60.913,59.618,40.382,19.34,384,1.000,bicubic,-44.327,-36.980,+115 hrnet_w18_ssld.paddle_in1k,39.067,60.933,60.650,39.350,21.30,288,1.000,bilinear,-42.981,-35.600,+275 tresnet_v2_l.miil_in21k_ft_in1k,39.010,60.990,59.477,40.523,46.17,224,0.875,bilinear,-44.884,-37.013,+57 xcit_small_24_p8_384.fb_dist_in1k,39.010,60.990,59.166,40.834,47.63,384,1.000,bicubic,-46.544,-38.404,-94 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,38.979,61.021,62.428,37.572,236.34,384,1.000,bicubic,-44.857,-34.698,+63 convformer_b36.sail_in1k_384,38.930,61.070,58.413,41.587,99.88,384,1.000,bicubic,-46.810,-39.111,-105 convnext_tiny.in12k_ft_in1k,38.912,61.088,59.862,40.138,28.59,288,1.000,bicubic,-45.538,-37.478,-5 maxvit_small_tf_224.in1k,38.871,61.129,59.162,40.838,68.93,224,0.950,bicubic,-45.555,-37.662,+3 coatnet_rmlp_2_rw_224.sw_in1k,38.869,61.131,58.018,41.982,73.88,224,0.950,bicubic,-45.739,-38.722,-24 vit_base_patch32_384.augreg_in21k_ft_in1k,38.796,61.204,60.329,39.671,88.30,384,1.000,bicubic,-44.556,-36.511,+115 tf_efficientnetv2_m.in1k,38.714,61.286,59.791,40.209,54.14,480,1.000,bicubic,-46.490,-37.573,-71 efficientvit_b3.r288_in1k,38.659,61.341,58.364,41.636,48.65,288,1.000,bicubic,-45.495,-38.372,+25 eca_nfnet_l2.ra3_in1k,38.655,61.345,59.445,40.555,56.72,384,1.000,bicubic,-46.045,-37.821,-32 davit_small.msft_in1k,38.631,61.369,58.203,41.797,49.75,224,0.950,bicubic,-45.621,-38.737,+12 efficientvit_b3.r256_in1k,38.621,61.379,58.641,41.359,48.65,256,1.000,bicubic,-45.181,-37.875,+59 mvitv2_small.fb_in1k,38.578,61.422,58.130,41.870,34.87,224,0.900,bicubic,-45.192,-38.446,+61 xcit_small_12_p8_384.fb_dist_in1k,38.547,61.453,58.795,41.205,26.21,384,1.000,bicubic,-46.531,-38.487,-63 convformer_m36.sail_in1k_384,38.531,61.469,57.736,42.264,57.05,384,1.000,bicubic,-47.049,-39.806,-109 xcit_small_24_p16_384.fb_dist_in1k,38.499,61.501,58.396,41.604,47.67,384,1.000,bicubic,-46.591,-38.916,-67 davit_base.msft_in1k,38.490,61.510,57.535,42.465,87.95,224,0.950,bicubic,-46.152,-39.485,-37 mvitv2_base.fb_in1k,38.458,61.542,57.934,42.066,51.47,224,0.900,bicubic,-45.992,-38.924,-18 rexnetr_300.sw_in12k_ft_in1k,38.431,61.569,60.612,39.388,34.81,288,1.000,bicubic,-46.115,-36.644,-31 convformer_s36.sail_in1k,38.405,61.595,57.710,42.290,40.01,224,1.000,bicubic,-45.655,-39.036,+22 xcit_small_12_p8_224.fb_dist_in1k,38.360,61.640,58.832,41.168,26.21,224,1.000,bicubic,-45.876,-38.038,+5 tf_efficientnet_b5.ra_in1k,38.331,61.669,59.928,40.072,30.39,456,0.934,bicubic,-45.483,-36.823,+46 fastvit_ma36.apple_dist_in1k,38.323,61.677,58.461,41.539,44.07,256,0.950,bicubic,-46.275,-38.541,-39 deit_base_distilled_patch16_384.fb_in1k,38.244,61.756,57.785,42.215,87.63,384,1.000,bicubic,-47.180,-39.621,-108 xcit_large_24_p8_224.fb_in1k,38.106,61.894,57.873,42.127,188.93,224,1.000,bicubic,-46.288,-38.791,-10 vit_base_patch16_384.orig_in21k_ft_in1k,38.105,61.895,60.426,39.574,86.86,384,1.000,bicubic,-46.096,-36.792,+4 resnetv2_152x2_bit.goog_in21k_ft_in1k,38.000,62.000,61.137,38.863,236.34,448,1.000,bilinear,-46.510,-36.297,-36 repvit_m2_3.dist_450e_in1k,37.996,62.004,58.154,41.846,23.69,224,0.950,bicubic,-45.746,-38.490,+51 pvt_v2_b4.in1k,37.953,62.047,58.217,41.783,62.56,224,0.900,bicubic,-45.759,-38.453,+55 coat_lite_medium.in1k,37.880,62.120,57.792,42.208,44.57,224,0.900,bicubic,-45.719,-38.935,+64 cait_s24_384.fb_dist_in1k,37.879,62.121,58.069,41.931,47.06,384,1.000,bicubic,-47.169,-39.277,-77 convnextv2_nano.fcmae_ft_in22k_in1k_384,37.875,62.125,59.443,40.557,15.62,384,1.000,bicubic,-45.499,-37.301,+88 resnet152d.ra2_in1k,37.853,62.147,58.362,41.638,60.21,320,1.000,bicubic,-45.831,-38.376,+56 convformer_s36.sail_in1k_384,37.812,62.188,57.488,42.512,40.01,384,1.000,bicubic,-47.566,-39.988,-112 resnetrs420.tf_in1k,37.745,62.255,58.209,41.791,191.89,416,1.000,bicubic,-47.259,-38.915,-78 deit3_medium_patch16_224.fb_in1k,37.709,62.291,57.087,42.913,38.85,224,0.900,bicubic,-45.377,-39.207,+114 xcit_small_24_p16_224.fb_dist_in1k,37.700,62.300,57.374,42.626,47.67,224,1.000,bicubic,-46.174,-39.362,+23 resnetrs350.tf_in1k,37.664,62.336,58.097,41.903,163.96,384,1.000,bicubic,-47.050,-38.895,-63 caformer_s18.sail_in1k_384,37.660,62.340,57.612,42.388,26.34,384,1.000,bicubic,-47.366,-39.746,-83 regnety_640.seer_ft_in1k,37.584,62.416,59.864,40.136,281.38,384,1.000,bicubic,-46.324,-37.058,+15 xcit_small_12_p16_384.fb_dist_in1k,37.582,62.418,57.761,42.239,26.25,384,1.000,bicubic,-47.130,-39.357,-65 pvt_v2_b5.in1k,37.548,62.452,57.301,42.699,81.96,224,0.900,bicubic,-46.192,-39.335,+38 resnet200d.ra2_in1k,37.503,62.497,58.301,41.699,64.69,320,1.000,bicubic,-46.461,-38.525,+8 maxvit_rmlp_tiny_rw_256.sw_in1k,37.381,62.619,57.193,42.807,29.15,256,0.950,bicubic,-46.843,-39.675,-16 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,37.342,62.658,59.404,40.596,236.34,224,0.875,bicubic,-45.534,-37.178,+122 efficientvit_b3.r224_in1k,37.340,62.660,57.122,42.878,48.65,224,0.950,bicubic,-46.120,-39.208,+62 regnety_1280.seer_ft_in1k,37.332,62.668,59.133,40.867,644.81,384,1.000,bicubic,-47.100,-37.959,-42 resnest269e.in1k,37.309,62.691,57.488,42.512,110.93,416,0.928,bicubic,-47.199,-39.502,-56 convnext_base.fb_in1k,37.307,62.693,57.319,42.681,88.59,288,1.000,bicubic,-47.121,-39.649,-43 resmlp_big_24_224.fb_in22k_ft_in1k,37.242,62.758,58.191,41.809,129.14,224,0.875,bicubic,-47.156,-38.921,-36 vit_small_r26_s32_224.augreg_in21k_ft_in1k,37.232,62.768,59.072,40.928,36.43,224,0.900,bicubic,-44.632,-36.950,+239 repvit_m2_3.dist_300e_in1k,37.210,62.790,57.234,42.766,23.69,224,0.950,bicubic,-46.294,-39.270,+49 pit_b_distilled_224.in1k,37.195,62.805,56.507,43.493,74.79,224,0.900,bicubic,-46.571,-39.961,+22 cait_s24_224.fb_dist_in1k,37.153,62.847,56.724,43.276,46.92,224,1.000,bicubic,-46.289,-39.850,+56 dm_nfnet_f2.dm_in1k,37.128,62.872,56.981,43.019,193.78,352,0.920,bicubic,-48.064,-40.365,-115 resnetaa101d.sw_in12k_ft_in1k,37.116,62.884,57.853,42.147,44.57,288,1.000,bicubic,-47.008,-39.253,-20 tiny_vit_21m_224.in1k,37.114,62.886,57.380,42.620,21.20,224,0.950,bicubic,-46.140,-39.212,+73 efficientformer_l7.snap_dist_in1k,37.112,62.888,56.900,43.100,82.23,224,0.950,bicubic,-46.270,-39.636,+61 pvt_v2_b3.in1k,37.108,62.892,57.335,42.665,45.24,224,0.900,bicubic,-46.010,-39.221,+87 fastvit_sa36.apple_dist_in1k,37.106,62.894,57.144,42.856,31.53,256,0.900,bicubic,-46.920,-39.710,-13 vit_base_patch32_224.augreg_in21k_ft_in1k,37.073,62.927,59.307,40.693,88.22,224,0.900,bicubic,-43.643,-36.259,+347 volo_d1_384.sail_in1k,37.069,62.931,57.138,42.862,26.78,384,1.000,bicubic,-48.175,-40.056,-131 efficientnetv2_rw_s.ra2_in1k,37.057,62.943,56.814,43.186,23.94,384,1.000,bicubic,-46.749,-39.918,+6 tf_efficientnet_b3.ap_in1k,37.051,62.949,57.238,42.762,12.23,300,0.904,bicubic,-44.769,-38.388,+230 maxvit_tiny_tf_224.in1k,37.020,62.980,56.904,43.096,30.92,224,0.950,bicubic,-46.382,-39.686,+49 swinv2_base_window16_256.ms_in1k,36.990,63.010,56.140,43.860,87.92,256,0.900,bicubic,-47.610,-40.950,-83 xcit_small_12_p16_224.fb_dist_in1k,36.975,63.025,56.725,43.275,26.25,224,1.000,bicubic,-46.353,-39.691,+60 regnetz_040_h.ra3_in1k,36.973,63.027,57.276,42.724,28.94,320,1.000,bicubic,-47.519,-39.482,-73 inception_next_small.sail_in1k,36.914,63.086,56.749,43.251,49.37,224,0.875,bicubic,-46.664,-39.849,+28 volo_d1_224.sail_in1k,36.888,63.112,56.635,43.365,26.63,224,0.960,bicubic,-47.274,-40.141,-37 seresnet152d.ra2_in1k,36.784,63.216,56.727,43.273,66.84,320,1.000,bicubic,-47.576,-40.313,-55 efficientformerv2_l.snap_dist_in1k,36.764,63.236,56.627,43.373,26.32,224,0.950,bicubic,-46.868,-39.931,+20 maxxvit_rmlp_small_rw_256.sw_in1k,36.707,63.293,56.012,43.988,66.01,256,0.950,bicubic,-47.917,-41.056,-92 seresnext101d_32x8d.ah_in1k,36.633,63.367,56.325,43.675,93.59,288,1.000,bicubic,-47.725,-40.596,-57 volo_d2_224.sail_in1k,36.627,63.373,56.470,43.530,58.68,224,0.960,bicubic,-48.575,-40.720,-136 caformer_s18.sail_in1k,36.578,63.422,55.831,44.169,26.34,224,1.000,bicubic,-47.076,-40.687,+15 xception65p.ra3_in1k,36.570,63.430,56.438,43.562,39.82,299,0.940,bicubic,-46.556,-40.044,+66 fastvit_ma36.apple_in1k,36.568,63.432,56.564,43.436,44.07,256,0.950,bicubic,-47.314,-40.178,-19 fastvit_sa36.apple_in1k,36.556,63.444,56.004,43.996,31.53,256,0.900,bicubic,-46.944,-40.626,+24 seresnextaa101d_32x8d.ah_in1k,36.532,63.468,56.409,43.591,93.59,288,1.000,bicubic,-48.034,-40.667,-95 focalnet_base_srf.ms_in1k,36.460,63.540,56.217,43.783,88.15,224,0.900,bicubic,-47.360,-40.464,-14 regnetz_d32.ra3_in1k,36.452,63.548,57.366,42.634,27.58,320,0.950,bicubic,-47.570,-39.502,-34 cait_xs24_384.fb_dist_in1k,36.416,63.584,56.944,43.056,26.67,384,1.000,bicubic,-47.645,-39.941,-42 efficientnet_b3.ra2_in1k,36.414,63.586,56.830,43.170,12.23,320,1.000,bicubic,-45.831,-39.288,+166 deit_base_distilled_patch16_224.fb_in1k,36.407,63.593,56.615,43.385,87.34,224,0.900,bicubic,-46.983,-39.873,+32 volo_d2_384.sail_in1k,36.407,63.593,56.323,43.677,58.87,384,1.000,bicubic,-49.635,-41.251,-209 resnetv2_101x3_bit.goog_in21k_ft_in1k,36.383,63.617,59.068,40.932,387.93,448,1.000,bilinear,-48.055,-38.314,-83 gcvit_base.in1k,36.381,63.619,55.880,44.120,90.32,224,0.875,bicubic,-48.063,-41.202,-85 dm_nfnet_f1.dm_in1k,36.326,63.674,55.747,44.253,132.63,320,0.910,bicubic,-48.376,-41.435,-112 resnetrs270.tf_in1k,36.310,63.690,56.566,43.434,129.86,352,1.000,bicubic,-48.118,-40.402,-83 tresnet_m.miil_in21k_ft_in1k,36.289,63.711,55.792,44.208,31.39,224,0.875,bilinear,-46.781,-40.318,+61 mixer_b16_224.miil_in21k_ft_in1k,36.267,63.733,55.965,44.035,59.88,224,0.875,bilinear,-46.039,-39.755,+152 convnext_small.fb_in1k,36.248,63.752,55.912,44.088,50.22,288,1.000,bicubic,-47.453,-40.896,-7 convformer_s18.sail_in1k_384,36.204,63.796,56.059,43.941,26.77,384,1.000,bicubic,-48.198,-41.053,-81 deit3_small_patch16_384.fb_in1k,36.193,63.807,55.558,44.442,22.21,384,1.000,bicubic,-47.235,-41.116,+16 tf_efficientnet_b2.ns_jft_in1k,36.167,63.833,57.559,42.441,9.11,260,0.890,bicubic,-46.211,-38.695,+133 mvitv2_tiny.fb_in1k,36.167,63.833,55.132,44.868,24.17,224,0.900,bicubic,-46.243,-41.020,+129 focalnet_base_lrf.ms_in1k,36.118,63.882,55.810,44.190,88.75,224,0.900,bicubic,-47.720,-40.798,-34 regnety_320.seer_ft_in1k,36.069,63.931,58.484,41.516,145.05,384,1.000,bicubic,-47.259,-38.224,+27 regnetz_040.ra3_in1k,36.053,63.947,55.735,44.265,27.12,320,1.000,bicubic,-48.187,-41.197,-75 resnest200e.in1k,35.929,64.071,55.847,44.153,70.20,320,0.909,bicubic,-47.915,-41.037,-39 resnet18.fb_swsl_ig1b_ft_in1k,35.874,64.126,58.447,41.553,11.69,224,0.875,bilinear,-37.414,-33.283,+666 sequencer2d_l.in1k,35.831,64.169,55.715,44.285,54.30,224,0.875,bicubic,-47.563,-40.781,+13 eca_nfnet_l1.ra2_in1k,35.815,64.185,55.953,44.047,41.41,320,1.000,bicubic,-48.197,-41.073,-54 gcvit_small.in1k,35.760,64.240,54.790,45.210,51.09,224,0.875,bicubic,-48.132,-41.868,-47 vit_base_patch16_224.orig_in21k_ft_in1k,35.754,64.246,57.401,42.599,86.57,224,0.900,bicubic,-46.036,-38.725,+191 vit_relpos_medium_patch16_cls_224.sw_in1k,35.725,64.275,54.919,45.081,38.76,224,0.900,bicubic,-46.847,-41.148,+101 xcit_small_24_p8_224.fb_in1k,35.556,64.444,54.780,45.220,47.63,224,1.000,bicubic,-48.278,-41.852,-42 xcit_small_12_p8_224.fb_in1k,35.522,64.478,55.507,44.493,26.21,224,1.000,bicubic,-47.812,-40.975,+15 xcit_large_24_p16_224.fb_in1k,35.522,64.478,54.741,45.259,189.10,224,1.000,bicubic,-47.380,-41.143,+56 coat_small.in1k,35.520,64.480,55.157,44.843,21.69,224,0.900,bicubic,-46.842,-41.051,+121 flexivit_base.1200ep_in1k,35.519,64.481,53.837,46.163,86.59,240,0.950,bicubic,-49.157,-43.157,-133 vit_small_patch16_384.augreg_in21k_ft_in1k,35.467,64.533,57.543,42.457,22.20,384,1.000,bicubic,-48.337,-39.557,-43 regnetz_d8_evos.ch_in1k,35.454,64.546,55.751,44.249,23.46,320,1.000,bicubic,-48.672,-41.261,-79 xcit_medium_24_p8_224.fb_in1k,35.452,64.548,54.823,45.177,84.32,224,1.000,bicubic,-48.294,-41.887,-37 swinv2_base_window8_256.ms_in1k,35.450,64.550,54.607,45.393,87.92,256,0.900,bicubic,-48.800,-42.317,-92 swinv2_small_window16_256.ms_in1k,35.428,64.572,54.623,45.377,49.73,256,0.900,bicubic,-48.796,-42.155,-90 dm_nfnet_f0.dm_in1k,35.407,64.594,55.525,44.475,71.49,256,0.900,bicubic,-48.080,-41.043,-12 resnest101e.in1k,35.373,64.627,55.790,44.210,48.28,256,0.875,bilinear,-47.511,-40.532,+47 resnet152.a1h_in1k,35.357,64.643,54.627,45.373,60.19,288,1.000,bicubic,-48.093,-41.911,-11 tf_efficientnet_b5.aa_in1k,35.316,64.684,56.038,43.962,30.39,456,0.934,bicubic,-48.372,-40.674,-33 convit_base.fb_in1k,35.302,64.698,54.939,45.061,86.54,224,0.875,bicubic,-46.988,-40.997,+123 focalnet_small_lrf.ms_in1k,35.277,64.723,54.888,45.112,50.34,224,0.900,bicubic,-48.217,-41.692,-18 efficientformer_l3.snap_dist_in1k,35.253,64.747,54.501,45.499,31.41,224,0.950,bicubic,-47.295,-41.749,+88 xcit_tiny_24_p8_224.fb_dist_in1k,35.241,64.759,55.267,44.733,12.11,224,1.000,bicubic,-47.325,-40.791,+85 edgenext_base.usi_in1k,35.216,64.784,55.126,44.874,18.51,320,1.000,bicubic,-48.742,-41.644,-74 fastvit_sa24.apple_dist_in1k,35.214,64.786,54.674,45.326,21.55,256,0.900,bicubic,-48.128,-41.878,-4 convformer_s18.sail_in1k,35.208,64.792,54.629,45.371,26.77,224,1.000,bicubic,-47.778,-41.621,+32 flexivit_base.600ep_in1k,35.137,64.863,53.652,46.348,86.59,240,0.950,bicubic,-49.387,-43.284,-139 twins_svt_large.in1k,35.084,64.916,54.719,45.281,99.27,224,0.900,bicubic,-48.594,-41.869,-40 repvgg_b3.rvgg_in1k,35.057,64.943,54.548,45.452,123.09,224,0.875,bilinear,-45.449,-40.706,+294 repvgg_b3g4.rvgg_in1k,35.049,64.951,54.788,45.212,83.83,224,0.875,bilinear,-45.167,-40.304,+327 convnextv2_tiny.fcmae_ft_in1k,35.045,64.955,54.224,45.776,28.64,288,1.000,bicubic,-48.419,-42.494,-26 repvit_m1_5.dist_450e_in1k,35.021,64.979,54.477,45.523,14.64,224,0.950,bicubic,-47.491,-41.635,+82 regnetz_d8.ra3_in1k,35.008,64.992,55.930,44.070,23.37,320,1.000,bicubic,-49.044,-41.066,-91 xcit_tiny_24_p8_384.fb_dist_in1k,34.915,65.085,55.148,44.852,12.11,384,1.000,bicubic,-48.831,-41.253,-59 resnet101d.ra2_in1k,34.876,65.124,54.210,45.790,44.57,320,1.000,bicubic,-48.144,-42.242,+19 rexnetr_200.sw_in12k_ft_in1k,34.870,65.130,55.857,44.143,16.52,288,1.000,bicubic,-48.268,-40.779,+2 repvit_m1_5.dist_300e_in1k,34.868,65.132,54.375,45.625,14.64,224,0.950,bicubic,-47.508,-41.655,+92 coatnet_1_rw_224.sw_in1k,34.850,65.150,53.442,46.558,41.72,224,0.950,bicubic,-48.746,-42.940,-45 coatnet_rmlp_1_rw_224.sw_in1k,34.803,65.197,53.953,46.047,41.69,224,0.950,bicubic,-48.559,-42.497,-20 swin_s3_base_224.ms_in1k,34.803,65.197,53.703,46.297,71.13,224,0.900,bicubic,-49.117,-42.969,-88 flexivit_base.300ep_in1k,34.799,65.201,53.161,46.839,86.59,240,0.950,bicubic,-49.607,-43.723,-131 seresnext101_32x8d.ah_in1k,34.789,65.211,53.452,46.548,93.57,288,1.000,bicubic,-49.397,-43.422,-113 resmlp_big_24_224.fb_distilled_in1k,34.788,65.213,54.642,45.358,129.14,224,0.875,bicubic,-48.804,-42.008,-49 maxvit_tiny_rw_224.sw_in1k,34.780,65.220,53.351,46.649,29.06,224,0.950,bicubic,-48.724,-43.163,-44 repvgg_d2se.rvgg_in1k,34.740,65.260,53.200,46.800,133.33,320,1.000,bilinear,-48.820,-43.458,-49 vit_relpos_base_patch16_clsgap_224.sw_in1k,34.725,65.275,54.214,45.786,86.43,224,0.900,bicubic,-48.035,-41.958,+29 vit_base_patch16_rpn_224.sw_in1k,34.715,65.285,54.658,45.342,86.54,224,0.900,bicubic,-47.487,-41.338,+108 sequencer2d_m.in1k,34.703,65.297,53.996,46.004,38.31,224,0.875,bicubic,-48.109,-42.278,+21 deit3_small_patch16_224.fb_in1k,34.685,65.315,53.173,46.827,22.06,224,0.900,bicubic,-46.685,-42.283,+188 davit_tiny.msft_in1k,34.673,65.326,54.344,45.656,28.36,224,0.950,bicubic,-48.023,-41.930,+33 vit_large_patch32_384.orig_in21k_ft_in1k,34.672,65.329,55.733,44.267,306.63,384,1.000,bicubic,-46.839,-40.357,+168 focalnet_small_srf.ms_in1k,34.670,65.330,54.420,45.580,49.89,224,0.900,bicubic,-48.746,-42.018,-42 ecaresnet101d.miil_in1k,34.644,65.356,54.499,45.501,44.57,288,0.950,bicubic,-48.340,-42.043,+6 resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,34.615,65.385,55.937,44.063,194.03,224,0.875,bilinear,-47.223,-40.155,+136 vit_relpos_base_patch16_224.sw_in1k,34.607,65.393,54.291,45.709,86.43,224,0.900,bicubic,-47.889,-41.847,+61 repvgg_b2g4.rvgg_in1k,34.593,65.407,54.768,45.232,61.76,224,0.875,bilinear,-44.789,-39.908,+359 gcvit_tiny.in1k,34.554,65.446,53.249,46.751,28.22,224,0.875,bicubic,-48.830,-43.149,-41 resnetrs200.tf_in1k,34.502,65.498,54.281,45.719,93.21,320,1.000,bicubic,-49.942,-42.561,-158 poolformerv2_m48.sail_in1k,34.485,65.515,54.027,45.973,73.35,224,1.000,bicubic,-48.133,-42.045,+35 efficientvit_b2.r256_in1k,34.430,65.570,53.593,46.407,24.33,256,1.000,bicubic,-48.260,-42.501,+24 convnextv2_nano.fcmae_ft_in22k_in1k,34.379,65.621,55.014,44.986,15.62,288,1.000,bicubic,-48.285,-41.506,+26 resnest50d_4s2x40d.in1k,34.369,65.631,54.725,45.275,30.42,224,0.875,bicubic,-46.751,-40.835,+199 resnetrs152.tf_in1k,34.351,65.649,53.564,46.436,86.62,320,1.000,bicubic,-49.351,-43.048,-80 pvt_v2_b2_li.in1k,34.318,65.682,54.104,45.896,22.55,224,0.900,bicubic,-47.876,-41.988,+93 crossvit_18_dagger_408.in1k,34.247,65.753,53.106,46.894,44.61,408,1.000,bicubic,-49.955,-43.712,-138 xcit_medium_24_p16_224.fb_in1k,34.241,65.759,53.157,46.843,84.40,224,1.000,bicubic,-48.399,-42.825,+24 efficientvit_b2.r288_in1k,34.224,65.776,53.546,46.454,24.33,288,1.000,bicubic,-48.876,-42.758,-20 pit_s_distilled_224.in1k,34.161,65.839,53.361,46.639,24.04,224,0.900,bicubic,-47.653,-42.369,+125 efficientnetv2_rw_t.ra2_in1k,34.159,65.841,53.135,46.865,13.65,288,1.000,bicubic,-48.191,-43.057,+65 tf_efficientnet_b1.ns_jft_in1k,34.151,65.849,55.503,44.497,7.79,240,0.882,bicubic,-47.237,-40.235,+164 twins_pcpvt_large.in1k,34.119,65.881,54.136,45.864,60.99,224,0.900,bicubic,-49.011,-42.468,-31 fastvit_sa24.apple_in1k,34.098,65.902,53.782,46.218,21.55,256,0.900,bicubic,-48.580,-42.490,+13 tf_efficientnet_b4.aa_in1k,34.058,65.942,54.212,45.788,19.34,380,0.922,bicubic,-48.960,-42.088,-18 efficientformerv2_s2.snap_dist_in1k,34.056,65.944,53.473,46.527,12.71,224,0.950,bicubic,-48.109,-42.437,+86 resnetv2_101.a1h_in1k,34.056,65.944,52.308,47.692,44.54,288,1.000,bicubic,-48.944,-44.146,-19 resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,34.033,65.967,55.596,44.404,88.79,224,0.875,bilinear,-47.573,-40.445,+131 xcit_small_24_p16_224.fb_in1k,34.005,65.995,53.281,46.719,47.67,224,1.000,bicubic,-48.571,-42.731,+28 tf_efficientnet_b6.aa_in1k,34.002,65.999,54.542,45.458,43.04,528,0.942,bicubic,-50.110,-42.342,-144 nfnet_l0.ra2_in1k,34.002,65.999,54.377,45.623,35.07,288,1.000,bicubic,-48.748,-42.139,-2 efficientnet_b3_pruned.in1k,33.992,66.008,54.110,45.890,9.86,300,0.904,bicubic,-46.860,-41.134,+213 regnety_160.deit_in1k,33.980,66.020,53.552,46.448,83.59,288,1.000,bicubic,-49.710,-43.228,-96 resnext101_64x4d.tv_in1k,33.966,66.034,52.530,47.470,83.46,224,0.875,bilinear,-49.026,-43.714,-25 gc_efficientnetv2_rw_t.agc_in1k,33.962,66.038,53.228,46.772,13.68,288,1.000,bicubic,-48.494,-43.068,+38 swinv2_cr_small_ns_224.sw_in1k,33.846,66.154,52.640,47.360,49.70,224,0.900,bicubic,-49.652,-43.844,-82 resnext101_64x4d.c1_in1k,33.842,66.158,52.161,47.839,83.46,288,1.000,bicubic,-49.314,-44.213,-48 poolformerv2_s36.sail_in1k,33.825,66.175,53.685,46.315,30.79,224,1.000,bicubic,-47.741,-42.005,+124 repvit_m3.dist_in1k,33.791,66.209,53.123,46.877,10.68,224,0.950,bicubic,-47.711,-42.444,+133 resnet101.a1h_in1k,33.781,66.219,53.104,46.896,44.55,288,1.000,bicubic,-48.997,-43.206,-15 xcit_small_12_p16_224.fb_in1k,33.756,66.244,53.228,46.772,26.25,224,1.000,bicubic,-48.214,-42.584,+91 swin_s3_small_224.ms_in1k,33.697,66.303,52.365,47.635,49.74,224,0.900,bicubic,-50.059,-44.087,-116 resnetv2_50x3_bit.goog_in21k_ft_in1k,33.671,66.329,55.888,44.112,217.32,448,1.000,bilinear,-50.349,-41.238,-144 swinv2_small_window8_256.ms_in1k,33.634,66.366,52.827,47.173,49.73,256,0.900,bicubic,-50.220,-43.817,-133 resnet152.tv2_in1k,33.622,66.378,51.656,48.344,60.19,224,0.965,bilinear,-48.664,-44.348,+51 inception_next_tiny.sail_in1k,33.583,66.417,52.978,47.022,28.06,224,0.875,bicubic,-48.895,-43.044,+25 resnet51q.ra2_in1k,33.555,66.445,53.023,46.977,35.70,288,1.000,bilinear,-48.805,-43.163,+37 xcit_tiny_24_p16_384.fb_dist_in1k,33.508,66.492,52.768,47.232,12.12,384,1.000,bicubic,-49.062,-43.508,+11 vit_relpos_medium_patch16_224.sw_in1k,33.498,66.502,52.620,47.380,38.75,224,0.900,bicubic,-48.964,-43.462,+23 regnety_080.ra3_in1k,33.455,66.545,52.939,47.061,39.18,288,1.000,bicubic,-50.471,-43.951,-148 cs3edgenet_x.c2_in1k,33.455,66.545,52.925,47.075,47.82,288,1.000,bicubic,-49.253,-43.445,-18 convmixer_1536_20.in1k,33.432,66.568,53.029,46.971,51.63,224,0.960,bicubic,-47.930,-42.585,+138 sequencer2d_s.in1k,33.430,66.570,52.383,47.617,27.65,224,0.875,bicubic,-48.910,-43.645,+35 regnety_032.ra_in1k,33.412,66.588,52.770,47.230,19.44,288,1.000,bicubic,-49.314,-43.646,-24 crossvit_18_240.in1k,33.392,66.608,52.245,47.755,43.27,240,0.875,bicubic,-49.008,-43.815,+22 vit_srelpos_medium_patch16_224.sw_in1k,33.373,66.627,52.459,47.541,38.74,224,0.900,bicubic,-48.867,-43.483,+45 gernet_l.idstcv_in1k,33.371,66.629,51.911,48.089,31.08,256,0.875,bilinear,-47.983,-43.619,+135 tf_efficientnetv2_b3.in21k_ft_in1k,33.353,66.647,54.922,45.078,14.36,300,0.900,bicubic,-49.317,-41.705,-20 regnetz_c16.ra3_in1k,33.337,66.663,54.242,45.758,13.46,320,1.000,bicubic,-49.295,-42.076,-15 crossvit_15_dagger_408.in1k,33.335,66.665,52.200,47.800,28.50,408,1.000,bicubic,-50.505,-44.578,-147 tiny_vit_5m_224.dist_in22k_ft_in1k,33.306,66.694,55.028,44.972,5.39,224,0.950,bicubic,-47.570,-40.636,+180 crossvit_18_dagger_240.in1k,33.284,66.716,52.190,47.810,44.27,240,0.875,bicubic,-49.234,-43.878,+3 swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,33.257,66.743,55.295,44.705,28.29,224,0.900,bicubic,-47.711,-40.719,+167 wide_resnet101_2.tv2_in1k,33.257,66.743,51.430,48.570,126.89,224,0.965,bilinear,-49.245,-44.586,+3 tresnet_xl.miil_in1k,33.251,66.749,52.298,47.702,78.44,224,0.875,bilinear,-48.823,-43.630,+56 nest_base_jx.goog_in1k,33.215,66.785,51.825,48.175,67.72,224,0.875,bicubic,-50.319,-44.549,-116 convnext_tiny.fb_in1k,33.168,66.832,52.677,47.323,28.59,288,1.000,bicubic,-49.530,-43.955,-33 resnest50d_1s4x24d.in1k,33.145,66.855,52.840,47.160,25.68,224,0.875,bicubic,-47.843,-42.485,+159 convnext_nano.in12k_ft_in1k,33.119,66.881,53.988,46.012,15.59,288,1.000,bicubic,-49.743,-42.568,-51 vit_relpos_medium_patch16_rpn_224.sw_in1k,33.092,66.908,52.363,47.637,38.73,224,0.900,bicubic,-49.218,-43.609,+23 resnet61q.ra2_in1k,33.090,66.910,51.746,48.254,36.85,288,1.000,bicubic,-49.434,-44.384,-7 maxxvit_rmlp_nano_rw_256.sw_in1k,33.074,66.926,51.852,48.148,16.78,256,0.950,bicubic,-49.968,-44.498,-67 nest_small_jx.goog_in1k,33.052,66.948,51.064,48.936,38.35,224,0.875,bicubic,-50.072,-45.256,-79 rexnet_300.nav_in1k,33.048,66.952,52.365,47.635,34.71,224,0.875,bicubic,-49.726,-43.873,-48 crossvit_base_240.in1k,33.035,66.965,51.390,48.610,105.03,240,0.875,bicubic,-49.179,-44.444,+30 poolformerv2_m36.sail_in1k,33.033,66.967,51.860,48.140,56.08,224,1.000,bicubic,-49.183,-44.064,+28 twins_pcpvt_base.in1k,33.021,66.979,52.502,47.498,43.83,224,0.900,bicubic,-49.693,-43.844,-46 pvt_v2_b2.in1k,33.017,66.983,52.001,47.999,25.36,224,0.900,bicubic,-49.067,-43.955,+42 xcit_tiny_24_p16_224.fb_dist_in1k,32.999,67.001,52.074,47.926,12.12,224,1.000,bicubic,-47.455,-43.144,+206 resnest50d.in1k,32.966,67.034,52.707,47.293,27.48,224,0.875,bilinear,-47.994,-42.675,+151 rexnet_200.nav_in1k,32.962,67.038,52.921,47.079,16.37,224,0.875,bicubic,-48.674,-42.745,+76 crossvit_15_dagger_240.in1k,32.925,67.075,51.807,48.193,28.21,240,0.875,bicubic,-49.405,-44.149,+8 convit_small.fb_in1k,32.923,67.077,52.115,47.885,27.78,224,0.875,bicubic,-48.497,-43.629,+100 tf_efficientnetv2_s.in1k,32.913,67.087,51.728,48.272,21.46,384,1.000,bicubic,-50.985,-44.968,-178 swin_base_patch4_window12_384.ms_in1k,32.905,67.095,51.750,48.250,87.90,384,1.000,bicubic,-51.571,-45.142,-237 vit_small_patch16_224.augreg_in21k_ft_in1k,32.885,67.115,53.923,46.077,22.05,224,0.900,bicubic,-48.501,-42.213,+101 convnext_tiny_hnf.a2h_in1k,32.881,67.119,51.194,48.806,28.59,288,1.000,bicubic,-49.703,-44.814,-33 tf_efficientnet_b3.aa_in1k,32.864,67.136,52.964,47.036,12.23,300,0.904,bicubic,-48.776,-42.758,+68 pnasnet5large.tf_in1k,32.862,67.138,50.516,49.484,86.06,331,0.911,bicubic,-49.920,-45.524,-65 twins_svt_base.in1k,32.832,67.168,51.565,48.435,56.07,224,0.900,bicubic,-50.288,-44.849,-95 regnetv_064.ra3_in1k,32.830,67.170,52.864,47.136,30.58,288,1.000,bicubic,-50.886,-43.878,-158 convnextv2_nano.fcmae_ft_in1k,32.815,67.185,52.656,47.344,15.62,288,1.000,bicubic,-49.671,-43.570,-23 nasnetalarge.tf_in1k,32.781,67.219,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.901,-48 gernet_m.idstcv_in1k,32.758,67.242,51.899,48.101,21.14,224,0.875,bilinear,-47.978,-43.291,+162 inception_resnet_v2.tf_in1k,32.734,67.266,50.640,49.360,55.84,299,0.897,bicubic,-47.724,-44.550,+187 resnet152d.gluon_in1k,32.730,67.270,51.080,48.920,60.21,224,0.875,bicubic,-47.746,-44.122,+183 repvit_m1_1.dist_450e_in1k,32.726,67.274,52.687,47.313,8.80,224,0.950,bicubic,-48.586,-42.849,+96 pit_b_224.in1k,32.722,67.278,49.854,50.146,73.76,224,0.900,bicubic,-49.716,-45.860,-24 tf_efficientnet_b2.ap_in1k,32.685,67.315,52.237,47.763,9.11,260,0.890,bicubic,-47.625,-42.789,+199 swin_base_patch4_window7_224.ms_in1k,32.644,67.356,51.573,48.427,87.77,224,0.900,bicubic,-50.962,-44.879,-157 fbnetv3_g.ra2_in1k,32.624,67.376,52.886,47.114,16.62,288,0.950,bilinear,-49.416,-43.174,+24 regnety_320.tv2_in1k,32.616,67.384,50.296,49.704,145.05,224,0.965,bicubic,-50.546,-46.118,-114 tresnet_l.miil_in1k,32.561,67.439,51.137,48.863,55.99,224,0.875,bilinear,-48.919,-44.487,+73 cait_xxs36_384.fb_dist_in1k,32.553,67.447,52.221,47.779,17.37,384,1.000,bicubic,-49.651,-43.923,+2 resnext101_32x8d.tv2_in1k,32.549,67.451,50.164,49.836,88.79,224,0.965,bilinear,-50.283,-46.068,-86 regnetz_c16_evos.ch_in1k,32.539,67.461,52.921,47.079,13.49,320,0.950,bicubic,-50.097,-43.553,-63 gmlp_s16_224.ra3_in1k,32.420,67.580,51.817,48.183,19.42,224,0.875,bicubic,-47.224,-42.805,+242 inception_resnet_v2.tf_ens_adv_in1k,32.368,67.632,50.429,49.571,55.84,299,0.897,bicubic,-47.609,-44.519,+211 deit_base_patch16_224.fb_in1k,32.363,67.637,50.991,49.009,86.57,224,0.900,bicubic,-49.629,-44.745,+20 maxvit_nano_rw_256.sw_in1k,32.355,67.645,50.622,49.378,15.45,256,0.950,bicubic,-50.573,-45.598,-96 swin_small_patch4_window7_224.ms_in1k,32.347,67.653,50.903,49.097,49.61,224,0.900,bicubic,-50.861,-45.413,-127 resnet152s.gluon_in1k,32.333,67.667,50.541,49.459,60.32,224,0.875,bicubic,-48.675,-44.875,+114 xcit_tiny_24_p8_224.fb_in1k,32.292,67.708,51.895,48.105,12.11,224,1.000,bicubic,-49.600,-44.075,+25 deit_small_distilled_patch16_224.fb_in1k,32.270,67.730,52.109,47.891,22.44,224,0.900,bicubic,-48.946,-43.514,+89 poolformerv2_s24.sail_in1k,32.266,67.734,51.492,48.508,21.34,224,1.000,bicubic,-48.482,-43.818,+140 repvit_m1_1.dist_300e_in1k,32.243,67.757,51.917,48.083,8.80,224,0.950,bicubic,-48.583,-43.253,+132 seresnext101_64x4d.gluon_in1k,32.192,67.808,50.313,49.687,88.23,224,0.875,bicubic,-48.702,-44.983,+121 regnetx_320.tv2_in1k,32.174,67.826,49.349,50.651,107.81,224,0.965,bicubic,-50.636,-46.859,-96 efficientvit_b2.r224_in1k,32.148,67.852,51.001,48.999,24.33,224,0.950,bicubic,-50.000,-44.705,-5 seresnext101_32x4d.gluon_in1k,32.121,67.879,51.243,48.757,48.96,224,0.875,bicubic,-48.771,-44.053,+119 coat_lite_small.in1k,32.113,67.887,49.928,50.072,19.84,224,0.900,bicubic,-50.199,-45.922,-29 flexivit_small.1200ep_in1k,32.095,67.905,50.323,49.677,22.06,240,0.950,bicubic,-50.431,-45.803,-59 tiny_vit_11m_224.in1k,32.072,67.928,51.278,48.722,11.00,224,0.950,bicubic,-49.420,-44.584,+50 coatnext_nano_rw_224.sw_in1k,32.070,67.930,51.017,48.983,14.70,224,0.900,bicubic,-49.872,-44.899,+11 maxxvitv2_nano_rw_256.sw_in1k,32.068,67.932,50.345,49.655,23.70,256,0.950,bicubic,-51.042,-45.979,-128 focalnet_tiny_lrf.ms_in1k,32.052,67.948,51.451,48.549,28.65,224,0.900,bicubic,-50.102,-44.497,-13 gcvit_xtiny.in1k,32.044,67.956,50.991,49.009,19.98,224,0.875,bicubic,-49.910,-44.975,+7 deit_base_patch16_384.fb_in1k,31.991,68.009,50.559,49.441,86.86,384,1.000,bicubic,-51.113,-45.809,-130 maxvit_rmlp_nano_rw_256.sw_in1k,31.976,68.025,50.618,49.382,15.50,256,0.950,bicubic,-50.978,-45.648,-117 xcit_tiny_12_p8_224.fb_dist_in1k,31.940,68.060,51.402,48.598,6.71,224,1.000,bicubic,-49.272,-44.200,+75 coatnet_bn_0_rw_224.sw_in1k,31.905,68.095,51.015,48.985,27.44,224,0.950,bicubic,-50.495,-45.171,-55 tf_efficientnet_b7.aa_in1k,31.877,68.123,51.909,48.091,66.35,600,0.949,bicubic,-52.539,-44.999,-271 levit_384.fb_dist_in1k,31.875,68.125,50.594,49.406,39.13,224,0.900,bicubic,-50.721,-45.424,-83 levit_conv_384.fb_dist_in1k,31.871,68.129,50.596,49.404,39.13,224,0.900,bicubic,-50.719,-45.420,-82 resnetrs101.tf_in1k,31.858,68.142,51.023,48.977,63.62,288,0.940,bicubic,-50.426,-44.991,-38 cs3se_edgenet_x.c2ns_in1k,31.808,68.192,50.769,49.231,50.72,320,1.000,bicubic,-51.738,-45.901,-187 vit_relpos_small_patch16_224.sw_in1k,31.781,68.219,50.620,49.380,21.98,224,0.900,bicubic,-49.681,-45.200,+41 convnext_tiny.fb_in22k_ft_in1k,31.667,68.333,51.785,48.215,28.59,288,1.000,bicubic,-47.231,-42.889,+268 poolformer_m48.sail_in1k,31.665,68.335,49.800,50.200,73.47,224,0.950,bicubic,-50.817,-46.165,-69 flexivit_small.600ep_in1k,31.647,68.353,49.384,50.616,22.06,240,0.950,bicubic,-50.715,-46.700,-57 tnt_s_patch16_224,31.634,68.366,51.153,48.847,23.76,224,0.900,bicubic,-49.902,-44.537,+25 eca_nfnet_l0.ra2_in1k,31.616,68.384,51.587,48.413,24.14,288,1.000,bicubic,-50.962,-44.905,-86 focalnet_tiny_srf.ms_in1k,31.606,68.394,50.864,49.136,28.43,224,0.900,bicubic,-50.532,-45.104,-27 resnetv2_50x1_bit.goog_distilled_in1k,31.581,68.419,51.267,48.733,25.55,224,0.875,bicubic,-51.243,-45.252,-124 coatnet_rmlp_nano_rw_224.sw_in1k,31.549,68.451,50.178,49.822,15.15,224,0.900,bicubic,-50.501,-45.700,-22 mobilevitv2_200.cvnets_in22k_ft_in1k,31.521,68.478,51.777,48.223,18.45,256,0.888,bicubic,-50.810,-44.165,-57 wide_resnet50_2.racm_in1k,31.521,68.478,50.388,49.612,68.88,288,0.950,bicubic,-50.758,-45.676,-49 xception41p.ra3_in1k,31.504,68.496,50.380,49.620,26.91,299,0.940,bicubic,-50.468,-45.404,-18 regnety_064.ra3_in1k,31.463,68.537,50.520,49.480,30.58,288,1.000,bicubic,-52.257,-46.202,-217 poolformer_m36.sail_in1k,31.447,68.553,50.017,49.983,56.17,224,0.950,bicubic,-50.655,-45.681,-31 flexivit_small.300ep_in1k,31.437,68.563,49.215,50.785,22.06,240,0.950,bicubic,-50.741,-46.823,-41 resnet152.a1_in1k,31.433,68.567,48.653,51.347,60.19,288,1.000,bicubic,-51.299,-47.067,-123 resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,31.429,68.571,52.127,47.873,44.18,224,0.875,bilinear,-49.495,-43.607,+82 repvit_m1_0.dist_450e_in1k,31.413,68.587,50.653,49.347,7.30,224,0.950,bicubic,-49.020,-44.265,+132 inception_v4.tf_in1k,31.386,68.614,49.235,50.765,42.68,299,0.875,bicubic,-48.770,-45.735,+159 rexnet_150.nav_in1k,31.376,68.624,51.284,48.716,9.73,224,0.875,bicubic,-48.948,-43.706,+140 efficientformer_l1.snap_dist_in1k,31.341,68.659,50.441,49.559,12.29,224,0.950,bicubic,-49.157,-44.547,+119 regnety_032.tv2_in1k,31.341,68.659,50.127,49.873,19.44,224,0.965,bicubic,-50.415,-45.717,-9 pit_s_224.in1k,31.339,68.661,49.677,50.323,23.46,224,0.900,bicubic,-49.747,-45.653,+62 resnet101.tv2_in1k,31.339,68.661,49.673,50.327,44.55,224,0.965,bilinear,-50.549,-46.095,-21 crossvit_15_240.in1k,31.319,68.681,50.182,49.818,27.53,240,0.875,bicubic,-50.217,-45.554,+6 swinv2_tiny_window16_256.ms_in1k,31.305,68.695,49.667,50.333,28.35,256,0.900,bicubic,-51.499,-46.569,-139 repvit_m1_0.dist_300e_in1k,31.274,68.726,50.801,49.199,7.30,224,0.950,bicubic,-48.852,-43.943,+152 cspresnet50.ra_in1k,31.272,68.728,51.225,48.775,21.62,256,0.887,bilinear,-48.310,-43.485,+188 cait_xxs36_224.fb_dist_in1k,31.272,68.728,50.614,49.386,17.30,224,1.000,bicubic,-48.474,-44.260,+174 crossvit_small_240.in1k,31.270,68.730,50.192,49.808,26.86,240,0.875,bicubic,-49.748,-45.264,+59 vit_srelpos_small_patch16_224.sw_in1k,31.260,68.740,50.233,49.767,21.97,224,0.900,bicubic,-49.832,-45.337,+52 swinv2_cr_small_224.sw_in1k,31.254,68.746,48.745,51.255,49.70,224,0.900,bicubic,-51.882,-47.363,-177 coatnet_0_rw_224.sw_in1k,31.252,68.748,48.633,51.367,27.44,224,0.950,bicubic,-51.138,-47.203,-91 swin_s3_tiny_224.ms_in1k,31.248,68.752,49.728,50.272,28.33,224,0.900,bicubic,-50.896,-46.226,-55 repvit_m2.dist_in1k,31.239,68.761,50.626,49.374,8.80,224,0.950,bicubic,-49.221,-44.542,+110 cspresnext50.ra_in1k,31.227,68.773,50.871,49.129,20.57,256,0.887,bilinear,-49.327,-44.454,+98 regnetv_040.ra3_in1k,31.225,68.775,50.115,49.885,20.64,288,1.000,bicubic,-51.965,-46.543,-188 convmixer_768_32.in1k,31.219,68.781,50.928,49.072,21.11,224,0.960,bicubic,-48.949,-44.145,+138 coat_mini.in1k,31.191,68.809,49.761,50.239,10.34,224,0.900,bicubic,-50.079,-45.621,+23 xcit_tiny_12_p8_384.fb_dist_in1k,31.182,68.818,50.508,49.492,6.71,384,1.000,bicubic,-51.206,-45.712,-97 fastvit_sa12.apple_dist_in1k,31.138,68.862,49.958,50.042,11.58,256,0.900,bicubic,-50.716,-45.752,-36 resnet101s.gluon_in1k,31.107,68.893,49.791,50.209,44.67,224,0.875,bicubic,-49.197,-45.361,+121 edgenext_small.usi_in1k,31.101,68.899,50.135,49.865,5.59,320,1.000,bicubic,-50.463,-45.577,-16 coatnet_nano_rw_224.sw_in1k,31.099,68.901,49.581,50.419,15.14,224,0.900,bicubic,-50.597,-46.066,-29 tf_efficientnet_cc_b0_8e.in1k,31.091,68.909,50.781,49.219,24.01,224,0.875,bicubic,-46.813,-42.881,+302 resmlp_36_224.fb_distilled_in1k,31.062,68.938,49.696,50.304,44.69,224,0.875,bicubic,-50.086,-45.782,+29 ecaresnet50t.ra2_in1k,31.050,68.950,50.573,49.427,25.57,320,0.950,bicubic,-51.302,-45.567,-98 repvit_m0_9.dist_300e_in1k,31.046,68.954,50.681,49.319,5.49,224,0.950,bicubic,-47.612,-43.435,+244 resnet152c.gluon_in1k,31.018,68.981,48.936,51.064,60.21,224,0.875,bicubic,-48.894,-45.910,+139 cs3sedarknet_x.c2ns_in1k,31.017,68.984,50.131,49.869,35.40,288,1.000,bicubic,-51.642,-46.219,-146 cspdarknet53.ra_in1k,31.001,68.999,50.412,49.588,27.64,256,0.887,bilinear,-49.067,-44.666,+130 resnext101_64x4d.gluon_in1k,30.993,69.007,48.549,51.451,83.46,224,0.875,bicubic,-49.607,-46.443,+81 tresnet_m.miil_in1k,30.987,69.013,48.686,51.314,31.39,224,0.875,bilinear,-49.811,-46.170,+61 twins_svt_small.in1k,30.983,69.017,49.219,50.781,24.06,224,0.900,bicubic,-50.693,-46.439,-38 regnety_160.tv2_in1k,30.942,69.058,49.060,50.940,83.59,224,0.965,bicubic,-51.704,-47.154,-150 gcresnet50t.ra2_in1k,30.936,69.064,50.034,49.966,25.90,288,1.000,bicubic,-50.520,-45.684,-13 tf_efficientnet_cc_b1_8e.in1k,30.903,69.097,50.078,49.922,39.72,240,0.882,bicubic,-48.399,-44.296,+183 resmlp_24_224.fb_distilled_in1k,30.901,69.099,50.174,49.826,30.02,224,0.875,bicubic,-49.855,-45.050,+60 regnety_080_tv.tv2_in1k,30.891,69.109,48.724,51.276,39.38,224,0.965,bicubic,-51.703,-47.524,-144 resnext101_32x4d.gluon_in1k,30.891,69.109,48.539,51.461,44.18,224,0.875,bicubic,-49.449,-46.391,+98 tf_efficientnetv2_b3.in1k,30.853,69.147,49.808,50.192,14.36,300,0.904,bicubic,-51.119,-45.994,-66 repvit_m0_9.dist_450e_in1k,30.846,69.154,50.119,49.881,5.49,224,0.950,bicubic,-48.220,-44.261,+200 tf_efficientnet_lite4.in1k,30.830,69.170,50.390,49.610,13.01,380,0.920,bilinear,-50.700,-45.274,-31 resnetaa50d.sw_in12k_ft_in1k,30.802,69.198,50.569,49.431,25.58,288,1.000,bicubic,-51.798,-45.929,-151 efficientvit_b1.r288_in1k,30.791,69.210,50.009,49.991,9.10,288,1.000,bicubic,-49.533,-45.167,+96 nf_resnet50.ra2_in1k,30.698,69.302,49.944,50.056,25.56,288,0.940,bicubic,-49.942,-45.390,+63 poolformer_s36.sail_in1k,30.690,69.310,49.445,50.555,30.86,224,0.900,bicubic,-50.740,-45.999,-22 xcit_tiny_24_p16_224.fb_in1k,30.671,69.329,50.416,49.584,12.12,224,1.000,bicubic,-48.777,-44.462,+158 resnet50.a1h_in1k,30.631,69.369,49.415,50.585,25.56,224,1.000,bicubic,-50.047,-45.891,+56 dpn107.mx_in1k,30.625,69.374,48.739,51.261,86.92,224,0.875,bicubic,-49.544,-46.203,+105 tresnet_xl.miil_in1k_448,30.622,69.379,49.077,50.923,78.44,448,0.875,bilinear,-52.437,-47.095,-204 resnet152.gluon_in1k,30.614,69.386,48.515,51.485,60.19,224,0.875,bicubic,-49.082,-46.215,+135 resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,30.602,69.398,50.667,49.333,25.03,224,0.875,bilinear,-49.732,-44.733,+86 haloregnetz_b.ra3_in1k,30.594,69.406,48.987,51.013,11.68,224,0.940,bicubic,-50.452,-46.213,+13 pit_xs_distilled_224.in1k,30.543,69.457,50.180,49.820,11.00,224,0.900,bicubic,-48.637,-44.186,+177 regnetx_080.tv2_in1k,30.521,69.479,48.030,51.970,39.57,224,0.965,bicubic,-51.019,-47.512,-47 resnet101d.gluon_in1k,30.506,69.494,47.973,52.027,44.57,224,0.875,bicubic,-49.920,-47.051,+74 resnest26d.gluon_in1k,30.490,69.510,50.663,49.337,17.07,224,0.875,bilinear,-47.992,-43.631,+223 mobilevitv2_200.cvnets_in22k_ft_in1k_384,30.490,69.510,50.575,49.425,18.45,384,1.000,bicubic,-52.910,-46.007,-249 resnet50.ram_in1k,30.451,69.549,48.997,51.003,25.56,288,0.950,bicubic,-49.525,-46.055,+104 efficientformerv2_s1.snap_dist_in1k,30.445,69.555,49.582,50.418,6.19,224,0.950,bicubic,-49.247,-45.134,+127 efficientnet_b2.ra_in1k,30.427,69.573,49.688,50.312,9.11,288,1.000,bicubic,-50.183,-45.625,+49 tf_efficientnet_b1.ap_in1k,30.423,69.577,49.555,50.445,7.79,240,0.882,bicubic,-48.853,-44.757,+158 cs3darknet_x.c2ns_in1k,30.409,69.591,49.197,50.803,35.05,288,1.000,bicubic,-51.813,-47.033,-117 xcit_tiny_12_p16_384.fb_dist_in1k,30.403,69.597,50.131,49.869,6.72,384,1.000,bicubic,-50.535,-45.283,+12 twins_pcpvt_small.in1k,30.396,69.604,49.384,50.616,24.11,224,0.900,bicubic,-50.696,-46.264,-4 ecaresnetlight.miil_in1k,30.382,69.618,49.146,50.854,30.16,288,0.950,bicubic,-51.026,-46.670,-39 resnet50d.ra2_in1k,30.376,69.624,48.794,51.206,25.58,288,0.950,bicubic,-50.980,-46.944,-33 ecaresnet101d_pruned.miil_in1k,30.356,69.644,48.810,51.190,24.88,288,0.950,bicubic,-51.642,-47.350,-97 visformer_small.in1k,30.339,69.661,48.285,51.715,40.22,224,0.900,bicubic,-51.767,-47.593,-108 resnet101.a1_in1k,30.297,69.703,46.584,53.416,44.55,288,1.000,bicubic,-52.025,-49.048,-136 tf_efficientnet_b5.in1k,30.295,69.705,49.753,50.247,30.39,456,0.934,bicubic,-52.881,-46.783,-241 regnety_040.ra3_in1k,30.266,69.734,48.930,51.070,20.65,288,1.000,bicubic,-52.778,-47.572,-225 regnetx_160.tv2_in1k,30.217,69.783,47.055,52.945,54.28,224,0.965,bicubic,-52.349,-49.117,-169 mobilevitv2_175.cvnets_in22k_ft_in1k,30.209,69.791,49.056,50.944,14.25,256,0.888,bicubic,-51.729,-46.734,-95 vit_relpos_base_patch32_plus_rpn_256.sw_in1k,30.201,69.799,48.688,51.312,119.42,256,0.900,bicubic,-49.283,-45.450,+127 resnet50.b1k_in1k,30.197,69.803,49.187,50.813,25.56,288,1.000,bicubic,-50.509,-46.245,+26 fastvit_s12.apple_dist_in1k,30.187,69.813,48.962,51.038,9.47,256,0.900,bicubic,-50.883,-46.323,-12 seresnext50_32x4d.racm_in1k,30.168,69.832,49.093,50.907,27.56,288,0.950,bicubic,-52.028,-47.055,-127 resnet50.b2k_in1k,30.152,69.848,48.244,51.756,25.56,288,1.000,bicubic,-50.302,-47.074,+48 regnety_016.tv2_in1k,30.138,69.862,49.231,50.769,11.20,224,0.965,bicubic,-50.528,-46.099,+26 wide_resnet50_2.tv2_in1k,30.116,69.883,48.362,51.638,68.88,224,0.965,bilinear,-51.489,-47.398,-78 dpn68b.ra_in1k,30.109,69.891,48.164,51.836,12.61,288,1.000,bicubic,-49.251,-46.272,+130 convmixer_1024_20_ks9_p14.in1k,30.093,69.907,49.934,50.066,24.38,224,0.960,bicubic,-46.843,-43.416,+294 efficientnet_el.ra_in1k,30.028,69.972,48.849,51.151,10.59,300,0.904,bicubic,-51.284,-46.640,-47 tf_efficientnet_b2.aa_in1k,30.012,69.988,49.590,50.410,9.11,260,0.890,bicubic,-50.072,-45.316,+74 xcit_tiny_12_p16_224.fb_dist_in1k,30.010,69.990,49.651,50.349,6.72,224,1.000,bicubic,-48.564,-44.547,+186 legacy_senet154.in1k,29.993,70.007,48.038,51.962,115.09,224,0.875,bilinear,-51.319,-47.522,-49 halo2botnet50ts_256.a1h_in1k,29.987,70.013,48.380,51.620,22.64,256,0.950,bicubic,-52.073,-47.254,-123 dpn98.mx_in1k,29.981,70.019,48.146,51.854,61.57,224,0.875,bicubic,-49.689,-46.508,+102 mobilevitv2_150.cvnets_in22k_ft_in1k,29.971,70.029,49.217,50.783,10.59,256,0.888,bicubic,-51.517,-46.451,-73 resnetv2_50d_gn.ah_in1k,29.957,70.043,48.195,51.805,25.57,288,1.000,bicubic,-52.001,-47.733,-115 dpn92.mx_in1k,29.946,70.054,49.113,50.887,37.67,224,0.875,bicubic,-50.092,-45.747,+69 resnext50_32x4d.a1h_in1k,29.942,70.058,48.234,51.766,25.03,288,1.000,bicubic,-52.072,-47.700,-124 convnextv2_pico.fcmae_ft_in1k,29.940,70.060,48.853,51.147,9.07,288,0.950,bicubic,-51.146,-46.627,-31 dpn131.mx_in1k,29.934,70.066,48.034,51.966,79.25,224,0.875,bicubic,-49.880,-46.666,+82 resnetv2_101x1_bit.goog_in21k_ft_in1k,29.898,70.102,51.109,48.891,44.54,448,1.000,bilinear,-52.444,-45.411,-166 senet154.gluon_in1k,29.887,70.113,47.879,52.121,115.09,224,0.875,bicubic,-51.339,-47.479,-54 tf_efficientnet_b4.in1k,29.861,70.139,49.014,50.986,19.34,380,0.922,bicubic,-52.747,-46.737,-208 legacy_xception.tf_in1k,29.855,70.145,48.684,51.316,22.86,299,0.897,bicubic,-49.185,-45.698,+143 resnet50.tv2_in1k,29.837,70.162,48.012,51.988,25.56,224,0.965,bilinear,-51.011,-47.422,-11 cs3sedarknet_l.c2ns_in1k,29.832,70.168,49.009,50.991,21.91,288,0.950,bicubic,-51.952,-46.955,-110 resnet152.a2_in1k,29.826,70.174,45.961,54.039,60.19,288,1.000,bicubic,-52.782,-50.167,-211 efficientvit_b1.r224_in1k,29.822,70.178,48.289,51.711,9.10,224,0.950,bicubic,-49.430,-46.015,+122 inception_v3.tf_adv_in1k,29.818,70.182,47.843,52.157,23.83,299,0.875,bicubic,-47.774,-45.887,+236 vit_base_patch16_384.augreg_in1k,29.792,70.208,48.327,51.673,86.86,384,1.000,bicubic,-51.310,-46.793,-46 resnetaa50.a1h_in1k,29.790,70.210,48.014,51.986,25.56,288,1.000,bicubic,-51.824,-47.788,-105 lamhalobotnet50ts_256.a1h_in1k,29.755,70.245,48.335,51.665,22.57,256,0.950,bicubic,-51.797,-47.157,-100 fbnetv3_d.ra2_in1k,29.739,70.261,49.472,50.528,10.31,256,0.950,bilinear,-49.943,-45.472,+81 ese_vovnet39b.ra_in1k,29.733,70.267,49.046,50.954,24.57,288,0.950,bicubic,-50.617,-46.320,+26 fastvit_sa12.apple_in1k,29.731,70.269,48.610,51.390,11.58,256,0.900,bicubic,-51.113,-46.730,-20 resmlp_36_224.fb_in1k,29.698,70.302,48.958,51.042,44.69,224,0.875,bicubic,-50.074,-45.926,+69 fastvit_t12.apple_dist_in1k,29.686,70.314,48.517,51.483,7.55,256,0.900,bicubic,-50.666,-46.525,+22 convnext_nano.d1h_in1k,29.684,70.316,47.920,52.080,15.59,288,1.000,bicubic,-51.798,-47.738,-95 resnet50.c1_in1k,29.672,70.328,48.458,51.542,25.56,288,1.000,bicubic,-51.240,-47.094,-35 vit_base_patch32_384.augreg_in1k,29.645,70.355,48.987,51.013,88.30,384,1.000,bicubic,-49.111,-45.239,+146 efficientvit_b1.r256_in1k,29.621,70.379,48.209,51.791,9.10,256,1.000,bicubic,-50.113,-46.571,+66 ecaresnet50d.miil_in1k,29.562,70.438,48.967,51.033,25.58,288,0.950,bicubic,-52.088,-46.915,-119 nest_tiny_jx.goog_in1k,29.558,70.442,46.983,53.017,17.06,224,0.875,bicubic,-51.868,-48.635,-93 resnet152.a3_in1k,29.525,70.475,47.038,52.962,60.19,224,0.950,bicubic,-51.021,-47.962,-5 gcresnext50ts.ch_in1k,29.517,70.483,47.851,52.149,15.67,288,1.000,bicubic,-51.713,-47.691,-78 resnext50_32x4d.tv2_in1k,29.480,70.520,47.232,52.768,25.03,224,0.965,bilinear,-51.702,-48.108,-72 cs3darknet_l.c2ns_in1k,29.466,70.534,48.234,51.766,21.16,288,0.950,bicubic,-51.430,-47.428,-42 efficientnet_em.ra2_in1k,29.464,70.536,48.922,51.078,6.90,240,0.882,bicubic,-49.780,-45.872,+103 resnext101_32x8d.tv_in1k,29.437,70.563,48.488,51.512,88.79,224,0.875,bilinear,-49.873,-46.032,+92 resnet50.fb_ssl_yfcc100m_ft_in1k,29.435,70.565,49.791,50.209,25.56,224,0.875,bilinear,-49.795,-45.035,+101 coat_lite_mini.in1k,29.435,70.565,47.718,52.282,11.01,224,0.900,bicubic,-49.667,-46.890,+111 resnetv2_50.a1h_in1k,29.435,70.565,47.441,52.559,25.55,288,1.000,bicubic,-51.963,-48.285,-99 deit_small_patch16_224.fb_in1k,29.425,70.575,48.250,51.750,22.05,224,0.900,bicubic,-50.423,-46.794,+45 sebotnet33ts_256.a1h_in1k,29.423,70.577,47.160,52.840,13.70,256,0.940,bicubic,-51.745,-48.009,-78 nf_regnet_b1.ra2_in1k,29.411,70.589,49.427,50.573,10.22,288,0.900,bicubic,-49.897,-45.313,+87 repvit_m1.dist_in1k,29.407,70.593,48.541,51.459,5.49,224,0.950,bicubic,-49.131,-45.529,+144 mobileone_s4.apple_in1k,29.405,70.595,47.967,52.033,14.95,224,0.900,bilinear,-50.021,-46.513,+76 cait_xxs24_384.fb_dist_in1k,29.391,70.609,48.751,51.249,12.03,384,1.000,bicubic,-51.581,-46.889,-62 resnetv2_50d_evos.ah_in1k,29.382,70.618,47.226,52.774,25.59,288,1.000,bicubic,-52.620,-48.674,-164 edgenext_small_rw.sw_in1k,29.346,70.654,48.722,51.278,7.83,320,1.000,bicubic,-51.112,-46.586,-9 convnext_nano_ols.d1h_in1k,29.325,70.675,47.478,52.522,15.65,288,1.000,bicubic,-52.275,-48.158,-132 regnetz_b16.ra3_in1k,29.321,70.679,47.885,52.115,9.72,288,1.000,bicubic,-51.407,-47.633,-37 swin_tiny_patch4_window7_224.ms_in1k,29.321,70.679,47.609,52.391,28.29,224,0.900,bicubic,-52.055,-47.934,-107 cait_xxs24_224.fb_dist_in1k,29.295,70.705,48.537,51.463,11.96,224,1.000,bicubic,-49.089,-45.779,+153 eca_resnet33ts.ra2_in1k,29.277,70.723,48.928,51.072,19.68,288,1.000,bicubic,-51.395,-46.436,-35 resnet50.d_in1k,29.250,70.750,47.213,52.787,25.56,288,1.000,bicubic,-51.722,-48.217,-69 resnet50.c2_in1k,29.244,70.756,47.165,52.835,25.56,288,1.000,bicubic,-51.626,-48.369,-56 pvt_v2_b1.in1k,29.242,70.758,48.962,51.038,14.01,224,0.900,bicubic,-49.462,-45.540,+124 maxvit_rmlp_pico_rw_256.sw_in1k,29.238,70.762,47.719,52.281,7.52,256,0.950,bicubic,-51.276,-47.495,-28 gcvit_xxtiny.in1k,29.218,70.781,48.348,51.652,12.00,224,0.875,bicubic,-50.508,-46.732,+38 poolformer_s24.sail_in1k,29.205,70.795,48.079,51.921,21.39,224,0.900,bicubic,-51.089,-46.995,-1 tresnet_l.miil_in1k_448,29.162,70.838,47.224,52.776,55.99,448,0.875,bilinear,-53.114,-48.754,-205 seresnet50.ra2_in1k,29.146,70.854,47.749,52.251,28.09,288,0.950,bicubic,-52.138,-47.903,-108 inception_v3.gluon_in1k,29.112,70.888,46.943,53.057,23.83,299,0.875,bicubic,-49.690,-47.433,+108 lambda_resnet50ts.a1h_in1k,29.108,70.891,46.961,53.039,21.54,256,0.950,bicubic,-52.050,-48.137,-97 resnet101.a2_in1k,29.091,70.909,45.762,54.238,44.55,288,1.000,bicubic,-53.145,-49.968,-206 xception71.tf_in1k,29.030,70.970,47.391,52.609,42.34,299,0.903,bicubic,-50.844,-47.537,+17 resnet34d.ra2_in1k,29.020,70.980,48.052,51.948,21.82,288,0.950,bicubic,-49.416,-46.292,+133 hrnet_w64.ms_in1k,28.987,71.013,47.138,52.862,128.06,224,0.875,bilinear,-50.489,-47.514,+49 xcit_tiny_12_p8_224.fb_in1k,28.979,71.021,47.501,52.499,6.71,224,1.000,bicubic,-50.709,-47.553,+33 regnetx_032.tv2_in1k,28.975,71.025,47.069,52.931,15.30,224,0.965,bicubic,-51.951,-48.209,-79 cs3darknet_focus_l.c2ns_in1k,28.939,71.061,47.639,52.361,21.15,288,0.950,bicubic,-51.937,-48.043,-74 tf_efficientnet_b0.ns_jft_in1k,28.902,71.098,49.007,50.993,5.29,224,0.875,bicubic,-49.766,-45.365,+110 xception65.tf_in1k,28.894,71.106,47.154,52.846,39.92,299,0.903,bicubic,-50.662,-47.504,+38 tf_efficientnet_b1.aa_in1k,28.886,71.114,47.523,52.477,7.79,240,0.882,bicubic,-49.942,-46.677,+94 vit_small_patch32_384.augreg_in21k_ft_in1k,28.875,71.125,48.889,51.111,22.92,384,1.000,bicubic,-51.611,-46.711,-41 mobilevitv2_150.cvnets_in22k_ft_in1k_384,28.871,71.129,47.932,52.068,10.59,384,1.000,bicubic,-53.715,-48.382,-266 resnet101.gluon_in1k,28.853,71.147,46.381,53.619,44.55,224,0.875,bicubic,-50.457,-48.141,+52 skresnext50_32x4d.ra_in1k,28.810,71.190,46.507,53.493,27.48,224,0.875,bicubic,-51.354,-48.133,-8 sehalonet33ts.ra2_in1k,28.780,71.220,46.574,53.426,13.69,256,0.940,bicubic,-52.178,-48.698,-90 levit_256.fb_dist_in1k,28.743,71.257,46.729,53.271,18.89,224,0.900,bicubic,-52.781,-48.765,-154 levit_conv_256.fb_dist_in1k,28.739,71.261,46.723,53.277,18.89,224,0.900,bicubic,-52.783,-48.767,-154 resnet50.ra_in1k,28.694,71.306,47.366,52.634,25.56,288,0.950,bicubic,-51.142,-47.600,+8 mobileone_s3.apple_in1k,28.676,71.324,47.582,52.418,10.17,224,0.900,bilinear,-49.316,-46.332,+151 resnetblur50.bt_in1k,28.662,71.338,46.908,53.092,25.56,288,0.950,bicubic,-51.572,-48.326,-20 tf_efficientnet_lite3.in1k,28.651,71.349,47.360,52.640,8.20,300,0.904,bilinear,-51.155,-47.554,+8 darknetaa53.c2ns_in1k,28.647,71.353,46.949,53.051,36.02,288,1.000,bilinear,-51.859,-48.373,-55 hrnet_w40.ms_in1k,28.645,71.355,47.452,52.548,57.56,224,0.875,bilinear,-50.287,-47.012,+74 skresnet34.ra_in1k,28.631,71.369,47.961,52.039,22.28,224,0.875,bicubic,-48.279,-45.183,+205 swinv2_tiny_window8_256.ms_in1k,28.627,71.373,46.189,53.811,28.35,256,0.900,bicubic,-53.193,-49.805,-189 seresnext50_32x4d.gluon_in1k,28.619,71.381,46.438,53.562,27.56,224,0.875,bicubic,-51.305,-48.386,-11 mobilevitv2_175.cvnets_in22k_ft_in1k_384,28.598,71.403,47.118,52.882,14.25,384,1.000,bicubic,-54.341,-49.308,-321 tf_efficientnet_b3.in1k,28.582,71.418,47.981,52.019,12.23,300,0.904,bicubic,-52.292,-47.319,-93 halonet50ts.a1h_in1k,28.580,71.420,46.183,53.817,22.73,256,0.940,bicubic,-53.082,-49.427,-183 tf_efficientnetv2_b0.in1k,28.570,71.430,47.077,52.923,7.14,224,0.875,bicubic,-49.788,-46.937,+114 poolformerv2_s12.sail_in1k,28.556,71.444,47.399,52.601,11.89,224,1.000,bicubic,-49.446,-46.465,+137 resnet152.tv_in1k,28.543,71.457,47.106,52.894,60.19,224,0.875,bilinear,-49.779,-46.940,+114 xcit_tiny_12_p16_224.fb_in1k,28.515,71.485,47.403,52.597,6.72,224,1.000,bicubic,-48.625,-46.313,+184 eva02_tiny_patch14_336.mim_in22k_ft_in1k,28.507,71.493,47.539,52.461,5.76,336,1.000,bicubic,-52.123,-47.987,-75 ecaresnet50t.a1_in1k,28.456,71.544,45.576,54.424,25.57,288,1.000,bicubic,-53.672,-50.066,-225 repvgg_b2.rvgg_in1k,28.421,71.579,47.044,52.956,89.02,224,0.875,bilinear,-50.371,-47.376,+73 hrnet_w48.ms_in1k,28.409,71.591,47.576,52.424,77.47,224,0.875,bilinear,-50.897,-46.940,+31 swinv2_cr_tiny_ns_224.sw_in1k,28.379,71.621,45.902,54.098,28.33,224,0.900,bicubic,-53.423,-49.916,-199 resnext50_32x4d.gluon_in1k,28.372,71.628,45.332,54.668,25.03,224,0.875,bicubic,-50.988,-49.098,+24 efficientnet_b2_pruned.in1k,28.360,71.640,47.057,52.943,8.31,260,0.890,bicubic,-51.560,-47.795,-24 tf_efficientnet_b0.ap_in1k,28.354,71.646,47.535,52.465,5.29,224,0.875,bicubic,-48.736,-45.727,+178 darknet53.c2ns_in1k,28.328,71.672,46.880,53.120,41.61,288,1.000,bicubic,-52.204,-48.552,-77 dla169.in1k,28.320,71.680,47.388,52.612,53.39,224,0.875,bilinear,-50.388,-46.956,+73 tf_efficientnet_cc_b0_4e.in1k,28.313,71.687,47.360,52.640,13.31,224,0.875,bicubic,-48.989,-45.976,+162 dla102x2.in1k,28.313,71.687,46.774,53.226,41.28,224,0.875,bilinear,-51.133,-47.858,+12 resnext50_32x4d.ra_in1k,28.303,71.697,46.081,53.919,25.03,288,0.950,bicubic,-52.395,-49.311,-93 mixnet_xl.ra_in1k,28.291,71.709,46.708,53.292,11.90,224,0.875,bicubic,-52.191,-48.229,-76 seresnet33ts.ra2_in1k,28.236,71.764,47.578,52.422,19.78,288,1.000,bicubic,-52.548,-47.784,-103 resnet50d.gluon_in1k,28.234,71.766,45.880,54.120,25.58,224,0.875,bicubic,-50.844,-48.586,+39 resnet50.a1_in1k,28.220,71.780,44.937,55.063,25.56,288,1.000,bicubic,-52.994,-50.165,-153 fastvit_s12.apple_in1k,28.126,71.874,46.649,53.351,9.47,256,0.900,bicubic,-51.816,-48.145,-37 densenet161.tv_in1k,28.108,71.892,46.653,53.347,28.68,224,0.875,bicubic,-49.250,-46.989,+151 resnet101c.gluon_in1k,28.104,71.896,45.953,54.047,44.57,224,0.875,bicubic,-51.434,-48.631,-4 resnet34.a1_in1k,28.100,71.900,45.707,54.293,21.80,288,1.000,bicubic,-49.818,-48.057,+121 wide_resnet101_2.tv_in1k,28.095,71.906,46.426,53.574,126.89,224,0.875,bilinear,-50.748,-47.855,+48 regnetx_320.pycls_in1k,28.079,71.921,45.118,54.882,107.81,224,0.875,bicubic,-52.167,-49.904,-58 regnety_320.pycls_in1k,28.075,71.925,45.460,54.540,145.05,224,0.875,bicubic,-52.735,-49.778,-115 resnext50_32x4d.a1_in1k,28.075,71.925,44.815,55.185,25.03,288,1.000,bicubic,-53.391,-50.359,-188 gernet_s.idstcv_in1k,28.051,71.949,46.727,53.273,8.17,224,0.875,bilinear,-48.859,-46.589,+171 ecaresnet50d_pruned.miil_in1k,28.043,71.957,47.038,52.962,19.94,288,0.950,bicubic,-52.747,-48.532,-116 levit_conv_192.fb_dist_in1k,28.032,71.968,45.874,54.126,10.95,224,0.900,bicubic,-51.806,-48.904,-37 mobilevitv2_175.cvnets_in1k,28.028,71.972,46.098,53.902,14.25,256,0.888,bicubic,-52.832,-49.158,-126 levit_192.fb_dist_in1k,28.028,71.972,45.870,54.130,10.95,224,0.900,bicubic,-51.810,-48.914,-37 efficientnet_el_pruned.in1k,28.004,71.996,46.804,53.196,10.59,300,0.904,bicubic,-52.294,-48.418,-71 vit_base_patch16_224.augreg_in1k,27.971,72.029,45.737,54.263,86.57,224,0.900,bicubic,-51.183,-48.353,+18 fastvit_t12.apple_in1k,27.935,72.065,46.393,53.607,7.55,256,0.900,bicubic,-51.329,-48.169,+7 resnet101.a3_in1k,27.925,72.075,45.014,54.986,44.55,224,0.950,bicubic,-51.888,-49.600,-39 resnet50_gn.a1h_in1k,27.916,72.084,46.075,53.925,25.56,288,0.950,bicubic,-53.300,-49.309,-173 xception41.tf_in1k,27.880,72.120,45.888,54.112,26.97,299,0.903,bicubic,-50.624,-48.388,+58 regnetx_160.pycls_in1k,27.827,72.173,45.631,54.369,54.28,224,0.875,bicubic,-52.039,-49.197,-49 dpn68b.mx_in1k,27.814,72.186,47.415,52.585,12.61,224,0.875,bicubic,-49.704,-46.437,+123 resnet50d.a1_in1k,27.790,72.210,44.377,55.623,25.58,288,1.000,bicubic,-53.660,-50.841,-199 inception_v3.tf_in1k,27.780,72.220,45.727,54.273,23.83,299,0.875,bicubic,-50.076,-48.139,+106 res2net101_26w_4s.in1k,27.778,72.222,45.159,54.841,45.21,224,0.875,bilinear,-51.422,-49.277,+5 tf_efficientnetv2_b1.in1k,27.745,72.255,46.580,53.420,8.14,240,0.882,bicubic,-51.715,-48.142,-21 repghostnet_200.in1k,27.719,72.281,46.322,53.678,9.80,224,0.875,bicubic,-51.087,-48.008,+30 vit_base_patch16_224.sam_in1k,27.703,72.297,45.100,54.900,86.57,224,0.900,bicubic,-52.535,-49.656,-78 fbnetv3_b.ra2_in1k,27.670,72.330,46.987,53.013,8.60,256,0.950,bilinear,-51.476,-47.757,+6 regnety_160.pycls_in1k,27.641,72.359,45.531,54.469,83.59,224,0.875,bicubic,-52.657,-49.433,-85 mobilevitv2_200.cvnets_in1k,27.637,72.363,45.784,54.216,18.45,256,0.888,bicubic,-53.497,-49.578,-175 repvgg_b1.rvgg_in1k,27.631,72.369,46.533,53.467,57.42,224,0.875,bilinear,-50.737,-47.563,+62 hrnet_w44.ms_in1k,27.627,72.373,45.837,54.163,67.06,224,0.875,bilinear,-51.267,-48.527,+18 resnet50.am_in1k,27.574,72.426,45.369,54.631,25.56,224,0.875,bicubic,-51.428,-49.029,+10 inception_v3.tv_in1k,27.556,72.444,45.265,54.735,23.83,299,0.875,bicubic,-49.878,-48.209,+116 resmlp_24_224.fb_in1k,27.517,72.483,45.690,54.310,30.02,224,0.875,bicubic,-51.857,-48.856,-24 pit_xs_224.in1k,27.485,72.515,45.910,54.090,10.62,224,0.900,bicubic,-50.691,-48.252,+69 tiny_vit_5m_224.in1k,27.483,72.517,45.855,54.145,5.39,224,0.950,bicubic,-51.687,-48.939,-5 gcresnet33ts.ra2_in1k,27.401,72.599,46.155,53.845,19.88,288,1.000,bicubic,-53.199,-49.167,-127 regnetx_080.pycls_in1k,27.397,72.603,45.012,54.988,39.57,224,0.875,bicubic,-51.801,-49.542,-9 hrnet_w30.ms_in1k,27.389,72.611,46.554,53.446,37.71,224,0.875,bilinear,-50.807,-47.668,+63 hrnet_w32.ms_in1k,27.381,72.619,46.006,53.994,41.23,224,0.875,bilinear,-51.061,-48.184,+43 convnext_pico.d1_in1k,27.358,72.642,45.660,54.340,9.05,288,0.950,bicubic,-53.058,-49.388,-111 vit_small_patch16_384.augreg_in1k,27.328,72.672,46.118,53.882,22.20,384,1.000,bicubic,-53.788,-49.456,-186 resnet50s.gluon_in1k,27.324,72.676,45.214,54.786,25.68,224,0.875,bicubic,-51.390,-49.028,+21 res2net50_26w_8s.in1k,27.306,72.694,44.815,55.185,48.40,224,0.875,bilinear,-51.635,-49.479,+1 convnext_pico_ols.d1_in1k,27.297,72.703,45.644,54.356,9.06,288,1.000,bicubic,-53.165,-49.608,-123 densenet201.tv_in1k,27.271,72.729,46.210,53.790,20.01,224,0.875,bicubic,-50.015,-47.270,+111 resnet33ts.ra2_in1k,27.257,72.743,45.183,54.817,19.68,288,1.000,bicubic,-52.469,-49.645,-64 ecaresnet50t.a2_in1k,27.246,72.754,44.047,55.953,25.57,288,1.000,bicubic,-54.412,-51.503,-252 regnety_064.pycls_in1k,27.238,72.762,44.866,55.134,30.58,224,0.875,bicubic,-52.478,-49.900,-65 ghostnetv2_160.in1k,27.232,72.768,46.366,53.634,12.39,224,0.875,bicubic,-50.600,-47.574,+79 efficientnet_b1_pruned.in1k,27.196,72.804,45.861,54.139,6.33,240,0.882,bicubic,-51.044,-47.973,+49 tf_efficientnetv2_b2.in1k,27.163,72.837,44.568,55.432,10.10,260,0.890,bicubic,-53.033,-50.474,-100 vit_base_patch32_224.augreg_in1k,27.140,72.861,45.175,54.825,88.22,224,0.900,bicubic,-47.755,-46.603,+178 seresnet50.a1_in1k,27.124,72.876,43.563,56.437,28.09,288,1.000,bicubic,-53.978,-51.765,-195 resnet50d.a2_in1k,27.120,72.880,43.811,56.189,25.58,288,1.000,bicubic,-54.044,-51.269,-204 resnetrs50.tf_in1k,27.098,72.902,45.027,54.973,35.69,224,0.910,bicubic,-52.796,-49.947,-89 rexnet_130.nav_in1k,27.081,72.919,45.957,54.043,7.56,224,0.875,bicubic,-52.425,-48.721,-58 gmixer_24_224.ra3_in1k,27.031,72.969,44.353,55.647,24.72,224,0.875,bicubic,-50.995,-49.315,+56 dla102x.in1k,27.022,72.978,45.505,54.495,26.31,224,0.875,bilinear,-51.490,-48.731,+16 resnet101.tv_in1k,26.963,73.037,45.234,54.766,44.55,224,0.875,bilinear,-50.417,-48.312,+91 regnetx_120.pycls_in1k,26.866,73.134,44.688,55.312,46.11,224,0.875,bicubic,-52.722,-50.055,-67 resnet32ts.ra2_in1k,26.849,73.151,45.041,54.959,17.96,288,1.000,bicubic,-52.539,-49.611,-54 rexnet_100.nav_in1k,26.841,73.159,45.379,54.621,4.80,224,0.875,bicubic,-51.015,-48.261,+64 legacy_seresnext101_32x4d.in1k,26.821,73.179,43.505,56.495,48.96,224,0.875,bilinear,-53.411,-51.515,-114 densenet169.tv_in1k,26.819,73.181,45.381,54.619,14.15,224,0.875,bicubic,-49.081,-47.647,+142 tinynet_a.in1k,26.815,73.185,45.082,54.918,6.19,192,0.875,bicubic,-50.833,-48.458,+69 regnetx_064.pycls_in1k,26.803,73.197,44.913,55.087,26.21,224,0.875,bicubic,-52.263,-49.547,-28 regnety_120.pycls_in1k,26.780,73.220,44.442,55.558,51.82,224,0.875,bicubic,-53.600,-50.684,-137 regnetx_032.pycls_in1k,26.717,73.283,45.230,54.770,15.30,224,0.875,bicubic,-51.451,-48.852,+36 res2net101d.in1k,26.711,73.289,44.336,55.664,45.23,224,0.875,bilinear,-54.507,-51.014,-227 resnext50_32x4d.a2_in1k,26.682,73.318,42.768,57.232,25.03,288,1.000,bicubic,-54.622,-52.328,-233 efficientvit_m5.r224_in1k,26.674,73.326,44.923,55.077,12.47,224,0.875,bicubic,-50.384,-48.261,+98 legacy_seresnet152.in1k,26.666,73.334,43.949,56.051,66.82,224,0.875,bilinear,-51.994,-50.421,-4 efficientnet_es.ra_in1k,26.619,73.381,45.096,54.904,5.44,224,0.875,bicubic,-51.439,-48.830,+38 res2net50_26w_6s.in1k,26.591,73.409,44.004,55.996,37.05,224,0.875,bilinear,-51.977,-50.118,-3 repvgg_b1g4.rvgg_in1k,26.579,73.421,45.100,54.900,39.97,224,0.875,bilinear,-51.009,-48.736,+64 dla60x.in1k,26.566,73.434,45.031,54.969,17.35,224,0.875,bilinear,-51.670,-48.995,+24 coat_lite_tiny.in1k,26.517,73.484,44.646,55.354,5.72,224,0.900,bicubic,-51.003,-49.276,+64 regnety_080.pycls_in1k,26.515,73.485,44.351,55.649,39.18,224,0.875,bicubic,-53.353,-50.481,-110 mobilenetv3_large_100.miil_in21k_ft_in1k,26.493,73.507,44.491,55.509,5.48,224,0.875,bilinear,-51.427,-48.423,+43 tf_efficientnet_b0.aa_in1k,26.483,73.517,45.642,54.358,5.29,224,0.875,bicubic,-50.361,-47.576,+99 res2net50_14w_8s.in1k,26.477,73.523,44.371,55.629,25.06,224,0.875,bilinear,-51.681,-49.475,+25 tf_efficientnet_b2.in1k,26.462,73.538,44.788,55.212,9.11,260,0.890,bicubic,-53.147,-49.926,-90 mobileone_s2.apple_in1k,26.456,73.544,44.566,55.434,7.88,224,0.900,bilinear,-51.060,-49.102,+60 resnet50.gluon_in1k,26.428,73.572,44.039,55.961,25.56,224,0.875,bicubic,-51.154,-49.681,+56 ecaresnet50t.a3_in1k,26.420,73.580,43.507,56.493,25.57,224,0.950,bicubic,-53.132,-51.188,-89 tf_efficientnet_el.in1k,26.353,73.647,44.173,55.827,10.59,300,0.904,bicubic,-53.895,-50.947,-141 levit_conv_128.fb_dist_in1k,26.330,73.670,44.120,55.880,9.21,224,0.900,bicubic,-52.164,-49.888,-11 levit_128.fb_dist_in1k,26.328,73.672,44.116,55.884,9.21,224,0.900,bicubic,-52.162,-49.896,-10 lambda_resnet26t.c1_in1k,26.326,73.674,44.430,55.570,10.96,256,0.940,bicubic,-52.762,-50.160,-54 resmlp_big_24_224.fb_in1k,26.318,73.682,43.554,56.446,129.14,224,0.875,bicubic,-54.718,-51.464,-225 resmlp_12_224.fb_distilled_in1k,26.316,73.684,44.874,55.126,15.35,224,0.875,bicubic,-51.638,-48.686,+29 visformer_tiny.in1k,26.257,73.743,44.182,55.818,10.32,224,0.900,bicubic,-51.903,-49.984,+13 regnetx_040.pycls_in1k,26.243,73.757,44.424,55.576,22.12,224,0.875,bicubic,-52.249,-49.818,-16 mobilevitv2_150.cvnets_in1k,26.178,73.822,43.762,56.238,10.59,256,0.888,bicubic,-54.192,-51.312,-163 crossvit_9_dagger_240.in1k,26.177,73.823,44.542,55.458,8.78,240,0.875,bicubic,-50.801,-49.076,+78 vit_small_patch32_224.augreg_in21k_ft_in1k,26.165,73.835,45.106,54.894,22.88,224,0.900,bicubic,-49.829,-47.694,+105 seresnet50.a2_in1k,26.165,73.835,42.675,57.325,28.09,288,1.000,bicubic,-54.941,-52.547,-240 densenetblur121d.ra_in1k,26.135,73.865,45.037,54.963,8.00,288,0.950,bicubic,-51.187,-48.751,+54 resnet50.a2_in1k,26.094,73.906,42.583,57.417,25.56,288,1.000,bicubic,-54.678,-52.405,-205 resnet50d.a3_in1k,26.090,73.910,42.970,57.030,25.58,224,0.950,bicubic,-52.630,-51.262,-39 resnext50_32x4d.a3_in1k,26.082,73.918,42.946,57.054,25.03,224,0.950,bicubic,-53.186,-51.360,-81 resnet34.a2_in1k,26.080,73.920,43.109,56.891,21.80,288,1.000,bicubic,-51.078,-50.165,+62 efficientnet_b1.ft_in1k,26.055,73.945,44.080,55.920,7.79,256,1.000,bicubic,-52.745,-50.262,-47 convnextv2_femto.fcmae_ft_in1k,26.039,73.961,44.302,55.698,5.23,288,0.950,bicubic,-53.299,-50.258,-92 fastvit_t8.apple_dist_in1k,26.033,73.967,44.397,55.603,4.03,256,0.900,bicubic,-51.143,-48.901,+57 lambda_resnet26rpt_256.c1_in1k,26.033,73.967,44.190,55.810,10.99,256,0.940,bicubic,-52.931,-50.246,-63 mobilevitv2_125.cvnets_in1k,26.021,73.979,43.668,56.332,7.48,256,0.888,bicubic,-53.659,-51.190,-119 dpn68.mx_in1k,26.006,73.994,44.084,55.916,12.61,224,0.875,bicubic,-50.340,-48.924,+88 hrnet_w18.ms_in1k,25.982,74.018,44.803,55.197,21.30,224,0.875,bilinear,-50.770,-48.641,+72 hardcorenas_f.miil_green_in1k,25.939,74.061,44.204,55.796,8.20,224,0.875,bilinear,-52.157,-49.598,-1 vit_small_patch16_224.augreg_in1k,25.935,74.065,43.964,56.036,22.05,224,0.900,bicubic,-52.913,-50.324,-61 repghostnet_150.in1k,25.923,74.077,44.328,55.672,6.58,224,0.875,bicubic,-51.537,-49.182,+34 regnety_040.pycls_in1k,25.909,74.091,43.854,56.146,20.65,224,0.875,bicubic,-53.311,-50.802,-87 hrnet_w18_small_v2.gluon_in1k,25.884,74.116,43.815,56.185,15.60,224,0.875,bicubic,-52.306,-50.087,-12 regnetx_016.tv2_in1k,25.878,74.122,43.353,56.647,9.19,224,0.965,bicubic,-53.558,-51.415,-110 fastvit_t8.apple_in1k,25.876,74.124,44.153,55.847,4.03,256,0.900,bicubic,-50.298,-48.899,+83 resnext50d_32x4d.bt_in1k,25.876,74.124,42.956,57.044,25.05,288,0.950,bicubic,-54.788,-52.464,-212 res2net50_26w_4s.in1k,25.872,74.128,43.163,56.837,25.70,224,0.875,bilinear,-52.078,-50.689,+3 tresnet_m.miil_in1k_448,25.860,74.140,42.868,57.132,31.39,448,0.875,bilinear,-55.850,-52.706,-328 coat_tiny.in1k,25.858,74.142,43.275,56.725,5.50,224,0.900,bicubic,-52.568,-50.773,-35 hardcorenas_c.miil_green_in1k,25.821,74.179,44.764,55.236,5.52,224,0.875,bilinear,-51.245,-48.398,+47 densenet121.ra_in1k,25.815,74.185,44.866,55.134,7.98,288,0.950,bicubic,-50.685,-48.502,+69 resnet50c.gluon_in1k,25.793,74.207,43.019,56.981,25.58,224,0.875,bicubic,-52.213,-50.973,-8 halonet26t.a1h_in1k,25.776,74.224,43.220,56.780,12.48,256,0.950,bicubic,-53.330,-51.086,-91 selecsls60.in1k,25.729,74.272,44.065,55.935,30.67,224,0.875,bicubic,-52.260,-49.765,-6 hardcorenas_e.miil_green_in1k,25.658,74.342,43.408,56.592,8.07,224,0.875,bilinear,-52.132,-50.292,+4 poolformer_s12.sail_in1k,25.654,74.346,44.167,55.833,11.92,224,0.900,bicubic,-51.586,-49.365,+31 dla60_res2next.in1k,25.654,74.346,43.675,56.325,17.03,224,0.875,bilinear,-52.786,-50.469,-44 dla60_res2net.in1k,25.648,74.352,43.583,56.417,20.85,224,0.875,bilinear,-52.816,-50.615,-48 ecaresnet26t.ra2_in1k,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.312,-51.430,-160 resmlp_12_224.fb_in1k,25.528,74.472,44.330,55.670,15.35,224,0.875,bicubic,-51.120,-48.848,+51 mixnet_l.ft_in1k,25.520,74.480,43.471,56.529,7.33,224,0.875,bicubic,-53.446,-50.711,-90 convnext_femto.d1_in1k,25.510,74.490,43.672,56.328,5.22,288,0.950,bicubic,-53.206,-50.759,-71 tf_efficientnet_lite1.in1k,25.509,74.492,43.573,56.427,5.42,240,0.882,bicubic,-51.136,-49.651,+49 res2net50d.in1k,25.497,74.503,43.041,56.959,25.72,224,0.875,bilinear,-54.757,-51.995,-191 cs3darknet_focus_m.c2ns_in1k,25.493,74.507,43.762,56.238,9.30,288,0.950,bicubic,-51.791,-50.204,+21 resnext50_32x4d.tv_in1k,25.467,74.533,42.787,57.213,25.03,224,0.875,bilinear,-52.155,-50.909,0 bat_resnext26ts.ch_in1k,25.455,74.545,43.194,56.806,10.73,256,0.900,bicubic,-52.797,-50.904,-41 botnet26t_256.c1_in1k,25.451,74.549,42.660,57.340,12.49,256,0.950,bicubic,-53.806,-51.872,-117 eca_halonext26ts.c1_in1k,25.442,74.558,43.182,56.818,10.76,256,0.940,bicubic,-54.044,-51.418,-141 repvgg_a2.rvgg_in1k,25.436,74.564,43.945,56.055,28.21,224,0.875,bilinear,-51.022,-49.057,+52 tf_mixnet_l.in1k,25.414,74.586,42.538,57.462,7.33,224,0.875,bicubic,-53.362,-51.464,-84 regnety_008_tv.tv2_in1k,25.406,74.594,43.434,56.566,6.43,224,0.965,bicubic,-53.260,-50.956,-76 hardcorenas_b.miil_green_in1k,25.396,74.603,44.188,55.812,5.18,224,0.875,bilinear,-51.151,-48.574,+42 res2next50.in1k,25.392,74.608,42.492,57.508,24.67,224,0.875,bilinear,-52.850,-51.399,-47 convnext_femto_ols.d1_in1k,25.387,74.613,43.137,56.863,5.23,288,0.950,bicubic,-53.537,-51.389,-100 efficientformerv2_s0.snap_dist_in1k,25.349,74.651,43.929,56.071,3.60,224,0.950,bicubic,-50.765,-48.929,+54 legacy_seresnet101.in1k,25.332,74.668,42.832,57.168,49.33,224,0.875,bilinear,-53.054,-51.430,-59 selecsls60b.in1k,25.326,74.674,43.556,56.444,32.77,224,0.875,bicubic,-53.086,-50.612,-62 hardcorenas_d.miil_green_in1k,25.326,74.674,43.137,56.863,7.50,224,0.875,bilinear,-52.108,-50.353,-2 resnetv2_50x1_bit.goog_in21k_ft_in1k,25.322,74.678,45.348,54.652,25.55,448,1.000,bilinear,-55.020,-50.334,-217 regnety_032.pycls_in1k,25.318,74.682,42.923,57.077,19.44,224,0.875,bicubic,-53.558,-51.485,-103 wide_resnet50_2.tv_in1k,25.310,74.690,42.182,57.818,68.88,224,0.875,bilinear,-53.166,-51.906,-73 dla102.in1k,25.294,74.706,43.846,56.154,33.27,224,0.875,bilinear,-52.730,-50.088,-40 resnest14d.gluon_in1k,25.280,74.719,44.106,55.894,10.61,224,0.875,bilinear,-50.227,-48.402,+60 legacy_seresnext50_32x4d.in1k,25.212,74.788,41.950,58.050,27.56,224,0.875,bilinear,-53.864,-52.482,-119 ghostnetv2_130.in1k,25.149,74.851,43.271,56.729,8.96,224,0.875,bicubic,-51.607,-50.091,+23 mixer_b16_224.goog_in21k_ft_in1k,25.111,74.888,41.225,58.775,59.88,224,0.875,bicubic,-51.491,-50.999,+26 res2net50_48w_2s.in1k,25.025,74.975,42.206,57.794,25.29,224,0.875,bilinear,-52.489,-51.344,-15 efficientnet_b0.ra_in1k,25.015,74.985,42.797,57.203,5.29,224,0.875,bicubic,-52.679,-50.735,-27 resnet50.a3_in1k,25.000,75.001,41.889,58.111,25.56,224,0.950,bicubic,-53.049,-51.891,-49 dla60.in1k,24.937,75.063,43.322,56.678,22.04,224,0.875,bilinear,-52.109,-49.996,+8 resnet34.gluon_in1k,24.937,75.063,42.237,57.763,21.80,224,0.875,bicubic,-49.643,-49.745,+75 mobilenetv2_120d.ra_in1k,24.915,75.085,43.039,56.961,5.83,224,0.875,bicubic,-52.393,-50.463,-11 convnextv2_atto.fcmae_ft_in1k,24.890,75.111,42.469,57.531,3.71,288,0.950,bicubic,-52.871,-51.257,-34 eca_botnext26ts_256.c1_in1k,24.868,75.132,42.943,57.057,10.59,256,0.950,bicubic,-54.400,-51.663,-147 resnet34.bt_in1k,24.828,75.171,42.080,57.920,21.80,288,0.950,bicubic,-51.652,-51.274,+25 regnety_016.pycls_in1k,24.823,75.177,42.610,57.390,11.20,224,0.875,bicubic,-53.045,-51.108,-43 xcit_nano_12_p8_224.fb_dist_in1k,24.801,75.199,43.072,56.928,3.05,224,1.000,bicubic,-51.531,-50.026,+28 seresnet50.a3_in1k,24.793,75.207,42.094,57.906,28.09,224,0.950,bicubic,-52.233,-50.978,+1 pit_ti_distilled_224.in1k,24.711,75.289,43.225,56.775,5.10,224,0.900,bicubic,-49.545,-48.727,+71 cs3darknet_m.c2ns_in1k,24.626,75.374,42.970,57.030,9.31,288,0.950,bicubic,-53.008,-51.046,-36 eca_resnext26ts.ch_in1k,24.559,75.441,42.536,57.464,10.30,288,1.000,bicubic,-53.441,-51.390,-56 resnet50.bt_in1k,24.559,75.441,41.445,58.555,25.56,288,0.950,bicubic,-55.081,-53.447,-183 mobilevitv2_100.cvnets_in1k,24.553,75.447,42.919,57.081,4.90,256,0.888,bicubic,-53.527,-51.251,-65 tf_efficientnet_lite2.in1k,24.532,75.468,42.290,57.710,6.09,260,0.890,bicubic,-52.930,-51.462,-31 seresnext26ts.ch_in1k,24.506,75.494,42.665,57.335,10.39,288,1.000,bicubic,-53.764,-51.427,-81 skresnet18.ra_in1k,24.497,75.504,42.534,57.466,11.96,224,0.875,bicubic,-48.538,-48.638,+84 regnetx_016.pycls_in1k,24.477,75.523,42.490,57.510,9.19,224,0.875,bicubic,-52.447,-50.925,-3 tf_efficientnet_lite0.in1k,24.373,75.627,42.510,57.490,4.65,224,0.875,bicubic,-50.459,-49.660,+51 hardcorenas_a.miil_green_in1k,24.367,75.633,43.316,56.684,5.26,224,0.875,bilinear,-51.571,-49.192,+24 hrnet_w18.ms_aug_in1k,24.341,75.659,42.897,57.103,21.30,224,0.950,bilinear,-53.781,-51.157,-74 efficientvit_m4.r224_in1k,24.286,75.714,41.758,58.242,8.80,224,0.875,bicubic,-50.082,-50.222,+58 gcresnext26ts.ch_in1k,24.154,75.846,41.306,58.694,10.48,288,1.000,bicubic,-54.260,-52.730,-97 resnet50.tv_in1k,24.096,75.904,41.323,58.677,25.56,224,0.875,bilinear,-52.032,-51.535,+15 tf_efficientnet_b1.in1k,24.070,75.930,41.512,58.488,7.79,240,0.882,bicubic,-54.492,-52.582,-114 levit_128s.fb_dist_in1k,24.060,75.940,41.001,58.999,7.78,224,0.900,bicubic,-52.466,-51.871,+1 levit_conv_128s.fb_dist_in1k,24.052,75.948,41.003,58.997,7.78,224,0.900,bicubic,-52.468,-51.863,+1 legacy_seresnet34.in1k,24.021,75.979,41.901,58.099,21.96,224,0.875,bilinear,-50.781,-50.225,+43 xcit_nano_12_p16_384.fb_dist_in1k,24.005,75.995,42.306,57.694,3.05,384,1.000,bicubic,-51.453,-50.392,+28 xcit_nano_12_p8_384.fb_dist_in1k,23.956,76.044,41.954,58.046,3.05,384,1.000,bicubic,-53.864,-52.086,-62 efficientnet_lite0.ra_in1k,23.905,76.095,42.109,57.891,4.65,224,0.875,bicubic,-51.577,-50.411,+24 repghostnet_130.in1k,23.852,76.148,41.569,58.431,5.48,224,0.875,bicubic,-52.524,-51.323,+1 densenet121.tv_in1k,23.840,76.160,41.928,58.072,7.98,224,0.875,bicubic,-50.924,-50.225,+39 efficientnet_es_pruned.in1k,23.815,76.185,41.991,58.009,5.44,224,0.875,bicubic,-51.191,-50.453,+33 regnetx_008.tv2_in1k,23.779,76.221,40.698,59.302,7.26,224,0.965,bicubic,-53.527,-52.966,-42 mixnet_m.ft_in1k,23.716,76.284,41.146,58.854,5.01,224,0.875,bicubic,-53.544,-52.272,-39 resnet26t.ra2_in1k,23.712,76.288,41.321,58.679,16.01,320,1.000,bicubic,-54.616,-52.803,-105 mobilenetv2_140.ra_in1k,23.695,76.305,41.469,58.531,6.11,224,0.875,bicubic,-52.821,-51.519,-9 dla34.in1k,23.685,76.315,41.538,58.462,15.74,224,0.875,bilinear,-50.955,-50.529,+35 legacy_seresnet50.in1k,23.640,76.360,40.079,59.921,28.09,224,0.875,bilinear,-54.004,-53.679,-66 convnext_atto.d2_in1k,23.589,76.411,41.087,58.913,3.70,288,0.950,bicubic,-53.419,-52.614,-30 resnext26ts.ra2_in1k,23.589,76.411,40.891,59.109,10.30,288,1.000,bicubic,-53.589,-52.573,-42 tf_mixnet_m.in1k,23.484,76.516,40.997,59.003,5.01,224,0.875,bicubic,-53.470,-52.157,-30 resnet34.tv_in1k,23.473,76.527,41.361,58.639,21.80,224,0.875,bilinear,-49.833,-50.059,+53 efficientvit_m3.r224_in1k,23.412,76.588,40.531,59.469,6.90,224,0.875,bicubic,-49.962,-50.817,+49 selecsls42b.in1k,23.369,76.632,40.681,59.319,32.46,224,0.875,bicubic,-53.802,-52.711,-44 tf_efficientnet_em.in1k,23.369,76.632,40.398,59.602,6.90,240,0.882,bicubic,-54.758,-53.650,-101 resnet34.a3_in1k,23.366,76.633,40.068,59.932,21.80,224,0.950,bicubic,-49.603,-51.038,+55 repvgg_b0.rvgg_in1k,23.319,76.681,41.164,58.836,15.82,224,0.875,bilinear,-51.825,-51.252,+12 regnety_004.tv2_in1k,23.292,76.708,40.987,59.013,4.34,224,0.965,bicubic,-52.302,-51.713,+2 xcit_nano_12_p16_224.fb_dist_in1k,23.249,76.751,41.368,58.632,3.05,224,1.000,bicubic,-49.061,-49.492,+61 convnext_atto_ols.a2_in1k,23.129,76.871,40.881,59.119,3.70,288,0.950,bicubic,-54.087,-52.795,-53 mobilenetv2_110d.ra_in1k,23.070,76.930,40.749,59.251,4.52,224,0.875,bicubic,-51.984,-51.434,+12 resnet18.a1_in1k,23.056,76.944,39.551,60.449,11.69,288,1.000,bicubic,-50.102,-51.475,+45 vit_base_patch32_224.sam_in1k,23.046,76.954,39.565,60.435,88.22,224,0.900,bicubic,-50.648,-51.449,+37 tinynet_b.in1k,23.017,76.983,40.979,59.021,3.73,188,0.875,bicubic,-51.961,-51.207,+12 resnet18d.ra2_in1k,23.001,76.999,41.197,58.803,11.71,288,0.950,bicubic,-50.793,-50.640,+34 repghostnet_111.in1k,22.881,77.119,40.439,59.561,4.54,224,0.875,bicubic,-52.175,-51.753,+6 mobileone_s1.apple_in1k,22.803,77.197,39.851,60.149,4.83,224,0.900,bilinear,-52.983,-52.941,-13 deit_tiny_distilled_patch16_224.fb_in1k,22.722,77.278,40.777,59.223,5.91,224,0.900,bicubic,-51.782,-51.113,+18 mobilenetv3_large_100.ra_in1k,22.657,77.343,40.763,59.237,5.48,224,0.875,bicubic,-53.109,-51.775,-14 repvgg_a1.rvgg_in1k,22.640,77.361,39.869,60.131,14.09,224,0.875,bilinear,-51.823,-51.987,+17 mobilenetv3_rw.rmsp_in1k,22.630,77.370,40.362,59.638,5.48,224,0.875,bicubic,-52.990,-52.342,-13 ghostnetv2_100.in1k,22.604,77.396,40.022,59.978,6.16,224,0.875,bicubic,-52.562,-52.332,-4 edgenext_x_small.in1k,22.590,77.410,39.500,60.500,2.34,288,1.000,bicubic,-53.098,-53.266,-17 tf_mobilenetv3_large_100.in1k,22.579,77.421,39.777,60.223,5.48,224,0.875,bilinear,-52.937,-52.817,-13 tf_efficientnet_b0.in1k,22.559,77.441,39.570,60.430,5.29,224,0.875,bicubic,-53.971,-53.438,-41 mobilevit_s.cvnets_in1k,22.478,77.522,38.657,61.343,5.58,256,0.900,bicubic,-55.834,-55.491,-134 xcit_nano_12_p8_224.fb_in1k,22.408,77.592,40.626,59.374,3.05,224,1.000,bicubic,-51.502,-51.542,+20 tf_efficientnet_es.in1k,22.406,77.594,39.089,60.911,5.44,224,0.875,bicubic,-54.192,-54.113,-46 hrnet_w18_small_v2.ms_in1k,22.341,77.659,39.857,60.143,15.60,224,0.875,bilinear,-52.769,-52.559,-8 convit_tiny.fb_in1k,22.268,77.732,39.675,60.325,5.71,224,0.875,bicubic,-50.844,-52.037,+28 regnetx_004_tv.tv2_in1k,22.213,77.787,39.126,60.874,5.50,224,0.965,bicubic,-52.387,-53.044,+3 regnety_008.pycls_in1k,22.109,77.891,38.902,61.098,6.26,224,0.875,bicubic,-54.193,-54.160,-37 ese_vovnet19b_dw.ra_in1k,22.072,77.928,39.464,60.536,6.54,288,0.950,bicubic,-55.672,-54.320,-104 regnety_006.pycls_in1k,21.981,78.019,38.959,61.041,6.06,224,0.875,bicubic,-53.287,-53.567,-17 vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,21.948,78.052,39.405,60.595,6.36,384,1.000,bicubic,-54.012,-53.857,-35 regnetx_008.pycls_in1k,21.948,78.052,38.928,61.072,7.26,224,0.875,bicubic,-53.080,-53.410,-11 semnasnet_100.rmsp_in1k,21.889,78.111,38.604,61.396,3.89,224,0.875,bicubic,-53.561,-53.994,-21 pit_ti_224.in1k,21.871,78.129,39.537,60.463,4.85,224,0.900,bicubic,-51.039,-51.867,+25 pvt_v2_b0.in1k,21.836,78.164,40.152,59.848,3.67,224,0.900,bicubic,-48.824,-50.044,+48 regnetx_006.pycls_in1k,21.747,78.253,38.914,61.086,6.20,224,0.875,bicubic,-52.121,-52.764,+8 vit_tiny_patch16_384.augreg_in21k_ft_in1k,21.720,78.280,39.319,60.681,5.79,384,1.000,bicubic,-56.704,-55.223,-158 crossvit_9_240.in1k,21.696,78.304,39.268,60.732,8.55,240,0.875,bicubic,-52.264,-52.694,+2 vgg19_bn.tv_in1k,21.623,78.376,39.276,60.724,143.68,224,0.875,bilinear,-52.592,-52.568,-3 semnasnet_075.rmsp_in1k,21.582,78.418,38.918,61.082,2.91,224,0.875,bicubic,-51.422,-52.222,+16 resnet18.gluon_in1k,21.545,78.455,38.875,61.125,11.69,224,0.875,bicubic,-49.289,-50.881,+40 mobilevitv2_075.cvnets_in1k,21.535,78.465,38.631,61.369,2.87,256,0.888,bicubic,-54.073,-54.113,-37 fbnetc_100.rmsp_in1k,21.492,78.508,38.179,61.821,5.57,224,0.875,bilinear,-53.638,-54.209,-27 repghostnet_100.in1k,21.459,78.541,38.682,61.318,4.07,224,0.875,bicubic,-52.748,-52.860,-7 xcit_nano_12_p16_224.fb_in1k,21.437,78.563,39.791,60.209,3.05,224,1.000,bicubic,-48.525,-49.971,+42 ghostnet_100.in1k,21.384,78.616,38.158,61.842,5.18,224,0.875,bicubic,-52.574,-53.374,-5 mnasnet_100.rmsp_in1k,21.346,78.653,37.709,62.291,4.38,224,0.875,bicubic,-53.306,-54.413,-20 resnet18.fb_ssl_yfcc100m_ft_in1k,21.293,78.707,39.114,60.886,11.69,224,0.875,bilinear,-51.305,-52.301,+11 lcnet_100.ra2_in1k,21.293,78.707,38.867,61.133,2.95,224,0.875,bicubic,-50.809,-51.487,+23 mixnet_s.ft_in1k,21.276,78.724,38.199,61.801,4.13,224,0.875,bicubic,-54.718,-55.071,-54 legacy_seresnext26_32x4d.in1k,21.089,78.911,37.627,62.373,16.79,224,0.875,bicubic,-56.019,-55.687,-92 efficientvit_m2.r224_in1k,21.081,78.919,37.690,62.310,4.19,224,0.875,bicubic,-49.733,-52.452,+30 crossvit_tiny_240.in1k,21.048,78.952,38.061,61.939,7.01,240,0.875,bicubic,-52.292,-53.847,-3 resnet18.a2_in1k,20.944,79.056,36.851,63.149,11.69,288,1.000,bicubic,-51.428,-53.745,+10 repvgg_a0.rvgg_in1k,20.922,79.078,37.539,62.461,9.11,224,0.875,bilinear,-51.486,-52.953,+6 regnetx_004.pycls_in1k,20.887,79.113,37.541,62.459,5.16,224,0.875,bicubic,-51.515,-53.285,+6 spnasnet_100.rmsp_in1k,20.867,79.133,37.896,62.104,4.42,224,0.875,bilinear,-53.227,-53.924,-19 seresnext26t_32x4d.bt_in1k,20.847,79.153,36.344,63.656,16.81,288,0.950,bicubic,-57.897,-57.968,-205 legacy_seresnet18.in1k,20.835,79.165,37.639,62.361,11.78,224,0.875,bicubic,-50.925,-52.693,+16 mobilenetv2_100.ra_in1k,20.761,79.239,37.757,62.243,3.50,224,0.875,bicubic,-52.207,-53.259,-2 tf_mixnet_s.in1k,20.474,79.526,36.621,63.379,4.13,224,0.875,bicubic,-55.178,-56.019,-58 vit_tiny_patch16_224.augreg_in21k_ft_in1k,20.460,79.540,37.601,62.399,5.72,224,0.900,bicubic,-55.002,-55.243,-52 regnety_004.pycls_in1k,20.411,79.589,37.014,62.986,4.34,224,0.875,bicubic,-53.615,-54.734,-24 tf_mobilenetv3_large_075.in1k,20.378,79.622,36.782,63.218,3.99,224,0.875,bilinear,-53.052,-54.570,-17 hrnet_w18_small.ms_in1k,20.364,79.636,37.093,62.907,13.19,224,0.875,bilinear,-51.972,-53.588,0 hrnet_w18_small.gluon_in1k,20.362,79.638,36.973,63.027,13.19,224,0.875,bicubic,-53.558,-54.221,-24 resnet26d.bt_in1k,20.266,79.734,36.348,63.652,16.01,288,0.950,bicubic,-57.142,-57.290,-125 resnet18.tv_in1k,20.230,79.770,37.258,62.742,11.69,224,0.875,bilinear,-49.530,-51.812,+21 mixer_l16_224.goog_in21k_ft_in1k,20.175,79.825,32.938,67.062,208.20,224,0.875,bicubic,-51.879,-54.736,+3 deit_tiny_patch16_224.fb_in1k,20.148,79.852,37.537,62.463,5.72,224,0.900,bicubic,-52.022,-53.579,0 tf_mobilenetv3_large_minimal_100.in1k,20.103,79.897,36.894,63.106,3.92,224,0.875,bilinear,-52.161,-53.746,-4 seresnext26d_32x4d.bt_in1k,20.067,79.933,35.233,64.766,16.81,288,0.950,bicubic,-58.747,-59.006,-226 vgg16_bn.tv_in1k,19.945,80.055,36.314,63.686,138.37,224,0.875,bilinear,-53.425,-55.200,-24 efficientvit_m1.r224_in1k,19.938,80.062,36.403,63.597,2.98,224,0.875,bicubic,-48.368,-52.267,+22 resnet26.bt_in1k,19.739,80.261,35.839,64.161,16.00,288,0.950,bicubic,-56.627,-57.341,-87 repghostnet_080.in1k,19.454,80.546,35.953,64.047,3.28,224,0.875,bicubic,-52.758,-54.531,-7 vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,19.334,80.666,36.059,63.941,6.34,224,0.900,bicubic,-52.464,-54.765,-3 mobileone_s0.apple_in1k,19.309,80.691,35.342,64.658,5.29,224,0.875,bilinear,-52.093,-54.500,0 tinynet_c.in1k,19.258,80.742,35.982,64.018,2.46,184,0.875,bicubic,-51.984,-53.750,+1 edgenext_xx_small.in1k,18.863,81.137,35.159,64.841,1.33,288,1.000,bicubic,-53.015,-55.393,-7 efficientvit_b0.r224_in1k,18.464,81.536,33.190,66.810,3.41,224,0.950,bicubic,-52.934,-56.238,-2 resnet18.a3_in1k,18.442,81.558,33.487,66.513,11.69,224,0.950,bicubic,-49.810,-54.685,+15 mobilevit_xs.cvnets_in1k,18.312,81.688,33.206,66.794,2.32,256,0.900,bicubic,-56.322,-59.142,-54 lcnet_075.ra2_in1k,18.128,81.872,34.371,65.629,2.36,224,0.875,bicubic,-50.654,-53.989,+9 vgg19.tv_in1k,17.941,82.059,33.058,66.942,143.67,224,0.875,bilinear,-54.437,-57.816,-22 vgg13_bn.tv_in1k,17.798,82.202,34.043,65.957,133.05,224,0.875,bilinear,-53.790,-56.335,-9 vgg16.tv_in1k,17.538,82.462,32.779,67.221,138.36,224,0.875,bilinear,-54.054,-57.605,-11 regnety_002.pycls_in1k,17.456,82.544,32.435,67.565,3.16,224,0.875,bicubic,-52.824,-57.095,-3 vgg11_bn.tv_in1k,17.397,82.603,33.001,66.999,132.87,224,0.875,bilinear,-52.985,-56.807,-5 mobilevitv2_050.cvnets_in1k,17.306,82.694,33.007,66.993,1.37,256,0.888,bicubic,-52.842,-56.911,-4 repghostnet_058.in1k,17.161,82.839,32.596,67.404,2.55,224,0.875,bicubic,-51.753,-55.824,+1 regnetx_002.pycls_in1k,16.959,83.041,32.225,67.775,2.68,224,0.875,bicubic,-51.793,-56.317,+2 mobilenetv3_small_100.lamb_in1k,16.803,83.197,32.518,67.482,2.54,224,0.875,bicubic,-50.855,-55.118,+7 resnet10t.c3_in1k,16.699,83.301,32.123,67.877,5.44,224,0.950,bicubic,-51.665,-55.913,+1 efficientvit_m0.r224_in1k,16.670,83.330,31.948,68.052,2.35,224,0.875,bicubic,-46.600,-53.228,+14 mobilenetv2_050.lamb_in1k,16.668,83.332,31.950,68.050,1.97,224,0.875,bicubic,-49.280,-54.134,+9 tinynet_d.in1k,16.658,83.342,32.449,67.551,2.34,152,0.875,bicubic,-50.314,-54.617,+4 mnasnet_small.lamb_in1k,16.638,83.362,31.909,68.091,2.03,224,0.875,bicubic,-49.558,-54.595,+5 dla60x_c.in1k,16.336,83.664,31.757,68.243,1.32,224,0.875,bilinear,-51.576,-56.675,0 tf_mobilenetv3_small_100.in1k,16.216,83.784,31.205,68.795,2.54,224,0.875,bilinear,-51.706,-56.467,-2 vgg13.tv_in1k,16.096,83.904,30.985,69.015,133.05,224,0.875,bilinear,-53.836,-58.265,-13 resnet14t.c3_in1k,15.925,84.075,30.003,69.997,10.08,224,0.950,bicubic,-56.329,-60.303,-34 vgg11.tv_in1k,15.723,84.278,30.451,69.549,132.86,224,0.875,bilinear,-53.300,-58.173,-13 repghostnet_050.in1k,15.589,84.411,30.189,69.811,2.31,224,0.875,bicubic,-51.377,-56.731,-2 mobilenetv3_small_075.lamb_in1k,14.948,85.052,29.733,70.267,2.04,224,0.875,bicubic,-50.288,-55.713,+2 tf_mobilenetv3_small_075.in1k,14.932,85.067,29.562,70.438,2.04,224,0.875,bilinear,-50.794,-56.570,0 dla46_c.in1k,14.665,85.335,29.397,70.603,1.30,224,0.875,bilinear,-50.207,-56.901,+1 mobilevit_xxs.cvnets_in1k,14.490,85.510,28.654,71.346,1.27,256,0.900,bicubic,-54.428,-60.291,-17 dla46x_c.in1k,14.380,85.620,29.197,70.803,1.07,224,0.875,bilinear,-51.612,-57.777,-5 lcnet_050.ra2_in1k,14.290,85.710,28.659,71.341,1.88,224,0.875,bicubic,-48.848,-55.724,0 tf_mobilenetv3_small_minimal_100.in1k,13.962,86.038,27.990,72.010,2.04,224,0.875,bilinear,-48.932,-56.248,0 tinynet_e.in1k,12.671,87.329,26.383,73.617,2.04,106,0.875,bicubic,-47.195,-55.379,0 mobilenetv3_small_050.lamb_in1k,11.038,88.962,23.477,76.523,1.59,224,0.875,bicubic,-46.878,-56.703,0
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/results-imagenet-r.csv	model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.623,9.377,97.913,2.087,846.47,256,1.000,bicubic,-7.127,-1.897,+18 eva_giant_patch14_336.clip_ft_in1k,90.550,9.450,97.230,2.770,"1,013.01",336,1.000,bicubic,-7.310,-2.650,+6 eva02_large_patch14_448.mim_m38m_ft_in1k,90.457,9.543,97.267,2.733,305.08,448,1.000,bicubic,-7.373,-2.553,+6 eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.293,9.707,97.170,2.830,305.08,448,1.000,bicubic,-7.737,-2.720,-2 eva_giant_patch14_224.clip_ft_in1k,89.843,10.157,97.023,2.977,"1,012.56",224,0.900,bicubic,-7.727,-2.687,+29 eva_giant_patch14_336.m30m_ft_in22k_in1k,88.590,11.410,95.920,4.080,"1,013.01",336,1.000,bicubic,-9.400,-3.980,-2 eva_giant_patch14_560.m30m_ft_in22k_in1k,88.397,11.603,95.610,4.390,"1,014.45",560,1.000,bicubic,-9.603,-4.250,-4 regnety_1280.swag_ft_in1k,88.257,11.743,96.477,3.523,644.81,384,1.000,bicubic,-9.523,-3.383,+6 eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,87.980,12.020,95.610,4.390,305.08,448,1.000,bicubic,-10.170,-4.270,-8 eva02_large_patch14_448.mim_in22k_ft_in1k,87.587,12.413,95.730,4.270,305.08,448,1.000,bicubic,-10.273,-4.060,-3 regnety_1280.swag_lc_in1k,86.943,13.057,95.737,4.263,644.81,224,0.965,bicubic,-10.447,-4.003,+36 convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,84.437,15.563,94.297,5.703,200.13,384,1.000,bicubic,-12.953,-5.433,+34 vit_large_patch14_clip_336.openai_ft_in12k_in1k,83.923,16.077,93.873,6.127,304.53,336,1.000,bicubic,-13.677,-5.857,+18 vit_large_patch14_clip_336.laion2b_ft_in1k,83.610,16.390,93.510,6.490,304.53,336,1.000,bicubic,-13.620,-6.210,+63 eva_large_patch14_336.in22k_ft_in1k,83.520,16.480,93.103,6.897,304.53,336,1.000,bicubic,-14.290,-6.757,-4 vit_huge_patch14_clip_224.laion2b_ft_in1k,83.263,16.737,93.133,6.867,632.05,224,1.000,bicubic,-13.837,-6.557,+80 vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,83.047,16.953,92.837,7.163,632.46,336,1.000,bicubic,-14.563,-6.943,+12 vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,82.797,17.203,92.613,7.387,632.05,224,1.000,bicubic,-14.563,-7.187,+36 vit_large_patch14_clip_224.openai_ft_in1k,82.323,17.677,92.907,7.093,304.20,224,1.000,bicubic,-15.117,-6.773,+24 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,82.217,17.783,92.433,7.567,200.13,384,1.000,bicubic,-15.253,-7.327,+19 vit_large_patch14_clip_224.laion2b_ft_in1k,81.700,18.300,92.263,7.737,304.20,224,1.000,bicubic,-15.320,-7.407,+90 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,81.347,18.653,91.963,8.037,200.13,320,1.000,bicubic,-15.913,-7.747,+48 regnety_320.swag_lc_in1k,81.317,18.683,93.153,6.847,145.05,224,0.965,bicubic,-15.463,-6.467,+123 eva_large_patch14_196.in22k_ft_in1k,81.300,18.700,91.533,8.467,304.14,196,1.000,bicubic,-16.220,-8.257,+12 regnety_320.swag_ft_in1k,81.203,18.797,92.707,7.293,145.05,384,1.000,bicubic,-16.177,-7.013,+23 convnext_large_mlp.clip_laion2b_augreg_ft_in1k,80.870,19.130,91.917,8.083,200.13,256,1.000,bicubic,-16.260,-7.863,+65 eva_large_patch14_336.in22k_ft_in22k_in1k,80.087,19.913,89.357,10.643,304.53,336,1.000,bicubic,-17.773,-10.443,-21 resnext101_32x32d.fb_wsl_ig1b_ft_in1k,79.467,20.533,89.197,10.803,468.53,224,0.875,bilinear,-17.303,-10.423,+124 resnext101_32x16d.fb_wsl_ig1b_ft_in1k,78.830,21.170,88.473,11.527,194.03,224,0.875,bilinear,-17.600,-11.157,+183 vit_large_patch14_clip_224.openai_ft_in12k_in1k,78.677,21.323,88.920,11.080,304.20,224,1.000,bicubic,-18.933,-10.810,0 eva_large_patch14_196.in22k_ft_in22k_in1k,78.500,21.500,88.330,11.670,304.14,196,1.000,bicubic,-19.110,-11.480,-3 vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.433,21.567,88.500,11.500,304.53,336,1.000,bicubic,-19.027,-11.280,+8 vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.267,21.733,88.673,11.327,304.20,224,1.000,bicubic,-19.123,-11.057,+12 regnety_160.swag_lc_in1k,78.187,21.813,91.663,8.337,83.59,224,0.965,bicubic,-18.263,-7.947,+172 regnety_160.swag_ft_in1k,77.683,22.317,90.737,9.263,83.59,384,1.000,bicubic,-19.487,-8.903,+50 eva02_base_patch14_448.mim_in22k_ft_in1k,77.610,22.390,89.307,10.693,87.12,448,1.000,bicubic,-20.110,-10.453,-14 eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,77.497,22.503,88.490,11.510,87.12,448,1.000,bicubic,-20.113,-11.330,-10 tf_efficientnet_l2.ns_jft_in1k_475,76.490,23.510,88.640,11.360,480.31,475,0.936,bicubic,-21.260,-11.150,-20 beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.353,23.647,87.090,12.910,304.43,224,0.950,bicubic,-21.397,-12.730,-19 resnext101_32x16d.fb_swsl_ig1b_ft_in1k,76.300,23.700,87.743,12.257,194.03,224,0.875,bilinear,-19.970,-11.757,+206 convnextv2_huge.fcmae_ft_in22k_in1k_384,75.953,24.047,86.637,13.363,660.29,384,1.000,bicubic,-21.917,-13.273,-36 convnextv2_huge.fcmae_ft_in22k_in1k_512,75.823,24.177,86.943,13.057,660.29,512,1.000,bicubic,-21.987,-12.847,-32 resnext101_32x8d.fb_wsl_ig1b_ft_in1k,75.797,24.203,86.197,13.803,88.79,224,0.875,bilinear,-20.143,-13.183,+277 resnext101_32x8d.fb_swsl_ig1b_ft_in1k,75.600,24.400,86.943,13.057,88.79,224,0.875,bilinear,-20.650,-12.597,+203 convnext_base.clip_laiona_augreg_ft_in1k_384,75.210,24.790,88.580,11.420,88.59,384,1.000,bicubic,-21.650,-11.110,+88 convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,75.173,24.827,88.460,11.540,88.59,384,1.000,bicubic,-21.867,-11.210,+58 tf_efficientnet_l2.ns_jft_in1k,74.657,25.343,87.557,12.443,480.31,800,0.960,bicubic,-23.123,-12.263,-34 convnext_base.clip_laion2b_augreg_ft_in12k_in1k,73.733,26.267,87.343,12.657,88.59,256,1.000,bicubic,-23.057,-12.337,+97 convnext_base.clip_laion2b_augreg_ft_in1k,73.400,26.600,87.147,12.853,88.59,256,1.000,bicubic,-23.160,-12.503,+134 beit_large_patch16_384.in22k_ft_in22k_in1k,73.277,26.723,85.030,14.970,305.00,384,1.000,bicubic,-24.533,-14.810,-38 beit_large_patch16_512.in22k_ft_in22k_in1k,73.160,26.840,85.090,14.910,305.67,512,1.000,bicubic,-24.620,-14.800,-36 resnext101_32x4d.fb_swsl_ig1b_ft_in1k,72.657,27.343,85.160,14.840,44.18,224,0.875,bilinear,-23.383,-14.250,+242 maxvit_xlarge_tf_512.in21k_ft_in1k,71.873,28.127,82.927,17.073,475.77,512,1.000,bicubic,-25.887,-16.893,-36 maxvit_xlarge_tf_384.in21k_ft_in1k,71.703,28.297,82.713,17.287,475.32,384,1.000,bicubic,-26.037,-17.137,-33 beit_large_patch16_224.in22k_ft_in22k_in1k,71.040,28.960,83.430,16.570,304.43,224,0.900,bicubic,-26.440,-16.260,-17 deit3_huge_patch14_224.fb_in22k_ft_in1k,70.817,29.183,82.200,17.800,632.13,224,1.000,bicubic,-26.433,-17.520,+17 vit_base_patch16_clip_384.laion2b_ft_in1k,70.777,29.223,83.820,16.180,86.86,384,1.000,bicubic,-26.123,-15.850,+69 caformer_b36.sail_in22k_ft_in1k_384,70.750,29.250,82.650,17.350,98.75,384,1.000,bicubic,-26.910,-17.210,-34 deit3_large_patch16_384.fb_in22k_ft_in1k,70.580,29.420,82.437,17.563,304.76,384,1.000,bicubic,-27.000,-17.273,-26 beitv2_large_patch16_224.in1k_ft_in1k,70.403,29.597,83.373,16.627,304.43,224,0.950,bicubic,-26.907,-16.387,0 maxvit_base_tf_512.in21k_ft_in1k,70.383,29.617,81.600,18.400,119.88,512,1.000,bicubic,-27.377,-18.260,-45 maxvit_large_tf_512.in21k_ft_in1k,70.380,29.620,81.650,18.350,212.33,512,1.000,bicubic,-27.290,-18.080,-39 maxvit_large_tf_384.in21k_ft_in1k,70.010,29.990,81.037,18.963,212.03,384,1.000,bicubic,-27.650,-18.783,-38 deit3_large_patch16_224.fb_in22k_ft_in1k,69.710,30.290,81.197,18.803,304.37,224,1.000,bicubic,-27.600,-18.483,-3 maxvit_base_tf_384.in21k_ft_in1k,69.557,30.443,80.730,19.270,119.65,384,1.000,bicubic,-28.003,-19.030,-30 maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,69.130,30.870,80.060,19.940,116.14,384,1.000,bicubic,-28.210,-19.630,-11 resnext50_32x4d.fb_swsl_ig1b_ft_in1k,68.970,31.030,82.803,17.197,25.03,224,0.875,bilinear,-26.660,-16.607,+317 vit_base_patch16_clip_224.laion2b_ft_in1k,68.780,31.220,82.503,17.497,86.57,224,1.000,bicubic,-27.540,-17.017,+165 convnextv2_large.fcmae_ft_in22k_in1k_384,68.653,31.347,81.070,18.930,197.96,384,1.000,bicubic,-28.977,-18.730,-43 caformer_b36.sail_in22k_ft_in1k,68.603,31.397,80.857,19.143,98.75,224,1.000,bicubic,-28.757,-18.973,-17 resnet50.fb_swsl_ig1b_ft_in1k,68.287,31.713,83.313,16.687,25.56,224,0.875,bilinear,-26.913,-16.077,+404 convnext_xlarge.fb_in22k_ft_in1k_384,68.157,31.843,80.453,19.547,350.20,384,1.000,bicubic,-29.433,-19.317,-40 maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,68.077,31.923,80.740,19.260,116.09,384,1.000,bicubic,-29.373,-19.020,-31 swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,67.667,32.333,80.100,19.900,196.74,384,1.000,bicubic,-29.613,-19.680,-7 tf_efficientnet_b7.ns_jft_in1k,67.507,32.493,81.383,18.617,66.35,600,0.949,bicubic,-29.683,-18.317,+8 vit_base_patch16_clip_384.openai_ft_in1k,67.350,32.650,81.687,18.313,86.86,384,1.000,bicubic,-29.460,-18.023,+65 vit_large_patch16_384.augreg_in21k_ft_in1k,67.057,32.943,78.697,21.303,304.72,384,1.000,bicubic,-30.353,-21.083,-33 convnextv2_base.fcmae_ft_in22k_in1k_384,67.030,32.970,79.800,20.200,88.72,384,1.000,bicubic,-30.350,-19.960,-29 convformer_b36.sail_in22k_ft_in1k_384,66.823,33.177,79.443,20.557,99.88,384,1.000,bicubic,-30.667,-20.317,-42 convnext_large.fb_in22k_ft_in1k_384,66.673,33.327,79.797,20.203,197.77,384,1.000,bicubic,-30.627,-19.963,-18 coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,66.580,33.420,78.413,21.587,73.88,384,1.000,bicubic,-30.790,-21.287,-30 maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,66.530,33.470,78.027,21.973,116.14,224,0.950,bicubic,-30.580,-21.573,+13 swin_large_patch4_window12_384.ms_in22k_ft_in1k,66.287,33.713,79.747,20.253,196.74,384,1.000,bicubic,-30.883,-19.993,+4 vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,66.147,33.853,78.883,21.117,86.86,384,1.000,bicubic,-31.073,-20.817,-5 vit_base_patch16_clip_224.openai_ft_in1k,66.027,33.973,80.980,19.020,86.57,224,0.900,bicubic,-30.283,-18.520,+151 beitv2_base_patch16_224.in1k_ft_in22k_in1k,65.757,34.243,78.893,21.107,86.53,224,0.900,bicubic,-31.153,-20.837,+38 swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,65.733,34.267,79.317,20.683,87.92,384,1.000,bicubic,-31.537,-20.473,-19 swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,65.640,34.360,78.480,21.520,196.74,256,0.900,bicubic,-31.600,-21.230,-13 tf_efficientnet_b6.ns_jft_in1k,65.583,34.417,79.557,20.443,43.04,528,0.942,bicubic,-31.437,-20.153,+20 caformer_m36.sail_in22k_ft_in1k_384,65.580,34.420,78.703,21.297,56.20,384,1.000,bicubic,-31.790,-21.087,-40 convformer_b36.sail_in22k_ft_in1k,65.513,34.487,78.140,21.860,99.88,224,1.000,bicubic,-31.747,-21.610,-22 convnext_xlarge.fb_in22k_ft_in1k,65.373,34.627,78.350,21.650,350.20,288,1.000,bicubic,-32.077,-21.470,-51 vit_base_patch16_clip_384.openai_ft_in12k_in1k,65.353,34.647,78.957,21.043,86.86,384,0.950,bicubic,-31.767,-20.613,-1 convnext_base.fb_in22k_ft_in1k_384,64.903,35.097,78.387,21.613,88.59,384,1.000,bicubic,-32.357,-21.353,-23 vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,64.767,35.233,77.770,22.230,86.57,224,0.950,bicubic,-31.853,-21.790,+79 convnextv2_large.fcmae_ft_in22k_in1k,64.710,35.290,78.157,21.843,197.96,288,1.000,bicubic,-32.600,-21.583,-37 vit_large_patch16_224.augreg_in21k_ft_in1k,64.360,35.640,76.180,23.820,304.33,224,0.900,bicubic,-32.340,-23.390,+64 convnext_large.fb_in22k_ft_in1k,64.263,35.737,77.773,22.227,197.77,288,1.000,bicubic,-32.957,-21.957,-20 vit_large_r50_s32_384.augreg_in21k_ft_in1k,64.103,35.897,75.847,24.153,329.09,384,1.000,bicubic,-32.847,-23.783,+18 maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,64.010,35.990,77.513,22.487,116.09,224,0.950,bicubic,-33.080,-22.167,-3 swin_large_patch4_window7_224.ms_in22k_ft_in1k,63.887,36.113,78.187,21.813,196.53,224,0.900,bicubic,-33.053,-21.483,+20 seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,63.653,36.347,76.623,23.377,149.39,384,1.000,bicubic,-33.637,-23.157,-37 beit_base_patch16_384.in22k_ft_in22k_in1k,63.623,36.377,78.103,21.897,86.74,384,1.000,bicubic,-33.697,-21.617,-46 caformer_m36.sail_in22k_ft_in1k,63.493,36.507,76.920,23.080,56.20,224,1.000,bicubic,-33.527,-22.810,+4 swin_base_patch4_window12_384.ms_in22k_ft_in1k,63.483,36.517,78.050,21.950,87.90,384,1.000,bicubic,-33.647,-21.670,-15 convnextv2_base.fcmae_ft_in22k_in1k,63.307,36.693,77.163,22.837,88.72,288,1.000,bicubic,-33.893,-22.597,-25 caformer_s36.sail_in22k_ft_in1k_384,63.263,36.737,77.460,22.540,39.30,384,1.000,bicubic,-34.027,-22.290,-41 swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,63.183,36.817,77.093,22.907,87.92,256,0.900,bicubic,-33.867,-22.497,-6 tf_efficientnet_b5.ns_jft_in1k,63.053,36.947,77.787,22.213,30.39,456,0.934,bicubic,-33.817,-21.853,+23 vit_base_patch16_clip_224.openai_ft_in12k_in1k,62.943,37.057,76.597,23.403,86.57,224,0.950,bicubic,-33.567,-22.953,+87 deit3_base_patch16_384.fb_in22k_ft_in1k,62.637,37.363,75.550,24.450,86.88,384,1.000,bicubic,-34.603,-24.190,-35 convnext_base.fb_in22k_ft_in1k,62.537,37.463,76.550,23.450,88.59,288,1.000,bicubic,-34.663,-23.210,-32 coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,62.417,37.583,75.130,24.870,73.88,224,0.950,bicubic,-34.763,-24.520,-29 vit_base_patch8_224.augreg2_in21k_ft_in1k,62.400,37.600,76.600,23.400,86.58,224,0.900,bicubic,-34.550,-23.010,+4 tf_efficientnetv2_l.in21k_ft_in1k,62.363,37.637,76.757,23.243,118.52,480,1.000,bicubic,-34.957,-22.883,-57 vit_base_patch8_224.augreg_in21k_ft_in1k,62.177,37.823,75.627,24.373,86.58,224,0.900,bicubic,-34.913,-23.983,-18 convformer_m36.sail_in22k_ft_in1k_384,62.103,37.897,75.540,24.460,57.05,384,1.000,bicubic,-35.267,-24.140,-65 tf_efficientnetv2_xl.in21k_ft_in1k,62.083,37.917,75.653,24.347,208.12,512,1.000,bicubic,-35.247,-23.947,-62 hrnet_w48_ssld.paddle_in1k,61.923,38.077,75.163,24.837,77.47,288,1.000,bilinear,-34.617,-24.477,+70 deit3_base_patch16_224.fb_in22k_ft_in1k,61.803,38.197,74.717,25.283,86.59,224,1.000,bicubic,-35.057,-24.903,+14 beitv2_base_patch16_224.in1k_ft_in1k,61.427,38.573,75.930,24.070,86.53,224,0.900,bicubic,-35.323,-23.670,+34 coatnet_2_rw_224.sw_in12k_ft_in1k,61.277,38.723,73.967,26.033,73.87,224,0.950,bicubic,-35.713,-25.693,-8 convformer_m36.sail_in22k_ft_in1k,61.257,38.743,74.620,25.380,57.05,224,1.000,bicubic,-35.813,-25.130,-23 tf_efficientnet_b4.ns_jft_in1k,61.233,38.767,76.160,23.840,19.34,380,0.922,bicubic,-35.477,-23.480,+35 convnextv2_huge.fcmae_ft_in1k,61.197,38.803,74.500,25.500,660.29,288,1.000,bicubic,-36.053,-25.220,-53 maxvit_base_tf_512.in1k,61.103,38.897,74.050,25.950,119.88,512,1.000,bicubic,-36.067,-25.630,-38 caformer_s36.sail_in22k_ft_in1k,60.767,39.233,75.167,24.833,39.30,224,1.000,bicubic,-36.053,-24.523,+12 vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,60.357,39.643,73.797,26.203,88.30,384,1.000,bicubic,-36.253,-25.683,+47 beit_base_patch16_224.in22k_ft_in22k_in1k,60.317,39.683,75.593,24.407,86.53,224,0.900,bicubic,-36.343,-24.067,+40 tf_efficientnetv2_m.in21k_ft_in1k,60.267,39.733,75.070,24.930,54.14,480,1.000,bicubic,-36.733,-24.560,-18 vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,60.230,39.770,73.550,26.450,88.34,448,1.000,bicubic,-36.330,-25.970,+54 vit_base_patch16_384.augreg_in21k_ft_in1k,60.183,39.817,73.843,26.157,86.86,384,1.000,bicubic,-36.837,-25.867,-22 convformer_s36.sail_in22k_ft_in1k_384,60.070,39.930,74.127,25.873,40.01,384,1.000,bicubic,-36.990,-25.583,-32 convnext_small.fb_in22k_ft_in1k_384,59.923,40.077,74.487,25.513,50.22,384,1.000,bicubic,-37.187,-25.153,-40 maxvit_large_tf_512.in1k,59.887,40.113,72.847,27.153,212.33,512,1.000,bicubic,-37.163,-26.813,-32 tiny_vit_21m_512.dist_in22k_ft_in1k,59.580,40.420,74.757,25.243,21.27,512,1.000,bicubic,-37.320,-24.933,-11 swin_base_patch4_window7_224.ms_in22k_ft_in1k,59.527,40.473,74.247,25.753,87.77,224,0.900,bicubic,-37.153,-25.423,+30 vit_base_patch32_clip_224.laion2b_ft_in1k,59.163,40.837,73.883,26.117,88.22,224,0.900,bicubic,-35.587,-25.187,+436 maxvit_base_tf_384.in1k,59.073,40.927,71.687,28.313,119.65,384,1.000,bicubic,-38.047,-27.953,-46 vit_base_patch16_224.augreg2_in21k_ft_in1k,59.060,40.940,73.603,26.397,86.57,224,0.900,bicubic,-37.450,-25.957,+56 seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,59.030,40.970,72.837,27.163,93.59,320,1.000,bicubic,-38.260,-26.883,-77 tiny_vit_21m_384.dist_in22k_ft_in1k,59.017,40.983,74.100,25.900,21.23,384,1.000,bicubic,-37.933,-25.610,-22 volo_d5_512.sail_in1k,58.927,41.073,73.210,26.790,296.09,512,1.150,bicubic,-38.373,-26.550,-80 convformer_s36.sail_in22k_ft_in1k,58.913,41.087,72.943,27.057,40.01,224,1.000,bicubic,-37.587,-26.517,+54 convnext_small.in12k_ft_in1k_384,58.807,41.193,72.797,27.203,50.22,384,1.000,bicubic,-38.183,-26.853,-32 volo_d5_448.sail_in1k,58.800,41.200,73.063,26.937,295.91,448,1.150,bicubic,-38.440,-26.607,-72 vit_large_r50_s32_224.augreg_in21k_ft_in1k,58.667,41.333,71.733,28.267,328.99,224,0.900,bicubic,-37.513,-27.777,+117 vit_base_patch32_clip_384.openai_ft_in12k_in1k,58.587,41.413,73.150,26.850,88.30,384,0.950,bicubic,-37.833,-26.350,+66 maxvit_large_tf_384.in1k,58.427,41.573,71.177,28.823,212.03,384,1.000,bicubic,-38.503,-28.393,-26 eva02_small_patch14_336.mim_in22k_ft_in1k,58.363,41.637,72.877,27.123,22.13,336,1.000,bicubic,-38.327,-26.733,+15 deit3_large_patch16_384.fb_in1k,58.357,41.643,72.950,27.050,304.76,384,1.000,bicubic,-38.493,-26.670,-16 deit3_huge_patch14_224.fb_in1k,58.137,41.863,72.143,27.857,632.13,224,0.900,bicubic,-38.443,-27.377,+27 convnextv2_large.fcmae_ft_in1k,58.103,41.897,72.627,27.373,197.96,288,1.000,bicubic,-38.727,-27.133,-16 tf_efficientnet_b8.ap_in1k,57.837,42.163,72.960,27.040,87.41,672,0.954,bicubic,-38.713,-26.600,+34 convnext_small.fb_in22k_ft_in1k,57.743,42.257,72.773,27.227,50.22,288,1.000,bicubic,-39.067,-26.857,-12 seresnextaa101d_32x8d.sw_in12k_ft_in1k,57.643,42.357,71.357,28.643,93.59,288,1.000,bicubic,-39.527,-28.423,-70 mvitv2_large.fb_in1k,57.497,42.503,70.750,29.250,217.99,224,0.900,bicubic,-38.903,-28.790,+62 cait_m48_448.fb_dist_in1k,57.477,42.523,71.857,28.143,356.46,448,1.000,bicubic,-39.403,-27.813,-28 cait_m36_384.fb_dist_in1k,57.473,42.527,72.307,27.693,271.22,384,1.000,bicubic,-39.367,-27.353,-23 tf_efficientnet_b3.ns_jft_in1k,57.417,42.583,72.370,27.630,12.23,300,0.904,bicubic,-38.683,-27.110,+119 volo_d4_448.sail_in1k,57.287,42.713,71.540,28.460,193.41,448,1.150,bicubic,-39.783,-28.090,-62 tiny_vit_21m_224.dist_in22k_ft_in1k,57.143,42.857,72.573,27.427,21.20,224,0.950,bicubic,-39.237,-26.847,+58 maxvit_small_tf_512.in1k,57.077,42.923,70.957,29.043,69.13,512,1.000,bicubic,-40.123,-28.663,-81 vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,57.070,42.930,71.267,28.733,88.22,224,0.900,bicubic,-38.160,-27.973,+302 vit_base_patch16_224.augreg_in21k_ft_in1k,56.833,43.167,70.633,29.367,86.57,224,0.900,bicubic,-39.467,-28.897,+75 deit3_medium_patch16_224.fb_in22k_ft_in1k,56.653,43.347,69.740,30.260,38.85,224,1.000,bicubic,-39.487,-29.550,+104 volo_d5_224.sail_in1k,56.507,43.493,70.660,29.340,295.46,224,0.960,bicubic,-40.373,-28.960,-39 deit3_large_patch16_224.fb_in1k,56.453,43.547,70.473,29.527,304.37,224,0.900,bicubic,-39.737,-29.027,+95 xcit_large_24_p8_384.fb_dist_in1k,56.360,43.640,71.307,28.693,188.93,384,1.000,bicubic,-40.400,-28.253,-16 flexivit_large.1200ep_in1k,56.290,43.710,71.560,28.440,304.36,240,0.950,bicubic,-40.490,-28.110,-21 convnextv2_tiny.fcmae_ft_in22k_in1k_384,56.100,43.900,71.897,28.103,28.64,384,1.000,bicubic,-40.520,-27.733,+2 flexivit_large.600ep_in1k,56.077,43.923,71.170,28.830,304.36,240,0.950,bicubic,-40.653,-28.390,-15 xcit_large_24_p8_224.fb_dist_in1k,56.020,43.980,70.670,29.330,188.93,224,1.000,bicubic,-40.610,-28.790,-2 caformer_s18.sail_in22k_ft_in1k_384,56.010,43.990,71.380,28.620,26.34,384,1.000,bicubic,-40.520,-28.200,+16 vit_base_patch32_clip_224.openai_ft_in1k,55.913,44.087,72.157,27.843,88.22,224,0.900,bicubic,-38.527,-26.953,+468 vit_medium_patch16_gap_384.sw_in12k_ft_in1k,55.783,44.217,70.997,29.003,39.03,384,0.950,bicubic,-40.707,-28.623,+24 flexivit_large.300ep_in1k,55.703,44.297,70.703,29.297,304.36,240,0.950,bicubic,-40.997,-28.877,-15 convnext_small.in12k_ft_in1k,55.700,44.300,70.727,29.273,50.22,288,1.000,bicubic,-40.900,-28.833,-1 caformer_b36.sail_in1k_384,55.193,44.807,68.070,31.930,98.75,384,1.000,bicubic,-41.967,-31.540,-90 convformer_s18.sail_in22k_ft_in1k_384,55.123,44.877,70.127,29.873,26.77,384,1.000,bicubic,-41.667,-29.583,-36 caformer_m36.sail_in1k_384,55.083,44.917,68.470,31.530,56.20,384,1.000,bicubic,-41.947,-31.240,-76 swin_small_patch4_window7_224.ms_in22k_ft_in1k,54.977,45.023,71.023,28.977,49.61,224,0.900,bicubic,-41.083,-28.387,+110 xcit_large_24_p16_384.fb_dist_in1k,54.907,45.093,69.850,30.150,189.10,384,1.000,bicubic,-42.033,-29.660,-61 volo_d4_224.sail_in1k,54.757,45.243,68.847,31.153,192.96,224,0.960,bicubic,-42.023,-30.763,-37 maxvit_tiny_tf_512.in1k,54.747,45.253,68.937,31.063,31.05,512,1.000,bicubic,-42.223,-30.733,-69 dm_nfnet_f5.dm_in1k,54.610,45.390,68.673,31.327,377.21,544,0.954,bicubic,-42.420,-31.007,-79 caformer_s36.sail_in1k_384,54.573,45.427,68.740,31.260,39.30,384,1.000,bicubic,-42.307,-30.930,-60 deit3_small_patch16_384.fb_in22k_ft_in1k,54.480,45.520,68.317,31.683,22.21,384,1.000,bicubic,-42.190,-31.323,-20 efficientnet_b5.sw_in12k_ft_in1k,54.413,45.587,69.873,30.127,30.39,448,1.000,bicubic,-42.357,-29.657,-38 vit_base_r50_s16_384.orig_in21k_ft_in1k,54.407,45.593,69.563,30.437,98.95,384,1.000,bicubic,-42.043,-30.047,+17 inception_next_base.sail_in1k_384,54.360,45.640,68.570,31.430,86.67,384,1.000,bicubic,-42.360,-31.040,-33 maxvit_small_tf_384.in1k,54.337,45.663,68.190,31.810,69.02,384,1.000,bicubic,-42.413,-31.350,-38 regnety_160.sw_in12k_ft_in1k,54.330,45.670,69.047,30.953,83.59,288,1.000,bicubic,-42.490,-30.573,-55 resnetv2_152x4_bit.goog_in21k_ft_in1k,54.317,45.683,70.170,29.830,936.53,480,1.000,bilinear,-42.563,-29.490,-65 xcit_large_24_p16_224.fb_dist_in1k,54.250,45.750,68.967,31.033,189.10,224,1.000,bicubic,-42.070,-30.533,+40 regnety_160.lion_in12k_ft_in1k,54.203,45.797,68.990,31.010,83.59,288,1.000,bicubic,-42.607,-30.520,-56 vit_small_r26_s32_384.augreg_in21k_ft_in1k,54.187,45.813,68.760,31.240,36.47,384,1.000,bicubic,-41.873,-30.740,+92 caformer_s18.sail_in22k_ft_in1k,54.113,45.887,69.713,30.287,26.34,224,1.000,bicubic,-41.897,-29.837,+102 volo_d3_448.sail_in1k,53.977,46.023,68.040,31.960,86.63,448,1.000,bicubic,-43.053,-31.630,-94 caformer_b36.sail_in1k,53.977,46.023,66.687,33.313,98.75,224,1.000,bicubic,-42.523,-32.943,0 tf_efficientnet_b5.ap_in1k,53.887,46.113,69.170,30.830,30.39,456,0.934,bicubic,-42.203,-30.370,+80 dm_nfnet_f6.dm_in1k,53.843,46.157,68.413,31.587,438.36,576,0.956,bicubic,-43.127,-31.347,-87 xcit_medium_24_p8_224.fb_dist_in1k,53.650,46.350,68.403,31.597,84.32,224,1.000,bicubic,-42.880,-31.107,-11 tf_efficientnet_b2.ns_jft_in1k,53.600,46.400,70.270,29.730,9.11,260,0.890,bicubic,-41.920,-29.070,+204 cait_s36_384.fb_dist_in1k,53.560,46.440,68.003,31.997,68.37,384,1.000,bicubic,-43.070,-31.607,-35 tf_efficientnet_b6.ap_in1k,53.553,46.447,68.563,31.437,43.04,528,0.942,bicubic,-42.817,-30.987,+16 vit_medium_patch16_gap_256.sw_in12k_ft_in1k,53.537,46.463,69.093,30.907,38.86,256,0.950,bicubic,-42.463,-30.327,+95 dm_nfnet_f3.dm_in1k,53.523,46.477,67.743,32.257,254.92,416,0.940,bicubic,-43.097,-31.837,-36 deit3_base_patch16_384.fb_in1k,53.483,46.517,67.623,32.377,86.88,384,1.000,bicubic,-42.747,-31.817,+47 deit3_base_patch16_224.fb_in1k,53.470,46.530,67.597,32.403,86.59,224,0.900,bicubic,-42.300,-31.673,+145 convformer_s18.sail_in22k_ft_in1k,53.437,46.563,68.680,31.320,26.77,224,1.000,bicubic,-42.663,-30.810,+67 tf_efficientnet_b8.ra_in1k,53.417,46.583,69.093,30.907,87.41,672,0.954,bicubic,-43.283,-30.557,-49 convnextv2_base.fcmae_ft_in1k,53.417,46.583,67.750,32.250,88.72,288,1.000,bicubic,-43.063,-31.800,-9 xcit_medium_24_p8_384.fb_dist_in1k,53.397,46.603,68.130,31.870,84.32,384,1.000,bicubic,-43.373,-31.470,-64 vit_base_patch32_384.augreg_in21k_ft_in1k,53.300,46.700,68.057,31.943,88.30,384,1.000,bicubic,-42.600,-31.283,+110 tf_efficientnet_b7.ap_in1k,53.257,46.743,68.870,31.130,66.35,600,0.949,bicubic,-43.093,-30.650,+9 convnext_large.fb_in1k,53.247,46.753,67.887,32.113,197.77,288,1.000,bicubic,-43.153,-31.563,+1 xcit_medium_24_p16_384.fb_dist_in1k,53.237,46.763,68.043,31.957,84.40,384,1.000,bicubic,-43.453,-31.557,-52 hrnet_w18_ssld.paddle_in1k,53.233,46.767,68.183,31.817,21.30,288,1.000,bilinear,-42.477,-31.107,+150 maxvit_base_tf_224.in1k,53.233,46.767,66.133,33.867,119.47,224,0.950,bicubic,-43.107,-33.447,+11 tf_efficientnetv2_l.in1k,53.160,46.840,67.827,32.173,118.52,480,1.000,bicubic,-43.580,-31.723,-65 tf_efficientnetv2_s.in21k_ft_in1k,53.127,46.873,69.000,31.000,21.46,384,1.000,bicubic,-43.343,-30.570,-18 tf_efficientnet_b4.ap_in1k,53.087,46.913,68.223,31.777,19.34,380,0.922,bicubic,-42.403,-31.197,+188 convnext_tiny.in12k_ft_in1k_384,53.063,46.937,68.503,31.497,28.59,384,1.000,bicubic,-43.497,-31.127,-40 regnetz_e8.ra3_in1k,53.003,46.997,67.147,32.853,57.70,320,1.000,bicubic,-43.597,-32.433,-50 maxvit_large_tf_224.in1k,53.003,46.997,65.337,34.663,211.79,224,0.950,bicubic,-43.327,-34.073,+7 coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,52.873,47.127,66.460,33.540,41.72,224,0.950,bicubic,-43.257,-32.880,+46 volo_d3_224.sail_in1k,52.697,47.303,66.307,33.693,86.33,224,0.960,bicubic,-43.733,-33.233,-17 deit3_small_patch16_224.fb_in22k_ft_in1k,52.690,47.310,66.867,33.133,22.06,224,1.000,bicubic,-43.140,-32.473,+110 regnety_120.sw_in12k_ft_in1k,52.637,47.363,67.637,32.363,51.82,288,1.000,bicubic,-43.913,-32.043,-44 dm_nfnet_f4.dm_in1k,52.460,47.540,67.117,32.883,316.07,512,0.951,bicubic,-44.490,-32.523,-112 maxvit_tiny_tf_384.in1k,52.457,47.543,66.777,33.223,30.98,384,1.000,bicubic,-44.143,-32.843,-54 tf_efficientnet_b7.ra_in1k,52.410,47.590,68.220,31.780,66.35,600,0.949,bicubic,-44.160,-31.250,-52 xcit_small_24_p8_384.fb_dist_in1k,52.377,47.623,66.847,33.153,47.63,384,1.000,bicubic,-44.433,-32.813,-92 efficientnetv2_rw_m.agc_in1k,52.343,47.657,67.223,32.777,53.24,416,1.000,bicubic,-43.927,-32.407,+10 resnet18.fb_swsl_ig1b_ft_in1k,52.323,47.677,70.483,29.517,11.69,224,0.875,bilinear,-38.777,-27.717,+762 convformer_b36.sail_in1k_384,52.300,47.700,66.543,33.457,99.88,384,1.000,bicubic,-44.570,-33.107,-106 convformer_m36.sail_in1k_384,52.283,47.717,66.150,33.850,57.05,384,1.000,bicubic,-44.497,-33.580,-90 inception_next_base.sail_in1k,52.273,47.727,65.930,34.070,86.67,224,0.950,bicubic,-43.647,-33.430,+84 deit_base_distilled_patch16_384.fb_in1k,52.260,47.740,67.747,32.253,87.63,384,1.000,bicubic,-44.250,-31.843,-45 xcit_medium_24_p16_224.fb_dist_in1k,52.197,47.803,66.917,33.083,84.40,224,1.000,bicubic,-44.053,-32.493,+10 xcit_small_24_p8_224.fb_dist_in1k,52.183,47.817,66.767,33.233,47.63,224,1.000,bicubic,-44.367,-32.773,-55 convnext_tiny.fb_in22k_ft_in1k_384,52.180,47.820,66.923,33.077,28.59,384,1.000,bicubic,-43.990,-32.577,+23 convformer_s36.sail_in1k_384,51.987,48.013,66.197,33.803,40.01,384,1.000,bicubic,-44.713,-33.333,-81 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,51.937,48.063,68.660,31.340,236.34,384,1.000,bicubic,-44.253,-30.640,+17 fastvit_ma36.apple_dist_in1k,51.923,48.077,67.013,32.987,44.07,256,0.950,bicubic,-44.377,-32.547,-3 convnextv2_tiny.fcmae_ft_in22k_in1k,51.903,48.097,67.800,32.200,28.64,288,1.000,bicubic,-44.437,-31.570,-18 resmlp_big_24_224.fb_in22k_ft_in1k,51.893,48.107,68.463,31.537,129.14,224,0.875,bicubic,-44.457,-31.127,-21 xcit_small_24_p16_384.fb_dist_in1k,51.883,48.117,66.367,33.633,47.67,384,1.000,bicubic,-44.457,-33.183,-21 cait_s24_384.fb_dist_in1k,51.787,48.213,66.307,33.693,47.06,384,1.000,bicubic,-44.783,-33.243,-70 resnetv2_152x2_bit.goog_in21k_ft_in1k,51.763,48.237,69.263,30.737,236.34,448,1.000,bilinear,-44.757,-30.307,-58 caformer_m36.sail_in1k,51.693,48.307,64.497,35.503,56.20,224,1.000,bicubic,-44.717,-35.063,-36 ecaresnet269d.ra2_in1k,51.677,48.323,66.040,33.960,102.09,352,1.000,bicubic,-44.773,-33.620,-44 regnety_2560.seer_ft_in1k,51.650,48.350,68.193,31.807,"1,282.60",384,1.000,bicubic,-44.880,-31.327,-63 rexnetr_300.sw_in12k_ft_in1k,51.627,48.373,68.020,31.980,34.81,288,1.000,bicubic,-44.463,-31.520,+25 caformer_s36.sail_in1k,51.593,48.407,64.907,35.093,39.30,224,1.000,bicubic,-44.487,-34.603,+27 coat_lite_medium_384.in1k,51.570,48.430,65.740,34.260,44.57,384,1.000,bicubic,-45.000,-33.780,-75 mvitv2_base.fb_in1k,51.550,48.450,65.633,34.367,51.47,224,0.900,bicubic,-44.430,-33.957,+50 vit_base_patch16_224_miil.in21k_ft_in1k,51.543,48.457,65.207,34.793,86.54,224,0.875,bilinear,-44.497,-34.143,+38 davit_small.msft_in1k,51.523,48.477,66.440,33.560,49.75,224,0.950,bicubic,-44.507,-32.960,+38 convnext_tiny.in12k_ft_in1k,51.440,48.560,67.063,32.937,28.59,288,1.000,bicubic,-44.800,-32.227,-9 maxvit_rmlp_small_rw_224.sw_in1k,51.430,48.570,65.190,34.810,64.90,224,0.900,bicubic,-44.530,-34.240,+54 tf_efficientnetv2_m.in1k,51.423,48.577,66.623,33.377,54.14,480,1.000,bicubic,-45.057,-32.897,-62 repvit_m2_3.dist_450e_in1k,51.383,48.617,66.737,33.263,23.69,224,0.950,bicubic,-44.607,-32.663,+42 caformer_s18.sail_in1k_384,51.353,48.647,65.657,34.343,26.34,384,1.000,bicubic,-45.057,-33.873,-50 edgenext_base.in21k_ft_in1k,51.283,48.717,65.657,34.343,18.51,320,1.000,bicubic,-44.917,-33.803,-6 davit_base.msft_in1k,51.267,48.733,65.223,34.777,87.95,224,0.950,bicubic,-44.973,-34.417,-14 convformer_b36.sail_in1k,51.203,48.797,64.280,35.720,99.88,224,1.000,bicubic,-45.037,-35.130,-14 maxvit_small_tf_224.in1k,51.190,48.810,65.260,34.740,68.93,224,0.950,bicubic,-45.010,-34.270,-8 xcit_small_12_p8_384.fb_dist_in1k,51.107,48.893,65.840,34.160,26.21,384,1.000,bicubic,-45.363,-33.650,-65 convnext_base.fb_in1k,51.057,48.943,65.880,34.120,88.59,288,1.000,bicubic,-45.253,-33.630,-33 convformer_m36.sail_in1k,51.020,48.980,63.653,36.347,57.05,224,1.000,bicubic,-45.060,-35.737,+13 volo_d2_384.sail_in1k,50.900,49.100,65.623,34.377,58.87,384,1.000,bicubic,-45.810,-33.977,-113 tf_efficientnet_b1.ns_jft_in1k,50.887,49.113,67.910,32.090,7.79,240,0.882,bicubic,-43.973,-31.340,+281 vit_base_patch16_384.orig_in21k_ft_in1k,50.880,49.120,65.277,34.723,86.86,384,1.000,bicubic,-45.320,-34.193,-16 convformer_s36.sail_in1k,50.863,49.137,64.083,35.917,40.01,224,1.000,bicubic,-45.247,-35.377,+1 tiny_vit_11m_224.dist_in22k_ft_in1k,50.830,49.170,66.870,33.130,11.00,224,0.950,bicubic,-44.880,-32.390,+95 xcit_small_24_p16_224.fb_dist_in1k,50.743,49.257,65.047,34.953,47.67,224,1.000,bicubic,-45.047,-34.243,+73 repvit_m2_3.dist_300e_in1k,50.737,49.263,66.743,33.257,23.69,224,0.950,bicubic,-44.853,-32.647,+113 convformer_s18.sail_in1k_384,50.687,49.313,65.637,34.363,26.77,384,1.000,bicubic,-45.563,-33.943,-31 flexivit_base.1200ep_in1k,50.687,49.313,65.133,34.867,86.59,240,0.950,bicubic,-45.433,-34.277,-7 coatnet_rmlp_2_rw_224.sw_in1k,50.603,49.397,63.363,36.637,73.88,224,0.950,bicubic,-45.607,-36.117,-24 xcit_small_12_p16_384.fb_dist_in1k,50.527,49.473,65.300,34.700,26.25,384,1.000,bicubic,-45.813,-34.190,-53 efficientnet_b4.ra2_in1k,50.500,49.500,65.730,34.270,19.34,384,1.000,bicubic,-45.030,-33.670,+122 volo_d1_384.sail_in1k,50.477,49.523,64.913,35.087,26.78,384,1.000,bicubic,-46.003,-34.697,-84 efficientvit_b3.r256_in1k,50.477,49.523,64.180,35.820,48.65,256,1.000,bicubic,-45.353,-35.040,+56 xcit_small_12_p8_224.fb_dist_in1k,50.437,49.563,65.420,34.580,26.21,224,1.000,bicubic,-45.523,-33.950,+25 fastvit_sa36.apple_dist_in1k,50.427,49.573,65.803,34.197,31.53,256,0.900,bicubic,-45.533,-33.577,+27 resnetv2_101x3_bit.goog_in21k_ft_in1k,50.393,49.607,67.783,32.217,387.93,448,1.000,bilinear,-45.857,-31.687,-41 flexivit_base.600ep_in1k,50.353,49.647,64.627,35.373,86.59,240,0.950,bicubic,-45.607,-34.793,+23 efficientvit_b3.r288_in1k,50.340,49.660,64.063,35.937,48.65,288,1.000,bicubic,-45.800,-35.297,-19 regnetz_040_h.ra3_in1k,50.317,49.683,65.623,34.377,28.94,320,1.000,bicubic,-46.003,-33.917,-58 inception_next_small.sail_in1k,50.273,49.727,65.097,34.903,49.37,224,0.875,bicubic,-45.407,-34.153,+84 mvitv2_small.fb_in1k,50.260,49.740,64.893,35.107,34.87,224,0.900,bicubic,-45.630,-34.467,+35 cait_s24_224.fb_dist_in1k,50.250,49.750,65.020,34.980,46.92,224,1.000,bicubic,-45.410,-34.370,+83 resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,50.230,49.770,66.033,33.967,194.03,224,0.875,bilinear,-45.170,-33.127,+134 pit_b_distilled_224.in1k,50.220,49.780,64.997,35.003,74.79,224,0.900,bicubic,-45.600,-34.293,+50 eca_nfnet_l2.ra3_in1k,50.203,49.797,65.440,34.560,56.72,384,1.000,bicubic,-46.247,-34.310,-90 resnest269e.in1k,50.187,49.813,64.680,35.320,110.93,416,0.928,bicubic,-45.923,-34.630,-24 vit_small_patch16_384.augreg_in21k_ft_in1k,50.173,49.827,65.790,34.210,22.20,384,1.000,bicubic,-45.807,-33.540,+7 tresnet_v2_l.miil_in21k_ft_in1k,50.163,49.837,65.123,34.877,46.17,224,0.875,bilinear,-45.657,-34.197,+45 pvt_v2_b5.in1k,50.147,49.853,65.027,34.973,81.96,224,0.900,bicubic,-45.793,-34.363,+17 deit3_medium_patch16_224.fb_in1k,50.140,49.860,64.710,35.290,38.85,224,0.900,bicubic,-45.240,-34.640,+133 deit_base_distilled_patch16_224.fb_in1k,50.077,49.923,66.230,33.770,87.34,224,0.900,bicubic,-45.673,-33.200,+55 tf_efficientnet_b3.ap_in1k,50.047,49.953,65.210,34.790,12.23,300,0.904,bicubic,-44.923,-33.900,+228 pvt_v2_b4.in1k,50.023,49.977,65.127,34.873,62.56,224,0.900,bicubic,-45.897,-34.093,+16 flexivit_base.300ep_in1k,50.003,49.997,64.103,35.897,86.59,240,0.950,bicubic,-45.947,-35.367,+11 coat_lite_medium.in1k,49.983,50.017,64.857,35.143,44.57,224,0.900,bicubic,-46.017,-34.643,-3 resnest200e.in1k,49.873,50.127,64.717,35.283,70.20,320,0.909,bicubic,-46.197,-34.763,-23 efficientformer_l7.snap_dist_in1k,49.837,50.163,66.033,33.967,82.23,224,0.950,bicubic,-45.763,-33.357,+79 volo_d2_224.sail_in1k,49.820,50.180,64.580,35.420,58.68,224,0.960,bicubic,-46.600,-34.880,-98 xception65.ra3_in1k,49.780,50.220,63.523,36.477,39.92,299,0.940,bicubic,-45.910,-35.797,+63 seresnextaa101d_32x8d.ah_in1k,49.747,50.253,64.443,35.557,93.59,288,1.000,bicubic,-46.693,-35.067,-103 swinv2_base_window16_256.ms_in1k,49.667,50.333,63.800,36.200,87.92,256,0.900,bicubic,-46.503,-35.590,-47 pvt_v2_b3.in1k,49.613,50.387,64.793,35.207,45.24,224,0.900,bicubic,-45.857,-34.517,+101 convnextv2_nano.fcmae_ft_in22k_in1k_384,49.603,50.397,65.657,34.343,15.62,384,1.000,bicubic,-46.187,-33.733,+36 cait_xs24_384.fb_dist_in1k,49.537,50.463,64.900,35.100,26.67,384,1.000,bicubic,-46.463,-34.530,-13 maxvit_rmlp_tiny_rw_256.sw_in1k,49.530,50.470,63.823,36.177,29.15,256,0.950,bicubic,-46.510,-35.717,-23 tf_efficientnet_b5.ra_in1k,49.523,50.477,65.653,34.347,30.39,456,0.934,bicubic,-46.447,-33.777,-10 fastvit_ma36.apple_in1k,49.500,50.500,63.620,36.380,44.07,256,0.950,bicubic,-46.470,-35.840,-9 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,49.480,50.520,65.623,34.377,236.34,224,0.875,bicubic,-46.270,-33.807,+37 resnet200d.ra2_in1k,49.463,50.537,64.330,35.670,64.69,320,1.000,bicubic,-46.647,-35.190,-46 efficientformerv2_l.snap_dist_in1k,49.457,50.543,64.920,35.080,26.32,224,0.950,bicubic,-46.303,-34.450,+33 xcit_small_12_p16_224.fb_dist_in1k,49.413,50.587,63.847,36.153,26.25,224,1.000,bicubic,-46.317,-35.453,+41 resnest101e.in1k,49.367,50.633,65.583,34.417,48.28,256,0.875,bilinear,-46.203,-33.787,+73 dm_nfnet_f2.dm_in1k,49.350,50.650,63.950,36.050,193.78,352,0.920,bicubic,-47.170,-35.640,-132 regnetz_040.ra3_in1k,49.297,50.703,64.060,35.940,27.12,320,1.000,bicubic,-46.883,-35.470,-62 vit_base_patch32_224.augreg_in21k_ft_in1k,49.267,50.733,64.340,35.660,88.22,224,0.900,bicubic,-45.133,-34.700,+324 tiny_vit_21m_224.in1k,49.267,50.733,64.303,35.697,21.20,224,0.950,bicubic,-46.383,-34.947,+53 resnet152d.ra2_in1k,49.263,50.737,64.413,35.587,60.21,320,1.000,bicubic,-46.607,-35.057,+6 seresnet152d.ra2_in1k,49.237,50.763,64.167,35.833,66.84,320,1.000,bicubic,-47.073,-35.243,-94 xcit_large_24_p8_224.fb_in1k,49.237,50.763,62.840,37.160,188.93,224,1.000,bicubic,-46.853,-36.300,-50 gcvit_base.in1k,49.153,50.847,63.950,36.050,90.32,224,0.875,bicubic,-46.927,-35.300,-49 maxxvit_rmlp_small_rw_256.sw_in1k,49.150,50.850,63.343,36.657,66.01,256,0.950,bicubic,-47.060,-35.937,-77 resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,49.093,50.907,65.490,34.510,88.79,224,0.875,bilinear,-46.227,-33.830,+110 resmlp_big_24_224.fb_distilled_in1k,49.093,50.907,65.473,34.527,129.14,224,0.875,bicubic,-46.777,-33.967,-1 convnext_small.fb_in1k,49.067,50.933,64.830,35.170,50.22,288,1.000,bicubic,-46.903,-34.530,-27 resnetaa101d.sw_in12k_ft_in1k,49.013,50.987,64.237,35.763,44.57,288,1.000,bicubic,-47.347,-35.233,-114 resnext101_64x4d.tv_in1k,48.990,51.010,63.510,36.490,83.46,224,0.875,bilinear,-46.830,-35.800,+5 volo_d1_224.sail_in1k,48.973,51.027,63.187,36.813,26.63,224,0.960,bicubic,-47.057,-36.203,-41 repvgg_b3.rvgg_in1k,48.913,51.087,64.880,35.120,123.09,224,0.875,bilinear,-45.657,-33.770,+275 efficientvit_b3.r224_in1k,48.877,51.123,62.947,37.053,48.65,224,0.950,bicubic,-46.663,-36.373,+63 resnetrs420.tf_in1k,48.863,51.137,63.433,36.567,191.89,416,1.000,bicubic,-47.537,-36.097,-126 maxvit_tiny_tf_224.in1k,48.807,51.193,62.933,37.067,30.92,224,0.950,bicubic,-47.003,-36.327,+4 convformer_s18.sail_in1k,48.787,51.213,62.930,37.070,26.77,224,1.000,bicubic,-46.543,-36.370,+99 caformer_s18.sail_in1k,48.753,51.247,62.870,37.130,26.34,224,1.000,bicubic,-46.927,-36.420,+30 deit3_small_patch16_384.fb_in1k,48.667,51.333,62.800,37.200,22.21,384,1.000,bicubic,-46.933,-36.640,+43 seresnext101d_32x8d.ah_in1k,48.603,51.397,62.960,37.040,93.59,288,1.000,bicubic,-47.757,-36.480,-125 swinv2_small_window16_256.ms_in1k,48.593,51.407,62.747,37.253,49.73,256,0.900,bicubic,-47.477,-36.703,-61 regnetz_d32.ra3_in1k,48.583,51.417,65.167,34.833,27.58,320,0.950,bicubic,-47.287,-34.263,-15 efficientnetv2_rw_s.ra2_in1k,48.577,51.423,63.837,36.163,23.94,384,1.000,bicubic,-47.133,-35.503,+17 efficientnet_b3.ra2_in1k,48.567,51.433,64.250,35.750,12.23,320,1.000,bicubic,-46.573,-35.050,+138 edgenext_base.usi_in1k,48.457,51.543,64.317,35.683,18.51,320,1.000,bicubic,-47.333,-34.983,-4 focalnet_base_lrf.ms_in1k,48.440,51.560,63.120,36.880,88.75,224,0.900,bicubic,-47.390,-36.080,-11 focalnet_base_srf.ms_in1k,48.423,51.577,63.103,36.897,88.15,224,0.900,bicubic,-47.477,-36.207,-29 vit_small_r26_s32_224.augreg_in21k_ft_in1k,48.377,51.623,63.800,36.200,36.43,224,0.900,bicubic,-46.743,-35.400,+139 swinv2_base_window8_256.ms_in1k,48.333,51.667,63.610,36.390,87.92,256,0.900,bicubic,-47.727,-35.940,-64 fastvit_sa36.apple_in1k,48.320,51.680,62.793,37.207,31.53,256,0.900,bicubic,-47.290,-36.527,+30 repvgg_b3g4.rvgg_in1k,48.310,51.690,64.780,35.220,83.83,224,0.875,bilinear,-46.190,-34.240,+275 vit_large_patch32_384.orig_in21k_ft_in1k,48.240,51.760,61.827,38.173,306.63,384,1.000,bicubic,-47.000,-37.403,+103 convit_base.fb_in1k,48.210,51.790,63.000,37.000,86.54,224,0.875,bicubic,-46.890,-36.150,+139 swin_s3_base_224.ms_in1k,48.153,51.847,62.243,37.757,71.13,224,0.900,bicubic,-47.887,-37.107,-66 sequencer2d_l.in1k,48.107,51.893,62.353,37.647,54.30,224,0.875,bicubic,-47.763,-37.207,-31 resnext101_32x8d.tv2_in1k,48.093,51.907,62.723,37.277,88.79,224,0.965,bilinear,-47.207,-36.507,+84 resnetrs350.tf_in1k,48.057,51.943,62.667,37.333,163.96,384,1.000,bicubic,-48.193,-36.923,-115 tf_efficientnetv2_b3.in21k_ft_in1k,48.030,51.970,64.747,35.253,14.36,300,0.900,bicubic,-47.570,-34.533,+24 gcvit_small.in1k,48.030,51.970,62.713,37.287,51.09,224,0.875,bicubic,-47.880,-36.567,-42 focalnet_small_lrf.ms_in1k,48.027,51.973,63.130,36.870,50.34,224,0.900,bicubic,-47.713,-36.030,-7 regnetz_d8.ra3_in1k,48.010,51.990,64.423,35.577,23.37,320,1.000,bicubic,-48.000,-35.097,-68 regnety_1280.seer_ft_in1k,47.990,52.010,64.230,35.770,644.81,384,1.000,bicubic,-48.320,-35.320,-131 twins_svt_large.in1k,47.963,52.037,62.907,37.093,99.27,224,0.900,bicubic,-47.737,-36.463,+2 fastvit_sa24.apple_dist_in1k,47.960,52.040,62.797,37.203,21.55,256,0.900,bicubic,-47.590,-36.513,+29 repvit_m1_5.dist_450e_in1k,47.950,52.050,63.643,36.357,14.64,224,0.950,bicubic,-47.330,-35.587,+83 vit_relpos_base_patch16_224.sw_in1k,47.923,52.077,62.840,37.160,86.43,224,0.900,bicubic,-47.207,-36.240,+117 repvgg_b2g4.rvgg_in1k,47.810,52.190,64.397,35.603,61.76,224,0.875,bilinear,-46.020,-34.533,+377 mixer_b16_224.miil_in21k_ft_in1k,47.793,52.207,63.403,36.597,59.88,224,0.875,bilinear,-47.087,-35.677,+175 repvgg_d2se.rvgg_in1k,47.780,52.220,62.770,37.230,133.33,320,1.000,bilinear,-48.170,-36.600,-60 vit_relpos_base_patch16_clsgap_224.sw_in1k,47.760,52.240,62.410,37.590,86.43,224,0.900,bicubic,-47.490,-36.800,+82 tf_efficientnet_b5.aa_in1k,47.737,52.263,63.910,36.090,30.39,456,0.934,bicubic,-48.143,-35.440,-49 repvit_m1_5.dist_300e_in1k,47.697,52.303,63.763,36.237,14.64,224,0.950,bicubic,-47.453,-35.387,+105 mvitv2_tiny.fb_in1k,47.667,52.333,62.830,37.170,24.17,224,0.900,bicubic,-47.733,-36.470,+51 eca_nfnet_l1.ra2_in1k,47.653,52.347,62.767,37.233,41.41,320,1.000,bicubic,-48.277,-36.723,-61 vit_relpos_medium_patch16_cls_224.sw_in1k,47.653,52.347,61.783,38.217,38.76,224,0.900,bicubic,-47.647,-37.317,+67 seresnext101_32x8d.ah_in1k,47.650,52.350,61.477,38.523,93.57,288,1.000,bicubic,-48.490,-38.013,-113 ecaresnet101d.miil_in1k,47.630,52.370,63.540,36.460,44.57,288,0.950,bicubic,-48.000,-35.750,0 regnetz_d8_evos.ch_in1k,47.623,52.377,63.807,36.193,23.46,320,1.000,bicubic,-48.517,-35.673,-117 resnetv2_50x3_bit.goog_in21k_ft_in1k,47.593,52.407,65.603,34.397,217.32,448,1.000,bilinear,-48.677,-33.957,-143 focalnet_small_srf.ms_in1k,47.547,52.453,62.510,37.490,49.89,224,0.900,bicubic,-48.083,-36.930,-2 pit_s_distilled_224.in1k,47.533,52.467,63.173,36.827,24.04,224,0.900,bicubic,-47.157,-35.977,+199 resnest50d_4s2x40d.in1k,47.483,52.517,63.803,36.197,30.42,224,0.875,bicubic,-47.217,-35.327,+194 dm_nfnet_f1.dm_in1k,47.457,52.543,62.083,37.917,132.63,320,0.910,bicubic,-48.843,-37.417,-150 efficientnet_b3_pruned.in1k,47.447,52.553,62.803,37.197,9.86,300,0.904,bicubic,-47.153,-36.287,+217 davit_tiny.msft_in1k,47.410,52.590,63.360,36.640,28.36,224,0.950,bicubic,-47.670,-35.900,+114 crossvit_18_dagger_408.in1k,47.390,52.610,60.927,39.073,44.61,408,1.000,bicubic,-48.760,-38.543,-126 poolformerv2_m48.sail_in1k,47.380,52.620,63.960,36.040,73.35,224,1.000,bicubic,-47.800,-35.160,+87 coatnet_rmlp_1_rw_224.sw_in1k,47.370,52.630,61.437,38.563,41.69,224,0.950,bicubic,-48.120,-37.923,+17 vit_base_patch16_224.orig_in21k_ft_in1k,47.347,52.653,61.617,38.383,86.57,224,0.900,bicubic,-47.853,-37.613,+80 xcit_small_24_p8_224.fb_in1k,47.290,52.710,60.990,39.010,47.63,224,1.000,bicubic,-48.610,-38.190,-70 efficientformer_l3.snap_dist_in1k,47.247,52.753,63.420,36.580,31.41,224,0.950,bicubic,-47.963,-35.890,+72 tresnet_m.miil_in21k_ft_in1k,47.233,52.767,62.003,37.997,31.39,224,0.875,bilinear,-48.147,-37.147,+37 tf_efficientnet_b6.aa_in1k,47.207,52.793,63.110,36.890,43.04,528,0.942,bicubic,-49.093,-36.420,-160 wide_resnet101_2.tv2_in1k,47.207,52.793,61.877,38.123,126.89,224,0.965,bilinear,-48.033,-37.323,+62 efficientvit_b2.r256_in1k,47.207,52.793,61.670,38.330,24.33,256,1.000,bicubic,-48.013,-37.590,+67 resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,47.187,52.813,63.387,36.613,44.18,224,0.875,bilinear,-47.953,-35.833,+84 swin_base_patch4_window12_384.ms_in1k,47.183,52.817,62.027,37.973,87.90,384,1.000,bicubic,-49.197,-37.473,-184 repvit_m3.dist_in1k,47.153,52.847,63.347,36.653,10.68,224,0.950,bicubic,-47.567,-35.483,+173 resnetrs270.tf_in1k,47.127,52.873,61.997,38.003,129.86,352,1.000,bicubic,-48.933,-37.483,-116 efficientvit_b2.r288_in1k,47.110,52.890,61.560,38.440,24.33,288,1.000,bicubic,-48.470,-37.710,-12 tf_efficientnet_b4.aa_in1k,47.083,52.917,62.860,37.140,19.34,380,0.922,bicubic,-48.507,-36.470,-16 regnety_320.tv2_in1k,47.073,52.927,62.050,37.950,145.05,224,0.965,bicubic,-48.487,-37.340,-11 vit_base_patch16_rpn_224.sw_in1k,47.063,52.937,62.397,37.603,86.54,224,0.900,bicubic,-47.757,-36.693,+150 rexnetr_200.sw_in12k_ft_in1k,47.053,52.947,63.973,36.027,16.52,288,1.000,bicubic,-48.257,-35.497,+35 convnextv2_tiny.fcmae_ft_in1k,47.043,52.957,62.503,37.497,28.64,288,1.000,bicubic,-48.787,-36.887,-73 swinv2_small_window8_256.ms_in1k,47.020,52.980,62.300,37.700,49.73,256,0.900,bicubic,-48.710,-37.060,-50 inception_next_tiny.sail_in1k,46.983,53.017,62.893,37.107,28.06,224,0.875,bicubic,-48.117,-36.247,+86 xcit_small_12_p8_224.fb_in1k,46.977,53.023,60.523,39.477,26.21,224,1.000,bicubic,-48.443,-38.667,+10 xcit_large_24_p16_224.fb_in1k,46.940,53.060,60.657,39.343,189.10,224,1.000,bicubic,-48.020,-38.173,+118 coat_small.in1k,46.937,53.063,61.307,38.693,21.69,224,0.900,bicubic,-48.253,-37.973,+60 xception65p.ra3_in1k,46.923,53.077,61.087,38.913,39.82,299,0.940,bicubic,-48.737,-38.183,-39 maxvit_tiny_rw_224.sw_in1k,46.907,53.093,60.897,39.103,29.06,224,0.950,bicubic,-48.833,-38.543,-58 resnet101d.ra2_in1k,46.890,53.110,62.340,37.660,44.57,320,1.000,bicubic,-48.850,-36.870,-61 swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,46.837,53.163,64.117,35.883,28.29,224,0.900,bicubic,-47.963,-35.173,+145 pvt_v2_b2_li.in1k,46.813,53.187,62.490,37.510,22.55,224,0.900,bicubic,-48.407,-36.640,+44 resnet152.tv2_in1k,46.803,53.197,61.070,38.930,60.19,224,0.965,bilinear,-48.237,-38.100,+95 regnety_640.seer_ft_in1k,46.707,53.293,63.233,36.767,281.38,384,1.000,bicubic,-49.353,-36.247,-135 fastvit_sa24.apple_in1k,46.653,53.347,61.710,38.290,21.55,256,0.900,bicubic,-48.627,-37.580,+27 seresnext101_64x4d.gluon_in1k,46.650,53.350,61.283,38.717,88.23,224,0.875,bicubic,-48.000,-37.687,+173 twins_pcpvt_large.in1k,46.613,53.387,62.263,37.737,60.99,224,0.900,bicubic,-49.107,-37.227,-62 convnextv2_nano.fcmae_ft_in22k_in1k,46.607,53.393,62.957,37.043,15.62,288,1.000,bicubic,-48.813,-36.353,-4 convnext_tiny.fb_in1k,46.587,53.413,63.187,36.813,28.59,288,1.000,bicubic,-48.623,-36.123,+40 swin_base_patch4_window7_224.ms_in1k,46.543,53.457,61.580,38.420,87.77,224,0.900,bicubic,-49.357,-37.860,-104 resnet152.a1h_in1k,46.540,53.460,60.403,39.597,60.19,288,1.000,bicubic,-49.210,-38.877,-75 efficientformerv2_s2.snap_dist_in1k,46.537,53.463,61.717,38.283,12.71,224,0.950,bicubic,-48.573,-37.403,+66 regnetv_064.ra3_in1k,46.480,53.520,62.250,37.750,30.58,288,1.000,bicubic,-49.300,-37.170,-80 crossvit_15_dagger_408.in1k,46.467,53.533,60.490,39.510,28.50,408,1.000,bicubic,-49.353,-38.720,-90 xcit_medium_24_p8_224.fb_in1k,46.467,53.533,59.653,40.347,84.32,224,1.000,bicubic,-49.393,-39.427,-98 resnetrs200.tf_in1k,46.440,53.560,61.067,38.933,93.21,320,1.000,bicubic,-49.910,-38.483,-211 swin_s3_small_224.ms_in1k,46.400,53.600,60.893,39.107,49.74,224,0.900,bicubic,-49.430,-38.287,-96 coatnet_1_rw_224.sw_in1k,46.393,53.607,60.063,39.937,41.72,224,0.950,bicubic,-49.227,-39.157,-52 gcvit_tiny.in1k,46.367,53.633,61.630,38.370,28.22,224,0.875,bicubic,-49.293,-37.700,-61 rexnet_300.nav_in1k,46.360,53.640,62.690,37.310,34.71,224,0.875,bicubic,-49.180,-36.500,-39 fbnetv3_g.ra2_in1k,46.340,53.660,62.387,37.613,16.62,288,0.950,bilinear,-48.780,-36.813,+54 sequencer2d_m.in1k,46.297,53.703,60.920,39.080,38.31,224,0.875,bicubic,-49.283,-38.300,-49 tresnet_xl.miil_in1k,46.290,53.710,61.943,38.057,78.44,224,0.875,bilinear,-48.790,-37.347,+62 xcit_tiny_24_p8_224.fb_dist_in1k,46.283,53.717,60.590,39.410,12.11,224,1.000,bicubic,-49.167,-38.770,-23 xcit_tiny_24_p8_384.fb_dist_in1k,46.260,53.740,60.730,39.270,12.11,384,1.000,bicubic,-49.970,-38.670,-191 deit_small_distilled_patch16_224.fb_in1k,46.173,53.827,62.423,37.577,22.44,224,0.900,bicubic,-48.427,-36.647,+160 regnety_160.deit_in1k,46.170,53.830,61.823,38.177,83.59,288,1.000,bicubic,-49.700,-37.617,-117 gernet_m.idstcv_in1k,46.163,53.837,62.687,37.313,21.14,224,0.875,bilinear,-48.377,-36.233,+174 repvit_m1_1.dist_450e_in1k,46.130,53.870,63.287,36.713,8.80,224,0.950,bicubic,-48.420,-35.863,+172 crossvit_base_240.in1k,46.120,53.880,60.207,39.793,105.03,240,0.875,bicubic,-48.950,-38.773,+59 resnest50d_1s4x24d.in1k,46.100,53.900,62.373,37.627,25.68,224,0.875,bicubic,-48.280,-36.697,+207 swinv2_cr_small_ns_224.sw_in1k,46.100,53.900,60.800,39.200,49.70,224,0.900,bicubic,-49.610,-38.600,-82 poolformerv2_m36.sail_in1k,46.050,53.950,62.247,37.753,56.08,224,1.000,bicubic,-49.000,-36.833,+61 tf_efficientnet_b0.ns_jft_in1k,46.043,53.957,63.277,36.723,5.29,224,0.875,bicubic,-47.707,-35.653,+307 poolformerv2_s36.sail_in1k,46.027,53.973,62.253,37.747,30.79,224,1.000,bicubic,-48.673,-36.977,+126 nest_base_jx.goog_in1k,46.027,53.973,60.100,39.900,67.72,224,0.875,bicubic,-49.513,-39.190,-53 vit_small_patch16_224.augreg_in21k_ft_in1k,46.020,53.980,61.830,38.170,22.05,224,0.900,bicubic,-48.870,-37.240,+89 resnet51q.ra2_in1k,46.020,53.980,60.903,39.097,35.70,288,1.000,bilinear,-49.180,-38.377,+18 vit_relpos_medium_patch16_224.sw_in1k,45.980,54.020,61.033,38.967,38.75,224,0.900,bicubic,-49.210,-38.187,+19 regnety_080.ra3_in1k,45.980,54.020,60.853,39.147,39.18,288,1.000,bicubic,-49.890,-38.577,-127 deit3_small_patch16_224.fb_in1k,45.943,54.057,58.883,41.117,22.06,224,0.900,bicubic,-48.747,-40.297,+127 resnest50d.in1k,45.933,54.067,62.620,37.380,27.48,224,0.875,bilinear,-48.667,-36.530,+147 convnext_nano.in12k_ft_in1k,45.900,54.100,62.693,37.307,15.59,288,1.000,bicubic,-49.450,-36.757,-25 crossvit_18_240.in1k,45.900,54.100,60.350,39.650,43.27,240,0.875,bicubic,-49.170,-38.680,+43 levit_384.fb_dist_in1k,45.877,54.123,61.683,38.317,39.13,224,0.900,bicubic,-49.333,-37.477,+7 regnety_032.ra_in1k,45.877,54.123,61.540,38.460,19.44,288,1.000,bicubic,-49.583,-37.850,-48 levit_conv_384.fb_dist_in1k,45.870,54.130,61.690,38.310,39.13,224,0.900,bicubic,-49.340,-37.590,+6 twins_svt_base.in1k,45.870,54.130,60.973,39.027,56.07,224,0.900,bicubic,-49.690,-38.257,-69 twins_pcpvt_base.in1k,45.857,54.143,61.330,38.670,43.83,224,0.900,bicubic,-49.613,-37.790,-55 convnext_tiny_hnf.a2h_in1k,45.853,54.147,60.180,39.820,28.59,288,1.000,bicubic,-49.397,-39.020,-10 crossvit_18_dagger_240.in1k,45.853,54.147,59.920,40.080,44.27,240,0.875,bicubic,-49.327,-39.240,+11 regnetz_c16.ra3_in1k,45.790,54.210,62.733,37.267,13.46,320,1.000,bicubic,-49.590,-36.687,-38 vit_relpos_medium_patch16_rpn_224.sw_in1k,45.743,54.257,60.983,39.017,38.73,224,0.900,bicubic,-49.317,-38.217,+38 vit_srelpos_medium_patch16_224.sw_in1k,45.723,54.277,61.073,38.927,38.74,224,0.900,bicubic,-49.217,-37.967,+62 crossvit_15_dagger_240.in1k,45.703,54.297,60.073,39.927,28.21,240,0.875,bicubic,-49.287,-39.117,+53 regnetx_320.tv2_in1k,45.673,54.327,60.233,39.767,107.81,224,0.965,bicubic,-49.607,-39.057,-22 convmixer_1536_20.in1k,45.663,54.337,61.743,38.257,51.63,224,0.960,bicubic,-49.307,-37.327,+54 gc_efficientnetv2_rw_t.agc_in1k,45.653,54.347,60.193,39.807,13.68,288,1.000,bicubic,-49.637,-39.187,-27 dm_nfnet_f0.dm_in1k,45.617,54.383,61.277,38.723,71.49,256,0.900,bicubic,-50.073,-38.073,-106 flexivit_small.1200ep_in1k,45.617,54.383,59.887,40.113,22.06,240,0.950,bicubic,-49.573,-39.333,0 efficientnetv2_rw_t.ra2_in1k,45.603,54.397,60.183,39.817,13.65,288,1.000,bicubic,-49.457,-39.037,+29 seresnext101_32x4d.gluon_in1k,45.600,54.400,61.160,38.840,48.96,224,0.875,bicubic,-48.830,-37.870,+164 xcit_tiny_24_p16_384.fb_dist_in1k,45.580,54.420,60.507,39.493,12.12,384,1.000,bicubic,-49.910,-38.883,-72 xcit_medium_24_p16_224.fb_in1k,45.537,54.463,59.007,40.993,84.40,224,1.000,bicubic,-49.593,-39.933,+9 repvit_m1_1.dist_300e_in1k,45.530,54.470,62.647,37.353,8.80,224,0.950,bicubic,-48.650,-36.433,+213 xcit_small_24_p16_224.fb_in1k,45.513,54.487,58.880,41.120,47.67,224,1.000,bicubic,-49.547,-40.190,+26 resnext101_64x4d.c1_in1k,45.450,54.550,59.033,40.967,83.46,288,1.000,bicubic,-50.080,-40.257,-81 resnet152d.gluon_in1k,45.427,54.573,60.080,39.920,60.21,224,0.875,bicubic,-49.003,-39.010,+160 regnety_320.seer_ft_in1k,45.420,54.580,62.227,37.773,145.05,384,1.000,bicubic,-50.370,-37.343,-140 nfnet_l0.ra2_in1k,45.417,54.583,62.087,37.913,35.07,288,1.000,bicubic,-49.963,-37.123,-57 resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,45.413,54.587,62.023,37.977,25.03,224,0.875,bilinear,-49.287,-37.057,+90 xcit_small_12_p16_224.fb_in1k,45.413,54.587,59.403,40.597,26.25,224,1.000,bicubic,-49.407,-39.657,+69 resnetv2_50x1_bit.goog_distilled_in1k,45.407,54.593,62.297,37.703,25.55,224,0.875,bicubic,-50.003,-37.023,-67 cs3se_edgenet_x.c2ns_in1k,45.393,54.607,60.447,39.553,50.72,320,1.000,bicubic,-50.607,-38.903,-192 resnet101.tv2_in1k,45.363,54.637,60.060,39.940,44.55,224,0.965,bilinear,-49.477,-38.970,+63 nest_small_jx.goog_in1k,45.350,54.650,59.040,40.960,38.35,224,0.875,bicubic,-50.190,-40.180,-93 tf_efficientnet_b7.aa_in1k,45.307,54.693,61.730,38.270,66.35,600,0.949,bicubic,-50.763,-37.610,-211 resnet61q.ra2_in1k,45.280,54.720,59.407,40.593,36.85,288,1.000,bicubic,-49.850,-39.853,-6 pvt_v2_b2.in1k,45.277,54.723,60.613,39.387,25.36,224,0.900,bicubic,-49.733,-38.437,+24 cs3edgenet_x.c2_in1k,45.257,54.743,60.260,39.740,47.82,288,1.000,bicubic,-50.203,-39.060,-81 nasnetalarge.tf_in1k,45.223,54.777,57.893,42.107,88.75,331,0.911,bicubic,-49.927,-41.377,-15 focalnet_tiny_lrf.ms_in1k,45.217,54.783,61.300,38.700,28.65,224,0.900,bicubic,-49.973,-37.880,-24 tresnet_xl.miil_in1k_448,45.213,54.787,61.447,38.553,78.44,448,0.875,bilinear,-50.297,-37.893,-95 flexivit_small.600ep_in1k,45.203,54.797,59.420,40.580,22.06,240,0.950,bicubic,-50.057,-39.960,-47 convit_small.fb_in1k,45.200,54.800,60.487,39.513,27.78,224,0.875,bicubic,-49.720,-38.693,+36 efficientvit_b2.r224_in1k,45.193,54.807,59.150,40.850,24.33,224,0.950,bicubic,-49.657,-39.970,+51 swin_small_patch4_window7_224.ms_in1k,45.183,54.817,60.357,39.643,49.61,224,0.900,bicubic,-50.527,-38.933,-138 sequencer2d_s.in1k,45.113,54.887,60.067,39.933,27.65,224,0.875,bicubic,-50.347,-39.193,-88 tf_efficientnet_b3.aa_in1k,45.110,54.890,60.637,39.363,12.23,300,0.904,bicubic,-49.800,-38.473,+34 rexnet_200.nav_in1k,45.063,54.937,62.323,37.677,16.37,224,0.875,bicubic,-49.607,-36.767,+87 maxxvit_rmlp_nano_rw_256.sw_in1k,45.050,54.950,59.663,40.337,16.78,256,0.950,bicubic,-50.290,-39.677,-70 resnetrs152.tf_in1k,44.950,55.050,59.690,40.310,86.62,320,1.000,bicubic,-51.010,-39.660,-199 deit_base_patch16_224.fb_in1k,44.867,55.133,59.170,40.830,86.57,224,0.900,bicubic,-50.153,-40.000,+9 flexivit_small.300ep_in1k,44.850,55.150,59.360,40.640,22.06,240,0.950,bicubic,-50.300,-39.770,-29 focalnet_tiny_srf.ms_in1k,44.833,55.167,61.040,38.960,28.43,224,0.900,bicubic,-50.207,-38.240,+2 coatnet_bn_0_rw_224.sw_in1k,44.803,55.197,60.913,39.087,27.44,224,0.950,bicubic,-50.177,-38.317,+13 tiny_vit_11m_224.in1k,44.803,55.197,60.097,39.903,11.00,224,0.950,bicubic,-50.437,-39.223,-54 deit_base_patch16_384.fb_in1k,44.770,55.230,59.600,40.400,86.86,384,1.000,bicubic,-50.870,-39.820,-136 resmlp_36_224.fb_distilled_in1k,44.763,55.237,61.073,38.927,44.69,224,0.875,bicubic,-49.797,-38.087,+99 cait_xxs36_384.fb_dist_in1k,44.760,55.240,59.387,40.613,17.37,384,1.000,bicubic,-50.480,-39.933,-59 resnet101.a1h_in1k,44.727,55.273,59.120,40.880,44.55,288,1.000,bicubic,-50.853,-40.130,-127 gernet_l.idstcv_in1k,44.720,55.280,58.950,41.050,31.08,256,0.875,bilinear,-50.210,-40.250,+16 tf_efficientnet_b2.ap_in1k,44.710,55.290,60.687,39.313,9.11,260,0.890,bicubic,-49.560,-38.353,+158 resmlp_24_224.fb_distilled_in1k,44.707,55.293,61.463,38.537,30.02,224,0.875,bicubic,-49.623,-37.627,+145 xcit_tiny_24_p16_224.fb_dist_in1k,44.707,55.293,59.417,40.583,12.12,224,1.000,bicubic,-49.533,-39.543,+162 repvit_m2.dist_in1k,44.673,55.327,61.730,38.270,8.80,224,0.950,bicubic,-49.727,-37.320,+128 regnety_032.tv2_in1k,44.650,55.350,60.897,39.103,19.44,224,0.965,bicubic,-50.220,-38.243,+24 ecaresnetlight.miil_in1k,44.580,55.420,60.383,39.617,30.16,288,0.950,bicubic,-49.950,-38.797,+95 swinv2_tiny_window16_256.ms_in1k,44.570,55.430,59.573,40.427,28.35,256,0.900,bicubic,-50.760,-39.577,-87 vit_relpos_small_patch16_224.sw_in1k,44.553,55.447,60.210,39.790,21.98,224,0.900,bicubic,-50.127,-38.890,+59 gmlp_s16_224.ra3_in1k,44.470,55.530,58.617,41.383,19.42,224,0.875,bicubic,-49.030,-40.223,+274 tiny_vit_5m_224.dist_in22k_ft_in1k,44.460,55.540,60.940,39.060,5.39,224,0.950,bicubic,-50.170,-38.200,+68 regnety_160.tv2_in1k,44.427,55.573,59.227,40.773,83.59,224,0.965,bicubic,-50.733,-40.023,-50 resnetv2_101.a1h_in1k,44.423,55.577,58.697,41.303,44.54,288,1.000,bicubic,-51.147,-40.573,-138 inception_resnet_v2.tf_ens_adv_in1k,44.393,55.607,58.113,41.887,55.84,299,0.897,bicubic,-49.727,-40.737,+175 tresnet_l.miil_in1k,44.387,55.613,59.947,40.053,55.99,224,0.875,bilinear,-50.513,-39.083,+9 repvit_m1_0.dist_450e_in1k,44.373,55.627,61.417,38.583,7.30,224,0.950,bicubic,-49.897,-37.623,+141 maxxvitv2_nano_rw_256.sw_in1k,44.373,55.627,58.830,41.170,23.70,256,0.950,bicubic,-51.057,-40.360,-114 repvit_m1_0.dist_300e_in1k,44.367,55.633,61.453,38.547,7.30,224,0.950,bicubic,-49.393,-37.467,+220 resnext101_32x4d.gluon_in1k,44.280,55.720,59.057,40.943,44.18,224,0.875,bicubic,-49.850,-39.883,+167 gcvit_xtiny.in1k,44.247,55.753,59.973,40.027,19.98,224,0.875,bicubic,-50.773,-39.187,-20 regnety_080_tv.tv2_in1k,44.183,55.817,58.787,41.213,39.38,224,0.965,bicubic,-51.117,-40.573,-94 maxvit_rmlp_nano_rw_256.sw_in1k,44.180,55.820,58.243,41.757,15.50,256,0.950,bicubic,-51.260,-40.817,-121 poolformer_m48.sail_in1k,44.160,55.840,59.153,40.847,73.47,224,0.950,bicubic,-50.940,-39.947,-43 regnetz_c16_evos.ch_in1k,44.143,55.857,61.067,38.933,13.49,320,0.950,bicubic,-51.497,-38.173,-164 vit_srelpos_small_patch16_224.sw_in1k,44.130,55.870,59.693,40.307,21.97,224,0.900,bicubic,-50.420,-39.397,+74 resnetv2_101x1_bit.goog_in21k_ft_in1k,44.127,55.873,61.950,38.050,44.54,448,1.000,bilinear,-51.193,-37.420,-103 crossvit_15_240.in1k,44.110,55.890,59.143,40.857,27.53,240,0.875,bicubic,-50.590,-40.097,+36 fastvit_sa12.apple_dist_in1k,44.103,55.897,59.110,40.890,11.58,256,0.900,bicubic,-50.577,-39.810,+41 convnextv2_nano.fcmae_ft_in1k,44.100,55.900,60.167,39.833,15.62,288,1.000,bicubic,-51.040,-39.043,-62 pit_b_224.in1k,44.080,55.920,58.023,41.977,73.76,224,0.900,bicubic,-50.730,-41.237,+14 resnet152s.gluon_in1k,44.060,55.940,58.710,41.290,60.32,224,0.875,bicubic,-50.650,-40.350,+28 resnet50.fb_ssl_yfcc100m_ft_in1k,44.023,55.977,61.890,38.110,25.56,224,0.875,bilinear,-50.287,-37.260,+118 poolformer_m36.sail_in1k,44.007,55.993,59.033,40.967,56.17,224,0.950,bicubic,-51.023,-40.067,-36 inception_resnet_v2.tf_in1k,44.007,55.993,57.920,42.080,55.84,299,0.897,bicubic,-50.353,-40.880,+110 pnasnet5large.tf_in1k,43.943,56.057,56.740,43.260,86.06,331,0.911,bicubic,-51.417,-42.390,-119 resnext101_64x4d.gluon_in1k,43.913,56.087,58.707,41.293,83.46,224,0.875,bicubic,-50.437,-40.363,+111 wide_resnet50_2.tv2_in1k,43.907,56.093,59.630,40.370,68.88,224,0.965,bilinear,-50.903,-39.350,+2 coatnext_nano_rw_224.sw_in1k,43.907,56.093,58.663,41.337,14.70,224,0.900,bicubic,-50.943,-40.537,-3 pit_s_224.in1k,43.907,56.093,58.640,41.360,23.46,224,0.900,bicubic,-50.683,-40.500,+52 coat_lite_small.in1k,43.817,56.183,57.127,42.873,19.84,224,0.900,bicubic,-51.253,-41.993,-53 mobilevitv2_200.cvnets_in22k_ft_in1k,43.810,56.190,59.490,40.510,18.45,256,0.888,bicubic,-51.240,-39.670,-46 tnt_s_patch16_224,43.777,56.223,59.220,40.780,23.76,224,0.900,bicubic,-50.783,-39.920,+53 regnetv_040.ra3_in1k,43.777,56.223,58.443,41.557,20.64,288,1.000,bicubic,-51.953,-40.937,-201 swinv2_cr_small_224.sw_in1k,43.767,56.233,57.717,42.283,49.70,224,0.900,bicubic,-51.643,-41.343,-137 cspresnext50.ra_in1k,43.763,56.237,60.147,39.853,20.57,256,0.887,bilinear,-50.477,-38.903,+120 cait_xxs36_224.fb_dist_in1k,43.757,56.243,58.740,41.260,17.30,224,1.000,bicubic,-50.153,-40.350,+172 pit_xs_distilled_224.in1k,43.723,56.277,60.657,39.343,11.00,224,0.900,bicubic,-49.567,-38.133,+271 swin_s3_tiny_224.ms_in1k,43.717,56.283,59.507,40.493,28.33,224,0.900,bicubic,-51.203,-39.593,-29 tf_efficientnetv2_s.in1k,43.710,56.290,58.593,41.407,21.46,384,1.000,bicubic,-52.000,-40.787,-204 rexnet_150.nav_in1k,43.693,56.307,60.923,39.077,9.73,224,0.875,bicubic,-50.587,-38.057,+105 xcit_tiny_12_p8_224.fb_dist_in1k,43.643,56.357,58.477,41.523,6.71,224,1.000,bicubic,-51.047,-40.283,+14 edgenext_small.usi_in1k,43.637,56.363,59.893,40.107,5.59,320,1.000,bicubic,-51.193,-39.517,-14 tf_efficientnet_b5.in1k,43.623,56.377,60.133,39.867,30.39,456,0.934,bicubic,-52.247,-39.257,-238 efficientformer_l1.snap_dist_in1k,43.593,56.407,59.957,40.043,12.29,224,0.950,bicubic,-50.347,-38.973,+158 maxvit_nano_rw_256.sw_in1k,43.523,56.477,57.610,42.390,15.45,256,0.950,bicubic,-51.947,-41.780,-160 wide_resnet50_2.racm_in1k,43.503,56.497,59.053,40.947,68.88,288,0.950,bicubic,-51.627,-40.237,-88 cs3sedarknet_x.c2ns_in1k,43.503,56.497,58.770,41.230,35.40,288,1.000,bicubic,-51.907,-40.660,-151 coatnet_rmlp_nano_rw_224.sw_in1k,43.503,56.497,58.610,41.390,15.15,224,0.900,bicubic,-51.587,-40.560,-75 crossvit_small_240.in1k,43.473,56.527,58.950,41.050,26.86,240,0.875,bicubic,-51.107,-40.100,+32 regnety_016.tv2_in1k,43.433,56.567,59.540,40.460,11.20,224,0.965,bicubic,-50.977,-39.500,+68 resnet101d.gluon_in1k,43.430,56.570,58.627,41.373,44.57,224,0.875,bicubic,-50.750,-40.383,+118 ecaresnet50t.ra2_in1k,43.417,56.583,59.313,40.687,25.57,320,0.950,bicubic,-51.663,-39.827,-79 resnet101s.gluon_in1k,43.370,56.630,58.517,41.483,44.67,224,0.875,bicubic,-50.810,-40.423,+115 efficientvit_b1.r288_in1k,43.367,56.633,57.850,42.150,9.10,288,1.000,bicubic,-50.863,-40.970,+104 cspdarknet53.ra_in1k,43.360,56.640,59.420,40.580,27.64,256,0.887,bilinear,-50.740,-39.560,+125 tf_efficientnet_b4.in1k,43.330,56.670,59.447,40.553,19.34,380,0.922,bicubic,-52.150,-39.823,-174 xcit_tiny_24_p8_224.fb_in1k,43.323,56.677,57.277,42.723,12.11,224,1.000,bicubic,-51.577,-41.743,-44 xcit_tiny_12_p8_384.fb_dist_in1k,43.317,56.683,58.177,41.823,6.71,384,1.000,bicubic,-52.023,-41.133,-149 visformer_small.in1k,43.273,56.727,57.977,42.023,40.22,224,0.900,bicubic,-51.697,-41.193,-60 convmixer_768_32.in1k,43.270,56.730,59.383,40.617,21.11,224,0.960,bicubic,-51.170,-39.497,+52 repvit_m0_9.dist_300e_in1k,43.243,56.757,60.377,39.623,5.49,224,0.950,bicubic,-50.217,-38.573,+222 eca_nfnet_l0.ra2_in1k,43.227,56.773,59.930,40.070,24.14,288,1.000,bicubic,-52.233,-39.350,-176 ecaresnet101d_pruned.miil_in1k,43.220,56.780,58.967,41.033,24.88,288,0.950,bicubic,-51.780,-40.263,-69 regnety_064.ra3_in1k,43.213,56.787,57.253,42.747,30.58,288,1.000,bicubic,-52.577,-42.097,-243 poolformerv2_s24.sail_in1k,43.190,56.810,60.430,39.570,21.34,224,1.000,bicubic,-51.280,-38.580,+40 regnetx_160.tv2_in1k,43.187,56.813,57.480,42.520,54.28,224,0.965,bicubic,-52.023,-41.680,-126 vit_relpos_base_patch32_plus_rpn_256.sw_in1k,43.183,56.817,58.403,41.597,119.42,256,0.900,bicubic,-49.957,-39.907,+258 vit_small_patch32_384.augreg_in21k_ft_in1k,43.150,56.850,59.313,40.687,22.92,384,1.000,bicubic,-51.440,-39.617,+11 resnest26d.gluon_in1k,43.140,56.860,60.637,39.363,17.07,224,0.875,bilinear,-50.080,-38.213,+246 twins_pcpvt_small.in1k,43.120,56.880,58.890,41.110,24.11,224,0.900,bicubic,-51.480,-40.210,+4 regnetx_080.tv2_in1k,43.090,56.910,57.923,42.077,39.57,224,0.965,bicubic,-51.640,-41.107,-25 repvit_m0_9.dist_450e_in1k,43.073,56.927,60.200,39.800,5.49,224,0.950,bicubic,-50.367,-38.450,+215 resmlp_36_224.fb_in1k,43.060,56.940,59.300,40.700,44.69,224,0.875,bicubic,-50.590,-39.350,+174 cspresnet50.ra_in1k,43.050,56.950,59.157,40.843,21.62,256,0.887,bilinear,-50.820,-39.733,+139 coatnet_nano_rw_224.sw_in1k,43.030,56.970,57.927,42.073,15.14,224,0.900,bicubic,-52.020,-41.223,-91 ecaresnet50d.miil_in1k,43.020,56.980,59.417,40.583,25.58,288,0.950,bicubic,-51.650,-39.813,-14 tf_efficientnet_lite4.in1k,42.990,57.010,57.630,42.370,13.01,380,0.920,bilinear,-51.880,-41.470,-55 twins_svt_small.in1k,42.917,57.083,58.460,41.540,24.06,224,0.900,bicubic,-51.843,-40.490,-39 dpn131.mx_in1k,42.917,57.083,57.130,42.870,79.25,224,0.875,bicubic,-50.863,-41.830,+149 mobilevitv2_200.cvnets_in22k_ft_in1k_384,42.913,57.087,58.973,41.027,18.45,384,1.000,bicubic,-52.477,-40.307,-179 resnet152.gluon_in1k,42.900,57.100,57.747,42.253,60.19,224,0.875,bicubic,-51.130,-41.103,+110 fastvit_sa12.apple_in1k,42.880,57.120,58.800,41.200,11.58,256,0.900,bicubic,-51.550,-40.200,+34 fbnetv3_d.ra2_in1k,42.850,57.150,59.677,40.323,10.31,256,0.950,bilinear,-51.020,-39.193,+129 levit_conv_256.fb_dist_in1k,42.823,57.177,57.897,42.103,18.89,224,0.900,bicubic,-51.577,-41.163,+36 resnet50.tv2_in1k,42.820,57.180,58.570,41.430,25.56,224,0.965,bilinear,-51.780,-40.460,-9 resnet152c.gluon_in1k,42.813,57.187,57.720,42.280,60.21,224,0.875,bicubic,-51.057,-41.080,+128 levit_256.fb_dist_in1k,42.810,57.190,57.897,42.103,18.89,224,0.900,bicubic,-51.590,-41.163,+32 tf_efficientnet_b1.ap_in1k,42.800,57.200,58.820,41.180,7.79,240,0.882,bicubic,-50.830,-39.980,+165 resnext50_32x4d.tv2_in1k,42.780,57.220,57.567,42.433,25.03,224,0.965,bilinear,-51.680,-41.333,+18 gcresnet50t.ra2_in1k,42.767,57.233,59.033,40.967,25.90,288,1.000,bicubic,-52.013,-40.087,-53 coatnet_0_rw_224.sw_in1k,42.753,57.247,56.233,43.767,27.44,224,0.950,bicubic,-52.147,-42.957,-76 tresnet_l.miil_in1k_448,42.740,57.260,58.943,41.057,55.99,448,0.875,bilinear,-52.660,-40.457,-193 cs3darknet_x.c2ns_in1k,42.727,57.273,58.190,41.810,35.05,288,1.000,bicubic,-52.553,-41.120,-171 dpn107.mx_in1k,42.710,57.290,57.160,42.840,86.92,224,0.875,bicubic,-51.300,-41.870,+102 seresnext50_32x4d.gluon_in1k,42.683,57.317,58.707,41.293,27.56,224,0.875,bicubic,-51.487,-40.213,+76 convnext_nano.d1h_in1k,42.677,57.323,57.577,42.423,15.59,288,1.000,bicubic,-52.193,-41.653,-75 tresnet_m.miil_in1k,42.670,57.330,58.160,41.840,31.39,224,0.875,bilinear,-51.410,-40.670,+88 fastvit_s12.apple_dist_in1k,42.643,57.357,58.160,41.840,9.47,256,0.900,bicubic,-51.637,-40.700,+48 resnetaa50d.sw_in12k_ft_in1k,42.610,57.390,58.490,41.510,25.58,288,1.000,bicubic,-52.680,-40.730,-180 xcit_tiny_12_p16_384.fb_dist_in1k,42.593,57.407,58.083,41.917,6.72,384,1.000,bicubic,-51.927,-40.887,-6 regnety_040.ra3_in1k,42.577,57.423,57.010,42.990,20.65,288,1.000,bicubic,-52.913,-42.240,-223 resnext101_32x8d.tv_in1k,42.563,57.437,58.303,41.697,88.79,224,0.875,bilinear,-51.217,-40.547,+124 efficientvit_b1.r256_in1k,42.527,57.473,57.427,42.573,9.10,256,1.000,bicubic,-51.183,-41.363,+136 nf_resnet50.ra2_in1k,42.517,57.483,59.530,40.470,25.56,288,0.940,bicubic,-51.863,-39.290,+21 mobilevitv2_175.cvnets_in22k_ft_in1k,42.517,57.483,58.117,41.883,14.25,256,0.888,bicubic,-52.263,-40.973,-66 seresnext50_32x4d.racm_in1k,42.433,57.567,58.093,41.907,27.56,288,0.950,bicubic,-52.567,-41.097,-111 resnetrs101.tf_in1k,42.417,57.583,57.283,42.717,63.62,288,0.940,bicubic,-52.833,-41.697,-180 poolformer_s36.sail_in1k,42.383,57.617,58.827,41.173,30.86,224,0.900,bicubic,-52.237,-40.293,-36 repvit_m1.dist_in1k,42.350,57.650,59.677,40.323,5.49,224,0.950,bicubic,-51.030,-38.973,+191 nest_tiny_jx.goog_in1k,42.320,57.680,57.067,42.933,17.06,224,0.875,bicubic,-52.600,-42.103,-99 mobileone_s4.apple_in1k,42.317,57.683,58.033,41.967,14.95,224,0.900,bilinear,-51.333,-40.917,+137 tf_efficientnetv2_b3.in1k,42.313,57.687,57.943,42.057,14.36,300,0.904,bicubic,-52.807,-41.277,-147 xcit_tiny_24_p16_224.fb_in1k,42.277,57.723,56.837,43.163,12.12,224,1.000,bicubic,-51.573,-41.913,+104 resnet152.a1_in1k,42.273,57.727,55.530,44.470,60.19,288,1.000,bicubic,-52.817,-43.460,-142 convmixer_1024_20_ks9_p14.in1k,42.267,57.733,59.723,40.277,24.38,224,0.960,bicubic,-50.063,-38.707,+292 deit_small_patch16_224.fb_in1k,42.263,57.737,58.030,41.970,22.05,224,0.900,bicubic,-51.727,-41.010,+83 mobileone_s3.apple_in1k,42.257,57.743,59.270,40.730,10.17,224,0.900,bilinear,-50.723,-39.350,+228 tf_efficientnet_cc_b1_8e.in1k,42.247,57.753,58.430,41.570,39.72,240,0.882,bicubic,-51.333,-40.260,+144 legacy_senet154.in1k,42.230,57.770,56.620,43.380,115.09,224,0.875,bilinear,-52.500,-42.480,-74 cait_xxs24_384.fb_dist_in1k,42.183,57.817,57.473,42.527,12.03,384,1.000,bicubic,-52.767,-41.657,-116 dpn98.mx_in1k,42.180,57.820,56.597,43.403,61.57,224,0.875,bicubic,-51.750,-42.313,+83 xception41p.ra3_in1k,42.160,57.840,56.883,43.117,26.91,299,0.940,bicubic,-52.890,-42.257,-140 tf_efficientnet_b2.aa_in1k,42.123,57.877,58.197,41.803,9.11,260,0.890,bicubic,-52.097,-40.733,+38 resnet50.b1k_in1k,42.080,57.920,58.153,41.847,25.56,288,1.000,bicubic,-52.430,-40.917,-24 resnext50_32x4d.gluon_in1k,42.050,57.950,57.673,42.327,25.03,224,0.875,bicubic,-51.620,-41.017,+121 convnext_nano_ols.d1h_in1k,42.023,57.977,56.867,43.133,15.65,288,1.000,bicubic,-52.557,-42.253,-44 pvt_v2_b1.in1k,41.953,58.047,59.593,40.407,14.01,224,0.900,bicubic,-51.537,-39.117,+148 xcit_tiny_12_p16_224.fb_dist_in1k,41.940,58.060,57.247,42.753,6.72,224,1.000,bicubic,-51.410,-41.443,+174 efficientnet_b2.ra_in1k,41.937,58.063,58.297,41.703,9.11,288,1.000,bicubic,-52.423,-40.743,+2 mobilevitv2_150.cvnets_in22k_ft_in1k,41.937,58.063,57.913,42.087,10.59,256,0.888,bicubic,-52.743,-41.197,-69 resnet50.b2k_in1k,41.910,58.090,57.673,42.327,25.56,288,1.000,bicubic,-52.390,-41.257,+12 efficientformerv2_s1.snap_dist_in1k,41.870,58.130,57.973,42.027,6.19,224,0.950,bicubic,-51.970,-40.917,+86 gcvit_xxtiny.in1k,41.837,58.163,58.440,41.560,12.00,224,0.875,bicubic,-52.213,-40.640,+54 fastvit_t12.apple_dist_in1k,41.817,58.183,57.597,42.403,7.55,256,0.900,bicubic,-52.283,-41.353,+47 tf_efficientnet_b3.in1k,41.803,58.197,58.057,41.943,12.23,300,0.904,bicubic,-52.487,-41.043,+9 mobilevitv2_150.cvnets_in22k_ft_in1k_384,41.790,58.210,57.807,42.193,10.59,384,1.000,bicubic,-53.560,-41.313,-228 resnet50d.ra2_in1k,41.787,58.213,58.023,41.977,25.58,288,0.950,bicubic,-53.023,-40.797,-105 resnext50_32x4d.a1h_in1k,41.753,58.247,56.443,43.557,25.03,288,1.000,bicubic,-53.237,-42.717,-142 gcresnext50ts.ch_in1k,41.707,58.293,57.403,42.597,15.67,288,1.000,bicubic,-52.783,-41.607,-36 efficientvit_b1.r224_in1k,41.703,58.297,56.600,43.400,9.10,224,0.950,bicubic,-51.627,-41.970,+166 poolformer_s24.sail_in1k,41.700,58.300,58.470,41.530,21.39,224,0.900,bicubic,-52.690,-40.590,-16 edgenext_small_rw.sw_in1k,41.677,58.323,58.517,41.483,7.83,320,1.000,bicubic,-52.683,-40.533,-10 mobilevitv2_175.cvnets_in22k_ft_in1k_384,41.673,58.327,58.010,41.990,14.25,384,1.000,bicubic,-53.587,-41.150,-218 hrnet_w64.ms_in1k,41.643,58.357,57.113,42.887,128.06,224,0.875,bilinear,-52.187,-41.827,+75 dla102x2.in1k,41.637,58.363,57.970,42.030,41.28,224,0.875,bilinear,-52.353,-40.990,+53 senet154.gluon_in1k,41.627,58.373,56.353,43.647,115.09,224,0.875,bicubic,-53.073,-42.617,-92 seresnet50.ra2_in1k,41.593,58.407,57.987,42.013,28.09,288,0.950,bicubic,-53.147,-41.123,-103 inception_v4.tf_in1k,41.557,58.443,55.380,44.620,42.68,299,0.875,bicubic,-52.823,-43.690,-20 cs3sedarknet_l.c2ns_in1k,41.553,58.447,57.350,42.650,21.91,288,0.950,bicubic,-53.557,-41.860,-183 convnext_tiny.fb_in22k_ft_in1k,41.533,58.467,55.470,44.530,28.59,288,1.000,bicubic,-51.997,-43.130,+121 haloregnetz_b.ra3_in1k,41.527,58.473,57.087,42.913,11.68,224,0.940,bicubic,-52.993,-41.893,-54 swinv2_cr_tiny_ns_224.sw_in1k,41.523,58.477,57.183,42.817,28.33,224,0.900,bicubic,-53.247,-41.927,-113 efficientnet_em.ra2_in1k,41.490,58.510,58.873,41.127,6.90,240,0.882,bicubic,-52.240,-40.057,+79 efficientnet_el.ra_in1k,41.490,58.510,58.293,41.707,10.59,300,0.904,bicubic,-53.180,-40.837,-89 tf_efficientnet_cc_b0_8e.in1k,41.483,58.517,57.370,42.630,24.01,224,0.875,bicubic,-51.387,-41.090,+209 convnextv2_pico.fcmae_ft_in1k,41.477,58.523,58.050,41.950,9.07,288,0.950,bicubic,-53.083,-41.120,-68 halo2botnet50ts_256.a1h_in1k,41.460,58.540,56.190,43.810,22.64,256,0.950,bicubic,-53.550,-42.950,-164 swin_tiny_patch4_window7_224.ms_in1k,41.453,58.547,57.303,42.697,28.29,224,0.900,bicubic,-53.167,-41.847,-87 resnetv2_50d_evos.ah_in1k,41.407,58.593,56.500,43.500,25.59,288,1.000,bicubic,-53.513,-42.600,-151 resnet50.a1h_in1k,41.387,58.613,56.677,43.323,25.56,224,1.000,bicubic,-52.813,-42.243,+6 cait_xxs24_224.fb_dist_in1k,41.370,58.630,57.520,42.480,11.96,224,1.000,bicubic,-52.080,-41.280,+124 swinv2_tiny_window8_256.ms_in1k,41.370,58.630,57.150,42.850,28.35,256,0.900,bicubic,-53.650,-41.820,-173 resnet152.tv_in1k,41.330,58.670,57.513,42.487,60.19,224,0.875,bilinear,-51.910,-41.187,+149 dpn68b.ra_in1k,41.297,58.703,55.077,44.923,12.61,288,1.000,bicubic,-52.493,-43.863,+61 resnet50.ram_in1k,41.297,58.703,55.033,44.967,25.56,288,0.950,bicubic,-52.703,-43.847,+32 cs3darknet_l.c2ns_in1k,41.277,58.723,57.377,42.623,21.16,288,0.950,bicubic,-53.393,-41.883,-103 xception71.tf_in1k,41.273,58.727,55.890,44.110,42.34,299,0.903,bicubic,-52.647,-43.060,+39 gernet_s.idstcv_in1k,41.260,58.740,58.823,41.177,8.17,224,0.875,bilinear,-51.180,-39.677,+232 inception_v3.tf_adv_in1k,41.253,58.747,56.327,43.673,23.83,299,0.875,bicubic,-51.757,-42.493,+171 resnet101.a1_in1k,41.207,58.793,54.273,45.727,44.55,288,1.000,bicubic,-53.733,-44.927,-164 dpn92.mx_in1k,41.203,58.797,56.217,43.783,37.67,224,0.875,bicubic,-52.947,-42.733,+2 resnetv2_50d_gn.ah_in1k,41.113,58.887,56.520,43.480,25.57,288,1.000,bicubic,-54.107,-42.510,-235 resnet50.c1_in1k,41.090,58.910,56.453,43.547,25.56,288,1.000,bicubic,-53.420,-42.547,-74 nf_regnet_b1.ra2_in1k,41.010,58.990,58.133,41.867,10.22,288,0.900,bicubic,-52.880,-40.757,+36 resnet50d.gluon_in1k,40.963,59.037,57.130,42.870,25.58,224,0.875,bicubic,-52.577,-41.580,+94 fbnetv3_b.ra2_in1k,40.960,59.040,58.650,41.350,8.60,256,0.950,bilinear,-52.670,-40.300,+76 resnet152.a3_in1k,40.940,59.060,55.030,44.970,60.19,224,0.950,bicubic,-53.500,-44.150,-63 inception_v3.gluon_in1k,40.910,59.090,55.623,44.377,23.83,299,0.875,bicubic,-52.650,-43.217,+86 ecaresnet50d_pruned.miil_in1k,40.907,59.093,57.633,42.367,19.94,288,0.950,bicubic,-53.393,-41.567,-37 cs3darknet_focus_l.c2ns_in1k,40.893,59.107,56.630,43.370,21.15,288,0.950,bicubic,-53.897,-42.530,-144 resnetv2_50.a1h_in1k,40.890,59.110,56.383,43.617,25.55,288,1.000,bicubic,-53.790,-42.707,-121 levit_conv_192.fb_dist_in1k,40.840,59.160,56.707,43.293,10.95,224,0.900,bicubic,-52.870,-42.113,+59 levit_192.fb_dist_in1k,40.837,59.163,56.710,43.290,10.95,224,0.900,bicubic,-52.873,-42.220,+57 tiny_vit_5m_224.in1k,40.827,59.173,57.257,42.743,5.39,224,0.950,bicubic,-52.963,-41.283,+39 regnety_320.pycls_in1k,40.810,59.190,56.110,43.890,145.05,224,0.875,bicubic,-53.710,-42.850,-92 regnetx_032.tv2_in1k,40.790,59.210,56.627,43.373,15.30,224,0.965,bicubic,-53.730,-42.543,-88 eva02_tiny_patch14_336.mim_in22k_ft_in1k,40.767,59.233,56.067,43.933,5.76,336,1.000,bicubic,-53.683,-43.033,-78 maxvit_rmlp_pico_rw_256.sw_in1k,40.757,59.243,55.203,44.797,7.52,256,0.950,bicubic,-53.453,-43.747,-24 legacy_xception.tf_in1k,40.753,59.247,56.397,43.603,22.86,299,0.897,bicubic,-52.867,-42.373,+65 lamhalobotnet50ts_256.a1h_in1k,40.753,59.247,56.103,43.897,22.57,256,0.950,bicubic,-54.057,-43.127,-156 resnet152.a2_in1k,40.737,59.263,54.280,45.720,60.19,288,1.000,bicubic,-54.233,-44.930,-189 vit_base_patch32_384.augreg_in1k,40.710,59.290,55.197,44.803,88.30,384,1.000,bicubic,-52.450,-43.413,+128 skresnext50_32x4d.ra_in1k,40.703,59.297,56.023,43.977,27.48,224,0.875,bicubic,-53.267,-42.807,+7 resnet101.gluon_in1k,40.693,59.307,56.137,43.863,44.55,224,0.875,bicubic,-53.067,-42.563,+35 hrnet_w40.ms_in1k,40.660,59.340,56.757,43.243,57.56,224,0.875,bilinear,-53.070,-42.043,+39 resmlp_24_224.fb_in1k,40.657,59.343,56.557,43.443,30.02,224,0.875,bicubic,-52.763,-42.273,+98 resnext50_32x4d.ra_in1k,40.627,59.373,56.340,43.660,25.03,288,0.950,bicubic,-53.703,-42.690,-58 resnet50.am_in1k,40.610,59.390,57.377,42.623,25.56,224,0.875,bicubic,-53.030,-41.493,+52 repvgg_b1.rvgg_in1k,40.607,59.393,57.843,42.157,57.42,224,0.875,bilinear,-52.833,-40.947,+90 ese_vovnet39b.ra_in1k,40.590,59.410,56.600,43.400,24.57,288,0.950,bicubic,-53.890,-42.460,-96 halonet50ts.a1h_in1k,40.577,59.423,55.193,44.807,22.73,256,0.940,bicubic,-54.143,-43.867,-153 tf_efficientnet_lite3.in1k,40.560,59.440,56.467,43.533,8.20,300,0.904,bilinear,-53.530,-42.373,-19 mobilevitv2_175.cvnets_in1k,40.537,59.463,56.273,43.727,14.25,256,0.888,bicubic,-53.693,-42.657,-44 xcit_tiny_12_p8_224.fb_in1k,40.537,59.463,55.647,44.353,6.71,224,1.000,bicubic,-53.813,-43.233,-69 dla169.in1k,40.523,59.477,57.240,42.760,53.39,224,0.875,bilinear,-53.277,-41.670,+17 tresnet_m.miil_in1k_448,40.520,59.480,56.687,43.313,31.39,448,0.875,bilinear,-54.140,-42.463,-139 regnetz_b16.ra3_in1k,40.507,59.493,56.003,43.997,9.72,288,1.000,bicubic,-54.163,-43.127,-142 pit_xs_224.in1k,40.493,59.507,56.540,43.460,10.62,224,0.900,bicubic,-52.397,-42.240,+152 resnetaa50.a1h_in1k,40.483,59.517,55.987,44.013,25.56,288,1.000,bicubic,-54.367,-42.983,-184 regnetx_320.pycls_in1k,40.470,59.530,55.653,44.347,107.81,224,0.875,bicubic,-53.760,-43.297,-53 vit_base_patch16_384.augreg_in1k,40.470,59.530,53.250,46.750,86.86,384,1.000,bicubic,-53.970,-45.780,-98 repvgg_b2.rvgg_in1k,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.113,-41.010,+48 coat_mini.in1k,40.420,59.580,55.200,44.800,10.34,224,0.900,bicubic,-54.340,-43.890,-172 eca_resnet33ts.ra2_in1k,40.400,59.600,57.340,42.660,19.68,288,1.000,bicubic,-53.850,-41.690,-60 resnet34d.ra2_in1k,40.400,59.600,56.170,43.830,21.82,288,0.950,bicubic,-53.200,-42.590,+41 skresnet34.ra_in1k,40.393,59.607,56.730,43.270,22.28,224,0.875,bicubic,-52.177,-41.790,+175 efficientnet_el_pruned.in1k,40.383,59.617,56.907,43.093,10.59,300,0.904,bicubic,-53.707,-42.113,-35 efficientnet_b2_pruned.in1k,40.373,59.627,56.520,43.480,8.31,260,0.890,bicubic,-53.427,-42.340,+3 wide_resnet101_2.tv_in1k,40.363,59.637,55.790,44.210,126.89,224,0.875,bilinear,-53.357,-43.010,+16 coat_lite_mini.in1k,40.357,59.643,55.697,44.303,11.01,224,0.900,bicubic,-53.113,-43.073,+60 sebotnet33ts_256.a1h_in1k,40.353,59.647,53.223,46.777,13.70,256,0.940,bicubic,-53.957,-45.377,-80 legacy_seresnext101_32x4d.in1k,40.347,59.653,54.817,45.183,48.96,224,0.875,bilinear,-53.773,-43.973,-45 tf_efficientnet_b0.ap_in1k,40.343,59.657,56.803,43.197,5.29,224,0.875,bicubic,-52.277,-41.567,+161 densenet201.tv_in1k,40.283,59.717,56.713,43.287,20.01,224,0.875,bicubic,-52.417,-41.887,+153 mobileone_s2.apple_in1k,40.263,59.737,57.967,42.033,7.88,224,0.900,bilinear,-52.397,-40.683,+156 regnetx_160.pycls_in1k,40.253,59.747,56.050,43.950,54.28,224,0.875,bicubic,-53.657,-42.840,-19 xception65.tf_in1k,40.253,59.747,55.263,44.737,39.92,299,0.903,bicubic,-53.497,-43.607,+5 resnet101.a2_in1k,40.190,59.810,54.223,45.777,44.55,288,1.000,bicubic,-54.700,-45.047,-210 resnet50.c2_in1k,40.187,59.813,55.247,44.753,25.56,288,1.000,bicubic,-54.083,-43.573,-80 resnet50.ra_in1k,40.180,59.820,56.197,43.803,25.56,288,0.950,bicubic,-53.910,-42.763,-46 resnetblur50.bt_in1k,40.163,59.837,56.190,43.810,25.56,288,0.950,bicubic,-54.007,-42.820,-61 poolformerv2_s12.sail_in1k,40.160,59.840,57.447,42.553,11.89,224,1.000,bicubic,-52.730,-41.083,+129 mobilevitv2_200.cvnets_in1k,40.140,59.860,55.517,44.483,18.45,256,0.888,bicubic,-54.380,-43.653,-137 fastvit_t12.apple_in1k,40.127,59.873,55.243,44.757,7.55,256,0.900,bicubic,-53.363,-43.617,+42 darknetaa53.c2ns_in1k,40.120,59.880,55.787,44.213,36.02,288,1.000,bilinear,-54.090,-43.213,-70 hrnet_w48.ms_in1k,40.110,59.890,56.633,43.367,77.47,224,0.875,bilinear,-53.900,-42.297,-43 vit_base_patch16_224.sam_in1k,40.100,59.900,55.423,44.577,86.57,224,0.900,bicubic,-53.790,-43.317,-28 resnet50_gn.a1h_in1k,40.057,59.943,54.853,45.147,25.56,288,0.950,bicubic,-54.563,-44.197,-168 legacy_seresnet152.in1k,40.050,59.950,55.837,44.163,66.82,224,0.875,bilinear,-53.360,-42.993,+56 resnet50.d_in1k,40.050,59.950,54.713,45.287,25.56,288,1.000,bicubic,-54.420,-44.287,-134 hrnet_w30.ms_in1k,40.043,59.957,57.110,42.890,37.71,224,0.875,bilinear,-53.367,-41.740,+55 seresnet33ts.ra2_in1k,39.990,60.010,56.403,43.597,19.78,288,1.000,bicubic,-54.270,-42.597,-90 regnetx_080.pycls_in1k,39.990,60.010,55.950,44.050,39.57,224,0.875,bicubic,-53.800,-42.960,-18 tf_efficientnet_b1.aa_in1k,39.970,60.030,56.127,43.873,7.79,240,0.882,bicubic,-53.750,-42.683,-8 resnet101c.gluon_in1k,39.953,60.047,55.287,44.713,44.57,224,0.875,bicubic,-53.747,-43.433,-3 fastvit_s12.apple_in1k,39.927,60.073,54.840,45.160,9.47,256,0.900,bicubic,-53.773,-43.920,-3 convnext_pico_ols.d1_in1k,39.887,60.113,55.617,44.383,9.06,288,1.000,bicubic,-54.123,-43.413,-52 resmlp_12_224.fb_distilled_in1k,39.833,60.167,57.430,42.570,15.35,224,0.875,bicubic,-53.037,-41.190,+115 tf_efficientnetv2_b0.in1k,39.800,60.200,56.287,43.713,7.14,224,0.875,bicubic,-53.260,-42.413,+80 res2net50_26w_8s.in1k,39.800,60.200,54.900,45.100,48.40,224,0.875,bilinear,-53.610,-43.850,+49 darknet53.c2ns_in1k,39.737,60.263,55.287,44.713,41.61,288,1.000,bicubic,-54.623,-43.763,-121 res2net101_26w_4s.in1k,39.720,60.280,54.550,45.450,45.21,224,0.875,bilinear,-53.810,-44.020,+17 lambda_resnet50ts.a1h_in1k,39.720,60.280,54.373,45.627,21.54,256,0.950,bicubic,-54.850,-44.537,-167 regnetx_120.pycls_in1k,39.687,60.313,55.643,44.357,46.11,224,0.875,bicubic,-54.573,-43.527,-103 hrnet_w44.ms_in1k,39.687,60.313,55.330,44.670,67.06,224,0.875,bilinear,-53.933,-43.630,0 vit_small_patch32_224.augreg_in21k_ft_in1k,39.677,60.323,55.253,44.747,22.88,224,0.900,bicubic,-52.463,-43.267,+162 resmlp_big_24_224.fb_in1k,39.637,60.363,54.830,45.170,129.14,224,0.875,bicubic,-54.633,-44.120,-106 vit_small_patch16_384.augreg_in1k,39.623,60.377,54.243,45.757,22.20,384,1.000,bicubic,-54.987,-44.897,-185 mixnet_xl.ra_in1k,39.620,60.380,55.870,44.130,11.90,224,0.875,bicubic,-54.610,-43.180,-99 xception41.tf_in1k,39.607,60.393,55.013,44.987,26.97,299,0.903,bicubic,-53.873,-43.737,+19 densenet161.tv_in1k,39.603,60.397,56.130,43.870,28.68,224,0.875,bicubic,-53.287,-42.660,+98 tf_efficientnetv2_b1.in1k,39.573,60.427,55.327,44.673,8.14,240,0.882,bicubic,-54.137,-43.463,-24 dla102x.in1k,39.567,60.433,56.320,43.680,26.31,224,0.875,bilinear,-53.973,-42.530,+5 xcit_tiny_12_p16_224.fb_in1k,39.540,60.460,55.050,44.950,6.72,224,1.000,bicubic,-52.940,-43.380,+133 sehalonet33ts.ra2_in1k,39.540,60.460,54.023,45.977,13.69,256,0.940,bicubic,-54.970,-44.737,-163 convnext_pico.d1_in1k,39.517,60.483,55.317,44.683,9.05,288,0.950,bicubic,-54.513,-43.693,-77 tf_efficientnet_b2.in1k,39.513,60.487,56.107,43.893,9.11,260,0.890,bicubic,-54.197,-42.703,-30 rexnet_130.nav_in1k,39.480,60.520,56.637,43.363,7.56,224,0.875,bicubic,-54.200,-42.063,-23 hrnet_w32.ms_in1k,39.467,60.533,56.120,43.880,41.23,224,0.875,bilinear,-53.483,-42.730,+79 levit_128.fb_dist_in1k,39.447,60.553,55.350,44.650,9.21,224,0.900,bicubic,-53.583,-43.360,+62 levit_conv_128.fb_dist_in1k,39.447,60.553,55.337,44.663,9.21,224,0.900,bicubic,-53.583,-43.363,+62 resnetv2_50x1_bit.goog_in21k_ft_in1k,39.433,60.567,57.853,42.147,25.55,448,1.000,bilinear,-55.307,-41.327,-229 ecaresnet50t.a1_in1k,39.417,60.583,53.680,46.320,25.57,288,1.000,bicubic,-55.473,-45.380,-256 regnety_064.pycls_in1k,39.387,60.613,55.770,44.230,30.58,224,0.875,bicubic,-54.743,-43.260,-100 regnety_120.pycls_in1k,39.343,60.657,55.277,44.723,51.82,224,0.875,bicubic,-54.667,-43.543,-81 gcresnet33ts.ra2_in1k,39.333,60.667,55.880,44.120,19.88,288,1.000,bicubic,-55.017,-43.220,-140 mobilevitv2_150.cvnets_in1k,39.323,60.677,55.230,44.770,10.59,256,0.888,bicubic,-54.727,-43.670,-89 resnet101.tv_in1k,39.290,60.710,55.787,44.213,44.55,224,0.875,bilinear,-53.590,-42.873,+84 tf_efficientnet_el.in1k,39.287,60.713,55.377,44.623,10.59,300,0.904,bicubic,-55.063,-43.583,-145 resnet50s.gluon_in1k,39.260,60.740,55.020,44.980,25.68,224,0.875,bicubic,-54.310,-43.820,-17 resnet101.a3_in1k,39.253,60.747,53.710,46.290,44.55,224,0.950,bicubic,-54.607,-45.050,-65 regnety_160.pycls_in1k,39.250,60.750,55.447,44.553,83.59,224,0.875,bicubic,-54.880,-43.573,-107 repghostnet_200.in1k,39.243,60.757,56.423,43.577,9.80,224,0.875,bicubic,-54.307,-42.397,-16 inception_v3.tf_in1k,39.223,60.777,54.300,45.700,23.83,299,0.875,bicubic,-53.967,-44.360,+34 resnext50_32x4d.a1_in1k,39.190,60.810,53.350,46.650,25.03,288,1.000,bicubic,-55.190,-45.430,-156 densenet169.tv_in1k,39.170,60.830,55.860,44.140,14.15,224,0.875,bicubic,-53.130,-42.750,+123 tf_efficientnetv2_b2.in1k,39.167,60.833,54.563,45.437,10.10,260,0.890,bicubic,-54.893,-44.367,-101 resnext50d_32x4d.bt_in1k,39.100,60.900,54.343,45.657,25.05,288,0.950,bicubic,-55.300,-44.447,-166 legacy_seresnet101.in1k,39.043,60.957,55.003,44.997,49.33,224,0.875,bilinear,-54.227,-43.737,+22 efficientnet_b1_pruned.in1k,39.003,60.997,55.630,44.370,6.33,240,0.882,bicubic,-53.977,-43.100,+57 repvgg_b1g4.rvgg_in1k,38.977,61.023,56.350,43.650,39.97,224,0.875,bilinear,-54.053,-42.250,+40 crossvit_9_dagger_240.in1k,38.970,61.030,54.867,45.133,8.78,240,0.875,bicubic,-53.800,-43.793,+81 resnet50.a1_in1k,38.967,61.033,53.243,46.757,25.56,288,1.000,bicubic,-55.253,-45.507,-131 inception_v3.tv_in1k,38.943,61.057,53.877,46.123,23.83,299,0.875,bicubic,-53.957,-44.853,+64 regnety_080.pycls_in1k,38.910,61.090,55.190,44.810,39.18,224,0.875,bicubic,-54.970,-43.810,-84 res2net101d.in1k,38.903,61.097,53.057,46.943,45.23,224,0.875,bilinear,-55.617,-45.853,-201 legacy_seresnext50_32x4d.in1k,38.890,61.110,54.577,45.423,27.56,224,0.875,bilinear,-54.560,-44.203,-13 dla102.in1k,38.840,61.160,55.317,44.683,33.27,224,0.875,bilinear,-54.430,-43.473,+12 visformer_tiny.in1k,38.830,61.170,55.023,44.977,10.32,224,0.900,bicubic,-54.150,-43.647,+44 regnety_040.pycls_in1k,38.823,61.177,55.580,44.420,20.65,224,0.875,bicubic,-54.807,-43.330,-49 efficientvit_m5.r224_in1k,38.820,61.180,54.980,45.020,12.47,224,0.875,bicubic,-53.330,-43.540,+116 regnetx_040.pycls_in1k,38.710,61.290,55.357,44.643,22.12,224,0.875,bicubic,-54.980,-43.573,-58 res2net50_14w_8s.in1k,38.710,61.290,54.073,45.927,25.06,224,0.875,bilinear,-54.320,-44.747,+31 dpn68.mx_in1k,38.687,61.313,54.687,45.313,12.61,224,0.875,bicubic,-53.613,-43.903,+104 regnetx_032.pycls_in1k,38.680,61.320,55.160,44.840,15.30,224,0.875,bicubic,-54.560,-43.560,+8 res2net50_26w_6s.in1k,38.680,61.320,53.773,46.227,37.05,224,0.875,bilinear,-54.910,-44.857,-49 resnet33ts.ra2_in1k,38.667,61.333,55.173,44.827,19.68,288,1.000,bicubic,-55.263,-43.707,-102 wide_resnet50_2.tv_in1k,38.650,61.350,54.467,45.533,68.88,224,0.875,bilinear,-54.810,-44.353,-26 selecsls60.in1k,38.620,61.380,55.617,44.383,30.67,224,0.875,bicubic,-54.390,-42.873,+28 regnetx_016.tv2_in1k,38.620,61.380,54.733,45.267,9.19,224,0.965,bicubic,-55.400,-44.197,-117 dla60x.in1k,38.610,61.390,55.403,44.597,17.35,224,0.875,bilinear,-54.580,-43.317,+5 tf_efficientnet_b0.aa_in1k,38.597,61.403,55.970,44.030,5.29,224,0.875,bicubic,-53.803,-42.500,+90 densenetblur121d.ra_in1k,38.593,61.407,55.637,44.363,8.00,288,0.950,bicubic,-54.437,-43.063,+22 dla60_res2net.in1k,38.593,61.407,54.563,45.437,20.85,224,0.875,bilinear,-54.777,-44.277,-13 repvgg_a2.rvgg_in1k,38.553,61.447,55.767,44.233,28.21,224,0.875,bilinear,-54.127,-42.513,+64 selecsls60b.in1k,38.550,61.450,55.310,44.690,32.77,224,0.875,bicubic,-54.950,-43.470,-43 seresnet50.a1_in1k,38.537,61.463,53.413,46.587,28.09,288,1.000,bicubic,-55.623,-45.437,-145 dpn68b.mx_in1k,38.530,61.470,55.183,44.817,12.61,224,0.875,bicubic,-54.250,-43.337,+51 resnet32ts.ra2_in1k,38.510,61.490,55.507,44.493,17.96,288,1.000,bicubic,-55.080,-43.233,-61 hardcorenas_f.miil_green_in1k,38.507,61.493,55.653,44.347,8.20,224,0.875,bilinear,-54.473,-42.977,+26 hrnet_w18_small_v2.gluon_in1k,38.460,61.540,56.180,43.820,15.60,224,0.875,bicubic,-54.540,-42.580,+20 resmlp_12_224.fb_in1k,38.440,61.560,56.327,43.673,15.35,224,0.875,bicubic,-53.680,-42.243,+98 tf_efficientnet_cc_b0_4e.in1k,38.430,61.570,55.177,44.823,13.31,224,0.875,bicubic,-54.390,-43.263,+44 dla60_res2next.in1k,38.423,61.577,54.930,45.070,17.03,224,0.875,bilinear,-55.157,-44.140,-63 regnetx_064.pycls_in1k,38.420,61.580,54.993,45.007,26.21,224,0.875,bicubic,-55.220,-44.047,-76 ghostnetv2_160.in1k,38.410,61.590,55.530,44.470,12.39,224,0.875,bicubic,-54.680,-43.210,+2 resnet50.gluon_in1k,38.410,61.590,54.830,45.170,25.56,224,0.875,bicubic,-54.150,-43.720,+65 resnet50d.a1_in1k,38.403,61.597,52.860,47.140,25.58,288,1.000,bicubic,-55.997,-46.200,-204 regnety_008_tv.tv2_in1k,38.337,61.663,54.270,45.730,6.43,224,0.965,bicubic,-54.813,-44.410,-7 hrnet_w18.ms_in1k,38.263,61.737,55.660,44.340,21.30,224,0.875,bilinear,-54.507,-42.960,+41 tinynet_a.in1k,38.230,61.770,55.193,44.807,6.19,192,0.875,bicubic,-54.580,-43.367,+37 ecaresnet50t.a3_in1k,38.227,61.773,53.650,46.350,25.57,224,0.950,bicubic,-55.633,-45.200,-117 resnet34.a1_in1k,38.220,61.780,52.373,47.627,21.80,288,1.000,bicubic,-54.780,-46.257,+9 densenet121.ra_in1k,38.180,61.820,55.137,44.863,7.98,288,0.950,bicubic,-54.520,-43.503,+44 mixnet_l.ft_in1k,38.173,61.827,54.797,45.203,7.33,224,0.875,bicubic,-55.067,-43.953,-21 regnety_032.pycls_in1k,38.167,61.833,54.363,45.637,19.44,224,0.875,bicubic,-55.293,-44.587,-55 poolformer_s12.sail_in1k,38.157,61.843,56.213,43.787,11.92,224,0.900,bicubic,-54.343,-42.177,+58 hardcorenas_e.miil_green_in1k,38.150,61.850,55.177,44.823,8.07,224,0.875,bilinear,-54.790,-43.333,+12 efficientnet_b1.ft_in1k,38.080,61.920,54.000,46.000,7.79,256,1.000,bicubic,-54.940,-44.710,-2 ecaresnet50t.a2_in1k,38.053,61.947,52.967,47.033,25.57,288,1.000,bicubic,-56.517,-46.073,-257 gmixer_24_224.ra3_in1k,38.050,61.950,52.077,47.923,24.72,224,0.875,bicubic,-54.630,-46.453,+39 vit_base_patch16_224.augreg_in1k,38.037,61.963,50.710,49.290,86.57,224,0.900,bicubic,-55.313,-48.050,-37 coat_lite_tiny.in1k,38.033,61.967,53.463,46.537,5.72,224,0.900,bicubic,-54.827,-45.177,+22 resnetrs50.tf_in1k,37.977,62.023,53.303,46.697,35.69,224,0.910,bicubic,-56.053,-45.437,-154 resnext50_32x4d.a2_in1k,37.933,62.067,52.353,47.647,25.03,288,1.000,bicubic,-56.287,-46.687,-182 mobilevitv2_125.cvnets_in1k,37.893,62.107,54.067,45.933,7.48,256,0.888,bicubic,-55.587,-44.773,-68 resnet50c.gluon_in1k,37.873,62.127,54.117,45.883,25.58,224,0.875,bicubic,-55.037,-44.573,+7 hardcorenas_c.miil_green_in1k,37.860,62.140,55.727,44.273,5.52,224,0.875,bilinear,-54.490,-42.613,+58 efficientformerv2_s0.snap_dist_in1k,37.823,62.177,54.053,45.947,3.60,224,0.950,bicubic,-54.037,-44.317,+82 res2net50_26w_4s.in1k,37.810,62.190,53.080,46.920,25.70,224,0.875,bilinear,-55.370,-45.580,-31 efficientnet_es.ra_in1k,37.787,62.213,54.960,45.040,5.44,224,0.875,bicubic,-55.123,-43.720,+4 resnest14d.gluon_in1k,37.777,62.223,56.480,43.520,10.61,224,0.875,bilinear,-53.373,-41.850,+109 convnext_femto.d1_in1k,37.733,62.267,54.100,45.900,5.22,288,0.950,bicubic,-55.707,-44.720,-65 resnext50_32x4d.tv_in1k,37.727,62.273,54.103,45.897,25.03,224,0.875,bilinear,-55.173,-44.217,+3 pit_ti_distilled_224.in1k,37.710,62.290,55.647,44.353,5.10,224,0.900,bicubic,-53.020,-42.603,+121 ecaresnet26t.ra2_in1k,37.667,62.333,54.357,45.643,16.01,320,0.950,bicubic,-56.273,-44.673,-153 seresnet50.a2_in1k,37.667,62.333,52.373,47.627,28.09,288,1.000,bicubic,-56.783,-46.517,-247 vit_base_patch32_224.augreg_in1k,37.563,62.437,51.810,48.190,88.22,224,0.900,bicubic,-53.027,-45.910,+122 fastvit_t8.apple_dist_in1k,37.557,62.443,53.830,46.170,4.03,256,0.900,bicubic,-55.033,-44.710,+30 hardcorenas_d.miil_green_in1k,37.543,62.457,54.720,45.280,7.50,224,0.875,bilinear,-55.067,-43.790,+26 res2next50.in1k,37.483,62.517,52.853,47.147,24.67,224,0.875,bilinear,-55.667,-45.787,-38 convnextv2_femto.fcmae_ft_in1k,37.477,62.523,53.507,46.493,5.23,288,0.950,bicubic,-56.273,-45.463,-131 resnet50.bt_in1k,37.420,62.580,53.860,46.140,25.56,288,0.950,bicubic,-56.540,-45.070,-162 hrnet_w18.ms_aug_in1k,37.337,62.663,54.123,45.877,21.30,224,0.950,bilinear,-56.143,-44.857,-87 lambda_resnet26t.c1_in1k,37.300,62.700,53.563,46.437,10.96,256,0.940,bicubic,-56.130,-45.167,-73 convnext_femto_ols.d1_in1k,37.253,62.747,53.047,46.953,5.23,288,0.950,bicubic,-56.137,-45.863,-67 mobilenetv3_large_100.miil_in21k_ft_in1k,37.243,62.757,53.543,46.457,5.48,224,0.875,bilinear,-55.017,-44.697,+47 hardcorenas_b.miil_green_in1k,37.233,62.767,55.033,44.967,5.18,224,0.875,bilinear,-54.687,-43.377,+60 resnet50d.a2_in1k,37.227,62.773,51.743,48.257,25.58,288,1.000,bicubic,-57.233,-47.287,-261 res2net50d.in1k,37.223,62.777,51.387,48.613,25.72,224,0.875,bilinear,-57.057,-47.703,-223 fastvit_t8.apple_in1k,37.217,62.783,53.133,46.867,4.03,256,0.900,bicubic,-54.713,-45.247,+56 eca_halonext26ts.c1_in1k,37.170,62.830,53.103,46.897,10.76,256,0.940,bicubic,-56.380,-45.497,-106 cs3darknet_focus_m.c2ns_in1k,37.140,62.860,53.910,46.090,9.30,288,0.950,bicubic,-55.960,-44.840,-46 res2net50_48w_2s.in1k,37.120,62.880,53.327,46.673,25.29,224,0.875,bilinear,-55.660,-45.143,-4 lambda_resnet26rpt_256.c1_in1k,37.103,62.897,53.860,46.140,10.99,256,0.940,bicubic,-56.327,-45.020,-84 vit_small_patch16_224.augreg_in1k,37.080,62.920,51.553,48.447,22.05,224,0.900,bicubic,-56.370,-47.227,-90 rexnet_100.nav_in1k,37.057,62.943,54.050,45.950,4.80,224,0.875,bicubic,-55.773,-44.550,-12 bat_resnext26ts.ch_in1k,37.057,62.943,53.743,46.257,10.73,256,0.900,bicubic,-56.063,-44.987,-51 resnet50.a2_in1k,37.050,62.950,51.347,48.653,25.56,288,1.000,bicubic,-57.070,-47.623,-201 dla60.in1k,37.040,62.960,54.220,45.780,22.04,224,0.875,bilinear,-55.610,-44.410,+3 tf_efficientnet_b1.in1k,37.017,62.983,53.417,46.583,7.79,240,0.882,bicubic,-55.913,-45.243,-29 regnety_016.pycls_in1k,37.013,62.987,54.083,45.917,11.20,224,0.875,bicubic,-55.997,-44.587,-43 botnet26t_256.c1_in1k,36.963,63.037,53.053,46.947,12.49,256,0.950,bicubic,-56.477,-45.857,-93 tf_mixnet_l.in1k,36.960,63.040,52.597,47.403,7.33,224,0.875,bicubic,-56.070,-45.933,-48 resnet34.a2_in1k,36.953,63.047,51.433,48.567,21.80,288,1.000,bicubic,-55.617,-47.137,+5 mobileone_s1.apple_in1k,36.930,63.070,54.603,45.397,4.83,224,0.900,bilinear,-54.860,-43.857,+46 ghostnetv2_130.in1k,36.887,63.113,54.137,45.863,8.96,224,0.875,bicubic,-55.353,-44.243,+28 legacy_seresnet50.in1k,36.863,63.137,53.473,46.527,28.09,224,0.875,bilinear,-55.797,-45.207,-6 halonet26t.a1h_in1k,36.843,63.157,52.270,47.730,12.48,256,0.950,bicubic,-56.747,-46.470,-130 densenet121.tv_in1k,36.807,63.193,54.030,45.970,7.98,224,0.875,bicubic,-54.593,-44.220,+60 tf_efficientnet_lite2.in1k,36.800,63.200,53.313,46.687,6.09,260,0.890,bicubic,-55.790,-45.117,-3 mobilenetv2_120d.ra_in1k,36.790,63.210,54.047,45.953,5.83,224,0.875,bicubic,-55.820,-44.383,-7 tf_efficientnet_lite1.in1k,36.730,63.270,53.583,46.417,5.42,240,0.882,bicubic,-55.560,-44.917,+17 regnetx_016.pycls_in1k,36.693,63.307,53.307,46.693,9.19,224,0.875,bicubic,-55.837,-45.243,0 eca_botnext26ts_256.c1_in1k,36.690,63.310,52.493,47.507,10.59,256,0.950,bicubic,-56.660,-46.197,-92 hardcorenas_a.miil_green_in1k,36.687,63.313,54.923,45.077,5.26,224,0.875,bilinear,-54.933,-43.357,+45 levit_conv_128s.fb_dist_in1k,36.623,63.377,53.137,46.863,7.78,224,0.900,bicubic,-54.877,-45.263,+48 levit_128s.fb_dist_in1k,36.623,63.377,53.130,46.870,7.78,224,0.900,bicubic,-54.887,-45.270,+48 repghostnet_150.in1k,36.617,63.383,54.110,45.890,6.58,224,0.875,bicubic,-55.763,-44.420,+4 efficientnet_b0.ra_in1k,36.593,63.407,53.477,46.523,5.29,224,0.875,bicubic,-55.887,-45.203,-3 efficientvit_m4.r224_in1k,36.587,63.413,53.267,46.733,8.80,224,0.875,bicubic,-54.153,-44.773,+74 resnext50_32x4d.a3_in1k,36.553,63.447,51.143,48.857,25.03,224,0.950,bicubic,-56.997,-47.537,-137 vit_base_patch32_224.sam_in1k,36.543,63.457,53.043,46.957,88.22,224,0.900,bicubic,-53.327,-44.557,+94 xcit_nano_12_p8_224.fb_dist_in1k,36.510,63.490,52.873,47.127,3.05,224,1.000,bicubic,-55.920,-45.667,-3 cs3darknet_m.c2ns_in1k,36.480,63.520,53.230,46.770,9.31,288,0.950,bicubic,-56.800,-45.490,-96 repvgg_a1.rvgg_in1k,36.450,63.550,53.770,46.230,14.09,224,0.875,bilinear,-54.670,-44.390,+57 tf_efficientnet_em.in1k,36.393,63.607,52.853,47.147,6.90,240,0.882,bicubic,-56.797,-45.637,-90 mobilevitv2_100.cvnets_in1k,36.387,63.613,53.083,46.917,4.90,256,0.888,bicubic,-56.753,-45.677,-84 resnet50d.a3_in1k,36.330,63.670,51.320,48.680,25.58,224,0.950,bicubic,-57.080,-47.370,-112 skresnet18.ra_in1k,36.313,63.687,54.187,45.813,11.96,224,0.875,bicubic,-53.867,-43.593,+83 repvgg_b0.rvgg_in1k,36.290,63.710,54.057,45.943,15.82,224,0.875,bilinear,-55.390,-44.393,+25 resnet34.bt_in1k,36.257,63.743,52.757,47.243,21.80,288,0.950,bicubic,-56.023,-45.843,-1 resnet50.tv_in1k,36.180,63.820,52.793,47.207,25.56,224,0.875,bilinear,-55.940,-45.617,+8 xcit_nano_12_p16_384.fb_dist_in1k,36.150,63.850,53.260,46.740,3.05,384,1.000,bicubic,-55.980,-45.250,+4 legacy_seresnet34.in1k,36.150,63.850,52.567,47.433,21.96,224,0.875,bilinear,-55.340,-45.633,+33 coat_tiny.in1k,36.117,63.883,51.047,48.953,5.50,224,0.900,bicubic,-57.393,-47.633,-144 efficientvit_m3.r224_in1k,36.090,63.910,52.453,47.547,6.90,224,0.875,bicubic,-53.910,-45.377,+77 resnet34.tv_in1k,36.070,63.930,53.537,46.463,21.80,224,0.875,bilinear,-54.230,-44.433,+70 deit_tiny_distilled_patch16_224.fb_in1k,36.023,63.977,54.243,45.757,5.91,224,0.900,bicubic,-55.057,-44.027,+50 mobilenetv2_140.ra_in1k,35.997,64.003,53.963,46.037,6.11,224,0.875,bicubic,-56.053,-44.287,+4 convnextv2_atto.fcmae_ft_in1k,35.990,64.010,51.187,48.813,3.71,288,0.950,bicubic,-56.930,-47.373,-68 resnet50.a3_in1k,35.957,64.043,50.483,49.517,25.56,224,0.950,bicubic,-56.983,-48.187,-71 tf_efficientnet_lite0.in1k,35.937,64.063,53.477,46.523,4.65,224,0.875,bicubic,-55.343,-44.363,+29 selecsls42b.in1k,35.813,64.187,52.473,47.527,32.46,224,0.875,bicubic,-56.667,-46.157,-25 xcit_nano_12_p8_384.fb_dist_in1k,35.797,64.203,52.300,47.700,3.05,384,1.000,bicubic,-57.503,-46.560,-118 seresnext26ts.ch_in1k,35.780,64.220,53.460,46.540,10.39,288,1.000,bicubic,-57.160,-45.120,-78 resnet34.gluon_in1k,35.780,64.220,52.183,47.817,21.80,224,0.875,bicubic,-55.310,-45.997,+42 convnext_atto.d2_in1k,35.777,64.223,52.323,47.677,3.70,288,0.950,bicubic,-56.993,-46.167,-56 seresnet50.a3_in1k,35.757,64.243,51.163,48.837,28.09,224,0.950,bicubic,-56.603,-47.167,-24 resnet26t.ra2_in1k,35.727,64.273,53.587,46.413,16.01,320,1.000,bicubic,-57.183,-45.113,-74 dla34.in1k,35.657,64.343,52.803,47.197,15.74,224,0.875,bilinear,-55.563,-45.367,+24 efficientnet_lite0.ra_in1k,35.640,64.360,53.663,46.337,4.65,224,0.875,bicubic,-55.610,-44.577,+22 mixnet_m.ft_in1k,35.633,64.367,52.423,47.577,5.01,224,0.875,bicubic,-56.637,-45.937,-20 resnet18.fb_ssl_yfcc100m_ft_in1k,35.613,64.387,53.753,46.247,11.69,224,0.875,bilinear,-55.087,-44.267,+43 mobilenetv3_rw.rmsp_in1k,35.543,64.457,53.707,46.293,5.48,224,0.875,bicubic,-56.007,-44.573,+9 regnetx_008.tv2_in1k,35.527,64.473,51.480,48.520,7.26,224,0.965,bicubic,-56.963,-46.950,-40 convnext_atto_ols.a2_in1k,35.403,64.597,51.390,48.610,3.70,288,0.950,bicubic,-57.577,-47.150,-93 efficientnet_es_pruned.in1k,35.393,64.607,52.840,47.160,5.44,224,0.875,bicubic,-56.307,-45.570,-3 mobilenetv2_110d.ra_in1k,35.313,64.687,52.870,47.130,4.52,224,0.875,bicubic,-56.017,-45.320,+12 repghostnet_130.in1k,35.263,64.737,52.567,47.433,5.48,224,0.875,bicubic,-56.677,-45.823,-14 tf_mixnet_m.in1k,35.193,64.807,50.987,49.013,5.01,224,0.875,bicubic,-57.017,-47.433,-25 hrnet_w18_small_v2.ms_in1k,35.163,64.837,52.420,47.580,15.60,224,0.875,bilinear,-55.997,-45.920,+17 xcit_nano_12_p16_224.fb_dist_in1k,35.120,64.880,52.543,47.457,3.05,224,1.000,bicubic,-55.070,-45.207,+49 resnet34.a3_in1k,35.033,64.967,50.503,49.497,21.80,224,0.950,bicubic,-55.207,-47.377,+45 convit_tiny.fb_in1k,35.027,64.973,51.777,48.223,5.71,224,0.875,bicubic,-55.523,-46.413,+36 gcresnext26ts.ch_in1k,35.017,64.983,51.493,48.507,10.48,288,1.000,bicubic,-57.723,-47.117,-70 eca_resnext26ts.ch_in1k,34.927,65.073,52.370,47.630,10.30,288,1.000,bicubic,-57.823,-46.340,-72 regnety_004.tv2_in1k,34.923,65.077,51.263,48.737,4.34,224,0.965,bicubic,-56.697,-46.907,-8 tinynet_b.in1k,34.867,65.133,51.997,48.003,3.73,188,0.875,bicubic,-56.243,-46.063,+15 resnext26ts.ra2_in1k,34.837,65.163,52.710,47.290,10.30,288,1.000,bicubic,-57.543,-45.680,-46 regnety_008.pycls_in1k,34.780,65.220,51.743,48.257,6.26,224,0.875,bicubic,-57.120,-46.667,-21 pit_ti_224.in1k,34.670,65.330,52.160,47.840,4.85,224,0.900,bicubic,-55.750,-45.850,+33 tf_efficientnet_b0.in1k,34.623,65.377,51.143,48.857,5.29,224,0.875,bicubic,-57.657,-47.407,-41 crossvit_9_240.in1k,34.613,65.387,51.767,48.233,8.55,240,0.875,bicubic,-56.437,-46.553,+16 mobilenetv3_large_100.ra_in1k,34.600,65.400,52.843,47.157,5.48,224,0.875,bicubic,-56.870,-45.477,-7 mixer_b16_224.goog_in21k_ft_in1k,34.427,65.573,48.107,51.893,59.88,224,0.875,bicubic,-56.713,-49.293,+6 pvt_v2_b0.in1k,34.400,65.600,53.103,46.897,3.67,224,0.900,bicubic,-54.570,-44.587,+55 tf_efficientnet_es.in1k,34.260,65.740,51.353,48.647,5.44,224,0.875,bicubic,-57.850,-47.077,-35 fbnetc_100.rmsp_in1k,34.243,65.757,51.190,48.810,5.57,224,0.875,bilinear,-57.037,-46.890,-6 resnet18d.ra2_in1k,34.213,65.787,51.763,48.237,11.71,288,0.950,bicubic,-56.587,-46.397,+12 regnety_006.pycls_in1k,34.153,65.847,51.253,48.747,6.06,224,0.875,bicubic,-57.407,-47.177,-18 repvgg_a0.rvgg_in1k,34.070,65.930,51.967,48.033,9.11,224,0.875,bilinear,-55.610,-45.793,+37 ghostnetv2_100.in1k,34.037,65.963,51.973,48.027,6.16,224,0.875,bicubic,-57.593,-46.317,-24 resnet18.a1_in1k,33.973,66.027,49.410,50.590,11.69,288,1.000,bicubic,-56.227,-48.350,+27 tf_mobilenetv3_large_100.in1k,33.963,66.037,51.470,48.530,5.48,224,0.875,bilinear,-57.457,-46.790,-16 regnetx_008.pycls_in1k,33.803,66.197,50.543,49.457,7.26,224,0.875,bicubic,-57.367,-47.827,-8 repghostnet_111.in1k,33.790,66.210,51.543,48.457,4.54,224,0.875,bicubic,-57.310,-46.687,0 mnasnet_100.rmsp_in1k,33.777,66.223,51.150,48.850,4.38,224,0.875,bicubic,-57.433,-47.070,-11 semnasnet_075.rmsp_in1k,33.773,66.227,52.403,47.597,2.91,224,0.875,bicubic,-56.447,-45.547,+21 lcnet_100.ra2_in1k,33.767,66.233,52.090,47.910,2.95,224,0.875,bicubic,-55.143,-45.290,+45 ese_vovnet19b_dw.ra_in1k,33.753,66.247,50.920,49.080,6.54,288,0.950,bicubic,-59.007,-47.730,-97 regnetx_004_tv.tv2_in1k,33.717,66.283,49.803,50.197,5.50,224,0.965,bicubic,-57.443,-48.297,-12 vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,33.633,66.367,50.687,49.313,6.36,384,1.000,bicubic,-58.097,-47.743,-40 mobilevit_s.cvnets_in1k,33.633,66.367,49.287,50.713,5.58,256,0.900,bicubic,-59.517,-49.493,-152 xcit_nano_12_p8_224.fb_in1k,33.580,66.420,50.233,49.767,3.05,224,1.000,bicubic,-57.520,-47.817,-10 vit_tiny_patch16_384.augreg_in21k_ft_in1k,33.550,66.450,51.077,48.923,5.79,384,1.000,bicubic,-59.870,-47.733,-183 semnasnet_100.rmsp_in1k,33.507,66.493,50.803,49.197,3.89,224,0.875,bicubic,-58.163,-47.487,-39 spnasnet_100.rmsp_in1k,33.490,66.510,51.287,48.713,4.42,224,0.875,bilinear,-57.100,-46.663,+1 mixnet_s.ft_in1k,33.467,66.533,51.007,48.993,4.13,224,0.875,bicubic,-58.313,-47.293,-46 crossvit_tiny_240.in1k,33.347,66.653,49.900,50.100,7.01,240,0.875,bicubic,-57.183,-48.050,+3 mobilevitv2_075.cvnets_in1k,33.340,66.660,50.110,49.890,2.87,256,0.888,bicubic,-58.630,-48.190,-56 efficientvit_m2.r224_in1k,33.293,66.707,49.810,50.190,4.19,224,0.875,bicubic,-55.617,-47.580,+32 vgg19_bn.tv_in1k,33.240,66.760,50.777,49.223,143.68,224,0.875,bilinear,-57.750,-47.323,-12 regnetx_006.pycls_in1k,33.147,66.853,50.243,49.757,6.20,224,0.875,bicubic,-57.643,-47.847,-11 repghostnet_100.in1k,33.140,66.860,50.770,49.230,4.07,224,0.875,bicubic,-57.550,-47.350,-7 edgenext_x_small.in1k,33.110,66.890,49.003,50.997,2.34,288,1.000,bicubic,-58.470,-49.187,-44 seresnext26t_32x4d.bt_in1k,33.083,66.917,50.270,49.730,16.81,288,0.950,bicubic,-60.267,-48.390,-183 resnet18.tv_in1k,33.057,66.943,51.173,48.827,11.69,224,0.875,bilinear,-55.093,-45.947,+34 seresnext26d_32x4d.bt_in1k,32.973,67.027,49.823,50.177,16.81,288,0.950,bicubic,-60.087,-48.887,-160 xcit_nano_12_p16_224.fb_in1k,32.953,67.047,49.977,50.023,3.05,224,1.000,bicubic,-56.017,-47.433,+23 mobileone_s0.apple_in1k,32.853,67.147,51.063,48.937,5.29,224,0.875,bilinear,-55.957,-46.157,+25 hrnet_w18_small.gluon_in1k,32.827,67.173,50.380,49.620,13.19,224,0.875,bicubic,-57.483,-47.370,-5 legacy_seresnext26_32x4d.in1k,32.773,67.227,49.217,50.783,16.79,224,0.875,bicubic,-59.827,-49.193,-106 hrnet_w18_small.ms_in1k,32.653,67.347,50.587,49.413,13.19,224,0.875,bilinear,-57.217,-47.303,+1 deit_tiny_patch16_224.fb_in1k,32.650,67.350,50.263,49.737,5.72,224,0.900,bicubic,-56.960,-47.697,+6 legacy_seresnet18.in1k,32.600,67.400,50.317,49.683,11.78,224,0.875,bicubic,-56.660,-47.373,+11 ghostnet_100.in1k,32.550,67.450,50.410,49.590,5.18,224,0.875,bicubic,-57.910,-47.500,-13 mobilenetv2_100.ra_in1k,32.513,67.487,50.790,49.210,3.50,224,0.875,bicubic,-57.357,-47.040,-2 regnetx_004.pycls_in1k,32.493,67.507,49.323,50.677,5.16,224,0.875,bicubic,-56.977,-48.437,+3 resnet26d.bt_in1k,32.420,67.580,49.997,50.003,16.01,288,0.950,bicubic,-60.130,-48.653,-107 resnet18.gluon_in1k,32.417,67.583,49.737,50.263,11.69,224,0.875,bicubic,-56.243,-47.363,+16 regnety_004.pycls_in1k,32.313,67.687,49.463,50.537,4.34,224,0.875,bicubic,-58.457,-48.607,-28 resnet18.a2_in1k,32.223,67.777,47.890,52.110,11.69,288,1.000,bicubic,-57.247,-49.520,0 tf_mixnet_s.in1k,32.200,67.800,48.523,51.477,4.13,224,0.875,bicubic,-59.480,-49.717,-67 resnet26.bt_in1k,32.173,67.827,49.443,50.557,16.00,288,0.950,bicubic,-59.937,-49.107,-83 vit_tiny_patch16_224.augreg_in21k_ft_in1k,32.020,67.980,49.017,50.983,5.72,224,0.900,bicubic,-59.900,-49.323,-77 tf_mobilenetv3_large_075.in1k,31.847,68.153,49.130,50.870,3.99,224,0.875,bilinear,-58.483,-48.740,-21 tf_mobilenetv3_large_minimal_100.in1k,31.600,68.400,49.353,50.647,3.92,224,0.875,bilinear,-57.560,-47.957,+2 efficientvit_m1.r224_in1k,31.297,68.703,48.197,51.803,2.98,224,0.875,bicubic,-55.923,-48.823,+18 repghostnet_080.in1k,30.873,69.127,48.797,51.203,3.28,224,0.875,bicubic,-58.597,-48.833,-6 vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,30.790,69.210,47.663,52.337,6.34,224,0.900,bicubic,-58.550,-50.037,-5 tinynet_c.in1k,30.517,69.483,48.477,51.523,2.46,184,0.875,bicubic,-57.883,-48.783,+6 lcnet_075.ra2_in1k,30.373,69.627,48.770,51.230,2.36,224,0.875,bicubic,-56.587,-47.780,+19 vgg16_bn.tv_in1k,30.357,69.643,47.280,52.720,138.37,224,0.875,bilinear,-60.183,-50.710,-32 resnet18.a3_in1k,30.093,69.907,46.333,53.667,11.69,224,0.950,bicubic,-56.977,-50.327,+15 efficientvit_b0.r224_in1k,30.060,69.940,46.603,53.397,3.41,224,0.950,bicubic,-58.240,-50.277,+4 edgenext_xx_small.in1k,29.750,70.250,46.487,53.513,1.33,288,1.000,bicubic,-60.050,-51.013,-19 regnety_002.pycls_in1k,29.707,70.293,46.827,53.173,3.16,224,0.875,bicubic,-58.463,-50.613,+3 mobilevit_xs.cvnets_in1k,29.587,70.413,46.037,53.963,2.32,256,0.900,bicubic,-61.623,-52.013,-63 mobilenetv3_small_100.lamb_in1k,29.050,70.950,47.200,52.800,2.54,224,0.875,bicubic,-57.130,-49.250,+14 mnasnet_small.lamb_in1k,28.953,71.047,47.287,52.713,2.03,224,0.875,bicubic,-56.527,-48.713,+15 vgg13_bn.tv_in1k,28.863,71.137,46.730,53.270,133.05,224,0.875,bilinear,-60.337,-50.800,-13 resnet10t.c3_in1k,28.857,71.143,46.903,53.097,5.44,224,0.950,bicubic,-57.823,-49.827,+10 regnetx_002.pycls_in1k,28.837,71.163,45.453,54.547,2.68,224,0.875,bicubic,-58.533,-51.547,+1 efficientvit_m0.r224_in1k,28.797,71.203,45.777,54.223,2.35,224,0.875,bicubic,-54.423,-49.923,+19 mobilenetv2_050.lamb_in1k,28.657,71.343,46.597,53.403,1.97,224,0.875,bicubic,-56.333,-49.023,+14 vgg19.tv_in1k,28.580,71.420,45.167,54.833,143.67,224,0.875,bilinear,-61.100,-52.383,-27 mobilevitv2_050.cvnets_in1k,28.560,71.440,45.203,54.797,1.37,256,0.888,bicubic,-60.470,-52.397,-17 dla60x_c.in1k,28.443,71.557,46.220,53.780,1.32,224,0.875,bilinear,-58.667,-50.920,0 vgg11_bn.tv_in1k,28.430,71.570,46.460,53.540,132.87,224,0.875,bilinear,-59.970,-50.790,-11 repghostnet_058.in1k,28.400,71.600,46.587,53.413,2.55,224,0.875,bicubic,-58.750,-50.193,-3 tinynet_d.in1k,27.960,72.040,45.877,54.123,2.34,152,0.875,bicubic,-57.480,-50.143,+7 vgg16.tv_in1k,27.877,72.123,44.680,55.320,138.36,224,0.875,bilinear,-61.493,-52.840,-28 resnet14t.c3_in1k,27.553,72.447,44.683,55.317,10.08,224,0.950,bicubic,-61.697,-52.757,-26 tf_mobilenetv3_small_100.in1k,27.293,72.707,44.410,55.590,2.54,224,0.875,bilinear,-58.657,-51.990,0 repghostnet_050.in1k,27.060,72.940,44.977,55.023,2.31,224,0.875,bicubic,-58.390,-51.173,+1 mixer_l16_224.goog_in21k_ft_in1k,26.860,73.140,37.913,62.087,208.20,224,0.875,bicubic,-60.130,-56.157,-6 vgg11.tv_in1k,26.537,73.463,43.470,56.530,132.86,224,0.875,bilinear,-60.813,-53.630,-12 mobilenetv3_small_075.lamb_in1k,26.533,73.467,43.877,56.123,2.04,224,0.875,bicubic,-57.587,-51.643,+4 mobilevit_xxs.cvnets_in1k,26.353,73.647,43.060,56.940,1.27,256,0.900,bicubic,-61.587,-54.130,-17 vgg13.tv_in1k,26.273,73.727,43.353,56.647,133.05,224,0.875,bilinear,-61.297,-53.757,-17 dla46x_c.in1k,26.207,73.793,43.777,56.223,1.07,224,0.875,bilinear,-59.233,-52.643,-4 lcnet_050.ra2_in1k,26.197,73.803,44.577,55.423,1.88,224,0.875,bicubic,-56.843,-50.443,+2 tf_mobilenetv3_small_075.in1k,26.197,73.803,43.617,56.383,2.04,224,0.875,bilinear,-58.303,-52.263,-2 dla46_c.in1k,25.507,74.493,43.777,56.223,1.30,224,0.875,bilinear,-59.193,-52.433,-4 tf_mobilenetv3_small_minimal_100.in1k,25.123,74.877,42.950,57.050,2.04,224,0.875,bilinear,-57.537,-52.080,0 tinynet_e.in1k,23.353,76.647,41.067,58.933,2.04,106,0.875,bicubic,-56.457,-52.903,0 mobilenetv3_small_050.lamb_in1k,21.737,78.263,38.757,61.243,1.59,224,0.875,bicubic,-56.343,-54.253,0
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv	model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count tinynet_e,49277.65,20.77,1024,106,0.03,0.69,2.04 mobilenetv3_small_050,45562.75,22.464,1024,224,0.03,0.92,1.59 lcnet_035,41026.68,24.949,1024,224,0.03,1.04,1.64 lcnet_050,37575.13,27.242,1024,224,0.05,1.26,1.88 mobilenetv3_small_075,33062.39,30.961,1024,224,0.05,1.3,2.04 mobilenetv3_small_100,30012.26,34.109,1024,224,0.06,1.42,2.54 tf_mobilenetv3_small_minimal_100,28698.14,35.672,1024,224,0.06,1.41,2.04 tf_mobilenetv3_small_075,27407.51,37.352,1024,224,0.05,1.3,2.04 tinynet_d,27236.47,37.585,1024,152,0.05,1.42,2.34 tf_mobilenetv3_small_100,25103.65,40.781,1024,224,0.06,1.42,2.54 lcnet_075,24140.95,42.406,1024,224,0.1,1.99,2.36 mnasnet_small,20706.43,49.443,1024,224,0.07,2.16,2.03 levit_128s,20595.72,49.709,1024,224,0.31,1.88,7.78 lcnet_100,19684.75,52.01,1024,224,0.16,2.52,2.95 mobilenetv2_035,18358.82,55.767,1024,224,0.07,2.86,1.68 regnetx_002,18244.04,56.117,1024,224,0.2,2.16,2.68 ghostnet_050,17564.96,58.287,1024,224,0.05,1.77,2.59 regnety_002,17006.07,60.202,1024,224,0.2,2.17,3.16 mnasnet_050,15925.32,64.29,1024,224,0.11,3.07,2.22 vit_tiny_r_s16_p8_224,15068.38,67.946,1024,224,0.44,2.06,6.34 mobilenetv2_050,14843.74,68.974,1024,224,0.1,3.64,1.97 tinynet_c,14634.69,69.959,1024,184,0.11,2.87,2.46 semnasnet_050,14248.78,71.855,1024,224,0.11,3.44,2.08 levit_128,14164.26,72.284,1024,224,0.41,2.71,9.21 vit_small_patch32_224,13811.36,74.131,1024,224,1.15,2.5,22.88 mixer_s32_224,13352.85,76.677,1024,224,1.0,2.28,19.1 cs3darknet_focus_s,12798.44,79.999,1024,256,0.69,2.7,3.27 lcnet_150,12783.12,80.094,1024,224,0.34,3.79,4.5 cs3darknet_s,12395.11,82.602,1024,256,0.72,2.97,3.28 regnetx_004,12366.39,82.791,1024,224,0.4,3.14,5.16 mobilenetv3_large_075,12001.32,85.313,1024,224,0.16,4.0,3.99 levit_192,11882.81,86.163,1024,224,0.66,3.2,10.95 resnet10t,11615.84,88.145,1024,224,1.1,2.43,5.44 ese_vovnet19b_slim_dw,11539.4,88.729,1024,224,0.4,5.28,1.9 gernet_s,11496.77,89.058,1024,224,0.75,2.65,8.17 mobilenetv3_rw,10873.77,94.16,1024,224,0.23,4.41,5.48 mobilenetv3_large_100,10705.06,95.645,1024,224,0.23,4.41,5.48 hardcorenas_a,10554.34,97.012,1024,224,0.23,4.38,5.26 tf_mobilenetv3_large_075,10511.12,97.41,1024,224,0.16,4.0,3.99 tf_mobilenetv3_large_minimal_100,10371.16,98.725,1024,224,0.22,4.4,3.92 mnasnet_075,10345.17,98.972,1024,224,0.23,4.77,3.17 hardcorenas_b,9695.74,105.601,1024,224,0.26,5.09,5.18 regnety_004,9655.22,106.046,1024,224,0.41,3.89,4.34 ghostnet_100,9483.99,107.96,1024,224,0.15,3.55,5.18 hardcorenas_c,9481.05,107.994,1024,224,0.28,5.01,5.52 tf_mobilenetv3_large_100,9456.79,108.271,1024,224,0.23,4.41,5.48 regnetx_006,9408.22,108.83,1024,224,0.61,3.98,6.2 mobilenetv2_075,9313.88,109.932,1024,224,0.22,5.86,2.64 tinynet_b,9291.99,110.191,1024,188,0.21,4.44,3.73 mnasnet_b1,9286.4,110.258,1024,224,0.33,5.46,4.38 mnasnet_100,9263.52,110.53,1024,224,0.33,5.46,4.38 gluon_resnet18_v1b,9078.31,112.785,1024,224,1.82,2.48,11.69 semnasnet_075,9069.42,112.895,1024,224,0.23,5.54,2.91 resnet18,9045.63,113.192,1024,224,1.82,2.48,11.69 ssl_resnet18,9045.4,113.196,1024,224,1.82,2.48,11.69 swsl_resnet18,9040.4,113.258,1024,224,1.82,2.48,11.69 levit_256,8921.47,114.768,1024,224,1.13,4.23,18.89 hardcorenas_d,8879.46,115.311,1024,224,0.3,4.93,7.5 regnety_006,8666.48,118.144,1024,224,0.61,4.33,6.06 seresnet18,8542.99,119.851,1024,224,1.82,2.49,11.78 mobilenetv2_100,8507.29,120.356,1024,224,0.31,6.68,3.5 spnasnet_100,8342.04,122.741,1024,224,0.35,6.03,4.42 legacy_seresnet18,8310.8,123.202,1024,224,1.82,2.49,11.78 semnasnet_100,8284.16,123.599,1024,224,0.32,6.23,3.89 mnasnet_a1,8283.57,123.607,1024,224,0.32,6.23,3.89 regnetx_008,7852.75,130.39,1024,224,0.81,5.15,7.26 hardcorenas_f,7809.07,131.117,1024,224,0.35,5.57,8.2 hardcorenas_e,7730.97,132.444,1024,224,0.35,5.65,8.07 efficientnet_lite0,7722.75,132.584,1024,224,0.4,6.74,4.65 levit_256d,7689.03,133.165,1024,224,1.4,4.93,26.21 xcit_nano_12_p16_224_dist,7674.8,133.413,1024,224,0.56,4.17,3.05 xcit_nano_12_p16_224,7670.11,133.492,1024,224,0.56,4.17,3.05 resnet18d,7636.48,134.082,1024,224,2.06,3.29,11.71 ghostnet_130,7625.58,134.274,1024,224,0.24,4.6,7.36 tf_efficientnetv2_b0,7614.25,134.473,1024,224,0.73,4.77,7.14 ese_vovnet19b_slim,7588.4,134.932,1024,224,1.69,3.52,3.17 deit_tiny_distilled_patch16_224,7449.3,137.451,1024,224,1.27,6.01,5.91 deit_tiny_patch16_224,7398.73,138.391,1024,224,1.26,5.97,5.72 vit_tiny_patch16_224,7390.78,138.538,1024,224,1.26,5.97,5.72 regnety_008,7366.88,138.989,1024,224,0.81,5.25,6.26 tinynet_a,7358.6,139.145,1024,192,0.35,5.41,6.19 dla46_c,7311.64,140.038,1024,224,0.58,4.5,1.3 fbnetc_100,7303.94,140.187,1024,224,0.4,6.51,5.57 mobilevitv2_050,7248.37,141.262,1024,256,0.48,8.04,1.37 tf_efficientnet_lite0,6816.26,150.218,1024,224,0.4,6.74,4.65 pit_ti_distilled_224,6788.49,150.832,1024,224,0.71,6.23,5.1 pit_ti_224,6762.99,151.401,1024,224,0.7,6.19,4.85 efficientnet_b0,6687.26,153.115,1024,224,0.4,6.75,5.29 visformer_tiny,6618.81,154.698,1024,224,1.27,5.72,10.32 rexnet_100,6608.65,154.937,1024,224,0.41,7.44,4.8 mnasnet_140,6580.58,155.597,1024,224,0.6,7.71,7.12 efficientnet_b1_pruned,6513.48,157.201,1024,240,0.4,6.21,6.33 rexnetr_100,6491.35,157.737,1024,224,0.43,7.72,4.88 mobilenetv2_110d,6395.98,160.089,1024,224,0.45,8.71,4.52 resnet14t,6341.58,161.462,1024,224,1.69,5.8,10.08 regnetz_005,6208.75,164.916,1024,224,0.52,5.86,7.12 dla46x_c,6145.64,166.61,1024,224,0.54,5.66,1.07 nf_regnet_b0,6055.0,169.104,1024,256,0.64,5.58,8.76 tf_efficientnet_b0,5992.76,170.862,1024,224,0.4,6.75,5.29 hrnet_w18_small,5908.15,173.308,1024,224,1.61,5.72,13.19 edgenext_xx_small,5886.07,173.957,1024,288,0.33,4.21,1.33 semnasnet_140,5856.63,174.833,1024,224,0.6,8.87,6.11 resnetblur18,5839.81,175.336,1024,224,2.34,3.39,11.69 ese_vovnet19b_dw,5825.11,175.779,1024,224,1.34,8.25,6.54 dla60x_c,5790.89,176.817,1024,224,0.59,6.01,1.32 mobilenetv2_140,5780.41,177.139,1024,224,0.6,9.57,6.11 skresnet18,5648.81,181.265,1024,224,1.82,3.24,11.96 mobilevit_xxs,5528.18,185.22,1024,256,0.42,8.34,1.27 efficientnet_b0_gn,5401.88,189.551,1024,224,0.42,6.75,5.29 convnext_atto,5364.13,190.886,1024,288,0.91,6.3,3.7 gluon_resnet34_v1b,5344.34,191.593,1024,224,3.67,3.74,21.8 resnet34,5335.05,191.926,1024,224,3.67,3.74,21.8 efficientnet_lite1,5334.12,191.959,1024,240,0.62,10.14,5.42 tv_resnet34,5332.7,192.011,1024,224,3.67,3.74,21.8 vit_base_patch32_224,5287.0,193.67,1024,224,4.41,5.01,88.22 vit_base_patch32_clip_224,5281.4,193.877,1024,224,4.41,5.01,88.22 levit_384,5276.74,194.047,1024,224,2.36,6.26,39.13 pit_xs_distilled_224,5241.4,195.357,1024,224,1.41,7.76,11.0 pit_xs_224,5237.09,195.517,1024,224,1.4,7.71,10.62 selecsls42,5225.99,195.932,1024,224,2.94,4.62,30.35 selecsls42b,5201.55,196.853,1024,224,2.98,4.62,32.46 gernet_m,5124.67,199.807,1024,224,3.02,5.24,21.14 pvt_v2_b0,5122.72,199.882,1024,224,0.57,7.99,3.67 tf_efficientnetv2_b1,5122.21,199.903,1024,240,1.21,7.34,8.14 mixnet_s,5079.84,201.57,1024,224,0.25,6.25,4.13 convnext_atto_ols,5062.64,202.255,1024,288,0.96,6.8,3.7 seresnet34,5028.88,203.611,1024,224,3.67,3.74,21.96 rexnetr_130,5003.96,204.626,1024,224,0.68,9.81,7.61 fbnetv3_b,5003.0,204.666,1024,256,0.55,9.1,8.6 mixer_b32_224,4982.51,205.508,1024,224,3.24,6.29,60.29 xcit_tiny_12_p16_224_dist,4879.26,209.853,1024,224,1.24,6.29,6.72 legacy_seresnet34,4875.12,210.034,1024,224,3.67,3.74,21.96 xcit_tiny_12_p16_224,4870.16,210.244,1024,224,1.24,6.29,6.72 resnet34d,4834.78,211.786,1024,224,3.91,4.54,21.82 tf_efficientnet_lite1,4822.03,212.348,1024,240,0.62,10.14,5.42 resnet26,4794.98,213.545,1024,224,2.36,7.35,16.0 mobilenetv2_120d,4786.27,213.934,1024,224,0.69,11.97,5.83 rexnet_130,4770.1,214.659,1024,224,0.68,9.71,7.56 efficientnet_b0_g16_evos,4743.69,215.854,1024,224,1.01,7.42,8.11 efficientnet_es,4736.89,216.163,1024,224,1.81,8.73,5.44 efficientnet_es_pruned,4735.25,216.239,1024,224,1.81,8.73,5.44 tf_mixnet_s,4735.17,216.242,1024,224,0.25,6.25,4.13 gmlp_ti16_224,4709.0,217.445,1024,224,1.34,7.55,5.87 convnext_femto,4672.08,219.162,1024,288,1.3,7.56,5.22 mobilevitv2_075,4638.17,220.764,1024,256,1.05,12.06,2.87 resmlp_12_224,4601.92,222.504,1024,224,3.01,5.5,15.35 resmlp_12_distilled_224,4597.97,222.695,1024,224,3.01,5.5,15.35 gmixer_12_224,4543.02,225.388,1024,224,2.67,7.26,12.7 fbnetv3_d,4532.2,225.927,1024,256,0.68,11.1,10.31 tf_efficientnet_es,4518.93,226.591,1024,224,1.81,8.73,5.44 selecsls60,4510.1,227.034,1024,224,3.59,5.52,30.67 mixer_s16_224,4509.29,227.075,1024,224,3.79,5.97,18.53 regnetx_016,4507.02,227.189,1024,224,1.62,7.93,9.19 selecsls60b,4490.35,228.033,1024,224,3.63,5.52,32.77 cs3darknet_focus_m,4487.64,228.171,1024,288,2.51,6.19,9.3 dla34,4481.03,228.505,1024,224,3.07,5.02,15.74 crossvit_tiny_240,4476.83,228.722,1024,240,1.57,9.08,7.01 convnext_femto_ols,4473.25,228.904,1024,288,1.35,8.06,5.23 vit_tiny_r_s16_p8_384,4463.13,229.423,1024,384,1.34,6.49,6.36 cs3darknet_m,4452.94,229.949,1024,288,2.63,6.69,9.31 repvgg_b0,4433.11,230.978,1024,224,3.41,6.15,15.82 resnet26d,4354.59,235.143,1024,224,2.6,8.15,16.01 rexnetr_150,4349.97,235.392,1024,224,0.89,11.13,9.78 resnetaa34d,4309.77,237.588,1024,224,4.43,5.07,21.82 efficientnet_b2_pruned,4309.58,237.598,1024,260,0.73,9.13,8.31 darknet17,4296.61,238.316,1024,256,3.26,7.18,14.3 vit_small_patch32_384,4250.58,240.897,1024,384,3.45,8.25,22.92 crossvit_9_240,4201.98,243.683,1024,240,1.85,9.52,8.55 nf_resnet26,4197.39,243.949,1024,224,2.41,7.35,16.0 efficientnet_b0_g8_gn,4190.39,244.357,1024,224,0.66,6.75,6.56 rexnet_150,4186.31,244.594,1024,224,0.9,11.21,9.73 ecaresnet50d_pruned,4182.62,244.81,1024,224,2.53,6.43,19.94 efficientformer_l1,4075.83,251.225,1024,224,1.3,5.53,12.29 poolformer_s12,4050.19,252.815,1024,224,1.82,5.53,11.92 regnety_016,4035.9,253.712,1024,224,1.63,8.04,11.2 efficientnet_lite2,4013.48,255.128,1024,260,0.89,12.9,6.09 crossvit_9_dagger_240,3992.98,256.437,1024,240,1.99,9.97,8.78 efficientnet_cc_b0_8e,3929.29,260.595,1024,224,0.42,9.42,24.01 efficientnet_cc_b0_4e,3918.01,261.346,1024,224,0.41,9.42,13.31 darknet21,3914.26,261.596,1024,256,3.93,7.47,20.86 efficientnet_b1,3876.9,264.116,1024,256,0.77,12.22,7.79 tf_efficientnet_b1,3834.3,267.052,1024,240,0.71,10.88,7.79 resnest14d,3793.21,269.944,1024,224,2.76,7.33,10.61 sedarknet21,3784.73,270.549,1024,256,3.93,7.47,20.95 resnext26ts,3775.5,271.211,1024,256,2.43,10.52,10.3 tf_efficientnetv2_b2,3727.06,274.735,1024,260,1.72,9.84,10.1 convnext_pico,3702.78,276.537,1024,288,2.27,10.08,9.05 edgenext_x_small,3692.42,277.311,1024,288,0.68,7.5,2.34 tf_efficientnet_cc_b0_8e,3691.33,277.395,1024,224,0.42,9.42,24.01 dpn48b,3689.99,277.494,1024,224,1.69,8.92,9.13 eca_resnext26ts,3675.59,278.583,1024,256,2.43,10.52,10.3 seresnext26ts,3670.33,278.98,1024,256,2.43,10.52,10.39 tf_efficientnet_cc_b0_4e,3665.41,279.357,1024,224,0.41,9.42,13.31 tf_efficientnet_lite2,3662.0,279.618,1024,260,0.89,12.9,6.09 nf_ecaresnet26,3619.99,282.862,1024,224,2.41,7.36,16.0 nf_seresnet26,3618.8,282.955,1024,224,2.41,7.36,17.4 gcresnext26ts,3594.7,284.852,1024,256,2.43,10.53,10.48 mobilevitv2_100,3589.19,213.964,768,256,1.84,16.08,4.9 gernet_l,3556.24,287.933,1024,256,4.57,8.0,31.08 legacy_seresnext26_32x4d,3545.88,288.774,1024,224,2.49,9.39,16.79 convnext_pico_ols,3532.27,289.886,1024,288,2.37,10.74,9.06 resnet26t,3503.33,292.28,1024,256,3.35,10.52,16.01 repvgg_a2,3454.82,296.386,1024,224,5.7,6.26,28.21 mixnet_m,3418.52,299.526,1024,224,0.36,8.19,5.01 efficientnet_b3_pruned,3356.7,305.049,1024,300,1.04,11.86,9.86 nf_regnet_b1,3352.23,305.456,1024,288,1.02,9.2,10.22 ecaresnext50t_32x4d,3339.2,306.649,1024,224,2.7,10.09,15.41 ecaresnext26t_32x4d,3337.18,306.833,1024,224,2.7,10.09,15.41 seresnext26tn_32x4d,3327.66,307.711,1024,224,2.7,10.09,16.81 seresnext26t_32x4d,3327.23,307.751,1024,224,2.7,10.09,16.81 seresnext26d_32x4d,3303.57,309.954,1024,224,2.73,10.19,16.81 tf_mixnet_m,3301.19,310.17,1024,224,0.36,8.19,5.01 convit_tiny,3286.62,311.554,1024,224,1.26,7.94,5.71 mobilevit_xs,3278.19,234.265,768,256,1.05,16.33,2.32 pit_s_224,3268.88,313.245,1024,224,2.88,11.56,23.46 pit_s_distilled_224,3266.72,313.452,1024,224,2.9,11.64,24.04 skresnet34,3242.45,315.8,1024,224,3.67,5.13,22.28 eca_botnext26ts_256,3224.24,317.583,1024,256,2.46,11.6,10.59 ecaresnet101d_pruned,3223.88,317.616,1024,224,3.48,7.69,24.88 deit_small_distilled_patch16_224,3220.79,317.922,1024,224,4.63,12.02,22.44 ecaresnetlight,3215.57,318.439,1024,224,4.11,8.42,30.16 deit_small_patch16_224,3209.05,319.085,1024,224,4.61,11.95,22.05 vit_small_patch16_224,3199.98,319.99,1024,224,4.61,11.95,22.05 eca_halonext26ts,3173.71,322.639,1024,256,2.44,11.46,10.76 convnextv2_atto,3162.98,323.733,1024,288,0.91,6.3,3.71 resnetv2_50,3158.28,324.214,1024,224,4.11,11.11,25.55 nf_regnet_b2,3133.63,326.765,1024,272,1.22,9.27,14.31 rexnetr_200,3133.12,245.111,768,224,1.59,15.11,16.52 botnet26t_256,3123.98,327.772,1024,256,3.32,11.98,12.49 coat_lite_tiny,3113.54,328.874,1024,224,1.6,11.65,5.72 vit_small_r26_s32_224,3112.34,329.001,1024,224,3.56,9.85,36.43 bat_resnext26ts,3103.95,329.89,1024,256,2.53,12.51,10.73 halonet26t,3103.39,329.95,1024,256,3.19,11.69,12.48 pvt_v2_b1,3095.14,330.828,1024,224,2.12,15.39,14.01 cspresnet50,3063.22,334.278,1024,256,4.54,11.5,21.62 resnet32ts,3055.79,335.09,1024,256,4.63,11.58,17.96 rexnet_200,3051.5,251.668,768,224,1.56,14.91,16.37 lambda_resnet26t,3046.2,336.144,1024,256,3.02,11.87,10.96 ssl_resnet50,3030.48,337.887,1024,224,4.11,11.11,25.56 gluon_resnet50_v1b,3027.43,338.23,1024,224,4.11,11.11,25.56 tv_resnet50,3027.39,338.232,1024,224,4.11,11.11,25.56 swsl_resnet50,3027.07,338.268,1024,224,4.11,11.11,25.56 resnet50,3025.4,338.455,1024,224,4.11,11.11,25.56 deit3_small_patch16_224_in21ft1k,3023.02,338.721,1024,224,4.61,11.95,22.06 deit3_small_patch16_224,3017.77,339.312,1024,224,4.61,11.95,22.06 tresnet_m,3006.54,340.578,1024,224,5.74,7.31,31.39 resnet33ts,3005.78,340.665,1024,256,4.76,11.66,19.68 vit_small_resnet26d_224,2994.08,341.995,1024,224,5.07,11.12,63.61 resnetv2_50t,2989.06,342.569,1024,224,4.32,11.82,25.57 regnetx_032,2988.15,342.675,1024,224,3.2,11.37,15.3 dpn68b,2981.13,343.481,1024,224,2.35,10.47,12.61 hrnet_w18_small_v2,2978.67,343.765,1024,224,2.62,9.65,15.6 dpn68,2975.29,344.155,1024,224,2.35,10.47,12.61 resnetv2_50d,2971.15,344.633,1024,224,4.35,11.92,25.57 efficientnet_em,2938.12,348.51,1024,240,3.04,14.34,6.9 vit_base_patch32_plus_256,2934.64,348.925,1024,256,7.79,7.76,119.48 coat_lite_mini,2921.75,350.462,1024,224,2.0,12.25,11.01 tf_efficientnet_b2,2919.63,350.718,1024,260,1.02,13.83,9.11 seresnet33ts,2919.51,350.732,1024,256,4.76,11.66,19.78 eca_resnet33ts,2917.21,351.008,1024,256,4.76,11.66,19.68 haloregnetz_b,2890.29,354.276,1024,224,1.97,11.94,11.68 coatnet_pico_rw_224,2884.58,354.98,1024,224,2.05,14.62,10.85 dla60,2883.99,355.049,1024,224,4.26,10.16,22.04 gluon_resnet50_v1c,2872.58,356.463,1024,224,4.35,11.92,25.58 resnet50t,2869.49,356.844,1024,224,4.32,11.82,25.57 gcresnet33ts,2863.36,357.609,1024,256,4.76,11.68,19.88 gluon_resnet50_v1d,2853.24,358.879,1024,224,4.35,11.92,25.58 cspresnet50d,2852.98,358.911,1024,256,4.86,12.55,21.64 resnet50d,2850.55,359.218,1024,224,4.35,11.92,25.58 vovnet39a,2845.31,359.878,1024,224,7.09,6.73,22.6 cspresnet50w,2835.31,361.148,1024,256,5.04,12.19,28.12 vgg11,2827.53,362.143,1024,224,7.61,7.44,132.86 tf_efficientnet_em,2826.28,362.303,1024,240,3.04,14.34,6.9 visformer_small,2818.88,363.251,1024,224,4.88,11.43,40.22 vit_relpos_small_patch16_224,2792.87,366.637,1024,224,4.59,13.05,21.98 vit_relpos_base_patch32_plus_rpn_256,2784.26,367.771,1024,256,7.68,8.01,119.42 vit_srelpos_small_patch16_224,2781.72,368.106,1024,224,4.59,12.16,21.97 resnest26d,2772.97,369.267,1024,224,3.64,9.97,17.07 cs3darknet_focus_l,2770.5,369.596,1024,288,5.9,10.16,21.15 efficientnet_b2a,2767.64,369.979,1024,288,1.12,16.2,9.11 efficientnet_b2,2766.98,370.065,1024,288,1.12,16.2,9.11 ese_vovnet39b,2760.12,370.986,1024,224,7.09,6.74,24.57 legacy_seresnet50,2753.49,371.881,1024,224,3.88,10.6,28.09 densenet121,2749.79,372.378,1024,224,2.87,6.9,7.98 tv_densenet121,2747.16,372.735,1024,224,2.87,6.9,7.98 eca_vovnet39b,2736.53,374.185,1024,224,7.09,6.74,22.6 coatnet_nano_cc_224,2716.19,376.986,1024,224,2.24,15.02,13.76 convnextv2_femto,2710.95,377.714,1024,288,1.3,7.56,5.23 resnetv2_50x1_bit_distilled,2704.93,378.554,1024,224,4.23,11.11,25.55 selecsls84,2697.2,379.64,1024,224,5.9,7.57,50.95 flexivit_small,2693.55,380.153,1024,240,5.35,14.18,22.06 twins_svt_small,2691.25,380.48,1024,224,2.94,13.75,24.06 mixnet_l,2678.25,382.327,1024,224,0.58,10.84,7.33 seresnet50,2674.61,382.848,1024,224,4.11,11.13,28.09 xcit_nano_12_p16_384_dist,2668.39,383.74,1024,384,1.64,12.15,3.05 cs3darknet_l,2649.93,386.412,1024,288,6.16,10.83,21.16 coatnet_nano_rw_224,2633.36,388.844,1024,224,2.41,15.41,15.14 coatnext_nano_rw_224,2627.24,389.75,1024,224,2.47,12.8,14.7 xcit_tiny_24_p16_224_dist,2617.14,391.253,1024,224,2.34,11.82,12.12 densenet121d,2616.98,391.278,1024,224,3.11,7.7,8.0 xcit_tiny_24_p16_224,2614.91,391.584,1024,224,2.34,11.82,12.12 resnet50_gn,2599.07,393.975,1024,224,4.14,11.11,25.56 vit_relpos_small_patch16_rpn_224,2596.73,394.33,1024,224,4.59,13.05,21.97 res2net50_48w_2s,2593.21,394.865,1024,224,4.18,11.72,25.29 mobilevit_s,2587.93,296.749,768,256,2.03,19.94,5.58 convnext_nano,2579.36,396.983,1024,288,4.06,13.84,15.59 tf_mixnet_l,2577.4,397.288,1024,224,0.58,10.84,7.33 resnetaa50d,2573.35,397.912,1024,224,5.39,12.44,25.58 vgg11_bn,2556.04,400.607,1024,224,7.62,7.44,132.87 seresnet50t,2550.33,401.504,1024,224,4.32,11.83,28.1 ecaresnet50d,2544.16,402.478,1024,224,4.35,11.93,25.58 gcvit_xxtiny,2518.13,406.639,1024,224,2.14,15.36,12.0 cs3sedarknet_l,2502.51,409.176,1024,288,6.16,10.83,21.91 resnetrs50,2497.73,409.96,1024,224,4.48,12.14,35.69 mobilevitv2_125,2489.87,308.438,768,256,2.86,20.1,7.48 resnetblur50,2484.87,412.08,1024,224,5.16,12.02,25.56 cspresnext50,2483.24,412.352,1024,256,4.05,15.86,20.57 gluon_resnet50_v1s,2459.02,416.413,1024,224,5.47,13.52,25.68 efficientnet_cc_b1_8e,2458.85,416.443,1024,240,0.75,15.44,39.72 vit_base_resnet26d_224,2458.01,416.584,1024,224,6.97,13.16,101.4 densenetblur121d,2444.58,418.873,1024,224,3.11,7.9,8.0 tv_resnext50_32x4d,2431.41,421.143,1024,224,4.26,14.4,25.03 ssl_resnext50_32x4d,2431.35,421.155,1024,224,4.26,14.4,25.03 swsl_resnext50_32x4d,2430.87,421.236,1024,224,4.26,14.4,25.03 resnext50_32x4d,2429.56,421.462,1024,224,4.26,14.4,25.03 gluon_resnext50_32x4d,2428.35,421.674,1024,224,4.26,14.4,25.03 dla60x,2414.82,424.035,1024,224,3.54,13.8,17.35 efficientnet_lite3,2407.43,212.664,512,300,1.65,21.85,8.2 regnetx_040,2406.98,425.416,1024,224,3.99,12.2,22.12 semobilevit_s,2404.63,319.371,768,256,2.03,19.95,5.74 gcresnext50ts,2402.57,426.196,1024,256,3.75,15.46,15.67 regnety_040s_gn,2385.11,429.317,1024,224,4.03,12.29,20.65 resnetblur50d,2367.52,432.507,1024,224,5.4,12.82,25.58 vovnet57a,2360.79,433.737,1024,224,8.95,7.52,36.64 tf_efficientnet_cc_b1_8e,2357.71,434.307,1024,240,0.75,15.44,39.72 resmlp_24_distilled_224,2351.85,435.39,1024,224,5.96,10.91,30.02 resmlp_24_224,2345.81,436.509,1024,224,5.96,10.91,30.02 res2net50_14w_8s,2341.48,437.317,1024,224,4.21,13.28,25.06 coatnet_rmlp_nano_rw_224,2340.53,437.494,1024,224,2.62,20.34,15.15 sehalonet33ts,2339.44,328.271,768,256,3.55,14.7,13.69 res2net50_26w_4s,2338.49,437.876,1024,224,4.28,12.61,25.7 convnext_nano_ols,2328.37,439.779,1024,288,4.38,15.5,15.65 lambda_resnet26rpt_256,2324.88,165.158,384,256,3.16,11.87,10.99 gmixer_24_224,2324.82,440.451,1024,224,5.28,14.45,24.72 gcresnet50t,2321.78,441.028,1024,256,5.42,14.67,25.9 resnext50d_32x4d,2317.05,441.929,1024,224,4.5,15.2,25.05 resnest50d_1s4x24d,2309.9,443.296,1024,224,4.43,13.57,25.68 seresnetaa50d,2309.78,443.319,1024,224,5.4,12.46,28.11 dla60_res2net,2301.91,444.834,1024,224,4.15,12.34,20.85 vit_base_r26_s32_224,2301.77,444.864,1024,224,6.81,12.36,101.38 twins_pcpvt_small,2290.09,447.132,1024,224,3.83,18.08,24.11 regnetz_b16,2286.62,447.81,1024,288,2.39,16.43,9.72 ese_vovnet57b,2267.23,451.64,1024,224,8.95,7.52,38.61 gluon_inception_v3,2265.31,452.024,1024,299,5.73,8.97,23.83 inception_v3,2260.97,452.888,1024,299,5.73,8.97,23.83 adv_inception_v3,2258.89,453.305,1024,299,5.73,8.97,23.83 tf_inception_v3,2255.73,453.943,1024,299,5.73,8.97,23.83 densenet169,2232.91,458.582,1024,224,3.4,7.3,14.15 tf_efficientnetv2_b3,2223.64,460.493,1024,300,3.04,15.74,14.36 nf_ecaresnet50,2211.52,463.019,1024,224,4.21,11.13,25.56 nf_seresnet50,2207.21,463.921,1024,224,4.21,11.13,28.09 skresnet50,2206.75,464.017,1024,224,4.11,12.5,25.8 edgenext_small,2206.31,464.109,1024,320,1.97,14.16,5.59 seresnext50_32x4d,2197.09,466.058,1024,224,4.26,14.42,27.56 gluon_seresnext50_32x4d,2196.94,466.091,1024,224,4.26,14.42,27.56 xcit_small_12_p16_224_dist,2195.81,466.33,1024,224,4.82,12.58,26.25 legacy_seresnext50_32x4d,2193.34,466.856,1024,224,4.26,14.42,27.56 xcit_small_12_p16_224,2190.16,467.534,1024,224,4.82,12.58,26.25 repvgg_b1g4,2188.83,467.817,1024,224,8.15,10.64,39.97 tf_efficientnet_lite3,2188.37,233.953,512,300,1.65,21.85,8.2 efficientnetv2_rw_t,2170.03,471.87,1024,288,3.19,16.42,13.65 gmlp_s16_224,2164.56,473.061,1024,224,4.42,15.1,19.42 dla60_res2next,2126.26,481.583,1024,224,3.49,13.17,17.03 gc_efficientnetv2_rw_t,2126.09,481.621,1024,288,3.2,16.45,13.68 skresnet50d,2112.57,484.703,1024,224,4.36,13.31,25.82 mobilevitv2_150,2105.0,243.219,512,256,4.09,24.11,10.59 mobilevitv2_150_in22ft1k,2104.51,243.274,512,256,4.09,24.11,10.59 convnextv2_pico,2092.16,489.434,1024,288,2.27,10.08,9.07 poolformer_s24,2090.38,489.851,1024,224,3.41,10.68,21.39 cs3sedarknet_xdw,2090.04,489.929,1024,256,5.97,17.18,21.6 res2next50,2085.23,491.055,1024,224,4.2,13.71,24.67 cspdarknet53,2084.51,491.231,1024,256,6.57,16.81,27.64 fbnetv3_g,2084.48,491.238,1024,288,1.77,21.09,16.62 crossvit_small_240,2074.04,493.709,1024,240,5.63,18.17,26.86 deit3_medium_patch16_224_in21ft1k,2064.27,496.046,1024,224,8.0,15.93,38.85 deit3_medium_patch16_224,2063.34,496.268,1024,224,8.0,15.93,38.85 xcit_nano_12_p8_224_dist,2049.01,499.742,1024,224,2.16,15.71,3.05 xcit_nano_12_p8_224,2044.48,500.848,1024,224,2.16,15.71,3.05 nf_regnet_b3,2035.39,503.085,1024,320,2.05,14.61,18.59 cs3darknet_focus_x,2017.73,507.488,1024,256,8.03,10.69,35.02 vit_relpos_medium_patch16_cls_224,2000.38,511.89,1024,224,8.03,18.24,38.76 lambda_resnet50ts,1991.21,514.246,1024,256,5.07,17.48,21.54 swin_tiny_patch4_window7_224,1978.72,517.495,1024,224,4.51,17.06,28.29 sebotnet33ts_256,1959.75,195.932,384,256,3.89,17.46,13.7 coatnet_0_rw_224,1957.32,523.148,1024,224,4.43,18.73,27.44 ecaresnet26t,1953.32,524.224,1024,320,5.24,16.44,16.01 regnetx_080,1942.5,527.144,1024,224,8.02,14.06,39.57 gcvit_xtiny,1941.57,527.393,1024,224,2.93,20.26,19.98 resnetv2_101,1925.46,531.806,1024,224,7.83,16.23,44.54 regnetx_064,1920.06,533.303,1024,224,6.49,16.37,26.21 mixnet_xl,1918.85,533.64,1024,224,0.93,14.57,11.9 edgenext_small_rw,1912.9,535.3,1024,320,2.46,14.85,7.83 vit_relpos_medium_patch16_224,1907.96,536.687,1024,224,7.97,17.02,38.75 vit_srelpos_medium_patch16_224,1900.57,538.773,1024,224,7.96,16.21,38.74 resnest50d,1896.74,539.858,1024,224,5.4,14.36,27.48 crossvit_15_240,1894.86,540.397,1024,240,5.81,19.77,27.53 vit_base_resnet50d_224,1892.78,540.989,1024,224,8.73,16.92,110.97 gluon_resnet101_v1b,1879.26,544.883,1024,224,7.83,16.23,44.55 tv_resnet101,1878.26,545.172,1024,224,7.83,16.23,44.55 resnet101,1875.25,546.047,1024,224,7.83,16.23,44.55 dla102,1873.79,546.472,1024,224,7.19,14.18,33.27 efficientformer_l3,1868.08,548.142,1024,224,3.93,12.01,31.41 maxvit_rmlp_pico_rw_256,1866.73,411.402,768,256,1.85,24.86,7.52 resnetv2_101d,1855.94,551.727,1024,224,8.07,17.04,44.56 pvt_v2_b2,1835.92,557.745,1024,224,4.05,27.53,25.36 maxvit_pico_rw_256,1829.44,419.787,768,256,1.83,22.3,7.46 vgg13,1820.36,562.512,1024,224,11.31,12.25,133.05 lamhalobotnet50ts_256,1818.57,563.067,1024,256,5.02,18.44,22.57 crossvit_15_dagger_240,1817.96,563.255,1024,240,6.13,20.43,28.21 gluon_resnet101_v1c,1816.14,563.82,1024,224,8.08,17.04,44.57 res2net50_26w_6s,1811.81,565.168,1024,224,6.33,15.28,37.05 gluon_resnet101_v1d,1808.21,566.295,1024,224,8.08,17.04,44.57 swin_s3_tiny_224,1803.67,567.72,1024,224,4.64,19.13,28.33 coatnet_rmlp_0_rw_224,1803.63,567.733,1024,224,4.72,24.89,27.45 vit_relpos_medium_patch16_rpn_224,1770.72,578.284,1024,224,7.97,17.02,38.73 halonet50ts,1765.73,579.917,1024,256,5.3,19.2,22.73 repvgg_b1,1760.92,581.5,1024,224,13.16,10.64,57.42 coatnet_bn_0_rw_224,1753.99,583.799,1024,224,4.67,22.04,27.44 wide_resnet50_2,1747.87,585.844,1024,224,11.43,14.4,68.88 efficientnet_b3,1741.21,294.036,512,320,2.01,26.52,12.23 efficientnet_b3a,1740.84,294.1,512,320,2.01,26.52,12.23 densenet201,1738.22,589.096,1024,224,4.34,7.85,20.01 coatnet_0_224,1727.45,296.376,512,224,4.58,24.01,25.04 darknetaa53,1721.33,594.876,1024,288,10.08,15.68,36.02 tf_efficientnet_b3,1720.61,297.558,512,300,1.87,23.83,12.23 cait_xxs24_224,1720.1,595.301,1024,224,2.53,20.29,11.96 vit_large_patch32_224,1718.53,595.845,1024,224,15.41,13.32,327.9 mobilevitv2_175,1697.71,301.572,512,256,5.54,28.13,14.25 mobilevitv2_175_in22ft1k,1697.51,301.606,512,256,5.54,28.13,14.25 xcit_tiny_12_p16_384_dist,1694.92,604.145,1024,384,3.64,18.26,6.72 pvt_v2_b2_li,1694.45,604.311,1024,224,3.91,27.6,22.55 coat_lite_small,1694.41,604.328,1024,224,3.96,22.09,19.84 resnetaa101d,1692.59,604.976,1024,224,9.12,17.56,44.57 legacy_seresnet101,1686.93,607.005,1024,224,7.61,15.74,49.33 tresnet_v2_l,1685.52,607.515,1024,224,8.81,16.34,46.17 hrnet_w18,1679.12,609.832,1024,224,4.32,16.31,21.3 vit_medium_patch16_gap_240,1667.0,614.264,1024,240,9.22,18.81,44.4 vit_tiny_patch16_384,1660.88,616.528,1024,384,4.7,25.39,5.79 regnetv_040,1659.81,616.926,1024,288,6.6,20.3,20.64 convnext_tiny_hnf,1659.73,616.951,1024,288,7.39,22.21,28.59 seresnet101,1655.13,618.666,1024,224,7.84,16.27,49.33 vit_base_patch32_384,1651.29,620.109,1024,384,13.06,16.5,88.3 vit_base_patch32_clip_384,1649.72,620.7,1024,384,13.06,16.5,88.3 regnety_040,1647.66,621.47,1024,288,6.61,20.3,20.65 regnety_032,1645.25,622.383,1024,288,5.29,18.61,19.44 gluon_resnet101_v1s,1642.29,623.505,1024,224,9.19,18.64,44.67 vgg13_bn,1634.19,626.596,1024,224,11.33,12.25,133.05 resnetaa50,1631.05,627.803,1024,288,8.52,19.24,25.56 mixer_b16_224_miil,1628.71,628.706,1024,224,12.62,14.53,59.88 mixer_b16_224,1627.79,629.061,1024,224,12.62,14.53,59.88 convnext_tiny,1626.95,629.384,1024,288,7.39,22.21,28.59 nf_resnet101,1620.77,631.785,1024,224,8.01,16.23,44.55 swinv2_cr_tiny_224,1618.15,632.807,1024,224,4.66,28.45,28.33 ecaresnet101d,1609.33,636.276,1024,224,8.08,17.07,44.57 twins_pcpvt_base,1605.41,637.831,1024,224,6.68,25.25,43.83 dla102x,1601.78,639.274,1024,224,5.89,19.42,26.31 ese_vovnet39b_evos,1601.47,639.4,1024,224,7.07,6.74,24.58 darknet53,1597.03,641.177,1024,288,11.78,15.68,41.61 resnetblur101d,1596.24,641.494,1024,224,9.12,17.94,44.57 resnet51q,1592.08,643.172,1024,288,8.07,20.94,35.7 swinv2_cr_tiny_ns_224,1591.39,643.448,1024,224,4.66,28.45,28.33 mixer_l32_224,1583.03,646.85,1024,224,11.27,19.86,206.94 resmlp_36_distilled_224,1577.86,648.967,1024,224,8.91,16.33,44.69 resmlp_36_224,1577.4,649.158,1024,224,8.91,16.33,44.69 resnetv2_50d_gn,1561.87,655.61,1024,288,7.24,19.7,25.57 botnet50ts_256,1556.81,246.643,384,256,5.54,22.23,22.74 nf_resnet50,1548.83,661.132,1024,288,6.88,18.37,25.56 resnetv2_50d_frn,1547.35,661.764,1024,224,4.33,11.92,25.59 halo2botnet50ts_256,1546.64,496.545,768,256,5.02,21.78,22.64 mvitv2_tiny,1534.63,667.247,1024,224,4.7,21.16,24.17 gluon_resnext101_32x4d,1505.04,680.366,1024,224,8.01,21.23,44.18 swsl_resnext101_32x4d,1504.46,680.63,1024,224,8.01,21.23,44.18 cs3darknet_x,1504.38,680.665,1024,288,10.6,14.36,35.05 ssl_resnext101_32x4d,1503.93,680.869,1024,224,8.01,21.23,44.18 resnext101_32x4d,1503.63,681.005,1024,224,8.01,21.23,44.18 resnest50d_4s2x40d,1497.58,683.755,1024,224,4.4,17.94,30.42 convnextv2_nano,1488.75,515.858,768,288,4.06,13.84,15.62 skresnext50_32x4d,1478.83,692.427,1024,224,4.5,17.18,27.48 mobilevitv2_200,1478.44,519.454,768,256,7.22,32.15,18.45 tresnet_l,1477.44,693.076,1024,224,10.88,11.9,55.99 mobilevitv2_200_in22ft1k,1477.37,519.83,768,256,7.22,32.15,18.45 vgg16,1475.59,693.946,1024,224,15.47,13.56,138.36 regnetz_c16,1475.58,693.953,1024,320,3.92,25.88,13.46 resnetv2_50d_evob,1468.61,697.244,1024,224,4.33,11.92,25.59 vit_medium_patch16_gap_256,1467.03,697.996,1024,256,10.59,22.15,38.86 res2net50_26w_8s,1466.52,698.239,1024,224,8.37,17.95,48.4 sequencer2d_s,1465.84,698.562,1024,224,4.96,11.31,27.65 eca_nfnet_l0,1461.61,700.586,1024,288,7.12,17.29,24.14 nfnet_l0,1460.27,701.228,1024,288,7.13,17.29,35.07 cs3sedarknet_x,1435.72,713.217,1024,288,10.6,14.37,35.4 resnet61q,1434.01,714.068,1024,288,9.87,21.52,36.85 res2net101_26w_4s,1424.71,718.728,1024,224,8.1,18.45,45.21 repvgg_b2g4,1415.15,723.581,1024,224,12.63,12.9,61.76 nest_tiny,1413.2,543.434,768,224,5.83,25.48,17.06 poolformer_s36,1408.65,726.922,1024,224,5.0,15.82,30.86 maxvit_rmlp_nano_rw_256,1404.06,546.971,768,256,4.47,31.92,15.5 convit_small,1397.72,732.608,1024,224,5.76,17.87,27.78 jx_nest_tiny,1387.89,553.347,768,224,5.83,25.48,17.06 maxvit_nano_rw_256,1378.18,557.246,768,256,4.46,30.28,15.45 nf_ecaresnet101,1373.28,745.649,1024,224,8.01,16.27,44.55 nf_seresnet101,1369.04,747.958,1024,224,8.02,16.27,49.33 gluon_seresnext101_32x4d,1358.35,753.84,1024,224,8.02,21.26,48.96 legacy_seresnext101_32x4d,1357.27,754.442,1024,224,8.02,21.26,48.96 efficientnet_b3_gn,1357.0,282.964,384,320,2.14,28.83,11.73 nfnet_f0,1356.65,754.786,1024,256,12.62,18.05,71.49 seresnext101_32x4d,1356.0,755.148,1024,224,8.02,21.26,48.96 resnetv2_152,1353.28,756.668,1024,224,11.55,22.56,60.19 xception,1353.17,567.542,768,299,8.4,35.83,22.86 twins_svt_base,1350.54,758.199,1024,224,8.59,26.33,56.07 crossvit_18_240,1343.82,761.996,1024,240,9.05,26.26,43.27 ese_vovnet99b_iabn,1343.72,762.049,1024,224,16.49,11.27,63.2 maxxvit_rmlp_nano_rw_256,1341.45,763.341,1024,256,4.37,26.05,16.78 regnetx_120,1339.05,764.708,1024,224,12.13,21.37,46.11 vgg16_bn,1336.79,765.998,1024,224,15.5,13.56,138.37 dpn92,1330.6,769.562,1024,224,6.54,18.21,37.67 tv_resnet152,1329.75,770.054,1024,224,11.56,22.56,60.19 gcvit_tiny,1328.61,770.718,1024,224,4.79,29.82,28.22 gluon_resnet152_v1b,1328.2,770.954,1024,224,11.56,22.56,60.19 resnet152,1327.13,771.578,1024,224,11.56,22.56,60.19 ese_vovnet99b,1316.93,777.554,1024,224,16.51,11.27,63.2 pvt_v2_b3,1316.31,777.917,1024,224,6.92,37.7,45.24 xcit_tiny_12_p8_224_dist,1300.55,787.348,1024,224,4.81,23.6,6.71 xcit_tiny_12_p8_224,1299.96,787.704,1024,224,4.81,23.6,6.71 crossvit_18_dagger_240,1298.96,788.312,1024,240,9.5,27.03,44.27 hrnet_w32,1297.82,789.002,1024,224,8.97,22.02,41.23 gluon_resnet152_v1c,1296.47,789.825,1024,224,11.8,23.36,60.21 resnetv2_152d,1296.37,789.881,1024,224,11.8,23.36,60.2 gluon_resnet152_v1d,1293.21,791.811,1024,224,11.8,23.36,60.21 vit_small_resnet50d_s16_224,1288.35,794.801,1024,224,13.48,24.82,57.53 cs3edgenet_x,1281.15,799.266,1024,288,14.59,16.36,47.82 edgenext_base,1272.74,804.548,1024,320,6.01,24.32,18.51 regnety_120,1268.38,807.318,1024,224,12.14,21.38,51.82 dla169,1258.34,813.753,1024,224,11.6,20.2,53.39 hrnet_w30,1252.2,817.74,1024,224,8.15,21.21,37.71 xception41p,1249.06,409.896,512,299,9.25,39.86,26.91 maxxvitv2_nano_rw_256,1248.81,819.967,1024,256,6.26,23.05,23.7 ecaresnet50t,1243.91,823.198,1024,320,8.82,24.13,25.57 vgg19,1237.03,827.774,1024,224,19.63,14.86,143.67 swin_small_patch4_window7_224,1228.67,833.406,1024,224,8.77,27.47,49.61 efficientnet_el_pruned,1220.93,838.69,1024,300,8.0,30.7,10.59 densenet161,1220.41,839.05,1024,224,7.79,11.06,28.68 efficientnet_el,1218.76,840.187,1024,300,8.0,30.7,10.59 deit_base_distilled_patch16_224,1211.4,845.292,1024,224,17.68,24.05,87.34 vit_base_patch16_224,1209.0,846.969,1024,224,17.58,23.9,86.57 vit_base_patch16_224_miil,1208.72,847.163,1024,224,17.59,23.91,94.4 deit_base_patch16_224,1208.56,847.275,1024,224,17.58,23.9,86.57 vit_base_patch16_clip_224,1205.77,849.236,1024,224,17.58,23.9,86.57 gluon_resnet152_v1s,1205.41,849.488,1024,224,12.92,24.96,60.32 coatnet_rmlp_1_rw_224,1201.89,851.979,1024,224,7.85,35.47,41.69 maxvit_tiny_rw_224,1200.3,853.107,1024,224,5.11,33.11,29.06 mixnet_xxl,1193.04,643.721,768,224,2.04,23.43,23.96 tf_efficientnet_el,1192.11,858.967,1024,300,8.0,30.7,10.59 swinv2_tiny_window8_256,1191.01,859.761,1024,256,5.96,24.57,28.35 volo_d1_224,1190.57,860.079,1024,224,6.94,24.43,26.63 repvgg_b2,1183.91,864.916,1024,224,20.45,12.9,89.02 legacy_seresnet152,1181.09,866.978,1024,224,11.33,22.08,66.82 xcit_small_24_p16_224_dist,1175.31,871.245,1024,224,9.1,23.64,47.67 xcit_small_24_p16_224,1174.76,871.656,1024,224,9.1,23.64,47.67 inception_v4,1168.76,876.127,1024,299,12.28,15.09,42.68 seresnet152,1166.02,878.19,1024,224,11.57,22.61,66.82 twins_pcpvt_large,1163.18,880.331,1024,224,9.84,35.82,60.99 deit3_base_patch16_224,1159.4,883.201,1024,224,17.58,23.9,86.59 deit3_base_patch16_224_in21ft1k,1159.14,883.404,1024,224,17.58,23.9,86.59 cait_xxs36_224,1156.4,885.493,1024,224,3.77,30.34,17.3 vit_base_patch32_clip_448,1154.9,886.645,1024,448,17.93,23.9,88.34 regnetx_160,1153.07,888.048,1024,224,15.99,25.52,54.28 dm_nfnet_f0,1152.75,888.293,1024,256,12.62,18.05,71.49 sequencer2d_m,1147.71,892.201,1024,224,6.55,14.26,38.31 repvgg_b3g4,1145.87,893.631,1024,224,17.89,15.1,83.83 mvitv2_small_cls,1144.7,894.542,1024,224,7.04,28.17,34.87 mvitv2_small,1143.83,895.224,1024,224,7.0,28.08,34.87 efficientnet_lite4,1139.64,336.935,384,380,4.04,45.66,13.01 tnt_s_patch16_224,1135.12,902.091,1024,224,5.24,24.37,23.76 convmixer_1024_20_ks9_p14,1130.85,905.497,1024,224,5.55,5.51,24.38 vgg19_bn,1127.16,908.464,1024,224,19.66,14.86,143.68 vit_relpos_base_patch16_clsgap_224,1124.58,910.547,1024,224,17.6,25.12,86.43 vit_relpos_base_patch16_cls_224,1122.76,912.026,1024,224,17.6,25.12,86.43 coatnet_rmlp_1_rw2_224,1119.61,914.591,1024,224,8.11,40.13,41.72 beit_base_patch16_224,1109.32,923.073,1024,224,17.58,23.9,86.53 xception41,1107.6,462.251,512,299,9.28,39.86,26.97 tresnet_xl,1106.51,925.423,1024,224,15.17,15.34,78.44 beitv2_base_patch16_224,1106.05,925.798,1024,224,17.58,23.9,86.53 coat_tiny,1099.16,931.604,1024,224,4.35,27.2,5.5 vit_base_patch16_gap_224,1085.51,943.323,1024,224,17.49,25.59,86.57 maxvit_tiny_tf_224,1081.57,710.062,768,224,5.6,35.78,30.92 vit_relpos_base_patch16_224,1078.21,949.713,1024,224,17.51,24.97,86.43 nf_regnet_b4,1075.82,951.823,1024,384,4.7,28.61,30.21 coatnet_1_rw_224,1074.48,953.005,1024,224,8.04,34.6,41.72 dla102x2,1070.83,956.252,1024,224,9.34,29.91,41.28 pit_b_224,1066.8,479.928,512,224,12.42,32.94,73.76 pit_b_distilled_224,1063.31,481.504,512,224,12.5,33.07,74.79 tf_efficientnet_lite4,1058.68,362.703,384,380,4.04,45.66,13.01 efficientnetv2_s,1057.28,968.508,1024,384,8.44,35.77,21.46 vit_large_r50_s32_224,1034.79,989.556,1024,224,19.58,24.41,328.99 vit_small_patch16_36x1_224,1032.1,992.142,1024,224,13.71,35.69,64.67 efficientnet_b3_g8_gn,1031.26,496.465,512,320,3.2,28.83,14.25 tf_efficientnetv2_s,1029.13,995.002,1024,384,8.44,35.77,21.46 flexivit_base,1028.55,995.558,1024,240,20.29,28.36,86.59 vit_base_patch16_rpn_224,1016.66,1007.208,1024,224,17.49,23.75,86.54 vit_small_r26_s32_384,1011.11,1012.73,1024,384,10.43,29.85,36.47 vit_small_patch16_18x2_224,1005.34,1018.547,1024,224,13.71,35.69,64.67 swinv2_cr_small_224,1000.71,1023.259,1024,224,9.07,50.27,49.7 efficientnetv2_rw_s,995.91,1028.19,1024,384,8.72,38.03,23.94 wide_resnet101_2,995.32,1028.801,1024,224,22.8,21.23,126.89 swinv2_cr_small_ns_224,989.25,1035.114,1024,224,9.08,50.27,49.7 vit_relpos_base_patch16_rpn_224,986.84,1037.641,1024,224,17.51,24.97,86.41 coatnet_1_224,984.69,519.944,512,224,8.7,39.0,42.23 resnet200,983.36,1041.314,1024,224,15.07,32.19,64.67 dpn98,982.09,1042.657,1024,224,11.73,25.2,61.57 convnext_small,981.97,1042.782,1024,288,14.39,35.65,50.22 cs3se_edgenet_x,975.89,1049.279,1024,320,18.01,20.21,50.72 regnety_080,969.67,1056.01,1024,288,13.22,29.69,39.18 poolformer_m36,966.97,1058.965,1024,224,8.8,22.02,56.17 resnest101e,963.69,1062.57,1024,256,13.38,28.66,48.28 regnetz_b16_evos,955.65,803.632,768,288,2.36,16.43,9.74 twins_svt_large,954.95,1072.291,1024,224,15.15,35.1,99.27 pvt_v2_b4,952.02,1075.594,1024,224,10.14,53.74,62.56 gluon_resnext101_64x4d,944.48,1084.183,1024,224,15.52,31.21,83.46 regnetv_064,944.32,1084.367,1024,288,10.55,27.11,30.58 regnety_064,944.18,1084.526,1024,288,10.56,27.11,30.58 maxvit_rmlp_tiny_rw_256,941.64,815.588,768,256,6.77,46.92,29.15 regnetz_d8,936.16,1093.814,1024,320,6.19,37.08,23.37 resnetrs101,936.12,1093.858,1024,288,13.56,28.53,63.62 regnetz_d32,933.58,1096.833,1024,320,9.33,37.08,27.58 ig_resnext101_32x8d,930.9,1099.997,1024,224,16.48,31.21,88.79 swsl_resnext101_32x8d,930.28,1100.725,1024,224,16.48,31.21,88.79 resnext101_32x8d,929.98,1101.084,1024,224,16.48,31.21,88.79 ssl_resnext101_32x8d,929.0,1102.24,1024,224,16.48,31.21,88.79 convnextv2_tiny,925.13,553.423,512,288,7.39,22.21,28.64 convnextv2_small,924.53,1107.57,1024,224,8.71,21.56,50.32 maxvit_tiny_rw_256,921.72,833.209,768,256,6.74,44.35,29.07 inception_resnet_v2,917.69,1115.834,1024,299,13.18,25.06,55.84 ens_adv_inception_resnet_v2,917.66,1115.871,1024,299,13.18,25.06,55.84 maxxvit_rmlp_tiny_rw_256,914.74,1119.428,1024,256,6.66,39.76,29.64 xcit_tiny_24_p16_384_dist,912.61,1122.045,1024,384,6.87,34.29,12.12 cait_s24_224,908.65,1126.929,1024,224,9.35,40.58,46.92 pvt_v2_b5,904.89,1131.615,1024,224,11.76,50.92,81.96 nest_small,902.63,850.834,768,224,10.35,40.04,38.35 repvgg_b3,901.73,1135.583,1024,224,29.16,15.1,123.09 maxvit_tiny_pm_256,896.67,1141.994,1024,256,6.61,47.9,30.09 xception65p,896.53,571.079,512,299,13.91,52.48,39.82 swin_s3_small_224,896.35,856.792,768,224,9.43,37.84,49.74 jx_nest_small,892.32,860.663,768,224,10.35,40.04,38.35 efficientnet_b4,890.89,431.018,384,384,4.51,50.04,19.34 gmlp_b16_224,885.75,1156.072,1024,224,15.78,30.21,73.08 gluon_seresnext101_64x4d,885.23,1156.747,1024,224,15.53,31.25,88.23 hrnet_w40,881.9,1161.12,1024,224,12.75,25.29,57.56 efficientformer_l7,877.43,1167.027,1024,224,10.17,24.45,82.23 coat_mini,874.29,1171.227,1024,224,6.82,33.68,10.34 resnet101d,871.81,1174.559,1024,320,16.48,34.77,44.57 swin_base_patch4_window7_224,870.1,1176.867,1024,224,15.47,36.63,87.77 regnetz_040,868.17,884.605,768,320,6.35,37.78,27.12 regnetz_040h,862.76,890.151,768,320,6.43,37.94,28.94 mobilevitv2_150_384_in22ft1k,848.7,301.627,256,384,9.2,54.25,10.59 resnetv2_50d_evos,844.34,909.573,768,288,7.15,19.7,25.59 tf_efficientnet_b4,838.16,458.136,384,380,4.49,49.49,19.34 crossvit_base_240,835.31,919.411,768,240,21.22,36.33,105.03 vit_base_r50_s16_224,821.15,1247.01,1024,224,21.67,35.31,114.69 xcit_medium_24_p16_224_dist,819.59,1249.397,1024,224,16.13,31.71,84.4 xcit_medium_24_p16_224,818.73,1250.697,1024,224,16.13,31.71,84.4 gcvit_small,807.46,1268.151,1024,224,8.57,41.61,51.09 gluon_xception65,806.21,635.055,512,299,13.96,52.48,39.92 xception65,800.01,639.983,512,299,13.96,52.48,39.92 mvitv2_base,799.31,1281.092,1024,224,10.16,40.5,51.47 hrnet_w44,789.29,1297.348,1024,224,14.94,26.92,67.06 vit_base_patch16_plus_240,780.68,1311.665,1024,240,27.41,33.08,117.56 hrnet_w48,780.39,1312.147,1024,224,17.34,28.56,77.47 swinv2_tiny_window16_256,778.19,657.926,512,256,6.68,39.02,28.35 tresnet_m_448,775.99,1319.596,1024,448,22.94,29.21,31.39 xcit_small_12_p16_384_dist,760.88,1345.804,1024,384,14.14,36.51,26.25 vit_small_patch16_384,750.95,1022.685,768,384,15.52,50.78,22.2 maxvit_rmlp_small_rw_224,745.49,1373.585,1024,224,10.75,49.3,64.9 sequencer2d_l,742.48,1379.149,1024,224,9.74,22.12,54.3 swinv2_small_window8_256,738.39,1386.788,1024,256,11.58,40.14,49.73 swin_s3_base_224,730.45,1401.854,1024,224,13.69,48.26,71.13 poolformer_m48,729.44,1403.808,1024,224,11.59,29.17,73.47 densenet264d_iabn,727.43,1407.671,1024,224,13.47,14.0,72.74 vit_relpos_base_patch16_plus_240,723.43,1415.468,1024,240,27.3,34.33,117.38 dpn131,722.72,1416.854,1024,224,16.09,32.97,79.25 tnt_b_patch16_224,722.12,1418.026,1024,224,14.09,39.01,65.41 deit3_small_patch16_384,717.36,1070.572,768,384,15.52,50.78,22.21 deit3_small_patch16_384_in21ft1k,716.76,1071.477,768,384,15.52,50.78,22.21 swinv2_cr_base_224,715.64,1430.874,1024,224,15.86,59.66,87.88 eca_nfnet_l1,713.15,1435.867,1024,320,14.92,34.42,41.41 coatnet_2_rw_224,709.88,721.237,512,224,15.09,49.22,73.87 swinv2_cr_base_ns_224,709.69,1442.871,1024,224,15.86,59.66,87.88 coatnet_rmlp_2_rw_224,708.85,722.285,512,224,15.18,54.78,73.88 convit_base,706.65,1449.076,1024,224,17.52,31.77,86.54 mobilevitv2_175_384_in22ft1k,703.41,363.928,256,384,12.47,63.29,14.25 maxvit_small_tf_224,701.58,729.767,512,224,11.66,53.17,68.93 densenet264,701.03,1460.686,1024,224,12.95,12.8,72.69 ecaresnet200d,694.19,1475.094,1024,256,20.0,43.15,64.69 resnetv2_50x1_bitm,691.29,740.624,512,448,16.62,44.46,25.55 seresnet200d,691.25,1481.355,1024,256,20.01,43.15,71.86 xcit_tiny_24_p8_224,684.73,1495.467,1024,224,9.21,45.39,12.11 xcit_tiny_24_p8_224_dist,684.22,1496.573,1024,224,9.21,45.39,12.11 convnext_base,682.42,1500.518,1024,288,25.43,47.53,88.59 volo_d2_224,663.51,1543.3,1024,224,14.34,41.34,58.68 coatnet_2_224,660.84,581.062,384,224,16.5,52.67,74.68 legacy_senet154,654.15,1565.387,1024,224,20.77,38.69,115.09 gluon_senet154,654.04,1565.641,1024,224,20.77,38.69,115.09 senet154,653.94,1565.866,1024,224,20.77,38.69,115.09 xcit_nano_12_p8_384_dist,646.53,1583.823,1024,384,6.34,46.08,3.05 dpn107,646.38,1584.202,1024,224,18.38,33.46,86.92 nest_base,640.55,799.298,512,224,17.96,53.39,67.72 jx_nest_base,633.53,808.151,512,224,17.96,53.39,67.72 mobilevitv2_200_384_in22ft1k,626.31,408.731,256,384,16.24,72.34,18.45 xception71,619.72,826.163,512,299,18.09,69.92,42.34 hrnet_w64,618.15,1656.539,1024,224,28.97,35.09,128.06 resnet152d,618.09,1656.699,1024,320,24.08,47.67,60.21 regnetz_c16_evos,604.19,847.399,512,320,3.86,25.88,13.49 gcvit_base,594.61,1722.135,1024,224,14.87,55.48,90.32 regnety_160,594.3,1292.258,768,288,26.37,38.07,83.59 maxxvit_rmlp_small_rw_256,588.15,1741.023,1024,256,14.67,58.38,66.01 xcit_small_12_p8_224,582.04,1759.324,1024,224,18.69,47.21,26.21 xcit_small_12_p8_224_dist,581.74,1760.224,1024,224,18.69,47.21,26.21 maxvit_rmlp_small_rw_256,575.72,1333.976,768,256,14.15,66.09,64.9 regnetx_320,551.07,1393.631,768,224,31.81,36.3,107.81 seresnet152d,547.51,1870.27,1024,320,24.09,47.72,66.84 resnetrs152,544.33,1881.196,1024,320,24.34,48.14,86.62 vit_large_patch32_384,543.23,1884.997,1024,384,45.31,43.86,306.63 halonet_h1,540.47,473.65,256,256,3.0,51.17,8.1 seresnet269d,540.42,1894.818,1024,256,26.59,53.6,113.67 swinv2_base_window8_256,529.22,1451.182,768,256,20.37,52.59,87.92 maxxvitv2_rmlp_base_rw_224,523.43,1956.308,1024,224,24.2,62.77,116.09 resnext101_64x4d,521.77,1962.525,1024,288,25.66,51.59,83.46 regnetz_e8,521.5,1472.647,768,320,15.46,63.94,57.7 mixer_l16_224,518.26,1975.807,1024,224,44.6,41.69,208.2 vit_medium_patch16_gap_384,508.63,1006.611,512,384,26.08,67.54,39.03 swin_large_patch4_window7_224,501.11,1532.586,768,224,34.53,54.94,196.53 regnety_320,490.98,2085.591,1024,224,32.34,30.26,145.05 swinv2_small_window16_256,487.64,1049.932,512,256,12.82,66.29,49.73 seresnext101_32x8d,483.23,2119.074,1024,288,27.24,51.63,93.57 vit_small_patch8_224,478.05,1071.009,512,224,22.44,80.84,21.67 ig_resnext101_32x16d,477.64,2143.862,1024,224,36.27,51.18,194.03 swsl_resnext101_32x16d,476.69,2148.145,1024,224,36.27,51.18,194.03 ssl_resnext101_32x16d,476.06,2150.954,1024,224,36.27,51.18,194.03 seresnext101d_32x8d,475.05,2155.547,1024,288,27.64,52.95,93.59 nf_regnet_b5,470.14,1089.029,512,456,11.7,61.95,49.74 xcit_large_24_p16_224_dist,468.86,2184.017,1024,224,35.86,47.27,189.1 xcit_large_24_p16_224,468.75,2184.529,1024,224,35.86,47.27,189.1 volo_d3_224,463.72,2208.199,1024,224,20.78,60.09,86.33 nfnet_f1,463.52,2209.163,1024,320,35.97,46.77,132.63 efficientnet_b5,460.91,555.412,256,448,9.59,93.56,30.39 resnet200d,453.15,2259.739,1024,320,31.25,67.33,64.69 efficientnetv2_m,451.89,2266.018,1024,416,18.6,67.5,54.14 seresnextaa101d_32x8d,447.26,2289.498,1024,288,28.51,56.44,93.59 efficientnetv2_rw_m,437.1,1757.005,768,416,21.49,79.62,53.24 swinv2_cr_large_224,422.08,1819.551,768,224,35.1,78.42,196.68 coatnet_rmlp_3_rw_224,421.87,910.226,384,224,33.56,79.47,165.15 xcit_tiny_12_p8_384_dist,421.04,2432.044,1024,384,14.13,69.14,6.71 swinv2_cr_tiny_384,419.77,609.847,256,384,15.34,161.01,28.33 maxvit_rmlp_base_rw_224,419.03,1832.808,768,224,23.15,92.64,116.14 resnetv2_152x2_bit_teacher,418.89,2444.553,1024,224,46.95,45.11,236.34 resnetv2_101x1_bitm,418.36,1223.813,512,448,31.65,64.93,44.54 dm_nfnet_f1,409.02,1877.643,768,320,35.97,46.77,132.63 xcit_small_24_p16_384_dist,407.47,2513.062,1024,384,26.72,68.58,47.67 coatnet_3_rw_224,404.39,633.033,256,224,33.44,73.83,181.81 tf_efficientnet_b5,403.59,634.298,256,456,10.46,98.86,30.39 convnextv2_base,402.92,1270.715,512,288,25.43,47.53,88.72 resnetrs200,396.11,2585.123,1024,320,31.51,67.81,93.21 tresnet_l_448,395.6,2588.481,1024,448,43.5,47.56,55.99 eva_large_patch14_196,391.22,2617.408,1024,196,61.57,63.52,304.14 vit_large_patch16_224,389.92,2626.132,1024,224,61.6,63.52,304.33 regnetz_d8_evos,389.86,1969.937,768,320,7.03,38.92,23.46 maxvit_base_tf_224,387.71,1320.545,512,224,24.04,95.01,119.47 coatnet_3_224,387.35,660.882,256,224,36.56,79.01,166.97 crossvit_15_dagger_408,386.57,662.227,256,408,21.45,95.05,28.5 vit_base_patch16_18x2_224,384.3,2664.545,1024,224,52.51,71.38,256.73 deit3_large_patch16_224,376.93,2716.643,1024,224,61.6,63.52,304.37 deit3_large_patch16_224_in21ft1k,376.54,2719.504,1024,224,61.6,63.52,304.37 tf_efficientnetv2_m,374.38,2051.373,768,480,24.76,89.84,54.14 convnext_large,371.39,1378.579,512,288,56.87,71.29,197.77 beitv2_large_patch16_224,360.12,2843.465,1024,224,61.6,63.52,304.43 beit_large_patch16_224,359.86,2845.558,1024,224,61.6,63.52,304.43 swinv2_base_window12to16_192to256_22kft1k,359.31,1068.705,384,256,22.02,84.71,87.92 swinv2_base_window16_256,359.09,1069.342,384,256,22.02,84.71,87.92 eca_nfnet_l2,347.1,2212.621,768,384,30.05,68.28,56.72 flexivit_large,333.31,3072.173,1024,240,70.99,75.39,304.36 vit_large_r50_s32_384,332.86,3076.333,1024,384,57.43,76.52,329.09 maxxvitv2_rmlp_large_rw_224,330.79,3095.576,1024,224,44.14,87.15,215.42 resnest200e,317.25,3227.754,1024,320,35.69,82.78,70.2 maxvit_tiny_tf_384,317.22,807.002,256,384,17.53,123.42,30.98 convmixer_768_32,309.28,3310.892,1024,224,19.55,25.95,21.11 deit_base_patch16_384,306.13,1254.335,384,384,55.54,101.56,86.86 vit_base_patch16_384,306.13,1254.349,384,384,55.54,101.56,86.86 vit_base_patch16_clip_384,305.56,1256.673,384,384,55.54,101.56,86.86 xcit_small_24_p8_224_dist,305.18,3355.41,1024,224,35.81,90.78,47.63 deit_base_distilled_patch16_384,304.96,1259.16,384,384,55.65,101.82,87.63 xcit_small_24_p8_224,304.86,3358.887,1024,224,35.81,90.78,47.63 nasnetalarge,300.31,1278.679,384,331,23.89,90.56,88.75 volo_d1_384,299.05,1712.072,512,384,22.75,108.55,26.78 volo_d4_224,295.86,3461.069,1024,224,44.34,80.22,192.96 deit3_base_patch16_384,294.03,1305.985,384,384,55.54,101.56,86.88 deit3_base_patch16_384_in21ft1k,293.78,1307.085,384,384,55.54,101.56,86.88 tresnet_xl_448,292.43,2626.294,768,448,60.65,61.31,78.44 pnasnet5large,285.95,1342.894,384,331,25.04,92.89,86.06 vit_large_patch14_224,285.66,3584.705,1024,224,81.08,88.79,304.2 vit_large_patch14_clip_224,285.43,3587.599,1024,224,81.08,88.79,304.2 crossvit_18_dagger_408,283.82,901.967,256,408,32.47,124.87,44.61 xcit_medium_24_p16_384_dist,282.22,3628.317,1024,384,47.39,91.64,84.4 cait_xxs24_384,275.38,3718.492,1024,384,9.63,122.66,12.03 regnety_640,271.79,2825.663,768,224,64.16,42.5,281.38 maxvit_large_tf_224,268.97,1427.67,384,224,43.68,127.35,211.79 nfnet_f2,263.0,3893.59,1024,352,63.22,79.06,193.78 beit_base_patch16_384,260.66,1473.146,384,384,55.54,101.56,86.74 swinv2_cr_small_384,258.79,989.214,256,384,29.7,298.03,49.7 ecaresnet269d,257.79,3972.16,1024,352,50.25,101.25,102.09 resnetrs270,249.11,4110.633,1024,352,51.13,105.48,129.86 mvitv2_large,248.64,2059.181,512,224,43.87,112.02,217.99 efficientnet_b6,246.42,519.432,128,528,19.4,167.39,43.04 convnext_xlarge,241.35,2121.412,512,288,100.8,95.05,350.2 convnextv2_large,238.64,1072.708,256,288,56.87,71.29,197.96 tf_efficientnet_b6,236.4,541.434,128,528,19.4,167.39,43.04 swin_base_patch4_window12_384,235.04,816.885,192,384,47.19,134.78,87.9 dm_nfnet_f2,234.34,3277.279,768,352,63.22,79.06,193.78 coatnet_4_224,228.52,1120.23,256,224,62.48,129.26,275.43 vit_base_r50_s16_384,227.31,1689.303,384,384,67.43,135.03,98.95 efficientnetv2_l,221.97,2306.653,512,480,56.4,157.99,118.52 xcit_tiny_24_p8_384_dist,221.23,4628.611,1024,384,27.05,132.95,12.11 ig_resnext101_32x32d,220.61,2320.857,512,224,87.29,91.12,468.53 swinv2_large_window12to16_192to256_22kft1k,219.46,1166.485,256,256,47.81,121.53,196.74 tf_efficientnetv2_l,219.35,2334.183,512,480,56.4,157.99,118.52 resmlp_big_24_224,214.31,4778.166,1024,224,100.23,87.31,129.14 resmlp_big_24_224_in22ft1k,214.13,4782.043,1024,224,100.23,87.31,129.14 resmlp_big_24_distilled_224,214.04,4784.169,1024,224,100.23,87.31,129.14 xcit_medium_24_p8_224_dist,210.1,4873.763,1024,224,63.53,121.23,84.32 xcit_medium_24_p8_224,210.01,4875.864,1024,224,63.53,121.23,84.32 maxvit_small_tf_384,208.79,919.556,192,384,35.87,183.65,69.02 vit_base_patch8_224,199.59,1282.637,256,224,78.22,161.69,86.58 eca_nfnet_l3,199.58,2565.434,512,448,52.55,118.4,72.04 volo_d5_224,196.25,5217.924,1024,224,72.4,118.11,295.46 xcit_small_12_p8_384_dist,194.27,2635.521,512,384,54.92,138.29,26.21 cait_xs24_384,192.73,3984.863,768,384,19.28,183.98,26.67 swinv2_cr_base_384,184.92,1384.392,256,384,50.57,333.68,87.88 cait_xxs36_384,184.35,5554.56,1024,384,14.35,183.7,17.37 swinv2_cr_huge_224,183.61,2091.395,384,224,115.97,121.08,657.83 convnext_xxlarge,183.01,2098.268,384,224,151.66,95.29,846.47 coatnet_rmlp_2_rw_384,178.88,715.532,128,384,47.69,209.43,73.88 convmixer_1536_20,173.51,5901.752,1024,224,48.68,33.03,51.63 volo_d2_384,168.46,1519.603,256,384,46.17,184.51,58.87 resnetrs350,168.28,6085.136,1024,384,77.59,154.74,163.96 xcit_large_24_p16_384_dist,160.71,4778.847,768,384,105.35,137.17,189.1 resnetv2_152x2_bit_teacher_384,159.55,1604.488,256,384,136.16,132.56,236.34 maxvit_xlarge_tf_224,155.79,1643.178,256,224,97.49,191.02,474.95 maxvit_tiny_tf_512,155.64,822.373,128,512,33.49,257.59,31.05 regnety_1280,155.18,2474.502,384,224,127.66,71.58,644.81 vit_huge_patch14_224,154.03,6647.897,1024,224,167.43,139.43,658.75 vit_huge_patch14_clip_224,153.92,6652.944,1024,224,167.4,139.41,632.05 maxxvitv2_rmlp_base_rw_384,153.34,1669.502,256,384,72.98,213.74,116.09 efficientnetv2_xl,152.49,3357.61,512,512,93.85,247.32,208.12 tf_efficientnetv2_xl,151.4,2536.254,384,512,93.85,247.32,208.12 deit3_huge_patch14_224_in21ft1k,149.08,6868.834,1024,224,167.4,139.41,632.13 deit3_huge_patch14_224,149.01,6871.974,1024,224,167.4,139.41,632.13 cait_s24_384,148.46,3448.684,512,384,32.17,245.31,47.06 resnest269e,147.61,3468.584,512,416,77.69,171.98,110.93 nfnet_f3,147.43,3472.717,512,416,115.58,141.78,254.92 efficientnet_b7,142.41,674.084,96,600,38.33,289.94,66.35 resnetv2_50x3_bitm,138.27,1388.564,192,448,145.7,133.37,217.32 tf_efficientnet_b7,137.89,696.181,96,600,38.33,289.94,66.35 swin_large_patch4_window12_384,137.6,930.229,128,384,104.08,202.16,196.74 ig_resnext101_32x48d,132.29,2902.628,384,224,153.57,131.06,828.41 dm_nfnet_f3,127.59,4012.898,512,416,115.58,141.78,254.92 coatnet_5_224,125.18,1022.512,128,224,145.49,194.24,687.47 maxvit_rmlp_base_rw_384,121.26,2111.079,256,384,70.97,318.95,116.14 xcit_large_24_p8_224,119.97,6401.598,768,224,141.23,181.56,188.93 xcit_large_24_p8_224_dist,119.94,6403.17,768,224,141.23,181.56,188.93 resnetrs420,119.93,6403.598,768,416,108.45,213.79,191.89 resnetv2_152x2_bitm,117.33,2181.801,256,448,184.99,180.43,236.34 maxvit_base_tf_384,113.69,1688.826,192,384,73.8,332.9,119.65 swinv2_cr_large_384,113.07,1132.03,128,384,108.95,404.96,196.68 eva_large_patch14_336,102.65,2493.904,256,336,191.1,270.24,304.53 vit_large_patch14_clip_336,102.47,2498.286,256,336,191.11,270.24,304.53 vit_large_patch16_384,102.37,2500.639,256,384,191.21,270.24,304.72 xcit_small_24_p8_384_dist,102.36,5001.728,512,384,105.24,265.91,47.63 eva_giant_patch14_224,101.75,10063.521,1024,224,267.18,192.64,1012.56 vit_giant_patch14_224,100.42,7648.057,768,224,267.18,192.64,1012.61 vit_giant_patch14_clip_224,100.32,7655.265,768,224,267.18,192.64,1012.65 cait_s36_384,99.37,5152.338,512,384,47.99,367.4,68.37 deit3_large_patch16_384,99.34,2577.037,256,384,191.21,270.24,304.76 deit3_large_patch16_384_in21ft1k,99.27,2578.907,256,384,191.21,270.24,304.76 regnety_2560,97.99,2612.623,256,224,257.07,87.48,826.14 maxvit_small_tf_512,97.85,981.11,96,512,67.26,383.77,69.13 swinv2_base_window12to24_192to384_22kft1k,95.95,666.98,64,384,55.25,280.36,87.92 efficientnet_b8,95.3,1007.298,96,672,63.48,442.89,87.41 tf_efficientnet_b8,92.65,1036.1,96,672,63.48,442.89,87.41 beit_large_patch16_384,88.55,2890.891,256,384,191.21,270.24,305.0 resnetv2_101x3_bitm,83.1,2310.491,192,448,280.33,194.78,387.93 maxvit_large_tf_384,80.34,1593.284,128,384,132.55,445.84,212.03 nfnet_f4,79.54,4827.723,384,512,216.26,262.26,316.07 volo_d3_448,73.5,2612.274,192,448,96.33,446.83,86.63 dm_nfnet_f4,71.41,3584.699,256,512,216.26,262.26,316.07 xcit_medium_24_p8_384_dist,70.91,5415.294,384,384,186.67,354.73,84.32 swinv2_large_window12to24_192to384_22kft1k,60.84,788.97,48,384,116.15,407.83,196.74 vit_gigantic_patch14_clip_224,60.15,8511.823,512,224,483.96,275.37,1844.91 vit_gigantic_patch14_224,60.11,8517.291,512,224,483.95,275.37,1844.44 nfnet_f5,58.02,4412.387,256,544,290.97,349.71,377.21 vit_huge_patch14_clip_336,57.29,4468.831,256,336,390.97,407.54,632.46 convnextv2_huge,56.06,1712.576,96,384,337.96,232.35,660.29 volo_d4_448,54.47,2349.801,128,448,197.13,527.35,193.41 tf_efficientnet_l2,54.12,1182.593,64,475,172.11,609.89,480.31 maxvit_base_tf_512,52.65,1823.292,96,512,138.02,703.99,119.88 swinv2_cr_giant_224,52.12,2455.882,128,224,483.85,309.15,2598.76 dm_nfnet_f5,50.7,5049.339,256,544,290.97,349.71,377.21 swinv2_cr_huge_384,48.86,1309.971,64,384,352.04,583.18,657.94 maxvit_xlarge_tf_384,46.24,2076.289,96,384,292.78,668.76,475.32 nfnet_f6,44.3,5778.548,256,576,378.69,452.2,438.36 xcit_large_24_p8_384_dist,40.2,6368.127,256,384,415.0,531.82,188.93 eva_giant_patch14_336,39.77,6436.237,256,336,620.64,550.67,1013.01 dm_nfnet_f6,39.62,6461.626,256,576,378.69,452.2,438.36 maxvit_large_tf_512,38.67,1654.908,64,512,244.75,942.15,212.33 volo_d5_448,37.56,3408.043,128,448,315.06,737.92,295.91 beit_large_patch16_512,35.36,2715.28,96,512,362.24,656.39,305.67 nfnet_f7,34.74,7370.0,256,608,480.39,570.85,499.5 cait_m36_384,32.36,7912.123,256,384,173.11,734.81,271.22 resnetv2_152x4_bitm,30.0,4266.89,128,480,844.84,414.26,936.53 volo_d5_512,26.35,4857.602,128,512,425.09,1105.37,296.09 maxvit_xlarge_tf_512,23.12,2076.455,48,512,534.14,1413.22,475.77 efficientnet_l2,21.26,1505.032,32,800,479.12,1707.39,480.31 swinv2_cr_giant_384,15.03,2129.6,32,384,1450.71,1394.86,2598.76 cait_m48_448,13.69,9353.048,128,448,329.41,1708.23,356.46 eva_giant_patch14_560,10.36,4631.037,48,560,1906.76,2577.17,1014.45
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt111-cu113-rtx3090.csv	model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count tinynet_e,68298.73,14.982,1024,106,2.04 mobilenetv3_small_050,48773.32,20.985,1024,224,1.59 lcnet_035,47045.94,21.755,1024,224,1.64 lcnet_050,41541.83,24.639,1024,224,1.88 mobilenetv3_small_075,37803.23,27.076,1024,224,2.04 mobilenetv3_small_100,34839.31,29.381,1024,224,2.54 tinynet_d,34615.54,29.571,1024,152,2.34 tf_mobilenetv3_small_minimal_100,31097.5,32.918,1024,224,2.04 tf_mobilenetv3_small_075,30498.6,33.564,1024,224,2.04 tf_mobilenetv3_small_100,28466.28,35.962,1024,224,2.54 lcnet_075,26999.4,37.915,1024,224,2.36 mnasnet_small,23228.74,44.072,1024,224,2.03 lcnet_100,22774.77,44.951,1024,224,2.95 levit_128s,21485.8,47.648,1024,224,7.78 mobilenetv2_035,20032.08,51.106,1024,224,1.68 ghostnet_050,18639.82,54.925,1024,224,2.59 mnasnet_050,18244.9,56.115,1024,224,2.22 regnetx_002,17821.98,57.446,1024,224,2.68 tinynet_c,17586.87,58.214,1024,184,2.46 regnety_002,16673.08,61.405,1024,224,3.16 mobilenetv2_050,16415.14,62.371,1024,224,1.97 semnasnet_050,16295.23,62.83,1024,224,2.08 lcnet_150,15040.68,68.071,1024,224,4.5 levit_128,14657.83,69.849,1024,224,9.21 regnetx_004,14440.03,70.903,1024,224,5.16 gernet_s,14051.59,72.863,1024,224,8.17 mobilenetv3_large_075,13658.47,74.961,1024,224,3.99 levit_192,12892.86,79.412,1024,224,10.95 mnasnet_075,12457.54,82.188,1024,224,3.17 mobilenetv3_rw,12442.0,82.291,1024,224,5.48 hardcorenas_a,12441.72,82.293,1024,224,5.26 mixer_s32_224,12325.03,83.072,1024,224,19.1 mobilenetv3_large_100,12253.83,83.554,1024,224,5.48 mobilenetv3_large_100_miil,12253.15,83.559,1024,224,5.48 vit_small_patch32_224,12098.59,84.625,1024,224,22.88 tf_mobilenetv3_large_075,11757.16,87.085,1024,224,3.99 tinynet_b,11714.84,87.399,1024,188,3.73 hardcorenas_b,11307.89,90.545,1024,224,5.18 hardcorenas_c,11295.65,90.643,1024,224,5.52 ese_vovnet19b_slim_dw,11295.18,90.646,1024,224,1.9 tf_mobilenetv3_large_minimal_100,11279.9,90.77,1024,224,3.92 mnasnet_b1,10903.65,93.902,1024,224,4.38 mnasnet_100,10903.28,93.906,1024,224,4.38 swsl_resnet18,10835.46,94.49,1024,224,11.69 ssl_resnet18,10829.31,94.547,1024,224,11.69 resnet18,10826.05,94.576,1024,224,11.69 gluon_resnet18_v1b,10791.4,94.879,1024,224,11.69 tf_mobilenetv3_large_100,10638.58,96.242,1024,224,5.48 hardcorenas_d,10551.44,97.037,1024,224,7.5 semnasnet_075,10519.79,97.329,1024,224,2.91 ghostnet_100,10434.77,98.122,1024,224,5.18 mobilenetv2_075,10372.86,98.708,1024,224,2.64 seresnet18,10183.7,100.541,1024,224,11.78 regnety_006,9982.58,102.567,1024,224,6.06 vit_tiny_r_s16_p8_224,9895.77,103.465,1024,224,6.34 spnasnet_100,9875.93,103.675,1024,224,4.42 legacy_seresnet18,9845.25,103.999,1024,224,11.78 regnety_004,9552.58,107.183,1024,224,4.34 levit_256,9434.24,108.53,1024,224,18.89 tinynet_a,9412.38,108.782,1024,192,6.19 hardcorenas_f,9390.96,109.029,1024,224,8.2 semnasnet_100,9334.36,109.69,1024,224,3.89 mnasnet_a1,9318.68,109.875,1024,224,3.89 mobilenetv2_100,9260.72,110.564,1024,224,3.5 hardcorenas_e,9255.53,110.624,1024,224,8.07 tf_efficientnetv2_b0,9250.71,110.683,1024,224,7.14 fbnetc_100,9032.97,113.35,1024,224,5.57 efficientnet_lite0,8999.14,113.778,1024,224,4.65 resnet18d,8913.81,114.867,1024,224,11.71 ese_vovnet19b_slim,8715.26,117.484,1024,224,3.17 regnetx_008,8458.52,121.05,1024,224,7.26 levit_256d,8024.27,127.602,1024,224,26.21 regnetx_006,7937.85,128.991,1024,224,6.2 regnety_008,7871.11,130.085,1024,224,6.26 tf_efficientnet_lite0,7813.92,131.036,1024,224,4.65 efficientnet_b0,7681.79,133.291,1024,224,5.29 ghostnet_130,7655.04,133.756,1024,224,7.36 mnasnet_140,7500.9,136.506,1024,224,7.12 xcit_nano_12_p16_224_dist,7406.15,138.252,1024,224,3.05 xcit_nano_12_p16_224,7390.69,138.541,1024,224,3.05 rexnetr_100,7266.82,140.903,1024,224,4.88 mobilenetv2_110d,7027.38,145.704,1024,224,4.52 tf_efficientnet_b0_ap,6815.39,150.235,1024,224,5.29 tf_efficientnet_b0,6815.06,150.243,1024,224,5.29 tf_efficientnet_b0_ns,6813.82,150.27,1024,224,5.29 regnetz_005,6657.81,153.793,1024,224,7.12 hrnet_w18_small,6626.19,154.526,1024,224,13.19 gernet_m,6452.0,158.699,1024,224,21.14 semnasnet_140,6407.4,159.804,1024,224,6.11 tv_resnet34,6289.18,162.807,1024,224,21.8 gluon_resnet34_v1b,6283.02,162.967,1024,224,21.8 resnet34,6262.0,163.515,1024,224,21.8 vit_tiny_patch16_224,6224.71,164.493,1024,224,5.72 mobilenetv2_140,6223.01,164.539,1024,224,6.11 deit_tiny_patch16_224,6218.5,164.657,1024,224,5.72 ese_vovnet19b_dw,6178.2,165.733,1024,224,6.54 deit_tiny_distilled_patch16_224,6117.9,167.365,1024,224,5.91 efficientnet_b1_pruned,6023.92,169.977,1024,240,6.33 efficientnet_lite1,5983.27,171.132,1024,240,5.42 tf_efficientnetv2_b1,5921.06,172.93,1024,240,8.14 selecsls42,5908.85,173.287,1024,224,30.35 fbnetv3_b,5903.05,173.458,1024,256,8.6 seresnet34,5898.43,173.594,1024,224,21.96 selecsls42b,5885.09,173.988,1024,224,32.46 rexnet_100,5871.63,174.387,1024,224,4.8 pit_ti_distilled_224,5837.73,175.397,1024,224,5.1 pit_ti_224,5807.14,176.321,1024,224,4.85 efficientnet_es,5711.75,179.268,1024,224,5.44 efficientnet_es_pruned,5710.76,179.298,1024,224,5.44 resnet26,5694.28,179.817,1024,224,16.0 legacy_seresnet34,5670.37,180.577,1024,224,21.96 levit_384,5643.3,181.443,1024,224,39.13 resnet34d,5571.88,183.768,1024,224,21.82 resnetblur18,5507.37,185.919,1024,224,11.69 rexnetr_130,5484.64,186.691,1024,224,7.61 nf_regnet_b0,5429.0,188.605,1024,256,8.76 tf_efficientnet_es,5424.57,188.76,1024,224,5.44 tf_efficientnet_lite1,5360.34,191.021,1024,240,5.42 skresnet18,5350.54,191.371,1024,224,11.96 selecsls60,5263.66,194.53,1024,224,30.67 selecsls60b,5251.8,194.969,1024,224,32.77 mobilenetv2_120d,5160.96,198.401,1024,224,5.83 mobilevit_xxs,5125.56,199.771,1024,256,1.27 repvgg_b0,5049.08,202.797,1024,224,15.82 resnet26d,4891.07,209.349,1024,224,16.01 fbnetv3_d,4812.34,212.774,1024,256,10.31 rexnetr_150,4791.0,213.722,1024,224,9.78 xcit_tiny_12_p16_224_dist,4778.77,214.269,1024,224,6.72 xcit_tiny_12_p16_224,4774.3,214.469,1024,224,6.72 visformer_tiny,4714.23,217.203,1024,224,10.32 nf_resnet26,4639.54,220.7,1024,224,16.0 efficientnet_lite2,4604.04,222.402,1024,260,6.09 pit_xs_224,4582.73,223.434,1024,224,10.62 pit_xs_distilled_224,4539.81,225.546,1024,224,11.0 resmlp_12_distilled_224,4519.92,226.541,1024,224,15.35 resmlp_12_224,4518.97,226.589,1024,224,15.35 tf_efficientnetv2_b2,4403.08,232.553,1024,260,10.1 vit_base_patch32_224_sam,4330.12,236.472,1024,224,88.22 vit_base_patch32_224,4316.0,237.246,1024,224,88.22 mixer_b32_224,4294.72,238.421,1024,224,60.29 tf_efficientnet_b1_ns,4267.56,239.936,1024,240,7.79 tf_efficientnet_b1_ap,4266.36,240.004,1024,240,7.79 tf_efficientnet_b1,4266.12,240.017,1024,240,7.79 efficientnet_b0_g16_evos,4260.91,240.312,1024,224,8.11 legacy_seresnext26_32x4d,4210.62,243.183,1024,224,16.79 mixer_s16_224,4210.38,243.197,1024,224,18.53 gernet_l,4176.68,245.159,1024,256,31.08 tf_efficientnet_lite2,4154.51,246.467,1024,260,6.09 gmixer_12_224,4127.0,248.109,1024,224,12.7 efficientnet_b1,4101.97,249.625,1024,256,7.79 resnext26ts,4091.38,250.27,1024,256,10.3 rexnet_130,4013.83,255.106,1024,224,7.56 seresnext26ts,3966.87,258.123,1024,256,10.39 nf_seresnet26,3954.94,258.905,1024,224,17.4 eca_resnext26ts,3953.54,258.996,1024,256,10.3 nf_ecaresnet26,3938.91,259.956,1024,224,16.0 repvgg_a2,3868.66,264.68,1024,224,28.21 gmlp_ti16_224,3865.39,264.903,1024,224,5.87 crossvit_tiny_240,3854.54,265.648,1024,240,7.01 efficientnet_b2_pruned,3821.39,267.953,1024,260,8.31 vit_small_patch32_384,3802.4,269.289,1024,384,22.92 resnet26t,3789.73,270.19,1024,256,16.01 regnetx_016,3776.49,271.139,1024,224,9.19 rexnet_150,3774.39,271.29,1024,224,9.73 seresnext26t_32x4d,3769.71,271.627,1024,224,16.81 seresnext26tn_32x4d,3769.68,271.629,1024,224,16.81 gcresnext26ts,3763.59,272.069,1024,256,10.48 seresnext26d_32x4d,3760.94,272.26,1024,224,16.81 ecaresnext50t_32x4d,3751.71,272.931,1024,224,15.41 ecaresnext26t_32x4d,3745.62,273.373,1024,224,15.41 ecaresnet50d_pruned,3714.28,275.68,1024,224,19.94 resnetv2_50,3703.17,276.505,1024,224,25.55 eca_botnext26ts_256,3685.99,277.797,1024,256,10.59 crossvit_9_240,3640.93,281.235,1024,240,8.55 ecaresnetlight,3588.5,285.344,1024,224,30.16 eca_halonext26ts,3573.18,286.568,1024,256,10.76 crossvit_9_dagger_240,3562.28,287.444,1024,240,8.78 poolformer_s12,3555.09,288.026,1024,224,11.92 swsl_resnet50,3536.6,289.53,1024,224,25.56 gluon_resnet50_v1b,3534.52,289.702,1024,224,25.56 tv_resnet50,3534.18,289.73,1024,224,25.56 resnet50,3528.73,290.177,1024,224,25.56 ssl_resnet50,3522.22,290.713,1024,224,25.56 rexnetr_200,3521.98,145.362,512,224,16.52 dla46_c,3494.33,293.033,1024,224,1.3 botnet26t_256,3447.34,297.026,1024,256,12.49 efficientnet_em,3434.61,298.129,1024,240,6.9 dpn68,3423.83,299.067,1024,224,12.61 resnet32ts,3414.56,299.879,1024,256,17.96 dpn68b,3412.63,300.049,1024,224,12.61 regnety_016,3408.85,300.382,1024,224,11.2 halonet26t,3379.19,303.017,1024,256,12.48 resnetv2_50t,3374.73,303.416,1024,224,25.57 resnetv2_50d,3370.34,303.812,1024,224,25.57 resnet33ts,3369.92,303.852,1024,256,19.68 nf_regnet_b1,3357.92,304.938,1024,288,10.22 gluon_resnet50_v1c,3339.61,306.611,1024,224,25.58 nf_regnet_b2,3329.81,307.512,1024,272,14.31 tf_efficientnet_b2_ap,3315.4,308.848,1024,260,9.11 tf_efficientnet_b2_ns,3314.91,308.894,1024,260,9.11 tf_efficientnet_em,3313.67,309.011,1024,240,6.9 tf_efficientnet_b2,3313.08,309.063,1024,260,9.11 seresnet33ts,3285.4,311.671,1024,256,19.78 eca_resnet33ts,3264.82,313.634,1024,256,19.68 resnet50t,3209.81,319.009,1024,224,25.57 bat_resnext26ts,3208.41,319.147,1024,256,10.73 legacy_seresnet50,3208.39,319.15,1024,224,28.09 gluon_resnet50_v1d,3207.43,319.246,1024,224,25.58 convnext_nano_hnf,3203.44,319.644,1024,224,15.59 resnet50d,3201.53,319.835,1024,224,25.58 convit_tiny,3182.1,321.788,1024,224,5.71 gcresnet33ts,3115.24,328.694,1024,256,19.88 vit_small_resnet26d_224,3114.54,328.764,1024,224,63.61 efficientnet_b2,3113.3,328.898,1024,288,9.11 efficientnet_b2a,3112.75,328.957,1024,288,9.11 efficientnet_b3_pruned,3098.33,330.489,1024,300,9.86 mobilevit_xs,3098.23,165.245,512,256,2.32 vovnet39a,3094.91,330.85,1024,224,22.6 seresnet50,3084.99,331.918,1024,224,28.09 haloregnetz_b,3050.82,335.635,1024,224,11.68 skresnet34,3016.36,339.47,1024,224,22.28 ese_vovnet39b,2991.04,342.343,1024,224,24.57 eca_vovnet39b,2984.95,343.042,1024,224,22.6 cspresnext50,2981.83,343.401,1024,224,20.57 selecsls84,2979.47,343.673,1024,224,50.95 res2net50_48w_2s,2961.27,345.783,1024,224,25.29 ssl_resnext50_32x4d,2883.39,355.125,1024,224,25.03 resnext50_32x4d,2879.89,355.557,1024,224,25.03 swsl_resnext50_32x4d,2879.83,355.563,1024,224,25.03 tv_resnext50_32x4d,2876.25,356.006,1024,224,25.03 gluon_resnext50_32x4d,2870.17,356.762,1024,224,25.03 resnetaa50d,2869.21,356.878,1024,224,25.58 tv_densenet121,2861.74,357.812,1024,224,7.98 densenet121,2859.4,358.103,1024,224,7.98 mixnet_s,2849.57,359.34,1024,224,4.13 seresnet50t,2844.85,359.936,1024,224,28.1 resnetrs50,2844.33,360.0,1024,224,35.69 deit_small_patch16_224,2839.86,360.568,1024,224,22.05 vit_small_patch16_224,2838.21,360.777,1024,224,22.05 ecaresnet101d_pruned,2833.47,361.381,1024,224,24.88 coat_lite_tiny,2832.3,361.531,1024,224,5.72 gluon_resnet50_v1s,2824.57,362.521,1024,224,25.68 pit_s_224,2821.07,362.969,1024,224,23.46 ecaresnet50d,2815.88,363.64,1024,224,25.58 rexnet_200,2812.74,182.018,512,224,16.37 pit_s_distilled_224,2802.25,365.406,1024,224,24.04 deit_small_distilled_patch16_224,2791.58,366.805,1024,224,22.44 dla34,2790.34,366.966,1024,224,15.74 dla46x_c,2773.06,369.253,1024,224,1.07 cspresnet50,2754.0,371.811,1024,256,21.62 hrnet_w18_small_v2,2736.24,374.217,1024,224,15.6 densenet121d,2731.76,374.837,1024,224,8.0 tf_mixnet_s,2728.68,375.26,1024,224,4.13 efficientnet_lite3,2718.45,188.332,512,300,8.2 regnetz_b16,2715.55,377.076,1024,288,9.72 dla60x_c,2712.17,377.543,1024,224,1.32 vit_tiny_r_s16_p8_384,2700.75,189.562,512,384,6.36 coat_lite_mini,2663.5,384.444,1024,224,11.01 resnext50d_32x4d,2662.22,384.629,1024,224,25.05 resnetblur50,2619.08,390.962,1024,224,25.56 cspresnet50d,2596.32,394.393,1024,256,21.64 vgg11_bn,2593.2,197.428,512,224,132.87 vit_base2_patch32_256,2576.99,397.35,1024,256,119.46 seresnetaa50d,2576.43,397.436,1024,224,28.11 cspresnet50w,2574.64,397.712,1024,256,28.12 seresnext50_32x4d,2574.54,397.729,1024,224,27.56 lambda_resnet26rpt_256,2573.59,397.876,1024,256,10.99 legacy_seresnext50_32x4d,2571.71,398.166,1024,224,27.56 xcit_nano_12_p16_384_dist,2569.25,398.547,1024,384,3.05 gluon_seresnext50_32x4d,2564.07,399.352,1024,224,27.56 xcit_tiny_24_p16_224_dist,2557.0,400.456,1024,224,12.12 xcit_tiny_24_p16_224,2556.28,400.571,1024,224,12.12 efficientnetv2_rw_t,2545.32,402.294,1024,288,13.65 vovnet57a,2538.22,403.418,1024,224,36.64 fbnetv3_g,2530.36,404.673,1024,288,16.62 gcresnet50t,2514.25,407.265,1024,256,25.9 res2net50_26w_4s,2505.47,408.693,1024,224,25.7 tf_efficientnetv2_b3,2496.85,410.104,1024,300,14.36 twins_svt_small,2488.33,411.508,1024,224,24.06 vit_base_resnet26d_224,2472.64,414.118,1024,224,101.4 ese_vovnet57b,2446.54,418.537,1024,224,38.61 densenetblur121d,2445.52,418.711,1024,224,8.0 tf_efficientnet_lite3,2442.63,209.598,512,300,8.2 resnetblur50d,2422.6,422.672,1024,224,25.58 mobilevit_s,2415.06,211.991,512,256,5.58 resnest14d,2409.35,424.998,1024,224,10.61 tf_inception_v3,2400.27,426.605,1024,299,23.83 gluon_inception_v3,2398.26,426.963,1024,299,23.83 nf_seresnet50,2397.62,427.078,1024,224,28.09 inception_v3,2397.12,427.166,1024,299,23.83 nf_ecaresnet50,2388.81,428.654,1024,224,25.56 adv_inception_v3,2388.59,428.689,1024,299,23.83 densenet169,2362.23,433.477,1024,224,14.15 sehalonet33ts,2337.63,219.014,512,256,13.69 resmlp_24_224,2312.09,442.876,1024,224,30.02 gc_efficientnetv2_rw_t,2311.11,443.065,1024,288,13.68 resmlp_24_distilled_224,2308.78,443.512,1024,224,30.02 convnext_tiny,2275.45,450.007,1024,224,28.59 gcresnext50ts,2263.71,452.341,1024,256,15.67 res2net50_14w_8s,2252.88,454.515,1024,224,25.06 semobilevit_s,2250.59,227.485,512,256,5.74 darknet53,2248.73,227.672,512,256,41.61 resnetv2_101,2235.55,458.038,1024,224,44.54 xcit_small_12_p16_224_dist,2216.94,461.885,1024,224,26.25 xcit_small_12_p16_224,2212.11,462.891,1024,224,26.25 skresnet50,2193.01,466.925,1024,224,25.8 resnet101,2170.93,471.673,1024,224,44.55 tv_resnet101,2164.75,473.02,1024,224,44.55 gluon_resnet101_v1b,2163.02,473.399,1024,224,44.55 res2next50,2156.68,474.791,1024,224,24.67 ecaresnet26t,2145.15,477.343,1024,320,16.01 nf_regnet_b3,2126.07,481.627,1024,320,18.59 gmixer_24_224,2110.93,485.081,1024,224,24.72 resnetv2_101d,2090.05,489.925,1024,224,44.56 gluon_resnet101_v1c,2088.96,490.184,1024,224,44.57 dla60,2087.19,490.597,1024,224,22.04 convnext_tiny_hnf,2073.34,493.877,1024,224,28.59 skresnet50d,2063.75,496.172,1024,224,25.82 twins_pcpvt_small,2048.61,499.837,1024,224,24.11 gluon_resnet101_v1d,2036.64,502.777,1024,224,44.57 vgg13,2019.99,506.92,1024,224,133.05 efficientnet_b0_gn,2017.66,253.748,512,224,5.29 wide_resnet50_2,2001.07,511.71,1024,224,68.88 sebotnet33ts_256,1999.77,192.009,384,256,13.7 xcit_nano_12_p8_224,1978.07,517.663,1024,224,3.05 xcit_nano_12_p8_224_dist,1977.13,517.91,1024,224,3.05 vit_base_resnet50d_224,1950.99,524.848,1024,224,110.97 repvgg_b1,1936.11,528.882,1024,224,57.42 legacy_seresnet101,1932.53,529.863,1024,224,49.33 dla60x,1896.77,539.852,1024,224,17.35 resnetaa101d,1893.61,540.751,1024,224,44.57 tf_efficientnet_b3_ns,1893.08,270.444,512,300,12.23 tf_efficientnet_b3_ap,1892.83,270.481,512,300,12.23 tf_efficientnet_b3,1892.52,270.524,512,300,12.23 seresnet101,1891.69,541.301,1024,224,49.33 gluon_resnet101_v1s,1874.89,546.152,1024,224,44.67 resnet51q,1869.52,547.722,1024,288,35.7 efficientnet_b3,1853.06,276.288,512,320,12.23 efficientnet_b3a,1852.72,276.339,512,320,12.23 crossvit_small_240,1844.12,555.265,1024,240,26.86 poolformer_s24,1843.54,555.44,1024,224,21.39 swin_tiny_patch4_window7_224,1831.44,559.111,1024,224,28.29 halonet50ts,1826.61,560.589,1024,256,22.73 densenet201,1818.74,563.014,1024,224,20.01 gmlp_s16_224,1810.78,565.489,1024,224,19.42 ssl_resnext101_32x4d,1796.49,569.986,1024,224,44.18 resnext101_32x4d,1794.8,570.526,1024,224,44.18 swsl_resnext101_32x4d,1794.2,570.714,1024,224,44.18 convnext_tiny_hnfd,1793.87,570.819,1024,224,28.63 gluon_resnext101_32x4d,1791.38,571.613,1024,224,44.18 cspdarknet53,1782.45,287.233,512,256,27.64 nf_resnet101,1778.39,575.79,1024,224,44.55 vit_small_r26_s32_224,1778.32,575.808,1024,224,36.43 ecaresnet101d,1778.2,575.85,1024,224,44.57 regnetz_c16,1772.58,288.833,512,320,13.46 res2net50_26w_6s,1761.45,581.325,1024,224,37.05 resnest26d,1761.21,581.405,1024,224,17.07 dla60_res2net,1720.09,595.302,1024,224,20.85 nf_resnet50,1719.27,595.589,1024,288,25.56 crossvit_15_240,1700.36,602.213,1024,240,27.53 resnetblur101d,1688.41,606.472,1024,224,44.57 resnet61q,1677.9,610.274,1024,288,36.85 swin_s3_tiny_224,1668.56,613.692,1024,224,28.33 xcit_tiny_12_p16_384_dist,1651.99,619.843,1024,384,6.72 crossvit_15_dagger_240,1651.71,619.951,1024,240,28.21 resnetv2_50d_frn,1650.73,620.316,1024,224,25.59 vgg13_bn,1642.03,311.797,512,224,133.05 efficientnet_b0_g8_gn,1637.47,312.665,512,224,6.56 vgg16,1632.03,627.428,1024,224,138.36 cait_xxs24_224,1630.9,627.86,1024,224,11.96 repvgg_b1g4,1614.79,634.126,1024,224,39.97 regnetx_032,1605.58,637.76,1024,224,15.3 seresnext101_32x4d,1600.47,639.797,1024,224,48.96 gluon_seresnext101_32x4d,1597.12,641.141,1024,224,48.96 legacy_seresnext101_32x4d,1596.46,641.406,1024,224,48.96 botnet50ts_256,1594.64,321.062,512,256,22.74 res2net101_26w_4s,1591.38,643.454,1024,224,45.21 regnetx_040,1586.28,645.521,1024,224,22.12 ese_vovnet39b_evos,1584.69,646.169,1024,224,24.58 resnetv2_50d_evob,1578.15,648.842,1024,224,25.59 resnetv2_50x1_bit_distilled,1562.05,655.535,1024,224,25.55 visformer_small,1557.14,657.604,1024,224,40.22 xception41p,1555.03,329.24,512,299,26.91 resnetv2_152,1552.32,659.642,1024,224,60.19 dla102,1552.08,659.747,1024,224,33.27 resmlp_36_224,1551.92,659.816,1024,224,44.69 dla60_res2next,1551.9,659.821,1024,224,17.03 resmlp_36_distilled_224,1551.61,659.949,1024,224,44.69 resnest50d_1s4x24d,1548.62,661.22,1024,224,25.68 xception,1546.37,331.084,512,299,22.86 hrnet_w32,1543.69,663.332,1024,224,41.23 halo2botnet50ts_256,1528.74,669.82,1024,256,22.64 tv_resnet152,1516.25,675.334,1024,224,60.19 coat_lite_small,1515.55,675.647,1024,224,19.84 mixer_b16_224,1515.3,675.76,1024,224,59.88 mixer_b16_224_miil,1514.28,676.214,1024,224,59.88 gluon_resnet152_v1b,1512.22,677.139,1024,224,60.19 resnet152,1507.31,679.341,1024,224,60.19 efficientnet_el,1505.49,340.076,512,300,10.59 efficientnet_el_pruned,1505.01,340.185,512,300,10.59 vit_large_patch32_224,1500.0,682.653,1024,224,306.54 swin_v2_cr_tiny_224,1497.15,683.946,1024,224,28.33 res2net50_26w_8s,1487.1,688.575,1024,224,48.4 nf_seresnet101,1486.78,688.723,1024,224,49.33 resnetv2_152d,1485.01,689.541,1024,224,60.2 nf_ecaresnet101,1477.89,692.868,1024,224,44.55 gluon_resnet152_v1c,1474.38,694.516,1024,224,60.21 swin_v2_cr_tiny_ns_224,1472.66,695.323,1024,224,28.33 tf_efficientnet_el,1464.69,349.551,512,300,10.59 vit_tiny_patch16_384,1460.68,701.028,1024,384,5.79 vit_base_r26_s32_224,1452.81,704.825,1024,224,101.38 gluon_resnet152_v1d,1447.4,707.462,1024,224,60.21 hrnet_w18,1432.55,714.792,1024,224,21.3 mixnet_m,1431.87,715.134,1024,224,5.01 mixer_l32_224,1426.61,717.775,1024,224,206.94 dla102x,1416.93,722.674,1024,224,26.31 tf_mixnet_m,1413.24,724.561,1024,224,5.01 convnext_small,1410.61,725.911,1024,224,50.22 twins_pcpvt_base,1405.32,728.645,1024,224,43.83 ecaresnet50t,1382.49,740.677,1024,320,25.57 nest_tiny,1378.24,371.475,512,224,17.06 vit_base_patch32_384,1377.87,743.165,1024,384,88.3 convit_small,1368.52,748.238,1024,224,27.78 regnety_032,1367.75,748.662,1024,288,19.44 vgg19,1367.17,748.977,1024,224,143.67 gluon_resnet152_v1s,1363.8,750.829,1024,224,60.32 vgg16_bn,1363.24,375.563,512,224,138.37 jx_nest_tiny,1354.35,378.027,512,224,17.06 ese_vovnet99b,1351.66,757.57,1024,224,63.2 xception41,1337.66,382.745,512,299,26.97 legacy_seresnet152,1335.99,766.462,1024,224,66.82 seresnet152,1317.5,777.213,1024,224,66.82 dpn92,1303.11,785.8,1024,224,37.67 efficientnet_lite4,1287.19,298.314,384,380,13.01 tresnet_m,1283.27,797.947,1024,224,31.39 inception_v4,1282.29,798.555,1024,299,42.68 densenet161,1276.9,801.927,1024,224,28.68 xcit_tiny_12_p8_224_dist,1263.32,810.55,1024,224,6.71 xcit_tiny_12_p8_224,1260.86,812.129,1024,224,6.71 regnetx_080,1259.62,812.931,1024,224,39.57 skresnext50_32x4d,1255.24,815.767,1024,224,27.48 vit_small_resnet50d_s16_224,1252.87,817.305,1024,224,57.53 twins_svt_base,1249.6,819.445,1024,224,56.07 poolformer_s36,1245.02,822.461,1024,224,30.86 repvgg_b2,1243.81,823.263,1024,224,89.02 volo_d1_224,1232.57,830.769,1024,224,26.63 hrnet_w30,1221.32,838.421,1024,224,37.71 crossvit_18_240,1205.55,849.387,1024,240,43.27 resnest50d,1199.06,853.99,1024,224,27.48 tf_efficientnet_lite4,1182.46,324.733,384,380,13.01 xcit_small_24_p16_224_dist,1182.35,866.056,1024,224,47.67 xcit_small_24_p16_224,1181.92,866.371,1024,224,47.67 crossvit_18_dagger_240,1172.17,873.578,1024,240,44.27 regnetx_064,1166.56,438.885,512,224,26.21 vgg19_bn,1163.98,439.857,512,224,143.68 nf_regnet_b4,1158.13,884.17,1024,384,30.21 efficientnetv2_s,1149.66,890.683,1024,384,21.46 regnetz_d8,1143.01,895.867,1024,320,23.37 resnet50_gn,1132.8,903.934,1024,224,25.56 dla169,1130.6,905.699,1024,224,53.39 wide_resnet101_2,1125.72,909.622,1024,224,126.89 swin_small_patch4_window7_224,1123.67,911.288,1024,224,49.61 tf_efficientnetv2_s,1123.66,911.292,1024,384,21.46 tf_efficientnetv2_s_in21ft1k,1121.7,912.889,1024,384,21.46 gluon_resnext101_64x4d,1118.73,915.313,1024,224,83.46 mixnet_l,1117.6,458.111,512,224,7.33 xception65p,1115.45,458.995,512,299,39.82 vit_base_patch16_224_miil,1108.89,923.433,1024,224,86.54 nfnet_l0,1101.58,929.561,1024,288,35.07 eca_nfnet_l0,1100.75,930.261,1024,288,24.14 tf_mixnet_l,1100.74,465.128,512,224,7.33 dpn98,1097.38,933.115,1024,224,61.57 resnet200,1096.34,934.004,1024,224,64.67 resnetrs101,1095.82,934.443,1024,288,63.62 cait_xxs36_224,1095.14,935.023,1024,224,17.3 efficientnetv2_rw_s,1089.64,939.747,1024,384,23.94 vit_base_patch16_224_sam,1073.62,953.767,1024,224,86.57 vit_base_patch16_224,1073.46,953.912,1024,224,86.57 deit_base_patch16_224,1071.76,955.424,1024,224,86.57 inception_resnet_v2,1061.78,964.405,1024,299,55.84 ens_adv_inception_resnet_v2,1058.32,967.554,1024,299,55.84 deit_base_distilled_patch16_224,1057.89,967.952,1024,224,87.34 tnt_s_patch16_224,1053.73,971.773,1024,224,23.76 dla102x2,1044.22,490.307,512,224,41.28 regnetz_040,1039.59,369.364,384,320,27.12 gluon_seresnext101_64x4d,1039.37,985.196,1024,224,88.23 regnetz_040h,1034.17,371.302,384,320,28.94 resnext101_32x8d,1033.81,990.5,1024,224,88.79 ssl_resnext101_32x8d,1033.7,990.597,1024,224,88.79 swsl_resnext101_32x8d,1033.38,990.91,1024,224,88.79 ig_resnext101_32x8d,1028.15,995.947,1024,224,88.79 regnetz_d32,1009.91,1013.934,1024,320,27.58 resnest50d_4s2x40d,1008.21,1015.647,1024,224,30.42 repvgg_b3,1001.81,1022.132,1024,224,123.09 twins_pcpvt_large,999.56,1024.437,1024,224,60.99 resnet101d,999.12,1024.886,1024,320,44.57 beit_base_patch16_224,993.76,1030.414,1024,224,86.53 convnext_base,982.94,1041.763,1024,224,88.59 convnext_base_in22ft1k,982.23,1042.511,1024,224,88.59 hrnet_w40,980.59,1044.254,1024,224,57.56 coat_tiny,977.86,1047.174,1024,224,5.5 efficientnet_b4,974.19,394.16,384,384,19.34 gluon_xception65,972.42,526.509,512,299,39.92 xception65,965.78,530.126,512,299,39.92 pit_b_224,946.3,541.042,512,224,73.76 regnetz_b16_evos,945.35,541.586,512,288,9.74 pit_b_distilled_224,942.16,543.418,512,224,74.79 tf_efficientnet_b4,925.87,414.732,384,380,19.34 tf_efficientnet_b4_ap,925.37,414.955,384,380,19.34 tf_efficientnet_b4_ns,925.29,414.99,384,380,19.34 vit_small_patch16_36x1_224,920.52,1112.401,1024,224,64.67 swin_v2_cr_small_224,915.78,1118.157,1024,224,49.7 vit_small_patch16_18x2_224,899.5,1138.389,1024,224,64.67 xcit_tiny_24_p16_384_dist,885.13,1156.874,1024,384,12.12 twins_svt_large,884.59,1157.58,1024,224,99.27 nest_small,880.02,581.79,512,224,38.35 hrnet_w48,878.91,1165.063,1024,224,77.47 cait_s24_224,871.81,1174.556,1024,224,46.92 jx_nest_small,870.35,588.255,512,224,38.35 poolformer_m36,860.43,1190.082,1024,224,56.17 regnety_040,849.33,602.817,512,288,20.65 regnetv_040,849.16,602.939,512,288,20.64 nfnet_f0,843.53,1213.933,1024,256,71.49 resnetv2_50d_evos,834.99,1226.346,1024,288,25.59 swin_s3_small_224,821.03,623.593,512,224,49.74 repvgg_b2g4,812.87,1259.724,1024,224,61.76 xcit_medium_24_p16_224_dist,811.19,1262.323,1024,224,84.4 xcit_medium_24_p16_224,810.62,1263.211,1024,224,84.4 dpn131,808.5,1266.522,1024,224,79.25 swin_base_patch4_window7_224,797.2,1284.488,1024,224,87.77 hrnet_w44,791.54,1293.66,1024,224,67.06 coat_mini,786.27,1302.337,1024,224,10.34 regnetx_120,777.95,658.124,512,224,46.11 gmlp_b16_224,772.78,1325.073,1024,224,73.08 dm_nfnet_f0,761.49,1344.713,1024,256,71.49 regnety_120,754.35,678.713,512,224,51.82 densenet264,750.52,1364.368,1024,224,72.69 mixnet_xl,748.42,684.091,512,224,11.9 crossvit_base_240,747.2,685.212,512,240,105.03 xcit_small_12_p16_384_dist,744.87,1374.713,1024,384,26.25 resnetv2_50d_gn,742.22,689.806,512,288,25.57 xception71,741.19,690.762,512,299,42.34 hrnet_w64,723.1,1416.105,1024,224,128.06 vit_large_r50_s32_224,721.71,1418.838,1024,224,328.99 regnety_040s_gn,714.09,716.982,512,224,20.65 seresnet200d,712.88,1436.411,1024,256,71.86 resnet152d,711.54,1439.114,1024,320,60.21 ecaresnet200d,707.4,1447.533,1024,256,64.69 dpn107,701.19,1460.351,1024,224,86.92 cspresnext50_iabn,693.03,1477.558,1024,256,20.57 senet154,692.07,1479.599,1024,224,115.09 legacy_senet154,691.5,1480.826,1024,224,115.09 gluon_senet154,689.56,1484.981,1024,224,115.09 convit_base,687.54,1489.363,1024,224,86.54 vit_small_patch16_384,683.58,748.981,512,384,22.2 volo_d2_224,677.6,1511.204,1024,224,58.68 tnt_b_patch16_224,675.63,1515.603,1024,224,65.41 xcit_nano_12_p8_384_dist,672.93,1521.688,1024,384,3.05 resnext101_64x4d,667.5,767.03,512,288,83.46 xcit_tiny_24_p8_224,665.57,1538.51,1024,224,12.11 swin_s3_base_224,665.44,1538.819,1024,224,71.13 xcit_tiny_24_p8_224_dist,665.24,1539.279,1024,224,12.11 swin_v2_cr_base_224,655.51,1562.118,1024,224,87.88 poolformer_m48,648.5,1579.004,1024,224,73.47 repvgg_b3g4,644.06,1589.905,1024,224,83.83 tresnet_l,638.84,1602.889,1024,224,55.99 resnetrs152,628.07,1630.372,1024,320,86.62 nest_base,626.36,817.402,512,224,67.72 seresnet152d,626.17,1635.31,1024,320,66.84 regnetx_160,623.38,821.322,512,224,54.28 jx_nest_base,620.6,824.996,512,224,67.72 regnetz_e8,607.41,842.906,512,320,57.7 ese_vovnet99b_iabn,606.83,1687.442,1024,224,63.2 regnetz_c16_evos,598.38,855.63,512,320,13.49 vit_base_r50_s16_224,595.98,1718.162,1024,224,98.66 resnest101e,584.85,875.416,512,256,48.28 vit_small_r26_s32_384,582.99,878.207,512,384,36.47 convmixer_768_32,574.69,1781.818,1024,224,21.11 seresnext101_32x8d,574.1,891.822,512,288,93.57 cspdarknet53_iabn,571.87,1790.614,1024,256,27.64 xcit_small_12_p8_224,568.57,1800.991,1024,224,26.21 xcit_small_12_p8_224_dist,567.71,1803.708,1024,224,26.21 seresnet269d,558.78,1832.551,1024,256,113.67 convnext_large_in22ft1k,544.26,940.706,512,224,197.77 convnext_large,544.24,940.754,512,224,197.77 regnety_080,536.38,954.53,512,288,39.18 resnet200d,524.48,1952.377,1024,320,64.69 mixnet_xxl,489.07,785.144,384,224,23.96 halonet_h1,488.76,523.759,256,256,8.1 eca_nfnet_l1,486.84,2103.345,1024,320,41.41 mixer_l16_224,485.71,2108.232,1024,224,208.2 efficientnetv2_m,483.16,2119.353,1024,416,54.14 vit_large_patch32_384,479.45,2135.754,1024,384,306.63 volo_d3_224,473.36,2163.252,1024,224,86.33 tresnet_xl,471.89,2169.959,1024,224,78.44 regnety_064,471.86,1085.046,512,288,30.58 regnetv_064,468.27,1093.376,512,288,30.58 efficientnet_b5,467.72,547.327,256,456,30.39 xcit_large_24_p16_224_dist,464.03,2206.748,1024,224,189.1 xcit_large_24_p16_224,463.92,2207.253,1024,224,189.1 efficientnet_b3_gn,459.87,417.496,192,320,11.73 swin_large_patch4_window7_224,457.64,1118.756,512,224,196.53 resnetrs200,456.26,2244.305,1024,320,93.21 tf_efficientnet_b5_ap,447.29,572.321,256,456,30.39 tf_efficientnet_b5,447.21,572.425,256,456,30.39 tf_efficientnet_b5_ns,446.89,572.83,256,456,30.39 xcit_tiny_12_p8_384_dist,429.76,2382.721,1024,384,6.71 regnety_320,415.34,1232.707,512,224,145.05 regnety_160,414.45,926.515,384,288,83.59 xcit_small_24_p16_384_dist,397.04,2579.039,1024,384,47.67 efficientnetv2_rw_m,393.18,1302.2,512,416,53.24 regnetz_d8_evos,387.65,1320.76,512,320,23.46 swin_v2_cr_large_224,386.59,1324.379,512,224,196.68 efficientnet_b3_g8_gn,385.43,498.136,192,320,14.25 swin_v2_cr_tiny_384,366.25,698.968,256,384,28.33 convnext_xlarge_in22ft1k,359.4,1424.579,512,224,350.2 tf_efficientnetv2_m,359.31,1424.935,512,480,54.14 tf_efficientnetv2_m_in21ft1k,358.34,1428.804,512,480,54.14 vit_large_patch16_224,354.58,2887.929,1024,224,304.33 crossvit_15_dagger_408,354.15,722.849,256,408,28.5 ssl_resnext101_32x16d,347.17,1474.787,512,224,194.03 swsl_resnext101_32x16d,346.95,1475.72,512,224,194.03 vit_base_patch16_18x2_224,346.41,2956.033,1024,224,256.73 ig_resnext101_32x16d,345.97,1479.87,512,224,194.03 convnext_base_384_in22ft1k,335.81,1143.488,384,384,88.59 beit_large_patch16_224,328.45,3117.61,1024,224,304.43 tresnet_m_448,321.84,3181.675,1024,448,31.39 volo_d1_384,310.06,1651.255,512,384,26.78 volo_d4_224,303.03,3379.23,1024,224,192.96 pnasnet5large,299.35,1282.762,384,331,86.06 xcit_small_24_p8_224,297.54,3441.571,1024,224,47.63 xcit_small_24_p8_224_dist,297.52,3441.791,1024,224,47.63 ecaresnet269d,293.49,3489.006,1024,352,102.09 nasnetalarge,291.8,1315.945,384,331,88.75 resnetrs270,287.51,3561.559,1024,352,129.86 nfnet_f1,286.44,3574.909,1024,320,132.63 resnetv2_152x2_bit_teacher,280.03,1828.366,512,224,236.34 xcit_medium_24_p16_384_dist,278.67,1837.25,512,384,84.4 vit_base_patch16_384,277.57,1383.439,384,384,86.86 deit_base_patch16_384,277.28,1384.877,384,384,86.86 deit_base_distilled_patch16_384,273.16,1405.736,384,384,87.63 cait_xxs24_384,268.69,3811.003,1024,384,12.03 efficientnet_b6,268.02,477.57,128,528,43.04 regnetx_320,262.23,1464.357,384,224,107.81 dm_nfnet_f1,261.81,3911.156,1024,320,132.63 vit_large_patch14_224,261.02,3923.008,1024,224,304.2 crossvit_18_dagger_408,259.11,740.985,192,408,44.61 tf_efficientnet_b6_ns,257.39,497.28,128,528,43.04 tf_efficientnet_b6_ap,257.15,497.757,128,528,43.04 tf_efficientnet_b6,257.08,497.876,128,528,43.04 beit_base_patch16_384,239.23,1605.166,384,384,86.74 vit_large_r50_s32_384,236.31,1624.94,384,384,329.09 eca_nfnet_l2,232.83,2198.978,512,384,56.72 xcit_tiny_24_p8_384_dist,226.12,4528.604,1024,384,12.11 swin_v2_cr_small_384,224.11,1142.266,256,384,49.7 swin_base_patch4_window12_384,212.75,902.462,192,384,87.9 resmlp_big_24_distilled_224,209.1,4897.073,1024,224,129.14 resmlp_big_24_224,208.19,4918.549,1024,224,129.14 resmlp_big_24_224_in22ft1k,208.06,4921.598,1024,224,129.14 xcit_medium_24_p8_224,208.01,4922.91,1024,224,84.32 xcit_medium_24_p8_224_dist,207.93,4924.607,1024,224,84.32 resnest200e,201.92,2535.671,512,320,70.2 volo_d5_224,199.55,5131.435,1024,224,295.46 xcit_small_12_p8_384_dist,193.71,2643.156,512,384,26.21 resnetrs350,188.78,2712.164,512,384,163.96 cait_xs24_384,188.52,2715.867,512,384,26.67 efficientnetv2_l,186.87,2739.852,512,480,118.52 convnext_large_384_in22ft1k,186.32,1373.958,256,384,197.77 tf_efficientnetv2_l,185.75,2756.36,512,480,118.52 tf_efficientnetv2_l_in21ft1k,185.69,2757.265,512,480,118.52 vit_base_patch8_224,182.98,1399.048,256,224,86.58 cait_xxs36_384,179.96,5689.985,1024,384,17.37 volo_d2_384,173.85,1472.545,256,384,58.87 vit_base_r50_s16_384,171.75,2235.786,384,384,98.95 vit_base_resnet50_384,171.74,2235.946,384,384,98.95 swin_v2_cr_huge_224,170.46,2252.707,384,224,657.83 densenet264d_iabn,163.84,6249.837,1024,224,72.74 efficientnet_b7,161.28,595.212,96,600,66.35 nfnet_f2,161.05,6358.083,1024,352,193.78 swin_v2_cr_base_384,160.58,1195.67,192,384,87.88 xcit_large_24_p16_384_dist,158.84,3223.264,512,384,189.1 tf_efficientnet_b7,155.96,615.536,96,600,66.35 tf_efficientnet_b7_ap,155.94,615.594,96,600,66.35 tf_efficientnet_b7_ns,155.94,615.59,96,600,66.35 tresnet_l_448,152.84,6699.593,1024,448,55.99 dm_nfnet_f2,147.62,3468.342,512,352,193.78 cait_s24_384,145.6,3516.436,512,384,47.06 efficientnetv2_xl,141.55,2712.868,384,512,208.12 vit_huge_patch14_224,141.3,7246.972,1024,224,632.05 tf_efficientnetv2_xl_in21ft1k,141.27,2718.106,384,512,208.12 resnetrs420,134.22,3814.641,512,416,191.89 swin_large_patch4_window12_384,125.78,1017.629,128,384,196.74 eca_nfnet_l3,122.95,4164.413,512,448,72.04 convnext_xlarge_384_in22ft1k,122.83,1563.128,192,384,350.2 xcit_large_24_p8_224,118.58,4317.779,512,224,188.93 xcit_large_24_p8_224_dist,118.55,4318.826,512,224,188.93 tresnet_xl_448,113.53,9019.968,1024,448,78.44 efficientnet_cc_b0_8e,109.15,9.151,1,224,24.01 efficientnet_cc_b0_4e,106.25,9.403,1,224,13.31 tf_efficientnet_cc_b0_4e,105.28,9.489,1,224,13.31 tf_efficientnet_cc_b0_8e,105.02,9.512,1,224,24.01 xcit_small_24_p8_384_dist,101.44,5047.074,512,384,47.63 efficientnet_b8,98.72,972.467,96,672,87.41 swin_v2_cr_large_384,98.1,1304.769,128,384,196.68 cait_s36_384,97.37,5258.401,512,384,68.37 resnetv2_152x2_bit_teacher_384,96.99,2639.495,256,384,236.34 tf_efficientnet_b8,96.21,997.82,96,672,87.41 tf_efficientnet_b8_ap,96.12,998.706,96,672,87.41 resnest269e,94.82,4049.739,384,416,110.93 vit_large_patch16_384,94.39,2712.048,256,384,304.72 resnetv2_50x3_bitm,92.92,1377.53,128,448,217.32 vit_giant_patch14_224,92.83,5515.228,512,224,1012.61 nfnet_f3,83.35,6142.681,512,416,254.92 convmixer_1024_20_ks9_p14,82.56,12403.804,1024,224,24.38 beit_large_patch16_384,82.25,3112.33,256,384,305.0 efficientnet_cc_b1_8e,77.14,12.953,1,240,39.72 dm_nfnet_f3,76.92,6655.946,512,416,254.92 volo_d3_448,75.82,2532.146,192,448,86.63 tf_efficientnet_cc_b1_8e,73.25,13.64,1,240,39.72 xcit_medium_24_p8_384_dist,70.98,3606.847,256,384,84.32 resnetv2_152x2_bitm,70.34,2729.533,192,448,236.34 resnetv2_101x3_bitm,57.82,2213.862,128,448,387.93 vit_gigantic_patch14_224,56.06,9133.289,512,224,1844.44 volo_d4_448,55.8,3441.115,192,448,193.41 swin_v2_cr_giant_224,49.27,2597.97,128,224,2598.76 nfnet_f4,44.93,8547.007,384,512,316.07 swin_v2_cr_huge_384,43.72,1463.762,64,384,657.94 dm_nfnet_f4,41.24,9312.061,384,512,316.07 xcit_large_24_p8_384_dist,40.33,4760.775,192,384,188.93 volo_d5_448,38.49,3325.731,128,448,295.91 beit_large_patch16_512,33.13,2897.67,96,512,305.67 nfnet_f5,33.1,11599.778,384,544,377.21 cait_m36_384,31.8,8050.244,256,384,271.22 dm_nfnet_f5,31.5,8127.503,256,544,377.21 volo_d5_512,26.9,4758.532,128,512,296.09 nfnet_f6,25.41,10073.116,256,576,438.36 dm_nfnet_f6,23.41,10936.755,256,576,438.36 nfnet_f7,20.63,9307.125,192,608,499.5 resnetv2_152x4_bitm,18.31,3496.111,64,480,936.53 swin_v2_cr_giant_384,13.76,2325.078,32,384,2598.76 cait_m48_448,13.51,9473.095,128,448,356.46 convmixer_1536_20,13.51,75823.541,1024,224,51.63
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/model_metadata-in1k.csv	model,pretrain adv_inception_v3,in1k-adv bat_resnext26ts,in1k beit_base_patch16_224,in21k-selfsl beit_base_patch16_384,in21k-selfsl beit_large_patch16_224,in21k-selfsl beit_large_patch16_384,in21k-selfsl beit_large_patch16_512,in21k-selfsl botnet26t_256,in1k cait_m36_384,in1k-dist cait_m48_448,in1k-dist cait_s24_224,in1k-dist cait_s24_384,in1k-dist cait_s36_384,in1k-dist cait_xs24_384,in1k-dist cait_xxs24_224,in1k-dist cait_xxs24_384,in1k-dist cait_xxs36_224,in1k-dist cait_xxs36_384,in1k-dist coat_lite_mini,in1k coat_lite_small,in1k coat_lite_tiny,in1k coat_mini,in1k coat_tiny,in1k convit_base,in1k convit_small,in1k convit_tiny,in1k convmixer_1024_20_ks9_p14,in1k convmixer_1536_20,in1k convmixer_768_32,in1k crossvit_15_240,in1k crossvit_15_dagger_240,in1k crossvit_15_dagger_408,in1k crossvit_18_240,in1k crossvit_18_dagger_240,in1k crossvit_18_dagger_408,in1k crossvit_9_240,in1k crossvit_9_dagger_240,in1k crossvit_base_240,in1k crossvit_small_240,in1k crossvit_tiny_240,in1k cspdarknet53,in1k cspresnet50,in1k cspresnext50,in1k deit_base_distilled_patch16_224,in1k-dist deit_base_distilled_patch16_384,in1k-dist deit_base_patch16_224,in1k deit_base_patch16_384,in1k deit_small_distilled_patch16_224,in1k-dist deit_small_patch16_224,in1k deit_tiny_distilled_patch16_224,in1k-dist deit_tiny_patch16_224,in1k densenet121,in1k densenet161,in1k densenet169,in1k densenet201,in1k densenetblur121d,in1k dla102,in1k dla102x,in1k dla102x2,in1k dla169,in1k dla34,in1k dla46_c,in1k dla46x_c,in1k dla60,in1k dla60_res2net,in1k dla60_res2next,in1k dla60x,in1k dla60x_c,in1k dm_nfnet_f0,in1k dm_nfnet_f1,in1k dm_nfnet_f2,in1k dm_nfnet_f3,in1k dm_nfnet_f4,in1k dm_nfnet_f5,in1k dm_nfnet_f6,in1k dpn107,in1k dpn131,in1k dpn68,in1k dpn68b,in1k dpn92,in1k dpn98,in1k eca_botnext26ts_256,in1k eca_halonext26ts,in1k eca_nfnet_l0,in1k eca_nfnet_l1,in1k eca_nfnet_l2,in1k eca_resnet33ts,in1k eca_resnext26ts,in1k ecaresnet101d,in1k ecaresnet101d_pruned,in1k ecaresnet269d,in1k ecaresnet26t,in1k ecaresnet50d,in1k ecaresnet50d_pruned,in1k ecaresnet50t,in1k ecaresnetlight,in1k efficientnet_b0,in1k efficientnet_b1,in1k efficientnet_b1_pruned,in1k efficientnet_b2,in1k efficientnet_b2_pruned,in1k efficientnet_b3,in1k efficientnet_b3_pruned,in1k efficientnet_b4,in1k efficientnet_el,in1k efficientnet_el_pruned,in1k efficientnet_em,in1k efficientnet_es,in1k efficientnet_es_pruned,in1k efficientnet_lite0,in1k efficientnetv2_rw_m,in1k efficientnetv2_rw_s,in1k efficientnetv2_rw_t,in1k ens_adv_inception_resnet_v2,in1k-adv ese_vovnet19b_dw,in1k ese_vovnet39b,in1k fbnetc_100,in1k gc_efficientnetv2_rw_t,in1k gcresnet33ts,in1k gcresnet50t,in1k gcresnext26ts,in1k gcresnext50ts,in1k gernet_l,in1k gernet_m,in1k gernet_s,in1k ghostnet_100,in1k gluon_inception_v3,in1k gluon_resnet101_v1b,in1k gluon_resnet101_v1c,in1k gluon_resnet101_v1d,in1k gluon_resnet101_v1s,in1k gluon_resnet152_v1b,in1k gluon_resnet152_v1c,in1k gluon_resnet152_v1d,in1k gluon_resnet152_v1s,in1k gluon_resnet18_v1b,in1k gluon_resnet34_v1b,in1k gluon_resnet50_v1b,in1k gluon_resnet50_v1c,in1k gluon_resnet50_v1d,in1k gluon_resnet50_v1s,in1k gluon_resnext101_32x4d,in1k gluon_resnext101_64x4d,in1k gluon_resnext50_32x4d,in1k gluon_senet154,in1k gluon_seresnext101_32x4d,in1k gluon_seresnext101_64x4d,in1k gluon_seresnext50_32x4d,in1k gluon_xception65,in1k gmixer_24_224,in1k gmlp_s16_224,in1k halo2botnet50ts_256,in1k halonet26t,in1k halonet50ts,in1k haloregnetz_b,in1k hardcorenas_a,in1k hardcorenas_b,in1k hardcorenas_c,in1k hardcorenas_d,in1k hardcorenas_e,in1k hardcorenas_f,in1k hrnet_w18,in1k hrnet_w18_small,in1k hrnet_w18_small_v2,in1k hrnet_w30,in1k hrnet_w32,in1k hrnet_w40,in1k hrnet_w44,in1k hrnet_w48,in1k hrnet_w64,in1k ig_resnext101_32x16d,ig1b-wsl ig_resnext101_32x32d,ig1b-wsl ig_resnext101_32x48d,ig1b-wsl ig_resnext101_32x8d,ig1b-wsl inception_resnet_v2,in1k inception_v3,in1k inception_v4,in1k jx_nest_base,in1k jx_nest_small,in1k jx_nest_tiny,in1k lambda_resnet26rpt_256,in1k lambda_resnet26t,in1k lambda_resnet50ts,in1k lamhalobotnet50ts_256,in1k legacy_senet154,in1k legacy_seresnet101,in1k legacy_seresnet152,in1k legacy_seresnet18,in1k legacy_seresnet34,in1k legacy_seresnet50,in1k legacy_seresnext101_32x4d,in1k legacy_seresnext26_32x4d,in1k legacy_seresnext50_32x4d,in1k levit_128,in1k-dist levit_128s,in1k-dist levit_192,in1k-dist levit_256,in1k-dist levit_384,in1k-dist mixer_b16_224,in1k mixer_b16_224_miil,in21k mixer_l16_224,in1k mixnet_l,in1k mixnet_m,in1k mixnet_s,in1k mixnet_xl,in1k mnasnet_100,in1k mobilenetv2_100,in1k mobilenetv2_110d,in1k mobilenetv2_120d,in1k mobilenetv2_140,in1k mobilenetv3_large_100,in1k mobilenetv3_large_100_miil,in21k mobilenetv3_rw,in1k nasnetalarge,in1k nf_regnet_b1,in1k nf_resnet50,in1k nfnet_l0,in1k pit_b_224,in1k pit_b_distilled_224,in1k-dist pit_s_224,in1k pit_s_distilled_224,in1k-dist pit_ti_224,in1k pit_ti_distilled_224,in1k-dist pit_xs_224,in1k pit_xs_distilled_224,in1k-dist pnasnet5large,in1k regnetx_002,in1k regnetx_004,in1k regnetx_006,in1k regnetx_008,in1k regnetx_016,in1k regnetx_032,in1k regnetx_040,in1k regnetx_064,in1k regnetx_080,in1k regnetx_120,in1k regnetx_160,in1k regnetx_320,in1k regnety_002,in1k regnety_004,in1k regnety_006,in1k regnety_008,in1k regnety_016,in1k regnety_032,in1k regnety_040,in1k regnety_064,in1k regnety_080,in1k regnety_120,in1k regnety_160,in1k regnety_320,in1k regnetz_b,in1k regnetz_c,in1k regnetz_d,in1k repvgg_a2,in1k repvgg_b0,in1k repvgg_b1,in1k repvgg_b1g4,in1k repvgg_b2,in1k repvgg_b2g4,in1k repvgg_b3,in1k repvgg_b3g4,in1k res2net101_26w_4s,in1k res2net50_14w_8s,in1k res2net50_26w_4s,in1k res2net50_26w_6s,in1k res2net50_26w_8s,in1k res2net50_48w_2s,in1k res2next50,in1k resmlp_12_224,in1k resmlp_12_distilled_224,in1k-dist resmlp_24_224,in1k resmlp_24_distilled_224,in1k-dist resmlp_36_224,in1k resmlp_36_distilled_224,in1k-dist resmlp_big_24_224,in1k resmlp_big_24_224_in22ft1k,in21k resmlp_big_24_distilled_224,in1k-dist resnest101e,in1k resnest14d,in1k resnest200e,in1k resnest269e,in1k resnest26d,in1k resnest50d,in1k resnest50d_1s4x24d,in1k resnest50d_4s2x40d,in1k resnet101d,in1k resnet152d,in1k resnet18,in1k resnet18d,in1k resnet200d,in1k resnet26,in1k resnet26d,in1k resnet26t,in1k resnet32ts,in1k resnet33ts,in1k resnet34,in1k resnet34d,in1k resnet50,in1k resnet50d,in1k resnet51q,in1k resnet61q,in1k resnetblur50,in1k resnetrs101,in1k resnetrs152,in1k resnetrs200,in1k resnetrs270,in1k resnetrs350,in1k resnetrs420,in1k resnetrs50,in1k resnetv2_101,in1k resnetv2_101x1_bitm,in21k resnetv2_101x3_bitm,in21k resnetv2_152x2_bit_teacher,in21k resnetv2_152x2_bit_teacher_384,in21k resnetv2_152x2_bitm,in21k resnetv2_152x4_bitm,in21k resnetv2_50,in1k resnetv2_50x1_bit_distilled,in1k-dist resnetv2_50x1_bitm,in21k resnetv2_50x3_bitm,in21k resnext101_32x8d,in1k resnext26ts,in1k resnext50_32x4d,in1k resnext50d_32x4d,in1k rexnet_100,in1k rexnet_130,in1k rexnet_150,in1k rexnet_200,in1k sehalonet33ts,in1k selecsls42b,in1k selecsls60,in1k selecsls60b,in1k semnasnet_100,in1k seresnet152d,in1k seresnet33ts,in1k seresnet50,in1k seresnext26d_32x4d,in1k seresnext26t_32x4d,in1k seresnext26ts,in1k seresnext50_32x4d,in1k skresnet18,in1k skresnet34,in1k skresnext50_32x4d,in1k spnasnet_100,in1k ssl_resnet18,yfc-semisl ssl_resnet50,yfc-semisl ssl_resnext101_32x16d,yfc-semisl ssl_resnext101_32x4d,yfc-semisl ssl_resnext101_32x8d,yfc-semisl ssl_resnext50_32x4d,yfc-semisl swin_base_patch4_window12_384,in21k swin_base_patch4_window7_224,in21k swin_large_patch4_window12_384,in21k swin_large_patch4_window7_224,in21k swin_small_patch4_window7_224,in1k swin_tiny_patch4_window7_224,in1k swsl_resnet18,ig1b-swsl swsl_resnet50,ig1b-swsl swsl_resnext101_32x16d,ig1b-swsl swsl_resnext101_32x4d,ig1b-swsl swsl_resnext101_32x8d,ig1b-swsl swsl_resnext50_32x4d,ig1b-swsl tf_efficientnet_b0,in1k tf_efficientnet_b0_ap,in1k-ap tf_efficientnet_b0_ns,jft300m-ns tf_efficientnet_b1,in1k tf_efficientnet_b1_ap,in1k-ap tf_efficientnet_b1_ns,jft300m-ns tf_efficientnet_b2,in1k tf_efficientnet_b2_ap,in1k-ap tf_efficientnet_b2_ns,jft300m-ns tf_efficientnet_b3,in1k tf_efficientnet_b3_ap,in1k-ap tf_efficientnet_b3_ns,jft300m-ns tf_efficientnet_b4,in1k tf_efficientnet_b4_ap,in1k-ap tf_efficientnet_b4_ns,jft300m-ns tf_efficientnet_b5,in1k tf_efficientnet_b5_ap,in1k-ap tf_efficientnet_b5_ns,jft300m-ns tf_efficientnet_b6,in1k tf_efficientnet_b6_ap,in1k-ap tf_efficientnet_b6_ns,jft300m-ns tf_efficientnet_b7,in1k tf_efficientnet_b7_ap,in1k-ap tf_efficientnet_b7_ns,jft300m-ns tf_efficientnet_b8,in1k tf_efficientnet_b8_ap,in1k-ap tf_efficientnet_cc_b0_4e,in1k tf_efficientnet_cc_b0_8e,in1k tf_efficientnet_cc_b1_8e,in1k tf_efficientnet_el,in1k tf_efficientnet_em,in1k tf_efficientnet_es,in1k tf_efficientnet_l2_ns,jft300m-ns tf_efficientnet_l2_ns_475,jft300m-ns tf_efficientnet_lite0,in1k tf_efficientnet_lite1,in1k tf_efficientnet_lite2,in1k tf_efficientnet_lite3,in1k tf_efficientnet_lite4,in1k tf_efficientnetv2_b0,in1k tf_efficientnetv2_b1,in1k tf_efficientnetv2_b2,in1k tf_efficientnetv2_b3,in1k tf_efficientnetv2_l,in1k tf_efficientnetv2_l_in21ft1k,in21k tf_efficientnetv2_m,in1k tf_efficientnetv2_m_in21ft1k,in21k tf_efficientnetv2_s,in1k tf_efficientnetv2_s_in21ft1k,in21k tf_efficientnetv2_xl_in21ft1k,in21k tf_inception_v3,in1k tf_mixnet_l,in1k tf_mixnet_m,in1k tf_mixnet_s,in1k tf_mobilenetv3_large_075,in1k tf_mobilenetv3_large_100,in1k tf_mobilenetv3_large_minimal_100,in1k tf_mobilenetv3_small_075,in1k tf_mobilenetv3_small_100,in1k tf_mobilenetv3_small_minimal_100,in1k tnt_s_patch16_224,in1k tresnet_l,in1k tresnet_l_448,in1k tresnet_m,in21k tresnet_m_448,in1k tresnet_xl,in1k tresnet_xl_448,in1k tv_densenet121,in1k tv_resnet101,in1k tv_resnet152,in1k tv_resnet34,in1k tv_resnet50,in1k tv_resnext50_32x4d,in1k twins_pcpvt_base,in1k twins_pcpvt_large,in1k twins_pcpvt_small,in1k twins_svt_base,in1k twins_svt_large,in1k twins_svt_small,in1k vgg11,in1k vgg11_bn,in1k vgg13,in1k vgg13_bn,in1k vgg16,in1k vgg16_bn,in1k vgg19,in1k vgg19_bn,in1k visformer_small,in1k vit_base_patch16_224,in21k vit_base_patch16_224_miil,in21k vit_base_patch16_384,in21k vit_base_patch16_224_sam,in1k vit_base_patch32_224,in21k vit_base_patch32_384,in21k vit_base_patch32_224_sam,in1k vit_base_r50_s16_384,in21k vit_large_patch16_224,in21k vit_large_patch16_384,in21k vit_large_patch32_384,in21k vit_large_r50_s32_224,in21k vit_large_r50_s32_384,in21k vit_small_patch16_224,in21k vit_small_patch16_384,in21k vit_small_patch32_224,in21k vit_small_patch32_384,in21k vit_small_r26_s32_224,in21k vit_small_r26_s32_384,in21k vit_tiny_patch16_224,in21k vit_tiny_patch16_384,in21k vit_tiny_r_s16_p8_224,in21k vit_tiny_r_s16_p8_384,in21k wide_resnet101_2,in1k wide_resnet50_2,in1k xception,in1k xception41,in1k xception65,in1k xception71,in1k xcit_large_24_p16_224,in1k xcit_large_24_p16_224_dist,in1k-dist xcit_large_24_p16_384_dist,in1k-dist xcit_large_24_p8_224,in1k xcit_large_24_p8_224_dist,in1k-dist xcit_large_24_p8_384_dist,in1k-dist xcit_medium_24_p16_224,in1k xcit_medium_24_p16_224_dist,in1k-dist xcit_medium_24_p16_384_dist,in1k-dist xcit_medium_24_p8_224,in1k xcit_medium_24_p8_224_dist,in1k-dist xcit_medium_24_p8_384_dist,in1k-dist xcit_nano_12_p16_224,in1k xcit_nano_12_p16_224_dist,in1k-dist xcit_nano_12_p16_384_dist,in1k-dist xcit_nano_12_p8_224,in1k xcit_nano_12_p8_224_dist,in1k-dist xcit_nano_12_p8_384_dist,in1k-dist xcit_small_12_p16_224,in1k xcit_small_12_p16_224_dist,in1k-dist xcit_small_12_p16_384_dist,in1k-dist xcit_small_12_p8_224,in1k xcit_small_12_p8_224_dist,in1k-dist xcit_small_12_p8_384_dist,in1k-dist xcit_small_24_p16_224,in1k xcit_small_24_p16_224_dist,in1k-dist xcit_small_24_p16_384_dist,in1k-dist xcit_small_24_p8_224,in1k xcit_small_24_p8_224_dist,in1k-dist xcit_small_24_p8_384_dist,in1k-dist xcit_tiny_12_p16_224,in1k xcit_tiny_12_p16_224_dist,in1k-dist xcit_tiny_12_p16_384_dist,in1k-dist xcit_tiny_12_p8_224,in1k xcit_tiny_12_p8_224_dist,in1k-dist xcit_tiny_12_p8_384_dist,in1k-dist xcit_tiny_24_p16_224,in1k xcit_tiny_24_p16_224_dist,in1k-dist xcit_tiny_24_p16_384_dist,in1k-dist xcit_tiny_24_p8_224,in1k xcit_tiny_24_p8_224_dist,in1k-dist xcit_tiny_24_p8_384_dist,in1k-dist
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/results/results-imagenet-real.csv	model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,91.129,8.871,98.713,1.287,305.08,448,1.000,bicubic,+1.077,-0.335,0 eva_giant_patch14_336.clip_ft_in1k,91.058,8.942,98.602,1.399,"1,013.01",336,1.000,bicubic,+1.592,-0.224,+5 eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,91.022,8.978,98.683,1.317,305.08,448,1.000,bicubic,+1.052,-0.329,-1 eva_giant_patch14_560.m30m_ft_in22k_in1k,90.969,9.031,98.672,1.328,"1,014.45",560,1.000,bicubic,+1.183,-0.320,-1 eva02_large_patch14_448.mim_in22k_ft_in1k,90.920,9.080,98.685,1.315,305.08,448,1.000,bicubic,+1.298,-0.265,-1 eva_giant_patch14_336.m30m_ft_in22k_in1k,90.907,9.093,98.661,1.339,"1,013.01",336,1.000,bicubic,+1.341,-0.291,0 eva_large_patch14_336.in22k_ft_in1k,90.905,9.095,98.785,1.215,304.53,336,1.000,bicubic,+2.235,+0.063,+6 eva_giant_patch14_224.clip_ft_in1k,90.900,9.100,98.680,1.319,"1,012.56",224,0.900,bicubic,+2.020,+0.000,+1 eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,90.896,9.104,98.802,1.198,87.12,448,1.000,bicubic,+2.206,+0.078,+2 eva02_large_patch14_448.mim_m38m_ft_in1k,90.890,9.110,98.653,1.347,305.08,448,1.000,bicubic,+1.316,-0.271,-5 eva_large_patch14_336.in22k_ft_in22k_in1k,90.862,9.138,98.715,1.285,304.53,336,1.000,bicubic,+1.656,-0.139,-3 eva02_base_patch14_448.mim_in22k_ft_in1k,90.800,9.200,98.742,1.258,87.12,448,1.000,bicubic,+2.548,+0.178,+14 caformer_b36.sail_in22k_ft_in1k_384,90.781,9.219,98.860,1.140,98.75,384,1.000,bicubic,+2.723,+0.278,+21 beit_large_patch16_512.in22k_ft_in22k_in1k,90.687,9.313,98.753,1.247,305.67,512,1.000,bicubic,+2.091,+0.097,+1 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,90.678,9.322,98.813,1.187,200.13,384,1.000,bicubic,+2.372,+0.231,+8 regnety_1280.swag_ft_in1k,90.657,9.343,98.819,1.181,644.81,384,1.000,bicubic,+2.427,+0.133,+12 convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.642,9.358,98.806,1.194,846.47,256,1.000,bicubic,+2.038,+0.099,-3 convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,90.633,9.367,98.755,1.245,200.13,384,1.000,bicubic,+2.785,+0.309,+24 volo_d5_512.sail_in1k,90.614,9.386,98.698,1.302,296.09,512,1.150,bicubic,+3.556,+0.728,+49 beit_large_patch16_384.in22k_ft_in22k_in1k,90.606,9.394,98.766,1.234,305.00,384,1.000,bicubic,+2.204,+0.158,-1 maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,90.584,9.416,98.617,1.383,116.14,384,1.000,bicubic,+2.756,+0.245,+22 volo_d5_448.sail_in1k,90.580,9.420,98.685,1.315,295.91,448,1.150,bicubic,+3.628,+0.747,+53 tf_efficientnet_l2.ns_jft_in1k,90.561,9.439,98.775,1.226,480.31,800,0.960,bicubic,+2.209,+0.127,-3 maxvit_base_tf_512.in21k_ft_in1k,90.561,9.439,98.702,1.298,119.88,512,1.000,bicubic,+2.341,+0.172,+7 eva_large_patch14_196.in22k_ft_in22k_in1k,90.557,9.443,98.698,1.302,304.14,196,1.000,bicubic,+1.983,+0.040,-8 convnextv2_huge.fcmae_ft_in22k_in1k_512,90.542,9.458,98.710,1.290,660.29,512,1.000,bicubic,+1.684,-0.038,-16 vit_large_patch14_clip_336.openai_ft_in12k_in1k,90.542,9.458,98.687,1.313,304.53,336,1.000,bicubic,+2.274,+0.161,-3 convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,90.540,9.460,98.806,1.194,200.13,320,1.000,bicubic,+2.582,+0.331,+8 tf_efficientnet_l2.ns_jft_in1k_475,90.537,9.463,98.710,1.290,480.31,475,0.936,bicubic,+2.303,+0.164,-2 eva_large_patch14_196.in22k_ft_in1k,90.535,9.465,98.770,1.230,304.14,196,1.000,bicubic,+2.603,+0.272,+7 volo_d4_448.sail_in1k,90.512,9.488,98.591,1.409,193.41,448,1.150,bicubic,+3.720,+0.707,+53 maxvit_xlarge_tf_512.in21k_ft_in1k,90.503,9.497,98.576,1.424,475.77,512,1.000,bicubic,+1.965,-0.068,-14 vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,90.501,9.499,98.631,1.369,632.46,336,1.000,bicubic,+1.909,-0.031,-17 convnextv2_huge.fcmae_ft_in22k_in1k_384,90.497,9.503,98.695,1.304,660.29,384,1.000,bicubic,+1.827,-0.043,-22 convnext_xlarge.fb_in22k_ft_in1k_384,90.495,9.505,98.768,1.232,350.20,384,1.000,bicubic,+2.743,+0.212,+9 caformer_m36.sail_in22k_ft_in1k_384,90.460,9.540,98.668,1.332,56.20,384,1.000,bicubic,+3.014,+0.360,+18 convformer_b36.sail_in22k_ft_in1k_384,90.448,9.552,98.772,1.228,99.88,384,1.000,bicubic,+2.846,+0.338,+10 maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,90.444,9.556,98.749,1.251,116.09,384,1.000,bicubic,+2.980,+0.375,+14 caformer_b36.sail_in22k_ft_in1k,90.431,9.569,98.764,1.236,98.75,224,1.000,bicubic,+3.011,+0.436,+16 vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,90.428,9.572,98.642,1.358,304.53,336,1.000,bicubic,+2.248,+0.070,-8 vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,90.418,9.582,98.644,1.356,632.05,224,1.000,bicubic,+2.162,+0.092,-16 swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,90.407,9.593,98.734,1.266,87.92,384,1.000,bicubic,+3.311,+0.500,+24 vit_large_patch14_clip_224.openai_ft_in12k_in1k,90.382,9.618,98.659,1.341,304.20,224,1.000,bicubic,+2.207,+0.113,-10 maxvit_xlarge_tf_384.in21k_ft_in1k,90.379,9.621,98.582,1.418,475.32,384,1.000,bicubic,+2.065,+0.038,-22 coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,90.377,9.623,98.648,1.351,73.88,384,1.000,bicubic,+2.995,+0.337,+12 beit_base_patch16_384.in22k_ft_in22k_in1k,90.367,9.633,98.725,1.275,86.74,384,1.000,bicubic,+3.567,+0.589,+36 convnextv2_large.fcmae_ft_in22k_in1k_384,90.367,9.633,98.663,1.337,197.96,384,1.000,bicubic,+2.169,+0.135,-16 seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,90.362,9.638,98.734,1.266,149.39,384,1.000,bicubic,+3.074,+0.400,+11 maxvit_large_tf_512.in21k_ft_in1k,90.362,9.638,98.642,1.358,212.33,512,1.000,bicubic,+2.138,+0.044,-19 maxvit_base_tf_384.in21k_ft_in1k,90.360,9.640,98.683,1.317,119.65,384,1.000,bicubic,+2.438,+0.139,-13 convnextv2_base.fcmae_ft_in22k_in1k_384,90.360,9.640,98.670,1.330,88.72,384,1.000,bicubic,+2.716,+0.254,-4 beitv2_large_patch16_224.in1k_ft_in22k_in1k,90.354,9.646,98.587,1.413,304.43,224,0.950,bicubic,+1.960,-0.011,-32 vit_large_patch14_clip_336.laion2b_ft_in1k,90.332,9.668,98.597,1.403,304.53,336,1.000,bicubic,+2.476,+0.229,-13 convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,90.330,9.670,98.770,1.230,88.59,384,1.000,bicubic,+3.196,+0.548,+10 maxvit_large_tf_384.in21k_ft_in1k,90.315,9.685,98.687,1.313,212.03,384,1.000,bicubic,+2.329,+0.119,-20 vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,90.311,9.689,98.668,1.332,304.20,224,1.000,bicubic,+2.417,+0.260,-17 convnext_large_mlp.clip_laion2b_augreg_ft_in1k,90.309,9.691,98.651,1.349,200.13,256,1.000,bicubic,+2.973,+0.433,+1 vit_large_patch14_clip_224.openai_ft_in1k,90.307,9.693,98.640,1.360,304.20,224,1.000,bicubic,+2.453,+0.214,-17 caformer_s36.sail_in22k_ft_in1k_384,90.305,9.695,98.794,1.206,39.30,384,1.000,bicubic,+3.447,+0.582,+19 convnext_base.fb_in22k_ft_in1k_384,90.283,9.717,98.800,1.200,88.59,384,1.000,bicubic,+3.487,+0.536,+23 convnext_large.fb_in22k_ft_in1k_384,90.279,9.721,98.659,1.341,197.77,384,1.000,bicubic,+2.807,+0.273,-10 deit3_large_patch16_384.fb_in22k_ft_in1k,90.247,9.753,98.625,1.375,304.76,384,1.000,bicubic,+2.527,+0.113,-17 dm_nfnet_f6.dm_in1k,90.241,9.759,98.625,1.375,438.36,576,0.956,bicubic,+3.879,+0.729,+44 seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,90.226,9.774,98.766,1.234,93.59,320,1.000,bicubic,+3.502,+0.590,+22 deit3_huge_patch14_224.fb_in22k_ft_in1k,90.226,9.774,98.640,1.360,632.13,224,1.000,bicubic,+3.040,+0.380,-1 regnety_320.swag_ft_in1k,90.213,9.787,98.764,1.236,145.05,384,1.000,bicubic,+3.379,+0.402,+14 vit_large_patch16_384.augreg_in21k_ft_in1k,90.213,9.787,98.661,1.339,304.72,384,1.000,bicubic,+3.129,+0.359,0 vit_base_patch16_clip_384.laion2b_ft_in1k,90.211,9.789,98.702,1.298,86.86,384,1.000,bicubic,+3.593,+0.694,+21 convformer_m36.sail_in22k_ft_in1k_384,90.204,9.796,98.651,1.349,57.05,384,1.000,bicubic,+3.312,+0.535,+8 convnextv2_huge.fcmae_ft_in1k,90.204,9.796,98.548,1.452,660.29,288,1.000,bicubic,+3.624,+0.576,+22 tf_efficientnetv2_l.in21k_ft_in1k,90.202,9.798,98.719,1.281,118.52,480,1.000,bicubic,+3.400,+0.583,+10 vit_base_patch16_clip_384.openai_ft_in12k_in1k,90.200,9.800,98.648,1.351,86.86,384,0.950,bicubic,+3.174,+0.466,-2 convformer_b36.sail_in22k_ft_in1k,90.189,9.811,98.695,1.304,99.88,224,1.000,bicubic,+3.191,+0.523,-3 vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,90.189,9.811,98.585,1.415,86.86,384,1.000,bicubic,+2.983,+0.550,-13 cait_m48_448.fb_dist_in1k,90.189,9.811,98.484,1.516,356.46,448,1.000,bicubic,+3.697,+0.732,+25 vit_huge_patch14_clip_224.laion2b_ft_in1k,90.181,9.819,98.544,1.456,632.05,224,1.000,bicubic,+2.593,+0.326,-28 volo_d3_448.sail_in1k,90.177,9.823,98.550,1.450,86.63,448,1.000,bicubic,+3.675,+0.840,+20 swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,90.157,9.843,98.614,1.386,196.74,384,1.000,bicubic,+2.693,+0.364,-25 beit_large_patch16_224.in22k_ft_in22k_in1k,90.149,9.851,98.725,1.275,304.43,224,0.900,bicubic,+2.671,+0.421,-29 beitv2_large_patch16_224.in1k_ft_in1k,90.100,9.900,98.439,1.561,304.43,224,0.950,bicubic,+2.688,+0.205,-24 tf_efficientnet_b7.ns_jft_in1k,90.098,9.902,98.614,1.386,66.35,600,0.949,bicubic,+3.258,+0.522,-2 vit_large_patch14_clip_224.laion2b_ft_in1k,90.095,9.905,98.555,1.445,304.20,224,1.000,bicubic,+2.809,+0.311,-21 convnextv2_base.fcmae_ft_in22k_in1k,90.068,9.932,98.676,1.324,88.72,288,1.000,bicubic,+3.070,+0.508,-11 convnext_xlarge.fb_in22k_ft_in1k,90.066,9.934,98.619,1.381,350.20,288,1.000,bicubic,+2.736,+0.291,-25 caformer_b36.sail_in1k_384,90.063,9.937,98.514,1.486,98.75,384,1.000,bicubic,+3.655,+0.700,+19 cait_m36_384.fb_dist_in1k,90.051,9.949,98.495,1.505,271.22,384,1.000,bicubic,+3.993,+0.765,+41 convnextv2_large.fcmae_ft_in22k_in1k,90.034,9.966,98.629,1.371,197.96,288,1.000,bicubic,+2.550,+0.273,-38 seresnextaa101d_32x8d.sw_in12k_ft_in1k,90.029,9.971,98.685,1.315,93.59,288,1.000,bicubic,+3.545,+0.655,+11 tiny_vit_21m_512.dist_in22k_ft_in1k,90.029,9.971,98.493,1.507,21.27,512,1.000,bicubic,+3.571,+0.609,+12 tf_efficientnetv2_m.in21k_ft_in1k,90.025,9.975,98.666,1.334,54.14,480,1.000,bicubic,+4.033,+0.722,+42 convformer_s36.sail_in22k_ft_in1k_384,90.023,9.977,98.619,1.381,40.01,384,1.000,bicubic,+3.645,+0.635,+14 swin_large_patch4_window12_384.ms_in22k_ft_in1k,90.019,9.981,98.661,1.339,196.74,384,1.000,bicubic,+2.887,+0.427,-27 deit3_large_patch16_224.fb_in22k_ft_in1k,90.008,9.992,98.659,1.341,304.37,224,1.000,bicubic,+3.026,+0.423,-20 convnext_base.clip_laiona_augreg_ft_in1k_384,90.001,9.998,98.548,1.452,88.59,384,1.000,bicubic,+3.499,+0.580,+2 swin_base_patch4_window12_384.ms_in22k_ft_in1k,89.997,10.003,98.700,1.300,87.90,384,1.000,bicubic,+3.559,+0.634,+8 vit_base_patch16_384.augreg_in21k_ft_in1k,89.989,10.011,98.678,1.322,86.86,384,1.000,bicubic,+3.995,+0.676,+35 maxvit_base_tf_512.in1k,89.974,10.026,98.433,1.567,119.88,512,1.000,bicubic,+3.372,+0.515,-7 caformer_m36.sail_in1k_384,89.933,10.067,98.454,1.546,56.20,384,1.000,bicubic,+3.767,+0.634,+20 convnextv2_large.fcmae_ft_in1k,89.931,10.069,98.559,1.441,197.96,288,1.000,bicubic,+3.813,+0.737,+22 maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,89.927,10.073,98.414,1.586,116.14,224,0.950,bicubic,+3.033,+0.400,-24 swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,89.925,10.075,98.505,1.494,196.74,256,0.900,bicubic,+2.973,+0.400,-27 regnety_160.swag_ft_in1k,89.918,10.082,98.644,1.356,83.59,384,1.000,bicubic,+3.898,+0.592,+27 convnext_small.fb_in22k_ft_in1k_384,89.916,10.084,98.685,1.315,50.22,384,1.000,bicubic,+4.138,+0.795,+43 efficientnet_b5.sw_in12k_ft_in1k,89.912,10.088,98.565,1.435,30.39,448,1.000,bicubic,+4.016,+0.829,+33 deit3_base_patch16_384.fb_in22k_ft_in1k,89.891,10.110,98.602,1.399,86.88,384,1.000,bicubic,+3.151,+0.486,-19 xcit_large_24_p8_384.fb_dist_in1k,89.886,10.114,98.384,1.616,188.93,384,1.000,bicubic,+3.890,+0.694,+24 volo_d5_224.sail_in1k,89.880,10.120,98.490,1.510,295.46,224,0.960,bicubic,+3.810,+0.915,+19 convnext_large.fb_in22k_ft_in1k,89.876,10.124,98.593,1.407,197.77,288,1.000,bicubic,+2.850,+0.389,-39 swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,89.867,10.133,98.644,1.356,87.92,256,0.900,bicubic,+3.599,+0.762,+2 tiny_vit_21m_384.dist_in22k_ft_in1k,89.863,10.137,98.499,1.501,21.23,384,1.000,bicubic,+3.755,+0.789,+12 convnext_base.fb_in22k_ft_in1k,89.858,10.142,98.691,1.309,88.59,288,1.000,bicubic,+3.584,+0.599,-1 caformer_m36.sail_in22k_ft_in1k,89.852,10.148,98.585,1.415,56.20,224,1.000,bicubic,+3.258,+0.561,-21 cait_s36_384.fb_dist_in1k,89.835,10.165,98.427,1.573,68.37,384,1.000,bicubic,+4.381,+0.949,+52 coatnet_2_rw_224.sw_in12k_ft_in1k,89.831,10.169,98.527,1.473,73.87,224,0.950,bicubic,+3.267,+0.631,-21 convformer_m36.sail_in22k_ft_in1k,89.818,10.182,98.548,1.452,57.05,224,1.000,bicubic,+3.670,+0.698,+5 xcit_medium_24_p8_384.fb_dist_in1k,89.816,10.184,98.365,1.635,84.32,384,1.000,bicubic,+4.000,+0.773,+25 convnext_base.clip_laion2b_augreg_ft_in12k_in1k,89.811,10.188,98.668,1.332,88.59,256,1.000,bicubic,+3.441,+0.684,-11 volo_d4_224.sail_in1k,89.811,10.188,98.427,1.573,192.96,224,0.960,bicubic,+3.939,+0.955,+20 maxvit_large_tf_512.in1k,89.799,10.201,98.330,1.670,212.33,512,1.000,bicubic,+3.273,+0.450,-25 vit_large_r50_s32_384.augreg_in21k_ft_in1k,89.792,10.208,98.522,1.478,329.09,384,1.000,bicubic,+3.610,+0.600,-4 swin_large_patch4_window7_224.ms_in22k_ft_in1k,89.786,10.214,98.640,1.360,196.53,224,0.900,bicubic,+3.474,+0.738,-13 tf_efficientnet_b6.ns_jft_in1k,89.786,10.214,98.508,1.492,43.04,528,0.942,bicubic,+3.328,+0.618,-20 volo_d2_384.sail_in1k,89.784,10.216,98.403,1.597,58.87,384,1.000,bicubic,+3.742,+0.829,+5 coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,89.775,10.225,98.469,1.531,73.88,224,0.950,bicubic,+3.271,+0.575,-29 tf_efficientnetv2_xl.in21k_ft_in1k,89.773,10.227,98.288,1.712,208.12,512,1.000,bicubic,+3.025,+0.274,-40 maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,89.752,10.248,98.484,1.516,116.09,224,0.950,bicubic,+3.110,+0.464,-38 beitv2_base_patch16_224.in1k_ft_in22k_in1k,89.743,10.257,98.585,1.415,86.53,224,0.900,bicubic,+3.269,+0.532,-27 dm_nfnet_f4.dm_in1k,89.741,10.259,98.409,1.591,316.07,512,0.951,bicubic,+3.905,+0.591,+12 dm_nfnet_f5.dm_in1k,89.737,10.263,98.441,1.559,377.21,544,0.954,bicubic,+3.637,+0.753,-6 xcit_small_24_p8_384.fb_dist_in1k,89.735,10.265,98.420,1.580,47.63,384,1.000,bicubic,+4.181,+0.850,+28 regnety_160.lion_in12k_ft_in1k,89.726,10.274,98.608,1.392,83.59,288,1.000,bicubic,+3.738,+0.774,+2 caformer_s18.sail_in22k_ft_in1k_384,89.720,10.280,98.578,1.422,26.34,384,1.000,bicubic,+4.306,+0.876,+36 vit_base_patch8_224.augreg2_in21k_ft_in1k,89.718,10.283,98.510,1.490,86.58,224,0.900,bicubic,+3.499,+0.678,-21 convformer_m36.sail_in1k_384,89.718,10.283,98.435,1.565,57.05,384,1.000,bicubic,+4.138,+0.893,+24 vit_base_patch16_clip_384.openai_ft_in1k,89.707,10.293,98.508,1.492,86.86,384,1.000,bicubic,+3.501,+0.632,-21 caformer_s36.sail_in22k_ft_in1k,89.701,10.300,98.638,1.362,39.30,224,1.000,bicubic,+3.910,+0.812,+8 volo_d1_384.sail_in1k,89.698,10.302,98.290,1.710,26.78,384,1.000,bicubic,+4.454,+1.096,+45 deit3_large_patch16_384.fb_in1k,89.688,10.312,98.390,1.610,304.76,384,1.000,bicubic,+3.876,+0.792,+4 caformer_s36.sail_in1k_384,89.679,10.321,98.324,1.676,39.30,384,1.000,bicubic,+3.937,+0.652,+9 xcit_large_24_p16_384.fb_dist_in1k,89.662,10.338,98.403,1.597,189.10,384,1.000,bicubic,+3.908,+0.865,+7 convformer_b36.sail_in1k_384,89.658,10.342,98.379,1.621,99.88,384,1.000,bicubic,+3.918,+0.855,+8 tf_efficientnet_b5.ns_jft_in1k,89.651,10.349,98.486,1.514,30.39,456,0.934,bicubic,+3.563,+0.730,-18 dm_nfnet_f3.dm_in1k,89.647,10.353,98.463,1.537,254.92,416,0.940,bicubic,+3.961,+0.893,+9 convnext_base.clip_laion2b_augreg_ft_in1k,89.645,10.355,98.463,1.537,88.59,256,1.000,bicubic,+3.487,+0.783,-25 regnety_2560.seer_ft_in1k,89.632,10.368,98.399,1.601,"1,282.60",384,1.000,bicubic,+4.482,+0.961,+48 convnext_small.in12k_ft_in1k_384,89.598,10.402,98.480,1.520,50.22,384,1.000,bicubic,+3.416,+0.558,-31 maxvit_base_tf_384.in1k,89.589,10.411,98.320,1.680,119.65,384,1.000,bicubic,+3.287,+0.522,-38 tf_efficientnet_b8.ap_in1k,89.579,10.421,98.305,1.695,87.41,672,0.954,bicubic,+4.215,+1.013,+28 regnety_160.sw_in12k_ft_in1k,89.570,10.430,98.546,1.454,83.59,288,1.000,bicubic,+3.584,+0.712,-15 vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,89.566,10.434,98.420,1.580,86.57,224,0.950,bicubic,+3.396,+0.664,-33 maxvit_tiny_tf_512.in1k,89.562,10.438,98.335,1.665,31.05,512,1.000,bicubic,+3.898,+0.751,+2 convformer_s18.sail_in22k_ft_in1k_384,89.553,10.447,98.531,1.469,26.77,384,1.000,bicubic,+4.555,+0.961,+57 volo_d3_224.sail_in1k,89.553,10.447,98.373,1.627,86.33,224,0.960,bicubic,+4.139,+1.097,+16 maxvit_large_tf_384.in1k,89.553,10.447,98.185,1.815,212.03,384,1.000,bicubic,+3.323,+0.497,-41 tf_efficientnetv2_l.in1k,89.540,10.460,98.339,1.661,118.52,480,1.000,bicubic,+3.876,+0.865,-1 flexivit_large.1200ep_in1k,89.534,10.466,98.414,1.586,304.36,240,0.950,bicubic,+3.890,+0.874,-1 xcit_large_24_p8_224.fb_dist_in1k,89.517,10.483,98.217,1.783,188.93,224,1.000,bicubic,+4.115,+0.815,+14 flexivit_large.600ep_in1k,89.510,10.490,98.392,1.608,304.36,240,0.950,bicubic,+3.970,+0.904,+1 xcit_small_12_p8_384.fb_dist_in1k,89.508,10.492,98.307,1.693,26.21,384,1.000,bicubic,+4.430,+1.025,+43 cait_s24_384.fb_dist_in1k,89.504,10.496,98.362,1.638,47.06,384,1.000,bicubic,+4.456,+1.016,+45 convformer_s36.sail_in22k_ft_in1k,89.496,10.505,98.454,1.546,40.01,224,1.000,bicubic,+4.082,+0.886,+8 convnextv2_tiny.fcmae_ft_in22k_in1k_384,89.485,10.515,98.484,1.516,28.64,384,1.000,bicubic,+4.379,+0.856,+34 convformer_s36.sail_in1k_384,89.481,10.520,98.369,1.631,40.01,384,1.000,bicubic,+4.103,+0.893,+10 xcit_medium_24_p16_384.fb_dist_in1k,89.481,10.520,98.296,1.704,84.40,384,1.000,bicubic,+4.056,+0.966,+2 convnext_tiny.in12k_ft_in1k_384,89.472,10.528,98.505,1.494,28.59,384,1.000,bicubic,+4.350,+0.900,+30 inception_next_base.sail_in1k_384,89.453,10.547,98.345,1.655,86.67,384,1.000,bicubic,+4.251,+0.931,+23 regnety_120.sw_in12k_ft_in1k,89.446,10.554,98.537,1.462,51.82,288,1.000,bicubic,+4.046,+0.956,+5 vit_base_patch16_224.augreg2_in21k_ft_in1k,89.446,10.554,98.441,1.559,86.57,224,0.900,bicubic,+4.352,+0.911,+31 deit3_base_patch16_224.fb_in22k_ft_in1k,89.444,10.556,98.557,1.443,86.59,224,1.000,bicubic,+3.744,+0.811,-18 maxvit_small_tf_512.in1k,89.444,10.556,98.354,1.646,69.13,512,1.000,bicubic,+3.360,+0.590,-45 vit_base_patch8_224.augreg_in21k_ft_in1k,89.436,10.564,98.484,1.516,86.58,224,0.900,bicubic,+3.638,+0.694,-28 vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,89.436,10.564,98.401,1.599,88.34,448,1.000,bicubic,+3.656,+0.763,-27 deit_base_distilled_patch16_384.fb_in1k,89.434,10.566,98.439,1.561,87.63,384,1.000,bicubic,+4.010,+1.033,-6 vit_base_patch16_clip_224.laion2b_ft_in1k,89.431,10.569,98.469,1.531,86.57,224,1.000,bicubic,+3.961,+0.893,-11 tf_efficientnet_b7.ap_in1k,89.431,10.569,98.345,1.655,66.35,600,0.949,bicubic,+4.307,+1.093,+20 convnextv2_base.fcmae_ft_in1k,89.421,10.579,98.360,1.640,88.72,288,1.000,bicubic,+3.947,+0.976,-13 caformer_b36.sail_in1k,89.408,10.592,98.222,1.778,98.75,224,1.000,bicubic,+3.904,+0.912,-15 vit_base_patch16_clip_224.openai_ft_in12k_in1k,89.404,10.596,98.394,1.606,86.57,224,0.950,bicubic,+3.462,+0.666,-42 hrnet_w48_ssld.paddle_in1k,89.401,10.598,98.382,1.618,77.47,288,1.000,bilinear,+4.921,+1.148,+69 beit_base_patch16_224.in22k_ft_in22k_in1k,89.395,10.605,98.529,1.471,86.53,224,0.900,bicubic,+4.183,+0.871,+7 regnetz_e8.ra3_in1k,89.378,10.622,98.459,1.542,57.70,320,1.000,bicubic,+4.344,+1.186,+25 deit3_small_patch16_384.fb_in22k_ft_in1k,89.363,10.637,98.386,1.614,22.21,384,1.000,bicubic,+4.539,+0.900,+39 tf_efficientnetv2_m.in1k,89.350,10.650,98.326,1.674,54.14,480,1.000,bicubic,+4.146,+0.962,+5 vit_medium_patch16_gap_384.sw_in12k_ft_in1k,89.348,10.652,98.495,1.505,39.03,384,0.950,bicubic,+3.818,+0.859,-24 tf_efficientnet_b8.ra_in1k,89.348,10.652,98.305,1.695,87.41,672,0.954,bicubic,+3.980,+0.911,-10 tf_efficientnet_b6.ap_in1k,89.344,10.656,98.283,1.717,43.04,528,0.942,bicubic,+4.556,+1.145,+38 volo_d2_224.sail_in1k,89.335,10.665,98.213,1.787,58.68,224,0.960,bicubic,+4.133,+1.023,+3 eva02_small_patch14_336.mim_in22k_ft_in1k,89.333,10.667,98.377,1.623,22.13,336,1.000,bicubic,+3.615,+0.743,-38 caformer_s18.sail_in1k_384,89.331,10.669,98.294,1.706,26.34,384,1.000,bicubic,+4.305,+0.936,+18 vit_large_patch16_224.augreg_in21k_ft_in1k,89.318,10.682,98.390,1.610,304.33,224,0.900,bicubic,+3.482,+0.726,-51 flexivit_large.300ep_in1k,89.310,10.690,98.324,1.676,304.36,240,0.950,bicubic,+4.022,+0.924,-12 tf_efficientnet_b4.ns_jft_in1k,89.301,10.699,98.345,1.655,19.34,380,0.922,bicubic,+4.141,+0.877,0 convnext_small.fb_in22k_ft_in1k,89.299,10.701,98.358,1.642,50.22,288,1.000,bicubic,+4.037,+0.676,-12 xcit_small_24_p16_384.fb_dist_in1k,89.295,10.705,98.326,1.674,47.67,384,1.000,bicubic,+4.205,+1.014,+6 xcit_medium_24_p8_224.fb_dist_in1k,89.293,10.707,98.185,1.815,84.32,224,1.000,bicubic,+4.219,+0.935,+8 beitv2_base_patch16_224.in1k_ft_in1k,89.271,10.729,98.262,1.738,86.53,224,0.900,bicubic,+3.677,+0.756,-40 deit3_huge_patch14_224.fb_in1k,89.218,10.782,98.164,1.836,632.13,224,0.900,bicubic,+3.994,+0.804,-12 coat_lite_medium_384.in1k,89.209,10.791,98.219,1.781,44.57,384,1.000,bicubic,+4.331,+0.847,+19 xcit_small_24_p8_224.fb_dist_in1k,89.199,10.801,98.243,1.757,47.63,224,1.000,bicubic,+4.331,+1.053,+19 vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,89.197,10.803,98.358,1.642,88.30,384,1.000,bicubic,+3.831,+0.698,-25 dm_nfnet_f2.dm_in1k,89.194,10.806,98.228,1.772,193.78,352,0.920,bicubic,+4.002,+0.882,-10 xcit_small_12_p16_384.fb_dist_in1k,89.194,10.806,98.217,1.783,26.25,384,1.000,bicubic,+4.482,+1.099,+25 swin_base_patch4_window7_224.ms_in22k_ft_in1k,89.173,10.827,98.433,1.567,87.77,224,0.900,bicubic,+3.901,+0.869,-23 vit_base_patch16_clip_224.openai_ft_in1k,89.171,10.829,98.269,1.732,86.57,224,0.900,bicubic,+3.879,+0.832,-26 eca_nfnet_l2.ra3_in1k,89.147,10.852,98.315,1.685,56.72,384,1.000,bicubic,+4.447,+1.049,+23 cait_xs24_384.fb_dist_in1k,89.147,10.852,98.292,1.708,26.67,384,1.000,bicubic,+5.085,+1.408,+87 fastvit_ma36.apple_dist_in1k,89.124,10.876,98.142,1.857,44.07,256,0.950,bicubic,+4.526,+1.141,+27 convformer_s18.sail_in1k_384,89.124,10.876,98.130,1.870,26.77,384,1.000,bicubic,+4.722,+1.018,+57 maxvit_tiny_tf_384.in1k,89.111,10.889,98.211,1.789,30.98,384,1.000,bicubic,+4.011,+0.833,-12 maxvit_small_tf_384.in1k,89.109,10.891,98.164,1.836,69.02,384,1.000,bicubic,+3.569,+0.702,-50 resnext101_32x32d.fb_wsl_ig1b_ft_in1k,89.107,10.893,98.189,1.810,468.53,224,0.875,bilinear,+4.009,+0.751,-13 tf_efficientnet_b7.ra_in1k,89.081,10.919,98.185,1.815,66.35,600,0.949,bicubic,+4.149,+0.977,+1 tiny_vit_21m_224.dist_in22k_ft_in1k,89.077,10.923,98.236,1.764,21.20,224,0.950,bicubic,+3.991,+0.870,-12 ecaresnet269d.ra2_in1k,89.071,10.929,98.234,1.766,102.09,352,1.000,bicubic,+4.103,+1.012,-3 regnety_1280.seer_ft_in1k,89.064,10.936,98.157,1.843,644.81,384,1.000,bicubic,+4.632,+1.065,+41 vit_base_patch32_clip_384.openai_ft_in12k_in1k,89.045,10.955,98.281,1.719,88.30,384,0.950,bicubic,+3.831,+0.877,-30 xcit_large_24_p16_224.fb_dist_in1k,89.045,10.955,98.059,1.941,189.10,224,1.000,bicubic,+4.129,+0.931,-2 convnext_small.in12k_ft_in1k,89.024,10.976,98.243,1.757,50.22,288,1.000,bicubic,+3.694,+0.697,-41 resmlp_big_24_224.fb_in22k_ft_in1k,89.019,10.981,98.215,1.785,129.14,224,0.875,bicubic,+4.621,+1.103,+46 dm_nfnet_f1.dm_in1k,89.017,10.983,98.256,1.744,132.63,320,0.910,bicubic,+4.315,+1.074,+8 xcit_small_12_p8_224.fb_dist_in1k,89.009,10.991,98.076,1.924,26.21,224,1.000,bicubic,+4.773,+1.206,+56 convnext_large.fb_in1k,88.994,11.006,98.040,1.960,197.77,288,1.000,bicubic,+4.148,+0.826,-2 efficientnetv2_rw_m.agc_in1k,88.985,11.015,98.219,1.781,53.24,416,1.000,bicubic,+4.175,+1.067,-1 caformer_m36.sail_in1k,88.985,11.015,98.016,1.984,56.20,224,1.000,bicubic,+3.753,+0.816,-39 regnety_1280.swag_lc_in1k,88.955,11.045,98.228,1.772,644.81,224,0.965,bicubic,+2.973,+0.378,-90 regnetz_d8_evos.ch_in1k,88.951,11.049,98.181,1.819,23.46,320,1.000,bicubic,+4.825,+1.169,+60 regnetz_040_h.ra3_in1k,88.949,11.051,98.209,1.791,28.94,320,1.000,bicubic,+4.457,+1.451,+19 edgenext_base.in21k_ft_in1k,88.942,11.057,98.279,1.721,18.51,320,1.000,bicubic,+4.888,+1.083,+66 tf_efficientnet_b5.ap_in1k,88.942,11.057,98.166,1.834,30.39,456,0.934,bicubic,+4.684,+1.192,+44 mvitv2_large.fb_in1k,88.936,11.064,97.965,2.035,217.99,224,0.900,bicubic,+3.692,+0.751,-47 caformer_s18.sail_in22k_ft_in1k,88.932,11.068,98.311,1.689,26.34,224,1.000,bicubic,+4.858,+1.113,+60 deit3_base_patch16_384.fb_in1k,88.923,11.077,98.042,1.958,86.88,384,1.000,bicubic,+3.849,+0.768,-28 deit3_medium_patch16_224.fb_in22k_ft_in1k,88.919,11.081,98.296,1.704,38.85,224,1.000,bicubic,+4.369,+1.108,+4 volo_d1_224.sail_in1k,88.915,11.085,98.027,1.973,26.63,224,0.960,bicubic,+4.753,+1.251,+50 convnext_tiny.in12k_ft_in1k,88.906,11.094,98.309,1.691,28.59,288,1.000,bicubic,+4.456,+0.969,+15 tf_efficientnetv2_s.in21k_ft_in1k,88.898,11.102,98.275,1.725,21.46,384,1.000,bicubic,+4.612,+1.023,+34 vit_base_patch16_224.augreg_in21k_ft_in1k,88.868,11.132,98.232,1.768,86.57,224,0.900,bicubic,+4.336,+0.938,+3 convnext_tiny.fb_in22k_ft_in1k_384,88.855,11.145,98.296,1.704,28.59,384,1.000,bicubic,+4.767,+1.152,+52 regnetz_d8.ra3_in1k,88.853,11.147,98.189,1.810,23.37,320,1.000,bicubic,+4.801,+1.194,+56 convformer_b36.sail_in1k,88.851,11.149,97.878,2.122,99.88,224,1.000,bicubic,+4.033,+0.932,-18 coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,88.842,11.158,97.869,2.131,41.72,224,0.950,bicubic,+3.932,+0.911,-25 resnetrs420.tf_in1k,88.838,11.162,98.031,1.968,191.89,416,1.000,bicubic,+3.834,+0.907,-34 resnext101_32x16d.fb_wsl_ig1b_ft_in1k,88.834,11.166,98.051,1.949,194.03,224,0.875,bilinear,+4.668,+0.853,+40 resnetrs270.tf_in1k,88.829,11.171,98.136,1.864,129.86,352,1.000,bicubic,+4.401,+1.168,+14 vit_small_r26_s32_384.augreg_in21k_ft_in1k,88.821,11.179,98.341,1.659,36.47,384,1.000,bicubic,+4.773,+1.013,+52 swin_small_patch4_window7_224.ms_in22k_ft_in1k,88.817,11.184,98.328,1.672,49.61,224,0.900,bicubic,+5.519,+1.364,+133 vit_base_r50_s16_384.orig_in21k_ft_in1k,88.806,11.194,98.232,1.768,98.95,384,1.000,bicubic,+3.830,+0.942,-37 xcit_medium_24_p16_224.fb_dist_in1k,88.806,11.194,98.038,1.962,84.40,224,1.000,bicubic,+4.520,+1.106,+23 tf_efficientnet_b7.aa_in1k,88.804,11.196,98.057,1.943,66.35,600,0.949,bicubic,+4.388,+1.149,+13 maxxvit_rmlp_small_rw_256.sw_in1k,88.795,11.205,98.064,1.937,66.01,256,0.950,bicubic,+4.171,+0.996,-18 fastvit_sa36.apple_dist_in1k,88.789,11.211,98.096,1.905,31.53,256,0.900,bicubic,+4.763,+1.242,+47 seresnet152d.ra2_in1k,88.787,11.213,98.172,1.828,66.84,320,1.000,bicubic,+4.427,+1.132,+14 xcit_tiny_24_p8_384.fb_dist_in1k,88.787,11.213,98.155,1.845,12.11,384,1.000,bicubic,+5.041,+1.755,+74 convformer_m36.sail_in1k,88.787,11.213,97.769,2.231,57.05,224,1.000,bicubic,+4.293,+0.903,-7 resnext101_32x8d.fb_swsl_ig1b_ft_in1k,88.782,11.218,98.151,1.849,88.79,224,0.875,bilinear,+4.480,+0.975,+14 resnetrs200.tf_in1k,88.763,11.237,98.115,1.885,93.21,320,1.000,bicubic,+4.319,+1.273,-5 convnext_base.fb_in1k,88.763,11.237,97.920,2.080,88.59,288,1.000,bicubic,+4.335,+0.952,+1 deit3_large_patch16_224.fb_in1k,88.763,11.237,97.920,2.080,304.37,224,0.900,bicubic,+3.989,+0.884,-32 tf_efficientnet_b6.aa_in1k,88.759,11.241,98.066,1.934,43.04,528,0.942,bicubic,+4.647,+1.182,+29 resnetrs350.tf_in1k,88.757,11.243,98.031,1.968,163.96,384,1.000,bicubic,+4.043,+1.039,-34 rexnetr_300.sw_in12k_ft_in1k,88.746,11.254,98.339,1.661,34.81,288,1.000,bicubic,+4.200,+1.083,-23 caformer_s36.sail_in1k,88.746,11.254,98.023,1.977,39.30,224,1.000,bicubic,+4.240,+1.027,-17 convnextv2_tiny.fcmae_ft_in22k_in1k,88.744,11.256,98.194,1.806,28.64,288,1.000,bicubic,+4.328,+0.934,-2 vit_base_patch16_224_miil.in21k_ft_in1k,88.742,11.258,98.027,1.973,86.54,224,0.875,bilinear,+4.476,+1.223,+8 edgenext_base.usi_in1k,88.735,11.265,98.147,1.853,18.51,320,1.000,bicubic,+4.777,+1.377,+38 regnetz_040.ra3_in1k,88.731,11.269,98.091,1.909,27.12,320,1.000,bicubic,+4.491,+1.159,+10 convformer_s18.sail_in22k_ft_in1k,88.727,11.273,98.194,1.806,26.77,224,1.000,bicubic,+4.989,+1.146,+65 resnetv2_152x2_bit.goog_in21k_ft_in1k,88.725,11.275,98.311,1.689,236.34,448,1.000,bilinear,+4.215,+0.877,-26 regnety_160.deit_in1k,88.703,11.297,98.068,1.932,83.59,288,1.000,bicubic,+5.013,+1.288,+69 davit_base.msft_in1k,88.701,11.299,97.874,2.127,87.95,224,0.950,bicubic,+4.059,+0.854,-39 regnety_640.seer_ft_in1k,88.674,11.326,98.166,1.834,281.38,384,1.000,bicubic,+4.766,+1.244,+35 regnetz_d32.ra3_in1k,88.652,11.348,98.078,1.922,27.58,320,0.950,bicubic,+4.630,+1.210,+27 vit_small_patch16_384.augreg_in21k_ft_in1k,88.648,11.352,98.230,1.770,22.20,384,1.000,bicubic,+4.844,+1.130,+47 flexivit_base.1200ep_in1k,88.648,11.352,97.935,2.065,86.59,240,0.950,bicubic,+3.972,+0.941,-43 mvitv2_base.fb_in1k,88.644,11.356,97.826,2.174,51.47,224,0.900,bicubic,+4.194,+0.968,-24 regnety_080.ra3_in1k,88.635,11.365,97.965,2.035,39.18,288,1.000,bicubic,+4.709,+1.075,+28 vit_medium_patch16_gap_256.sw_in12k_ft_in1k,88.633,11.367,98.189,1.810,38.86,256,0.950,bicubic,+4.187,+0.980,-25 davit_small.msft_in1k,88.631,11.369,97.953,2.047,49.75,224,0.950,bicubic,+4.379,+1.013,-4 eca_nfnet_l1.ra2_in1k,88.622,11.378,98.132,1.868,41.41,320,1.000,bicubic,+4.610,+1.106,+22 repvgg_d2se.rvgg_in1k,88.599,11.401,97.984,2.015,133.33,320,1.000,bilinear,+5.039,+1.326,+69 maxvit_base_tf_224.in1k,88.588,11.412,97.850,2.150,119.47,224,0.950,bicubic,+3.728,+0.862,-62 swinv2_base_window16_256.ms_in1k,88.586,11.414,97.908,2.092,87.92,256,0.900,bicubic,+3.986,+0.818,-48 resnetaa101d.sw_in12k_ft_in1k,88.573,11.427,98.076,1.924,44.57,288,1.000,bicubic,+4.449,+0.970,+4 regnety_320.seer_ft_in1k,88.571,11.429,98.106,1.894,145.05,384,1.000,bicubic,+5.243,+1.398,+93 efficientvit_b3.r288_in1k,88.562,11.438,97.707,2.293,48.65,288,1.000,bicubic,+4.409,+0.971,0 resnetv2_152x4_bit.goog_in21k_ft_in1k,88.554,11.446,98.192,1.808,936.53,480,1.000,bilinear,+3.638,+0.754,-73 resnet200d.ra2_in1k,88.554,11.446,97.961,2.039,64.69,320,1.000,bicubic,+4.590,+1.135,+16 xcit_small_24_p16_224.fb_dist_in1k,88.545,11.455,98.004,1.996,47.67,224,1.000,bicubic,+4.671,+1.268,+22 flexivit_base.600ep_in1k,88.545,11.455,97.933,2.067,86.59,240,0.950,bicubic,+4.021,+0.997,-47 seresnextaa101d_32x8d.ah_in1k,88.537,11.463,98.002,1.998,93.59,288,1.000,bicubic,+3.971,+0.926,-54 maxvit_rmlp_small_rw_224.sw_in1k,88.522,11.478,97.773,2.227,64.90,224,0.900,bicubic,+4.030,+0.763,-44 resnest269e.in1k,88.520,11.480,98.029,1.971,110.93,416,0.928,bicubic,+4.012,+1.039,-49 coatnet_rmlp_2_rw_224.sw_in1k,88.513,11.487,97.566,2.434,73.88,224,0.950,bicubic,+3.905,+0.826,-60 efficientformerv2_l.snap_dist_in1k,88.509,11.491,97.963,2.037,26.32,224,0.950,bicubic,+4.877,+1.405,+49 repvit_m2_3.dist_300e_in1k,88.503,11.497,97.942,2.058,23.69,224,0.950,bicubic,+4.999,+1.438,+57 gcvit_base.in1k,88.501,11.499,97.769,2.231,90.32,224,0.875,bicubic,+4.056,+0.687,-42 swinv2_base_window8_256.ms_in1k,88.498,11.502,97.891,2.109,87.92,256,0.900,bicubic,+4.248,+0.967,-22 seresnext101_32x8d.ah_in1k,88.494,11.506,97.884,2.116,93.57,288,1.000,bicubic,+4.308,+1.010,-16 repvit_m2_3.dist_450e_in1k,88.488,11.512,98.059,1.941,23.69,224,0.950,bicubic,+4.746,+1.415,+31 convformer_s36.sail_in1k,88.486,11.514,97.763,2.237,40.01,224,1.000,bicubic,+4.426,+1.017,-7 flexivit_base.300ep_in1k,88.479,11.521,97.846,2.154,86.59,240,0.950,bicubic,+4.073,+0.962,-38 crossvit_18_dagger_408.in1k,88.473,11.527,97.897,2.103,44.61,408,1.000,bicubic,+4.271,+1.079,-22 efficientnetv2_rw_s.ra2_in1k,88.469,11.531,97.978,2.022,23.94,384,1.000,bicubic,+4.663,+1.246,+17 resnetv2_101x3_bit.goog_in21k_ft_in1k,88.466,11.534,98.157,1.843,387.93,448,1.000,bilinear,+4.028,+0.775,-49 fastvit_sa24.apple_dist_in1k,88.460,11.540,97.957,2.043,21.55,256,0.900,bicubic,+5.118,+1.405,+69 maxvit_large_tf_224.in1k,88.460,11.540,97.809,2.191,211.79,224,0.950,bicubic,+3.518,+0.839,-94 maxvit_small_tf_224.in1k,88.456,11.544,97.880,2.120,68.93,224,0.950,bicubic,+4.030,+1.056,-48 resnetv2_50x3_bit.goog_in21k_ft_in1k,88.447,11.553,98.200,1.800,217.32,448,1.000,bilinear,+4.427,+1.074,-8 cait_s24_224.fb_dist_in1k,88.443,11.557,97.961,2.039,46.92,224,1.000,bicubic,+5.001,+1.387,+52 resmlp_big_24_224.fb_distilled_in1k,88.441,11.559,97.938,2.062,129.14,224,0.875,bicubic,+4.849,+1.287,+37 regnetv_064.ra3_in1k,88.436,11.563,98.061,1.939,30.58,288,1.000,bicubic,+4.721,+1.319,+23 resnest200e.in1k,88.434,11.566,98.040,1.960,70.20,320,0.909,bicubic,+4.590,+1.156,+1 vit_large_r50_s32_224.augreg_in21k_ft_in1k,88.432,11.568,98.083,1.917,328.99,224,0.900,bicubic,+4.014,+0.911,-54 inception_next_base.sail_in1k,88.432,11.568,97.773,2.227,86.67,224,0.950,bicubic,+4.340,+0.977,-24 tf_efficientnet_b3.ns_jft_in1k,88.428,11.572,98.031,1.968,12.23,300,0.904,bicubic,+4.376,+1.113,-20 seresnext101d_32x8d.ah_in1k,88.428,11.572,97.961,2.039,93.59,288,1.000,bicubic,+4.070,+1.041,-47 swin_base_patch4_window12_384.ms_in1k,88.426,11.574,97.803,2.197,87.90,384,1.000,bicubic,+3.950,+0.911,-68 tf_efficientnetv2_s.in1k,88.409,11.591,97.927,2.073,21.46,384,1.000,bicubic,+4.511,+1.231,-11 convnext_small.fb_in1k,88.407,11.593,98.012,1.988,50.22,288,1.000,bicubic,+4.707,+1.204,+18 tf_efficientnet_b5.aa_in1k,88.407,11.593,97.931,2.069,30.39,456,0.934,bicubic,+4.719,+1.219,+19 regnety_320.swag_lc_in1k,88.398,11.602,98.115,1.885,145.05,224,0.965,bicubic,+3.850,+0.673,-83 vit_base_patch16_384.orig_in21k_ft_in1k,88.392,11.608,98.160,1.840,86.86,384,1.000,bicubic,+4.192,+0.942,-41 regnetz_c16_evos.ch_in1k,88.381,11.619,98.040,1.960,13.49,320,0.950,bicubic,+5.745,+1.566,+121 tresnet_v2_l.miil_in21k_ft_in1k,88.375,11.625,97.925,2.075,46.17,224,0.875,bilinear,+4.481,+1.435,-16 swinv2_small_window16_256.ms_in1k,88.372,11.628,97.848,2.152,49.73,256,0.900,bicubic,+4.148,+1.070,-47 efficientnet_b4.ra2_in1k,88.366,11.634,97.961,2.039,19.34,384,1.000,bicubic,+4.952,+1.363,+38 resnet152d.ra2_in1k,88.362,11.638,97.931,2.069,60.21,320,1.000,bicubic,+4.678,+1.193,+13 fastvit_ma36.apple_in1k,88.358,11.643,97.923,2.077,44.07,256,0.950,bicubic,+4.475,+1.181,-18 maxvit_rmlp_tiny_rw_256.sw_in1k,88.355,11.645,97.820,2.180,29.15,256,0.950,bicubic,+4.131,+0.952,-50 tf_efficientnet_b4.ap_in1k,88.347,11.653,97.891,2.109,19.34,380,0.922,bicubic,+5.097,+1.495,+52 deit3_small_patch16_224.fb_in22k_ft_in1k,88.338,11.662,98.132,1.868,22.06,224,1.000,bicubic,+5.262,+1.356,+71 regnety_064.ra3_in1k,88.323,11.677,97.865,2.135,30.58,288,1.000,bicubic,+4.603,+1.143,+1 efficientvit_b3.r256_in1k,88.319,11.681,97.560,2.440,48.65,256,1.000,bicubic,+4.517,+1.044,-11 convnextv2_nano.fcmae_ft_in22k_in1k_384,88.315,11.685,97.938,2.062,15.62,384,1.000,bicubic,+4.941,+1.194,+37 tf_efficientnet_b5.ra_in1k,88.313,11.687,97.914,2.086,30.39,456,0.934,bicubic,+4.499,+1.162,-17 crossvit_15_dagger_408.in1k,88.313,11.687,97.874,2.127,28.50,408,1.000,bicubic,+4.473,+1.096,-21 cs3se_edgenet_x.c2ns_in1k,88.300,11.700,97.933,2.067,50.72,320,1.000,bicubic,+4.754,+1.263,+13 deit3_small_patch16_384.fb_in1k,88.296,11.704,97.888,2.112,22.21,384,1.000,bicubic,+4.868,+1.214,+24 pvt_v2_b4.in1k,88.285,11.715,97.816,2.184,62.56,224,0.900,bicubic,+4.573,+1.146,-4 efficientformer_l7.snap_dist_in1k,88.278,11.722,97.882,2.118,82.23,224,0.950,bicubic,+4.896,+1.346,+30 mvitv2_small.fb_in1k,88.264,11.736,97.692,2.308,34.87,224,0.900,bicubic,+4.494,+1.116,-16 inception_next_small.sail_in1k,88.253,11.747,97.816,2.184,49.37,224,0.875,bicubic,+4.675,+1.218,+6 resnetrs152.tf_in1k,88.249,11.751,97.737,2.263,86.62,320,1.000,bicubic,+4.547,+1.125,-7 deit3_base_patch16_224.fb_in1k,88.242,11.758,97.818,2.182,86.59,224,0.900,bicubic,+4.456,+1.232,-21 xcit_small_12_p16_224.fb_dist_in1k,88.240,11.760,97.841,2.159,26.25,224,1.000,bicubic,+4.912,+1.425,+33 gcvit_small.in1k,88.223,11.777,97.788,2.212,51.09,224,0.875,bicubic,+4.331,+1.130,-37 deit_base_distilled_patch16_224.fb_in1k,88.210,11.790,97.910,2.090,87.34,224,0.900,bicubic,+4.820,+1.422,+21 regnetv_040.ra3_in1k,88.208,11.792,97.972,2.028,20.64,288,1.000,bicubic,+5.018,+1.314,+38 xception65p.ra3_in1k,88.189,11.811,97.788,2.212,39.82,299,0.940,bicubic,+5.063,+1.306,+45 swinv2_small_window8_256.ms_in1k,88.172,11.828,97.777,2.223,49.73,256,0.900,bicubic,+4.318,+1.133,-38 caformer_s18.sail_in1k,88.163,11.837,97.728,2.272,26.34,224,1.000,bicubic,+4.509,+1.210,-9 xcit_tiny_24_p16_384.fb_dist_in1k,88.161,11.839,97.942,2.058,12.12,384,1.000,bicubic,+5.591,+1.666,+108 xcit_large_24_p8_224.fb_in1k,88.159,11.841,97.391,2.609,188.93,224,1.000,bicubic,+3.765,+0.727,-87 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,88.148,11.852,98.051,1.949,236.34,384,1.000,bicubic,+4.312,+0.925,-38 tiny_vit_21m_224.in1k,88.146,11.854,97.850,2.150,21.20,224,0.950,bicubic,+4.892,+1.258,+26 coat_lite_medium.in1k,88.144,11.856,97.899,2.101,44.57,224,0.900,bicubic,+4.544,+1.171,-11 resnext101_32x8d.fb_wsl_ig1b_ft_in1k,88.144,11.856,97.859,2.142,88.79,224,0.875,bilinear,+5.446,+1.715,+77 cait_xxs36_384.fb_dist_in1k,88.140,11.860,97.903,2.097,17.37,384,1.000,bicubic,+5.936,+1.759,+149 dm_nfnet_f0.dm_in1k,88.133,11.867,97.903,2.097,71.49,256,0.900,bicubic,+4.647,+1.335,-2 resnext101_32x4d.fb_swsl_ig1b_ft_in1k,88.112,11.888,97.965,2.035,44.18,224,0.875,bilinear,+4.886,+1.205,+24 pit_b_distilled_224.in1k,88.110,11.890,97.654,2.346,74.79,224,0.900,bicubic,+4.344,+1.186,-35 xcit_tiny_12_p8_384.fb_dist_in1k,88.103,11.897,97.925,2.075,6.71,384,1.000,bicubic,+5.715,+1.705,+116 tiny_vit_11m_224.dist_in22k_ft_in1k,88.103,11.897,97.790,2.210,11.00,224,0.950,bicubic,+4.875,+1.160,+20 pvt_v2_b5.in1k,88.103,11.897,97.694,2.306,81.96,224,0.900,bicubic,+4.363,+1.058,-31 rexnetr_200.sw_in12k_ft_in1k,88.099,11.901,98.006,1.994,16.52,288,1.000,bicubic,+4.961,+1.370,+25 pvt_v2_b3.in1k,88.099,11.901,97.777,2.223,45.24,224,0.900,bicubic,+4.981,+1.221,+32 fastvit_sa36.apple_in1k,88.088,11.912,97.790,2.210,31.53,256,0.900,bicubic,+4.588,+1.160,-13 hrnet_w18_ssld.paddle_in1k,88.067,11.933,97.824,2.176,21.30,288,1.000,bilinear,+6.019,+1.574,+157 xception65.ra3_in1k,88.067,11.933,97.750,2.250,39.92,299,0.940,bicubic,+4.887,+1.158,+18 efficientvit_b3.r224_in1k,88.065,11.935,97.566,2.434,48.65,224,0.950,bicubic,+4.605,+1.236,-11 swin_s3_base_224.ms_in1k,88.046,11.954,97.662,2.338,71.13,224,0.900,bicubic,+4.126,+0.990,-66 xcit_tiny_24_p8_224.fb_dist_in1k,88.037,11.963,97.818,2.182,12.11,224,1.000,bicubic,+5.471,+1.760,+89 convnextv2_tiny.fcmae_ft_in1k,88.035,11.965,97.859,2.142,28.64,288,1.000,bicubic,+4.571,+1.141,-15 regnety_160.swag_lc_in1k,88.033,11.967,98.042,1.958,83.59,224,0.965,bicubic,+4.251,+0.762,-51 resnet152.a1h_in1k,88.033,11.967,97.696,2.304,60.19,288,1.000,bicubic,+4.583,+1.158,-16 focalnet_base_srf.ms_in1k,88.033,11.967,97.656,2.344,88.15,224,0.900,bicubic,+4.213,+0.976,-56 maxvit_tiny_tf_224.in1k,88.027,11.973,97.816,2.184,30.92,224,0.950,bicubic,+4.625,+1.226,-12 focalnet_base_lrf.ms_in1k,88.003,11.997,97.609,2.391,88.75,224,0.900,bicubic,+4.165,+1.001,-63 gcvit_tiny.in1k,88.001,11.999,97.722,2.278,28.22,224,0.875,bicubic,+4.617,+1.324,-10 eca_nfnet_l0.ra2_in1k,87.980,12.020,97.871,2.129,24.14,288,1.000,bicubic,+5.402,+1.379,+77 tf_efficientnet_b5.in1k,87.975,12.025,97.933,2.067,30.39,456,0.934,bicubic,+4.799,+1.397,+6 cs3sedarknet_x.c2ns_in1k,87.975,12.025,97.794,2.205,35.40,288,1.000,bicubic,+5.317,+1.445,+60 nfnet_l0.ra2_in1k,87.967,12.033,97.867,2.133,35.07,288,1.000,bicubic,+5.217,+1.351,+46 efficientformer_l3.snap_dist_in1k,87.963,12.037,97.711,2.289,31.41,224,0.950,bicubic,+5.415,+1.461,+79 tf_efficientnet_b4.aa_in1k,87.958,12.042,97.739,2.261,19.34,380,0.922,bicubic,+4.940,+1.439,+23 xcit_small_24_p8_224.fb_in1k,87.956,12.044,97.581,2.419,47.63,224,1.000,bicubic,+4.122,+0.949,-69 regnetz_c16.ra3_in1k,87.952,12.048,97.779,2.220,13.46,320,1.000,bicubic,+5.320,+1.462,+58 regnety_032.ra_in1k,87.950,12.050,97.897,2.103,19.44,288,1.000,bicubic,+5.224,+1.481,+43 coatnet_1_rw_224.sw_in1k,87.943,12.057,97.453,2.547,41.72,224,0.950,bicubic,+4.347,+1.071,-43 resnet101d.ra2_in1k,87.937,12.063,97.910,2.090,44.57,320,1.000,bicubic,+4.917,+1.458,+17 regnety_040.ra3_in1k,87.933,12.067,97.880,2.120,20.65,288,1.000,bicubic,+4.889,+1.378,+13 mobilevitv2_200.cvnets_in22k_ft_in1k_384,87.933,12.067,97.822,2.178,18.45,384,1.000,bicubic,+4.533,+1.240,-25 swinv2_cr_small_ns_224.sw_in1k,87.933,12.067,97.668,2.332,49.70,224,0.900,bicubic,+4.435,+1.184,-38 focalnet_small_lrf.ms_in1k,87.930,12.069,97.696,2.304,50.34,224,0.900,bicubic,+4.436,+1.116,-38 repvit_m1_5.dist_450e_in1k,87.928,12.072,97.703,2.297,14.64,224,0.950,bicubic,+5.416,+1.591,+72 resnetv2_101.a1h_in1k,87.924,12.076,97.651,2.349,44.54,288,1.000,bicubic,+4.924,+1.197,+13 efficientvit_b2.r288_in1k,87.920,12.080,97.600,2.400,24.33,288,1.000,bicubic,+4.820,+1.296,+3 vit_base_patch32_384.augreg_in21k_ft_in1k,87.909,12.091,98.010,1.990,88.30,384,1.000,bicubic,+4.557,+1.170,-25 sequencer2d_l.in1k,87.907,12.093,97.703,2.297,54.30,224,0.875,bicubic,+4.513,+1.207,-32 twins_svt_large.in1k,87.903,12.097,97.581,2.419,99.27,224,0.900,bicubic,+4.225,+0.993,-59 coatnet_rmlp_1_rw_224.sw_in1k,87.892,12.108,97.624,2.376,41.69,224,0.950,bicubic,+4.530,+1.174,-29 twins_pcpvt_large.in1k,87.869,12.131,97.859,2.142,60.99,224,0.900,bicubic,+4.739,+1.255,-9 swin_base_patch4_window7_224.ms_in1k,87.866,12.134,97.564,2.436,87.77,224,0.900,bicubic,+4.260,+1.112,-59 maxvit_tiny_rw_224.sw_in1k,87.856,12.144,97.643,2.357,29.06,224,0.950,bicubic,+4.352,+1.129,-51 swin_s3_small_224.ms_in1k,87.849,12.151,97.431,2.568,49.74,224,0.900,bicubic,+4.093,+0.980,-78 convformer_s18.sail_in1k,87.845,12.155,97.551,2.449,26.77,224,1.000,bicubic,+4.859,+1.301,+5 deit_base_patch16_384.fb_in1k,87.845,12.155,97.508,2.492,86.86,384,1.000,bicubic,+4.741,+1.140,-8 mobilevitv2_175.cvnets_in22k_ft_in1k_384,87.841,12.159,97.728,2.272,14.25,384,1.000,bicubic,+4.903,+1.302,+6 ecaresnet101d.miil_in1k,87.839,12.161,97.899,2.101,44.57,288,0.950,bicubic,+4.855,+1.357,+3 convnext_nano.in12k_ft_in1k,87.837,12.164,97.888,2.112,15.59,288,1.000,bicubic,+4.975,+1.332,+9 xcit_small_12_p8_224.fb_in1k,87.822,12.178,97.568,2.432,26.21,224,1.000,bicubic,+4.488,+1.086,-35 flexivit_small.600ep_in1k,87.811,12.189,97.577,2.423,22.06,240,0.950,bicubic,+5.449,+1.493,+72 vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,87.809,12.191,97.756,2.244,88.22,224,0.900,bicubic,+4.513,+1.228,-35 flexivit_small.1200ep_in1k,87.809,12.191,97.613,2.387,22.06,240,0.950,bicubic,+5.283,+1.487,+51 focalnet_small_srf.ms_in1k,87.809,12.191,97.575,2.425,49.89,224,0.900,bicubic,+4.393,+1.137,-51 tf_efficientnetv2_b3.in21k_ft_in1k,87.807,12.193,97.895,2.105,14.36,300,0.900,bicubic,+5.137,+1.269,+21 maxxvit_rmlp_nano_rw_256.sw_in1k,87.807,12.193,97.752,2.248,16.78,256,0.950,bicubic,+4.765,+1.402,-11 deit3_medium_patch16_224.fb_in1k,87.807,12.193,97.654,2.346,38.85,224,0.900,bicubic,+4.721,+1.360,-15 tresnet_xl.miil_in1k_448,87.796,12.204,97.459,2.541,78.44,448,0.875,bilinear,+4.738,+1.287,-15 resnetv2_50x1_bit.goog_distilled_in1k,87.790,12.210,97.899,2.101,25.55,224,0.875,bicubic,+4.966,+1.381,+1 repvit_m1_5.dist_300e_in1k,87.772,12.227,97.649,2.351,14.64,224,0.950,bicubic,+5.396,+1.619,+61 convnext_tiny.fb_in1k,87.770,12.230,97.585,2.415,28.59,288,1.000,bicubic,+5.072,+0.953,+13 regnety_320.tv2_in1k,87.743,12.257,97.673,2.327,145.05,224,0.965,bicubic,+4.581,+1.259,-34 resnext101_64x4d.tv_in1k,87.740,12.259,97.592,2.408,83.46,224,0.875,bilinear,+4.748,+1.348,-14 convnextv2_nano.fcmae_ft_in22k_in1k,87.732,12.268,97.886,2.114,15.62,288,1.000,bicubic,+5.068,+1.366,+15 twins_pcpvt_base.in1k,87.730,12.270,97.728,2.272,43.83,224,0.900,bicubic,+5.016,+1.382,+6 tresnet_m.miil_in21k_ft_in1k,87.725,12.274,97.517,2.483,31.39,224,0.875,bilinear,+4.656,+1.407,-24 mvitv2_tiny.fb_in1k,87.719,12.281,97.553,2.447,24.17,224,0.900,bicubic,+5.309,+1.401,+48 gc_efficientnetv2_rw_t.agc_in1k,87.717,12.283,97.803,2.197,13.68,288,1.000,bicubic,+5.261,+1.507,+45 maxvit_rmlp_nano_rw_256.sw_in1k,87.715,12.285,97.577,2.423,15.50,256,0.950,bicubic,+4.761,+1.311,-17 resnetv2_101x1_bit.goog_in21k_ft_in1k,87.683,12.317,97.940,2.060,44.54,448,1.000,bilinear,+5.341,+1.420,+57 rexnet_300.nav_in1k,87.683,12.317,97.611,2.389,34.71,224,0.875,bicubic,+4.909,+1.373,-5 swin_small_patch4_window7_224.ms_in1k,87.662,12.338,97.568,2.432,49.61,224,0.900,bicubic,+4.454,+1.252,-48 efficientnetv2_rw_t.ra2_in1k,87.651,12.349,97.690,2.310,13.65,288,1.000,bicubic,+5.301,+1.498,+53 twins_svt_base.in1k,87.651,12.349,97.525,2.474,56.07,224,0.900,bicubic,+4.531,+1.111,-39 mobilevitv2_150.cvnets_in22k_ft_in1k_384,87.649,12.351,97.647,2.353,10.59,384,1.000,bicubic,+5.063,+1.333,+18 coat_small.in1k,87.647,12.354,97.530,2.470,21.69,224,0.900,bicubic,+5.285,+1.322,+46 fastvit_sa24.apple_in1k,87.638,12.362,97.726,2.274,21.55,256,0.900,bicubic,+4.960,+1.454,-1 maxxvitv2_nano_rw_256.sw_in1k,87.638,12.362,97.525,2.474,23.70,256,0.950,bicubic,+4.528,+1.201,-42 efficientvit_b2.r256_in1k,87.638,12.362,97.453,2.547,24.33,256,1.000,bicubic,+4.948,+1.359,-1 pnasnet5large.tf_in1k,87.636,12.364,97.487,2.513,86.06,331,0.911,bicubic,+4.854,+1.447,-16 resnet101.a1h_in1k,87.634,12.366,97.558,2.442,44.55,288,1.000,bicubic,+4.856,+1.248,-16 resnetaa50d.sw_in12k_ft_in1k,87.623,12.377,97.803,2.197,25.58,288,1.000,bicubic,+5.023,+1.305,+7 cs3edgenet_x.c2_in1k,87.621,12.379,97.654,2.346,47.82,288,1.000,bicubic,+4.913,+1.284,-11 flexivit_small.300ep_in1k,87.621,12.379,97.613,2.387,22.06,240,0.950,bicubic,+5.443,+1.575,+66 xcit_medium_24_p8_224.fb_in1k,87.612,12.388,97.201,2.799,84.32,224,1.000,bicubic,+3.866,+0.491,-117 swinv2_tiny_window16_256.ms_in1k,87.610,12.390,97.560,2.440,28.35,256,0.900,bicubic,+4.806,+1.324,-23 resnext101_32x16d.fb_swsl_ig1b_ft_in1k,87.606,12.394,97.816,2.184,194.03,224,0.875,bilinear,+4.270,+0.970,-73 resnext50_32x4d.fb_swsl_ig1b_ft_in1k,87.600,12.400,97.649,2.351,25.03,224,0.875,bilinear,+5.428,+1.425,+63 nest_base_jx.goog_in1k,87.597,12.403,97.521,2.479,67.72,224,0.875,bicubic,+4.063,+1.147,-99 maxvit_nano_rw_256.sw_in1k,87.595,12.405,97.519,2.481,15.45,256,0.950,bicubic,+4.667,+1.299,-36 tf_efficientnet_b4.in1k,87.585,12.415,97.602,2.398,19.34,380,0.922,bicubic,+4.977,+1.850,-4 davit_tiny.msft_in1k,87.565,12.435,97.585,2.415,28.36,224,0.950,bicubic,+4.869,+1.311,-17 levit_384.fb_dist_in1k,87.563,12.437,97.538,2.462,39.13,224,0.900,bicubic,+4.967,+1.520,-3 levit_conv_384.fb_dist_in1k,87.561,12.439,97.538,2.462,39.13,224,0.900,bicubic,+4.971,+1.522,-2 sequencer2d_m.in1k,87.559,12.441,97.581,2.419,38.31,224,0.875,bicubic,+4.747,+1.307,-34 convnextv2_nano.fcmae_ft_in1k,87.553,12.447,97.666,2.334,15.62,288,1.000,bicubic,+5.067,+1.440,+13 tf_efficientnet_b2.ns_jft_in1k,87.553,12.447,97.626,2.374,9.11,260,0.890,bicubic,+5.175,+1.372,+23 ecaresnet50t.ra2_in1k,87.544,12.456,97.649,2.351,25.57,320,0.950,bicubic,+5.192,+1.509,+27 regnetx_320.tv2_in1k,87.538,12.462,97.564,2.436,107.81,224,0.965,bicubic,+4.728,+1.356,-37 vit_base_patch32_clip_224.laion2b_ft_in1k,87.529,12.471,97.547,2.453,88.22,224,0.900,bicubic,+4.947,+1.347,-5 efficientformerv2_s2.snap_dist_in1k,87.516,12.484,97.615,2.385,12.71,224,0.950,bicubic,+5.350,+1.705,+51 inception_next_tiny.sail_in1k,87.516,12.484,97.549,2.451,28.06,224,0.875,bicubic,+5.038,+1.527,+9 coatnet_bn_0_rw_224.sw_in1k,87.508,12.492,97.551,2.449,27.44,224,0.950,bicubic,+5.108,+1.365,+13 vit_base_patch16_rpn_224.sw_in1k,87.504,12.496,97.489,2.511,86.54,224,0.900,bicubic,+5.302,+1.493,+43 pvt_v2_b2_li.in1k,87.501,12.499,97.470,2.530,22.55,224,0.900,bicubic,+5.307,+1.378,+44 resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,87.495,12.505,97.818,2.182,236.34,224,0.875,bicubic,+4.619,+1.236,-49 edgenext_small.usi_in1k,87.493,12.507,97.587,2.413,5.59,320,1.000,bicubic,+5.929,+1.875,+96 nest_small_jx.goog_in1k,87.493,12.507,97.513,2.487,38.35,224,0.875,bicubic,+4.369,+1.193,-74 vit_relpos_base_patch16_clsgap_224.sw_in1k,87.471,12.529,97.525,2.474,86.43,224,0.900,bicubic,+4.711,+1.353,-42 vit_relpos_base_patch16_224.sw_in1k,87.467,12.533,97.558,2.442,86.43,224,0.900,bicubic,+4.971,+1.419,-2 regnety_080_tv.tv2_in1k,87.465,12.535,97.636,2.364,39.38,224,0.965,bicubic,+4.871,+1.388,-20 fbnetv3_g.ra2_in1k,87.446,12.554,97.545,2.455,16.62,288,0.950,bilinear,+5.406,+1.485,+53 resnet61q.ra2_in1k,87.439,12.561,97.598,2.402,36.85,288,1.000,bicubic,+4.915,+1.468,-9 wide_resnet50_2.racm_in1k,87.437,12.563,97.543,2.458,68.88,288,0.950,bicubic,+5.157,+1.479,+24 poolformerv2_m48.sail_in1k,87.435,12.565,97.421,2.579,73.35,224,1.000,bicubic,+4.817,+1.349,-29 efficientnet_b3.ra2_in1k,87.433,12.567,97.679,2.321,12.23,320,1.000,bicubic,+5.187,+1.561,+24 regnetx_160.tv2_in1k,87.433,12.567,97.444,2.556,54.28,224,0.965,bicubic,+4.867,+1.272,-16 resnext101_64x4d.c1_in1k,87.433,12.567,97.444,2.556,83.46,288,1.000,bicubic,+4.277,+1.070,-89 cait_xxs24_384.fb_dist_in1k,87.414,12.586,97.621,2.378,12.03,384,1.000,bicubic,+6.442,+1.981,+149 resnet51q.ra2_in1k,87.397,12.603,97.583,2.417,35.70,288,1.000,bilinear,+5.037,+1.397,+4 cs3darknet_x.c2ns_in1k,87.395,12.605,97.611,2.389,35.05,288,1.000,bicubic,+5.173,+1.381,+22 cs3sedarknet_l.c2ns_in1k,87.395,12.605,97.570,2.430,21.91,288,0.950,bicubic,+5.611,+1.606,+66 coat_lite_small.in1k,87.390,12.610,97.374,2.626,19.84,224,0.900,bicubic,+5.078,+1.524,+9 pvt_v2_b2.in1k,87.382,12.618,97.519,2.481,25.36,224,0.900,bicubic,+5.298,+1.563,+36 tresnet_l.miil_in1k_448,87.382,12.618,97.487,2.513,55.99,448,0.875,bilinear,+5.106,+1.509,+14 resnetv2_50d_gn.ah_in1k,87.380,12.620,97.536,2.464,25.57,288,1.000,bicubic,+5.422,+1.608,+47 sequencer2d_s.in1k,87.377,12.623,97.387,2.613,27.65,224,0.875,bicubic,+5.037,+1.359,+1 swinv2_cr_small_224.sw_in1k,87.377,12.623,97.346,2.654,49.70,224,0.900,bicubic,+4.242,+1.238,-97 xcit_tiny_24_p8_224.fb_in1k,87.373,12.627,97.626,2.374,12.11,224,1.000,bicubic,+5.481,+1.656,+49 fastvit_sa12.apple_dist_in1k,87.363,12.637,97.493,2.507,11.58,256,0.900,bicubic,+5.509,+1.783,+51 vit_relpos_medium_patch16_cls_224.sw_in1k,87.363,12.637,97.451,2.549,38.76,224,0.900,bicubic,+4.791,+1.383,-33 nasnetalarge.tf_in1k,87.360,12.640,97.417,2.583,88.75,331,0.911,bicubic,+4.734,+1.375,-47 seresnext50_32x4d.racm_in1k,87.356,12.644,97.617,2.383,27.56,288,0.950,bicubic,+5.160,+1.469,+15 crossvit_18_dagger_240.in1k,87.350,12.650,97.453,2.547,44.27,240,0.875,bicubic,+4.832,+1.385,-29 repvit_m3.dist_in1k,87.326,12.674,97.481,2.519,10.68,224,0.950,bicubic,+5.824,+1.913,+77 ecaresnet50d.miil_in1k,87.322,12.678,97.666,2.334,25.58,288,0.950,bicubic,+5.672,+1.784,+59 wide_resnet101_2.tv2_in1k,87.322,12.678,97.404,2.596,126.89,224,0.965,bilinear,+4.820,+1.388,-30 resnext101_32x8d.tv2_in1k,87.318,12.682,97.560,2.440,88.79,224,0.965,bilinear,+4.486,+1.328,-80 crossvit_18_240.in1k,87.318,12.682,97.487,2.513,43.27,240,0.875,bicubic,+4.918,+1.427,-21 focalnet_tiny_srf.ms_in1k,87.301,12.699,97.414,2.586,28.43,224,0.900,bicubic,+5.163,+1.446,+16 resnest101e.in1k,87.288,12.712,97.562,2.438,48.28,256,0.875,bilinear,+4.404,+1.240,-85 gcvit_xtiny.in1k,87.279,12.721,97.478,2.522,19.98,224,0.875,bicubic,+5.325,+1.513,+32 tiny_vit_11m_224.in1k,87.277,12.723,97.487,2.513,11.00,224,0.950,bicubic,+5.785,+1.625,+69 coatnet_rmlp_nano_rw_224.sw_in1k,87.277,12.723,97.447,2.554,15.15,224,0.900,bicubic,+5.227,+1.569,+20 ecaresnet101d_pruned.miil_in1k,87.269,12.731,97.713,2.287,24.88,288,0.950,bicubic,+5.271,+1.553,+23 vit_relpos_medium_patch16_rpn_224.sw_in1k,87.269,12.731,97.447,2.554,38.73,224,0.900,bicubic,+4.959,+1.475,-13 swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,87.249,12.751,97.782,2.218,28.29,224,0.900,bicubic,+6.281,+1.768,+123 resnetrs101.tf_in1k,87.241,12.759,97.455,2.545,63.62,288,0.940,bicubic,+4.957,+1.441,-11 coatnext_nano_rw_224.sw_in1k,87.239,12.761,97.543,2.458,14.70,224,0.900,bicubic,+5.297,+1.627,+26 poolformer_m48.sail_in1k,87.237,12.763,97.318,2.682,73.47,224,0.950,bicubic,+4.755,+1.352,-40 regnety_160.tv2_in1k,87.232,12.768,97.461,2.539,83.59,224,0.965,bicubic,+4.586,+1.247,-70 mixer_b16_224.miil_in21k_ft_in1k,87.230,12.770,97.414,2.586,59.88,224,0.875,bilinear,+4.924,+1.694,-18 tresnet_xl.miil_in1k,87.230,12.770,97.397,2.603,78.44,224,0.875,bilinear,+5.156,+1.469,+8 xcit_tiny_12_p8_224.fb_dist_in1k,87.224,12.776,97.449,2.551,6.71,224,1.000,bicubic,+6.012,+1.847,+91 xcit_tiny_12_p16_384.fb_dist_in1k,87.209,12.791,97.466,2.534,6.72,384,1.000,bicubic,+6.271,+2.052,+117 convit_base.fb_in1k,87.209,12.791,97.284,2.716,86.54,224,0.875,bicubic,+4.919,+1.348,-20 resnetv2_50d_evos.ah_in1k,87.205,12.795,97.421,2.579,25.59,288,1.000,bicubic,+5.203,+1.521,+10 resnet152.tv2_in1k,87.188,12.812,97.387,2.613,60.19,224,0.965,bilinear,+4.901,+1.383,-22 tf_efficientnet_b3.ap_in1k,87.188,12.812,97.382,2.618,12.23,300,0.904,bicubic,+5.368,+1.756,+25 vit_base_patch32_clip_224.openai_ft_in1k,87.179,12.821,97.466,2.534,88.22,224,0.900,bicubic,+5.249,+1.500,+16 visformer_small.in1k,87.179,12.821,97.323,2.677,40.22,224,0.900,bicubic,+5.073,+1.445,-2 vit_srelpos_medium_patch16_224.sw_in1k,87.177,12.823,97.310,2.690,38.74,224,0.900,bicubic,+4.937,+1.368,-21 focalnet_tiny_lrf.ms_in1k,87.175,12.825,97.370,2.630,28.65,224,0.900,bicubic,+5.021,+1.422,-10 crossvit_15_dagger_240.in1k,87.166,12.834,97.431,2.568,28.21,240,0.875,bicubic,+4.836,+1.475,-34 convnext_tiny_hnf.a2h_in1k,87.147,12.853,97.280,2.720,28.59,288,1.000,bicubic,+4.563,+1.272,-71 vit_relpos_medium_patch16_224.sw_in1k,87.145,12.855,97.500,2.500,38.75,224,0.900,bicubic,+4.683,+1.418,-54 swin_s3_tiny_224.ms_in1k,87.143,12.857,97.308,2.692,28.33,224,0.900,bicubic,+4.999,+1.354,-12 coatnet_0_rw_224.sw_in1k,87.128,12.872,97.233,2.767,27.44,224,0.950,bicubic,+4.738,+1.397,-50 xcit_small_24_p16_224.fb_in1k,87.119,12.881,97.267,2.733,47.67,224,1.000,bicubic,+4.543,+1.255,-72 repvit_m1_1.dist_450e_in1k,87.104,12.896,97.412,2.588,8.80,224,0.950,bicubic,+5.792,+1.876,+63 swinv2_tiny_window8_256.ms_in1k,87.089,12.911,97.510,2.490,28.35,256,0.900,bicubic,+5.269,+1.516,+12 pit_s_distilled_224.in1k,87.081,12.919,97.363,2.637,24.04,224,0.900,bicubic,+5.267,+1.633,+11 efficientvit_b2.r224_in1k,87.081,12.919,97.203,2.797,24.33,224,0.950,bicubic,+4.933,+1.497,-19 ecaresnet50t.a1_in1k,87.081,12.919,97.122,2.878,25.57,288,1.000,bicubic,+4.953,+1.480,-15 mobilevitv2_200.cvnets_in22k_ft_in1k,87.057,12.943,97.431,2.568,18.45,256,0.888,bicubic,+4.725,+1.490,-46 resnext50_32x4d.a1h_in1k,87.057,12.943,97.331,2.669,25.03,288,1.000,bicubic,+5.043,+1.397,-10 xception41p.ra3_in1k,87.051,12.949,97.201,2.799,26.91,299,0.940,bicubic,+5.079,+1.417,-7 regnetz_b16.ra3_in1k,87.047,12.953,97.412,2.588,9.72,288,1.000,bicubic,+6.319,+1.894,+119 crossvit_15_240.in1k,87.042,12.958,97.423,2.577,27.53,240,0.875,bicubic,+5.506,+1.687,+27 convit_small.fb_in1k,87.042,12.958,97.350,2.650,27.78,224,0.875,bicubic,+5.622,+1.606,+44 tf_efficientnetv2_b3.in1k,87.027,12.973,97.301,2.699,14.36,300,0.904,bicubic,+5.055,+1.499,-10 gcresnet50t.ra2_in1k,87.025,12.975,97.391,2.609,25.90,288,1.000,bicubic,+5.569,+1.673,+37 xcit_small_12_p16_224.fb_in1k,87.010,12.990,97.242,2.759,26.25,224,1.000,bicubic,+5.040,+1.430,-11 nest_tiny_jx.goog_in1k,87.008,12.992,97.378,2.622,17.06,224,0.875,bicubic,+5.582,+1.760,+37 poolformerv2_m36.sail_in1k,87.008,12.992,97.284,2.716,56.08,224,1.000,bicubic,+4.792,+1.360,-42 deit3_small_patch16_224.fb_in1k,87.008,12.992,97.171,2.829,22.06,224,0.900,bicubic,+5.638,+1.715,+45 deit_small_distilled_patch16_224.fb_in1k,87.004,12.996,97.323,2.677,22.44,224,0.900,bicubic,+5.788,+1.699,+56 swinv2_cr_tiny_ns_224.sw_in1k,87.002,12.998,97.280,2.720,28.33,224,0.900,bicubic,+5.200,+1.462,-2 resnet101.a1_in1k,87.000,13.000,96.960,3.040,44.55,288,1.000,bicubic,+4.678,+1.328,-58 resnet152.a2_in1k,86.995,13.005,96.923,3.077,60.19,288,1.000,bicubic,+4.387,+0.795,-102 coatnet_nano_rw_224.sw_in1k,86.993,13.007,97.248,2.752,15.14,224,0.900,bicubic,+5.297,+1.602,0 resmlp_36_224.fb_distilled_in1k,86.987,13.013,97.276,2.724,44.69,224,0.875,bicubic,+5.839,+1.798,+59 poolformer_m36.sail_in1k,86.955,13.045,97.141,2.859,56.17,224,0.950,bicubic,+4.853,+1.443,-34 mobilevitv2_175.cvnets_in22k_ft_in1k,86.951,13.050,97.335,2.665,14.25,256,0.888,bicubic,+5.013,+1.545,-18 regnety_032.tv2_in1k,86.942,13.058,97.410,2.590,19.44,224,0.965,bicubic,+5.186,+1.566,-6 resnet101.a2_in1k,86.942,13.058,96.990,3.010,44.55,288,1.000,bicubic,+4.706,+1.260,-54 xcit_large_24_p16_224.fb_in1k,86.942,13.058,96.921,3.079,189.10,224,1.000,bicubic,+4.040,+1.037,-142 resnet101.tv2_in1k,86.936,13.065,97.248,2.752,44.55,224,0.965,bilinear,+5.047,+1.480,-19 xcit_medium_24_p16_224.fb_in1k,86.933,13.067,97.103,2.897,84.40,224,1.000,bicubic,+4.293,+1.121,-117 resnetv2_50.a1h_in1k,86.929,13.071,97.340,2.660,25.55,288,1.000,bicubic,+5.531,+1.614,+25 poolformerv2_s36.sail_in1k,86.921,13.079,97.353,2.647,30.79,224,1.000,bicubic,+5.355,+1.663,+1 tnt_s_patch16_224,86.914,13.086,97.361,2.639,23.76,224,0.900,bicubic,+5.378,+1.671,+4 vit_relpos_small_patch16_224.sw_in1k,86.891,13.109,97.489,2.511,21.98,224,0.900,bicubic,+5.429,+1.669,+15 vit_small_patch16_224.augreg_in21k_ft_in1k,86.871,13.129,97.609,2.391,22.05,224,0.900,bicubic,+5.486,+1.473,+23 vit_small_r26_s32_224.augreg_in21k_ft_in1k,86.856,13.143,97.530,2.470,36.43,224,0.900,bicubic,+4.992,+1.508,-26 resnet152.a1_in1k,86.856,13.143,96.793,3.207,60.19,288,1.000,bicubic,+4.124,+1.073,-136 ecaresnetlight.miil_in1k,86.852,13.148,97.508,2.492,30.16,288,0.950,bicubic,+5.444,+1.692,+17 resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,86.848,13.152,97.521,2.479,194.03,224,0.875,bilinear,+5.010,+1.429,-26 convmixer_1536_20.in1k,86.840,13.161,97.355,2.645,51.63,224,0.960,bicubic,+5.478,+1.741,+21 tf_efficientnet_b3.aa_in1k,86.840,13.161,97.297,2.703,12.23,300,0.904,bicubic,+5.200,+1.575,-15 rexnet_200.nav_in1k,86.840,13.161,97.276,2.724,16.37,224,0.875,bicubic,+5.204,+1.610,-13 resnet50.fb_swsl_ig1b_ft_in1k,86.827,13.173,97.493,2.507,25.56,224,0.875,bilinear,+5.655,+1.507,+33 repvit_m1_1.dist_300e_in1k,86.827,13.173,97.318,2.682,8.80,224,0.950,bicubic,+6.001,+2.148,+75 deit_base_patch16_224.fb_in1k,86.827,13.173,97.052,2.949,86.57,224,0.900,bicubic,+4.835,+1.316,-43 tresnet_m.miil_in1k_448,86.816,13.184,97.216,2.784,31.39,448,0.875,bilinear,+5.106,+1.642,-25 tf_efficientnet_lite4.in1k,86.799,13.201,97.265,2.735,13.01,380,0.920,bilinear,+5.269,+1.601,-9 coat_mini.in1k,86.799,13.201,97.156,2.844,10.34,224,0.900,bicubic,+5.529,+1.774,+22 resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,86.795,13.205,97.472,2.528,88.79,224,0.875,bilinear,+5.189,+1.432,-19 eva02_tiny_patch14_336.mim_in22k_ft_in1k,86.784,13.216,97.269,2.731,5.76,336,1.000,bicubic,+6.154,+1.743,+87 seresnet50.ra2_in1k,86.778,13.223,97.361,2.639,28.09,288,0.950,bicubic,+5.493,+1.709,+17 vit_base_patch16_224.orig_in21k_ft_in1k,86.773,13.227,97.442,2.558,86.57,224,0.900,bicubic,+4.983,+1.316,-35 convnextv2_pico.fcmae_ft_in1k,86.773,13.227,97.331,2.669,9.07,288,0.950,bicubic,+5.687,+1.851,+39 regnetx_080.tv2_in1k,86.771,13.229,97.197,2.803,39.57,224,0.965,bicubic,+5.231,+1.655,-18 cs3darknet_l.c2ns_in1k,86.767,13.233,97.459,2.541,21.16,288,0.950,bicubic,+5.871,+1.797,+53 resnetaa50.a1h_in1k,86.765,13.235,97.391,2.609,25.56,288,1.000,bicubic,+5.151,+1.589,-27 tresnet_l.miil_in1k,86.763,13.237,97.273,2.727,55.99,224,0.875,bilinear,+5.283,+1.650,-10 resnet50d.ra2_in1k,86.760,13.239,97.372,2.628,25.58,288,0.950,bicubic,+5.404,+1.634,+4 ese_vovnet39b.ra_in1k,86.756,13.244,97.372,2.628,24.57,288,0.950,bicubic,+6.406,+2.006,+104 twins_svt_small.in1k,86.752,13.248,97.180,2.820,24.06,224,0.900,bicubic,+5.076,+1.522,-37 resnet50_gn.a1h_in1k,86.750,13.250,97.449,2.551,25.56,288,0.950,bicubic,+5.534,+2.065,+12 tiny_vit_5m_224.dist_in22k_ft_in1k,86.748,13.252,97.316,2.684,5.39,224,0.950,bicubic,+5.872,+1.652,+50 seresnet50.a1_in1k,86.746,13.255,96.951,3.049,28.09,288,1.000,bicubic,+5.644,+1.623,+25 mobilevitv2_150.cvnets_in22k_ft_in1k,86.743,13.257,97.214,2.786,10.59,256,0.888,bicubic,+5.255,+1.546,-19 fastvit_s12.apple_dist_in1k,86.741,13.259,97.207,2.793,9.47,256,0.900,bicubic,+5.671,+1.923,+28 crossvit_base_240.in1k,86.733,13.267,97.122,2.878,105.03,240,0.875,bicubic,+4.519,+1.288,-90 levit_256.fb_dist_in1k,86.731,13.269,97.254,2.746,18.89,224,0.900,bicubic,+5.207,+1.760,-27 levit_conv_256.fb_dist_in1k,86.726,13.274,97.256,2.744,18.89,224,0.900,bicubic,+5.204,+1.766,-28 ecaresnet50t.a2_in1k,86.726,13.274,97.081,2.919,25.57,288,1.000,bicubic,+5.068,+1.531,-43 cs3darknet_focus_l.c2ns_in1k,86.722,13.278,97.376,2.624,21.15,288,0.950,bicubic,+5.846,+1.694,+41 convnext_nano_ols.d1h_in1k,86.718,13.282,97.047,2.953,15.65,288,1.000,bicubic,+5.118,+1.411,-39 vit_srelpos_small_patch16_224.sw_in1k,86.707,13.293,97.252,2.748,21.97,224,0.900,bicubic,+5.615,+1.682,+18 resnet50.ram_in1k,86.699,13.301,97.199,2.801,25.56,288,0.950,bicubic,+6.723,+2.147,+118 crossvit_small_240.in1k,86.686,13.314,97.276,2.724,26.86,240,0.875,bicubic,+5.668,+1.820,+22 halo2botnet50ts_256.a1h_in1k,86.686,13.314,97.098,2.902,22.64,256,0.950,bicubic,+4.626,+1.464,-82 pit_b_224.in1k,86.686,13.314,96.894,3.107,73.76,224,0.900,bicubic,+4.248,+1.180,-131 resnet50d.a1_in1k,86.671,13.329,96.693,3.307,25.58,288,1.000,bicubic,+5.221,+1.475,-26 ecaresnet50d_pruned.miil_in1k,86.669,13.331,97.429,2.571,19.94,288,0.950,bicubic,+5.879,+1.859,+44 tf_efficientnet_b1.ns_jft_in1k,86.669,13.331,97.382,2.618,7.79,240,0.882,bicubic,+5.281,+1.644,-22 swin_tiny_patch4_window7_224.ms_in1k,86.658,13.342,97.203,2.797,28.29,224,0.900,bicubic,+5.282,+1.659,-21 poolformer_s36.sail_in1k,86.654,13.346,97.152,2.848,30.86,224,0.900,bicubic,+5.224,+1.708,-29 gernet_l.idstcv_in1k,86.643,13.357,97.188,2.812,31.08,256,0.875,bilinear,+5.289,+1.658,-19 efficientnet_el.ra_in1k,86.630,13.370,97.190,2.810,10.59,300,0.904,bicubic,+5.318,+1.700,-18 twins_pcpvt_small.in1k,86.622,13.378,97.344,2.656,24.11,224,0.900,bicubic,+5.530,+1.696,+5 repvit_m2.dist_in1k,86.615,13.385,97.207,2.793,8.80,224,0.950,bicubic,+6.155,+2.039,+67 resmlp_24_224.fb_distilled_in1k,86.615,13.385,97.137,2.863,30.02,224,0.875,bicubic,+5.859,+1.913,+39 gcresnext50ts.ch_in1k,86.609,13.391,97.188,2.812,15.67,288,1.000,bicubic,+5.379,+1.646,-17 resnet50.c2_in1k,86.605,13.395,97.338,2.662,25.56,288,1.000,bicubic,+5.735,+1.804,+26 nf_resnet50.ra2_in1k,86.592,13.408,97.295,2.705,25.56,288,0.940,bicubic,+5.952,+1.961,+47 resnest50d_4s2x40d.in1k,86.588,13.412,97.267,2.733,30.42,224,0.875,bicubic,+5.468,+1.707,-6 sebotnet33ts_256.a1h_in1k,86.585,13.415,96.793,3.207,13.70,256,0.940,bicubic,+5.417,+1.625,-12 efficientnet_b3_pruned.in1k,86.579,13.421,97.184,2.816,9.86,300,0.904,bicubic,+5.727,+1.940,+24 repvgg_b3.rvgg_in1k,86.579,13.421,97.139,2.861,123.09,224,0.875,bilinear,+6.073,+1.885,+53 wide_resnet50_2.tv2_in1k,86.566,13.434,97.248,2.752,68.88,224,0.965,bilinear,+4.960,+1.488,-64 sehalonet33ts.ra2_in1k,86.566,13.434,97.004,2.995,13.69,256,0.940,bicubic,+5.608,+1.732,+9 fastvit_sa12.apple_in1k,86.564,13.436,97.233,2.767,11.58,256,0.900,bicubic,+5.720,+1.893,+22 resnet50.a1_in1k,86.541,13.459,96.838,3.162,25.56,288,1.000,bicubic,+5.327,+1.736,-22 repvit_m1_0.dist_450e_in1k,86.538,13.462,97.098,2.902,7.30,224,0.950,bicubic,+6.104,+2.180,+59 resnet50.c1_in1k,86.536,13.464,97.235,2.765,25.56,288,1.000,bicubic,+5.624,+1.683,+7 convnext_nano.d1h_in1k,86.536,13.464,97.182,2.818,15.59,288,1.000,bicubic,+5.054,+1.524,-53 xcit_tiny_24_p16_224.fb_dist_in1k,86.534,13.466,97.218,2.782,12.12,224,1.000,bicubic,+6.080,+2.000,+55 seresnet50.a2_in1k,86.519,13.481,96.987,3.013,28.09,288,1.000,bicubic,+5.413,+1.765,-16 vit_small_patch16_384.augreg_in1k,86.496,13.504,97.182,2.818,22.20,384,1.000,bicubic,+5.380,+1.608,-18 halonet50ts.a1h_in1k,86.487,13.513,97.148,2.852,22.73,256,0.940,bicubic,+4.825,+1.538,-80 resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,86.483,13.517,97.474,2.526,44.18,224,0.875,bilinear,+5.559,+1.740,+1 maxvit_rmlp_pico_rw_256.sw_in1k,86.481,13.519,97.201,2.799,7.52,256,0.950,bicubic,+5.967,+1.987,+38 haloregnetz_b.ra3_in1k,86.466,13.534,96.947,3.053,11.68,224,0.940,bicubic,+5.420,+1.747,-13 resnet152s.gluon_in1k,86.464,13.536,97.109,2.891,60.32,224,0.875,bicubic,+5.456,+1.693,-11 seresnet33ts.ra2_in1k,86.462,13.538,97.190,2.810,19.78,288,1.000,bicubic,+5.678,+1.828,+14 repvit_m1_0.dist_300e_in1k,86.457,13.543,97.054,2.946,7.30,224,0.950,bicubic,+6.331,+2.310,+75 mobilevitv2_200.cvnets_in1k,86.457,13.543,96.970,3.030,18.45,256,0.888,bicubic,+5.323,+1.608,-27 resnext50d_32x4d.bt_in1k,86.455,13.545,97.165,2.835,25.05,288,0.950,bicubic,+5.791,+1.745,+22 resnet50.d_in1k,86.455,13.545,97.056,2.944,25.56,288,1.000,bicubic,+5.483,+1.626,-12 resnetv2_50x1_bit.goog_in21k_ft_in1k,86.440,13.560,97.605,2.396,25.55,448,1.000,bilinear,+6.098,+1.922,+49 resnet50.tv2_in1k,86.440,13.560,97.145,2.855,25.56,224,0.965,bilinear,+5.592,+1.711,+3 resnet50.b1k_in1k,86.438,13.562,97.235,2.765,25.56,288,1.000,bicubic,+5.732,+1.803,+14 resnest50d_1s4x24d.in1k,86.432,13.568,97.152,2.848,25.68,224,0.875,bicubic,+5.444,+1.826,-19 poolformerv2_s24.sail_in1k,86.389,13.611,97.150,2.850,21.34,224,1.000,bicubic,+5.641,+1.840,+8 regnety_016.tv2_in1k,86.372,13.628,97.188,2.812,11.20,224,0.965,bicubic,+5.706,+1.858,+15 repvgg_b3g4.rvgg_in1k,86.368,13.632,97.047,2.953,83.83,224,0.875,bilinear,+6.152,+1.955,+60 darknetaa53.c2ns_in1k,86.359,13.641,97.165,2.835,36.02,288,1.000,bilinear,+5.853,+1.843,+24 efficientformer_l1.snap_dist_in1k,86.357,13.643,97.019,2.981,12.29,224,0.950,bicubic,+5.859,+2.031,+25 darknet53.c2ns_in1k,86.350,13.649,97.130,2.869,41.61,288,1.000,bicubic,+5.819,+1.698,+20 lamhalobotnet50ts_256.a1h_in1k,86.350,13.649,97.041,2.959,22.57,256,0.950,bicubic,+4.798,+1.549,-89 fastvit_t12.apple_dist_in1k,86.344,13.656,97.098,2.902,7.55,256,0.900,bicubic,+5.992,+2.056,+37 legacy_senet154.in1k,86.344,13.656,96.934,3.066,115.09,224,0.875,bilinear,+5.032,+1.374,-60 resnet50.a1h_in1k,86.340,13.660,97.060,2.940,25.56,224,1.000,bicubic,+5.662,+1.754,+5 cait_xxs36_224.fb_dist_in1k,86.329,13.671,97.118,2.882,17.30,224,1.000,bicubic,+6.583,+2.244,+81 tf_efficientnet_b3.in1k,86.325,13.675,96.964,3.036,12.23,300,0.904,bicubic,+5.451,+1.664,-16 mobilevitv2_175.cvnets_in1k,86.321,13.679,96.985,3.015,14.25,256,0.888,bicubic,+5.461,+1.729,-15 gernet_m.idstcv_in1k,86.319,13.681,97.098,2.902,21.14,224,0.875,bilinear,+5.582,+1.908,-4 resnet50d.a2_in1k,86.314,13.686,96.674,3.326,25.58,288,1.000,bicubic,+5.150,+1.594,-52 vit_small_patch32_384.augreg_in21k_ft_in1k,86.312,13.688,97.419,2.581,22.92,384,1.000,bicubic,+5.826,+1.819,+15 pit_s_224.in1k,86.308,13.692,97.049,2.951,23.46,224,0.900,bicubic,+5.222,+1.719,-42 efficientnet_b2.ra_in1k,86.306,13.694,96.987,3.013,9.11,288,1.000,bicubic,+5.696,+1.673,+3 gcresnet33ts.ra2_in1k,86.301,13.699,97.060,2.940,19.88,288,1.000,bicubic,+5.701,+1.738,+3 resnext50_32x4d.a1_in1k,86.284,13.716,96.710,3.290,25.03,288,1.000,bicubic,+4.818,+1.536,-89 resnext50_32x4d.a2_in1k,86.280,13.720,96.684,3.316,25.03,288,1.000,bicubic,+4.976,+1.588,-71 resnet50.b2k_in1k,86.272,13.729,97.071,2.929,25.56,288,1.000,bicubic,+5.818,+1.753,+16 senet154.gluon_in1k,86.272,13.729,96.957,3.042,115.09,224,0.875,bicubic,+5.046,+1.599,-69 resnext50_32x4d.tv2_in1k,86.267,13.733,97.054,2.946,25.03,224,0.965,bilinear,+5.085,+1.714,-64 eca_resnet33ts.ra2_in1k,86.257,13.743,97.150,2.850,19.68,288,1.000,bicubic,+5.585,+1.786,-9 resnest50d.in1k,86.248,13.752,97.069,2.931,27.48,224,0.875,bilinear,+5.288,+1.687,-41 gcvit_xxtiny.in1k,86.244,13.756,97.107,2.893,12.00,224,0.875,bicubic,+6.518,+2.027,+67 regnetx_032.tv2_in1k,86.244,13.756,97.092,2.908,15.30,224,0.965,bicubic,+5.318,+1.814,-40 vit_base_patch16_384.augreg_in1k,86.231,13.769,96.964,3.036,86.86,384,1.000,bicubic,+5.129,+1.844,-59 convmixer_768_32.in1k,86.220,13.780,97.037,2.963,21.11,224,0.960,bicubic,+6.052,+1.963,+36 efficientnet_el_pruned.in1k,86.192,13.807,97.028,2.972,10.59,300,0.904,bicubic,+5.894,+1.806,+23 tresnet_m.miil_in1k,86.188,13.812,96.667,3.333,31.39,224,0.875,bilinear,+5.390,+1.811,-28 cspdarknet53.ra_in1k,86.178,13.822,97.009,2.991,27.64,256,0.887,bilinear,+6.110,+1.931,+38 rexnet_150.nav_in1k,86.169,13.831,97.060,2.940,9.73,224,0.875,bicubic,+5.845,+2.070,+16 inception_v4.tf_in1k,86.163,13.837,96.919,3.081,42.68,299,0.875,bicubic,+6.007,+1.949,+33 efficientvit_b1.r288_in1k,86.154,13.846,96.932,3.068,9.10,288,1.000,bicubic,+5.830,+1.756,+15 inception_resnet_v2.tf_in1k,86.133,13.867,97.045,2.955,55.84,299,0.897,bicubic,+5.675,+1.855,-1 xcit_tiny_12_p8_224.fb_in1k,86.114,13.886,97.088,2.912,6.71,224,1.000,bicubic,+6.425,+2.034,+61 resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,86.101,13.899,97.212,2.788,25.03,224,0.875,bilinear,+5.767,+1.812,+10 res2net101d.in1k,86.099,13.901,96.851,3.149,45.23,224,0.875,bilinear,+4.881,+1.501,-85 resnet50.a2_in1k,86.097,13.903,96.701,3.299,25.56,288,1.000,bicubic,+5.325,+1.713,-34 tf_efficientnet_el.in1k,86.082,13.918,96.955,3.045,10.59,300,0.904,bicubic,+5.834,+1.835,+15 mobilevitv2_150.cvnets_in1k,86.082,13.918,96.857,3.143,10.59,256,0.888,bicubic,+5.712,+1.783,+2 cspresnext50.ra_in1k,86.077,13.923,97.107,2.893,20.57,256,0.887,bilinear,+5.523,+1.781,-20 convnext_pico_ols.d1_in1k,86.069,13.931,97.017,2.983,9.06,288,1.000,bicubic,+5.607,+1.765,-11 resnet101s.gluon_in1k,86.067,13.933,97.030,2.970,44.67,224,0.875,bicubic,+5.763,+1.878,+7 edgenext_small_rw.sw_in1k,86.054,13.946,96.928,3.072,7.83,320,1.000,bicubic,+5.596,+1.620,-10 lambda_resnet50ts.a1h_in1k,86.049,13.950,96.742,3.258,21.54,256,0.950,bicubic,+4.891,+1.644,-84 seresnext101_32x4d.gluon_in1k,86.028,13.972,96.972,3.027,48.96,224,0.875,bicubic,+5.136,+1.676,-56 convnext_pico.d1_in1k,86.015,13.985,96.932,3.068,9.05,288,0.950,bicubic,+5.599,+1.884,-8 poolformer_s24.sail_in1k,86.013,13.987,97.030,2.970,21.39,224,0.900,bicubic,+5.719,+1.956,+5 resnetblur50.bt_in1k,85.992,14.008,96.985,3.015,25.56,288,0.950,bicubic,+5.758,+1.751,+9 resnet152.a3_in1k,85.985,14.015,96.849,3.151,60.19,224,0.950,bicubic,+5.439,+1.849,-28 ecaresnet26t.ra2_in1k,85.981,14.019,97.043,2.957,16.01,320,0.950,bicubic,+6.131,+1.953,+29 tf_efficientnet_b2.ap_in1k,85.981,14.019,96.812,3.188,9.11,260,0.890,bicubic,+5.671,+1.786,-3 seresnext101_64x4d.gluon_in1k,85.973,14.027,96.981,3.019,88.23,224,0.875,bicubic,+5.079,+1.685,-64 efficientformerv2_s1.snap_dist_in1k,85.962,14.038,96.874,3.126,6.19,224,0.950,bicubic,+6.270,+2.158,+41 vit_base_patch32_224.augreg_in21k_ft_in1k,85.956,14.044,97.128,2.872,88.22,224,0.900,bicubic,+5.240,+1.562,-46 resnext50_32x4d.ra_in1k,85.934,14.066,97.032,2.968,25.03,288,0.950,bicubic,+5.236,+1.640,-45 fbnetv3_d.ra2_in1k,85.930,14.070,97.026,2.974,10.31,256,0.950,bilinear,+6.248,+2.082,+40 vit_large_patch32_384.orig_in21k_ft_in1k,85.915,14.085,97.370,2.630,306.63,384,1.000,bicubic,+4.405,+1.280,-137 resnet152d.gluon_in1k,85.915,14.085,96.814,3.186,60.21,224,0.875,bicubic,+5.439,+1.612,-28 tf_efficientnet_b2.aa_in1k,85.896,14.104,96.864,3.136,9.11,260,0.890,bicubic,+5.812,+1.958,+7 tf_efficientnetv2_b2.in1k,85.887,14.113,96.887,3.113,10.10,260,0.890,bicubic,+5.691,+1.845,0 vit_base_patch16_224.sam_in1k,85.872,14.128,96.693,3.307,86.57,224,0.900,bicubic,+5.634,+1.937,-5 repvgg_b2g4.rvgg_in1k,85.862,14.138,96.804,3.196,61.76,224,0.875,bilinear,+6.480,+2.128,+55 resnet101d.gluon_in1k,85.862,14.138,96.674,3.326,44.57,224,0.875,bicubic,+5.436,+1.650,-26 efficientvit_b1.r256_in1k,85.830,14.170,96.782,3.217,9.10,256,1.000,bicubic,+6.096,+2.002,+25 mixnet_xl.ra_in1k,85.804,14.196,96.716,3.284,11.90,224,0.875,bicubic,+5.322,+1.780,-37 inception_resnet_v2.tf_ens_adv_in1k,85.768,14.232,96.759,3.241,55.84,299,0.897,bicubic,+5.790,+1.811,+3 repvit_m0_9.dist_450e_in1k,85.757,14.243,96.810,3.190,5.49,224,0.950,bicubic,+6.691,+2.430,+81 tf_efficientnet_lite3.in1k,85.755,14.245,96.891,3.109,8.20,300,0.904,bilinear,+5.949,+1.977,+18 resnext101_32x4d.gluon_in1k,85.753,14.247,96.637,3.363,44.18,224,0.875,bicubic,+5.413,+1.707,-25 xcit_tiny_24_p16_224.fb_in1k,85.746,14.254,96.932,3.068,12.12,224,1.000,bicubic,+6.298,+2.054,+42 legacy_seresnext101_32x4d.in1k,85.742,14.258,96.772,3.228,48.96,224,0.875,bilinear,+5.510,+1.752,-13 resnet101.a3_in1k,85.736,14.264,96.501,3.499,44.55,224,0.950,bicubic,+5.922,+1.887,+13 res2net50d.in1k,85.729,14.271,96.763,3.237,25.72,224,0.875,bilinear,+5.475,+1.727,-20 regnety_320.pycls_in1k,85.727,14.273,96.725,3.275,145.05,224,0.875,bicubic,+4.917,+1.487,-75 cspresnet50.ra_in1k,85.719,14.281,96.799,3.200,21.62,256,0.887,bilinear,+6.137,+2.090,+28 resmlp_big_24_224.fb_in1k,85.701,14.299,96.426,3.574,129.14,224,0.875,bicubic,+4.665,+1.408,-102 resnext101_64x4d.gluon_in1k,85.693,14.307,96.641,3.358,83.46,224,0.875,bicubic,+5.093,+1.649,-58 xception71.tf_in1k,85.689,14.311,96.774,3.226,42.34,299,0.903,bicubic,+5.815,+1.846,-3 resnet33ts.ra2_in1k,85.689,14.311,96.757,3.243,19.68,288,1.000,bicubic,+5.963,+1.929,+13 deit_small_patch16_224.fb_in1k,85.676,14.324,96.906,3.094,22.05,224,0.900,bicubic,+5.828,+1.862,0 resnet50.ra_in1k,85.674,14.326,96.889,3.111,25.56,288,0.950,bicubic,+5.838,+1.923,+2 efficientnet_em.ra2_in1k,85.672,14.328,96.951,3.049,6.90,240,0.882,bicubic,+6.428,+2.157,+49 dpn107.mx_in1k,85.672,14.328,96.757,3.243,86.92,224,0.875,bicubic,+5.502,+1.815,-21 ecaresnet50t.a3_in1k,85.672,14.328,96.725,3.275,25.57,224,0.950,bicubic,+6.120,+2.031,+22 efficientnet_b2_pruned.in1k,85.642,14.358,96.742,3.258,8.31,260,0.890,bicubic,+5.722,+1.890,-12 resmlp_36_224.fb_in1k,85.623,14.377,96.795,3.205,44.69,224,0.875,bicubic,+5.850,+1.911,+1 tiny_vit_5m_224.in1k,85.605,14.395,96.953,3.047,5.39,224,0.950,bicubic,+6.435,+2.159,+52 mobilevitv2_125.cvnets_in1k,85.578,14.422,96.665,3.335,7.48,256,0.888,bicubic,+5.898,+1.807,+9 resnet50.bt_in1k,85.576,14.425,96.832,3.168,25.56,288,0.950,bicubic,+5.936,+1.940,+11 levit_conv_192.fb_dist_in1k,85.571,14.429,96.742,3.258,10.95,224,0.900,bicubic,+5.733,+1.964,-9 resnet32ts.ra2_in1k,85.569,14.431,96.866,3.134,17.96,288,1.000,bicubic,+6.181,+2.214,+24 levit_192.fb_dist_in1k,85.569,14.431,96.740,3.260,10.95,224,0.900,bicubic,+5.731,+1.956,-9 resnet152c.gluon_in1k,85.569,14.431,96.644,3.356,60.21,224,0.875,bicubic,+5.657,+1.798,-19 pit_xs_distilled_224.in1k,85.561,14.439,96.686,3.314,11.00,224,0.900,bicubic,+6.381,+2.320,+44 tf_efficientnetv2_b1.in1k,85.556,14.444,96.731,3.269,8.14,240,0.882,bicubic,+6.096,+2.009,+16 regnety_120.pycls_in1k,85.554,14.446,96.774,3.226,51.82,224,0.875,bicubic,+5.174,+1.648,-57 fbnetv3_b.ra2_in1k,85.520,14.480,96.862,3.139,8.60,256,0.950,bilinear,+6.374,+2.118,+44 regnetx_320.pycls_in1k,85.520,14.480,96.671,3.329,107.81,224,0.875,bicubic,+5.274,+1.649,-43 nf_regnet_b1.ra2_in1k,85.507,14.493,96.789,3.211,10.22,288,0.900,bicubic,+6.199,+2.049,+25 convnextv2_femto.fcmae_ft_in1k,85.503,14.497,96.806,3.194,5.23,288,0.950,bicubic,+6.165,+2.246,+21 dpn92.mx_in1k,85.501,14.499,96.650,3.350,37.67,224,0.875,bicubic,+5.463,+1.790,-33 fastvit_s12.apple_in1k,85.492,14.508,96.721,3.280,9.47,256,0.900,bicubic,+5.550,+1.927,-31 regnety_160.pycls_in1k,85.486,14.514,96.616,3.384,83.59,224,0.875,bicubic,+5.188,+1.652,-52 resnet152.gluon_in1k,85.482,14.518,96.550,3.450,60.19,224,0.875,bicubic,+5.786,+1.820,-11 resnetrs50.tf_in1k,85.469,14.531,96.733,3.267,35.69,224,0.910,bicubic,+5.575,+1.759,-30 rexnet_130.nav_in1k,85.465,14.536,96.680,3.320,7.56,224,0.875,bicubic,+5.959,+2.002,+1 dpn131.mx_in1k,85.450,14.550,96.620,3.380,79.25,224,0.875,bicubic,+5.636,+1.920,-23 regnetx_160.pycls_in1k,85.396,14.604,96.650,3.350,54.28,224,0.875,bicubic,+5.530,+1.822,-30 tf_efficientnet_b2.in1k,85.396,14.604,96.586,3.414,9.11,260,0.890,bicubic,+5.788,+1.872,-8 convnext_tiny.fb_in22k_ft_in1k,85.381,14.619,96.804,3.196,28.59,288,1.000,bicubic,+6.483,+2.130,+46 dla102x2.in1k,85.375,14.625,96.629,3.371,41.28,224,0.875,bilinear,+5.929,+1.997,+2 repvit_m0_9.dist_300e_in1k,85.373,14.627,96.627,3.373,5.49,224,0.950,bicubic,+6.715,+2.511,+66 dpn98.mx_in1k,85.351,14.649,96.509,3.491,61.57,224,0.875,bicubic,+5.681,+1.855,-15 regnetx_016.tv2_in1k,85.349,14.651,96.817,3.183,9.19,224,0.965,bicubic,+5.913,+2.049,-1 gmlp_s16_224.ra3_in1k,85.349,14.651,96.644,3.356,19.42,224,0.875,bicubic,+5.705,+2.022,-15 botnet26t_256.c1_in1k,85.332,14.668,96.631,3.369,12.49,256,0.950,bicubic,+6.074,+2.099,+15 skresnext50_32x4d.ra_in1k,85.328,14.672,96.390,3.610,27.48,224,0.875,bicubic,+5.164,+1.750,-54 seresnext50_32x4d.gluon_in1k,85.321,14.679,96.674,3.326,27.56,224,0.875,bicubic,+5.397,+1.850,-46 xception65.tf_in1k,85.315,14.685,96.646,3.354,39.92,299,0.903,bicubic,+5.759,+1.988,-15 resnet101c.gluon_in1k,85.311,14.689,96.411,3.589,44.57,224,0.875,bicubic,+5.773,+1.827,-14 lambda_resnet26t.c1_in1k,85.302,14.698,96.721,3.280,10.96,256,0.940,bicubic,+6.214,+2.130,+23 resnext50_32x4d.a3_in1k,85.298,14.702,96.328,3.672,25.03,224,0.950,bicubic,+6.030,+2.022,+7 regnety_064.pycls_in1k,85.283,14.717,96.648,3.352,30.58,224,0.875,bicubic,+5.567,+1.882,-31 resnet34d.ra2_in1k,85.272,14.728,96.701,3.299,21.82,288,0.950,bicubic,+6.836,+2.357,+68 coat_lite_mini.in1k,85.272,14.728,96.686,3.314,11.01,224,0.900,bicubic,+6.170,+2.078,+19 resmlp_24_224.fb_in1k,85.264,14.736,96.490,3.510,30.02,224,0.875,bicubic,+5.890,+1.944,-8 convnext_femto_ols.d1_in1k,85.253,14.747,96.772,3.228,5.23,288,0.950,bicubic,+6.329,+2.246,+28 cait_xxs24_224.fb_dist_in1k,85.238,14.762,96.714,3.286,11.96,224,1.000,bicubic,+6.854,+2.398,+71 regnety_080.pycls_in1k,85.238,14.762,96.635,3.365,39.18,224,0.875,bicubic,+5.370,+1.803,-52 efficientvit_b1.r224_in1k,85.232,14.768,96.462,3.538,9.10,224,0.950,bicubic,+5.980,+2.158,+2 pvt_v2_b1.in1k,85.210,14.790,96.631,3.369,14.01,224,0.900,bicubic,+6.506,+2.129,+43 xcit_tiny_12_p16_224.fb_dist_in1k,85.200,14.800,96.597,3.403,6.72,224,1.000,bicubic,+6.626,+2.399,+47 halonet26t.a1h_in1k,85.191,14.809,96.454,3.546,12.48,256,0.950,bicubic,+6.085,+2.148,+9 resnext101_32x8d.tv_in1k,85.189,14.811,96.456,3.544,88.79,224,0.875,bilinear,+5.879,+1.936,-11 inception_v3.gluon_in1k,85.187,14.813,96.535,3.465,23.83,299,0.875,bicubic,+6.385,+2.159,+29 fastvit_t12.apple_in1k,85.180,14.819,96.607,3.393,7.55,256,0.900,bicubic,+5.916,+2.045,-6 convnext_femto.d1_in1k,85.163,14.837,96.704,3.296,5.22,288,0.950,bicubic,+6.447,+2.273,+34 resnet101.gluon_in1k,85.161,14.839,96.366,3.634,44.55,224,0.875,bicubic,+5.851,+1.844,-16 hrnet_w48.ms_in1k,85.159,14.841,96.490,3.510,77.47,224,0.875,bilinear,+5.853,+1.974,-14 regnetx_120.pycls_in1k,85.138,14.862,96.475,3.525,46.11,224,0.875,bicubic,+5.550,+1.733,-38 eca_halonext26ts.c1_in1k,85.131,14.869,96.580,3.420,10.76,256,0.940,bicubic,+5.645,+1.980,-33 resnet50.am_in1k,85.131,14.869,96.571,3.429,25.56,224,0.875,bicubic,+6.129,+2.173,+8 tf_efficientnet_b1.ap_in1k,85.131,14.869,96.407,3.593,7.79,240,0.882,bicubic,+5.855,+2.095,-16 repvit_m1.dist_in1k,85.121,14.879,96.597,3.403,5.49,224,0.950,bicubic,+6.583,+2.527,+36 eca_botnext26ts_256.c1_in1k,85.121,14.879,96.511,3.489,10.59,256,0.950,bicubic,+5.853,+1.905,-16 legacy_xception.tf_in1k,85.121,14.879,96.469,3.531,22.86,299,0.897,bicubic,+6.081,+2.087,+4 hrnet_w64.ms_in1k,85.114,14.886,96.738,3.262,128.06,224,0.875,bilinear,+5.638,+2.086,-37 lambda_resnet26rpt_256.c1_in1k,85.099,14.901,96.565,3.435,10.99,256,0.940,bicubic,+6.135,+2.129,+4 res2net101_26w_4s.in1k,85.097,14.903,96.381,3.619,45.21,224,0.875,bilinear,+5.897,+1.945,-13 resnet50.fb_ssl_yfcc100m_ft_in1k,85.093,14.907,96.862,3.139,25.56,224,0.875,bilinear,+5.863,+2.036,-16 mobileone_s4.apple_in1k,85.082,14.918,96.434,3.566,14.95,224,0.900,bilinear,+5.656,+1.954,-36 dpn68b.ra_in1k,85.061,14.939,96.449,3.551,12.61,288,1.000,bicubic,+5.701,+2.013,-33 tf_efficientnet_cc_b1_8e.in1k,85.061,14.939,96.430,3.570,39.72,240,0.882,bicubic,+5.759,+2.056,-27 resnest26d.gluon_in1k,85.016,14.984,96.639,3.361,17.07,224,0.875,bilinear,+6.534,+2.345,+34 xcit_nano_12_p8_384.fb_dist_in1k,85.014,14.986,96.629,3.371,3.05,384,1.000,bicubic,+7.194,+2.589,+83 resnext50_32x4d.gluon_in1k,85.005,14.995,96.428,3.572,25.03,224,0.875,bicubic,+5.645,+1.998,-36 tf_efficientnet_b0.ns_jft_in1k,84.999,15.001,96.505,3.495,5.29,224,0.875,bicubic,+6.331,+2.133,+18 coat_tiny.in1k,84.971,15.029,96.422,3.578,5.50,224,0.900,bicubic,+6.545,+2.374,+36 regnety_040.pycls_in1k,84.952,15.048,96.612,3.388,20.65,224,0.875,bicubic,+5.732,+1.956,-24 dla169.in1k,84.931,15.069,96.541,3.459,53.39,224,0.875,bilinear,+6.223,+2.197,+13 tf_efficientnet_b1.aa_in1k,84.918,15.082,96.366,3.634,7.79,240,0.882,bicubic,+6.090,+2.166,0 resnet50d.a3_in1k,84.901,15.099,96.285,3.715,25.58,224,0.950,bicubic,+6.181,+2.053,+8 legacy_seresnext50_32x4d.in1k,84.897,15.103,96.428,3.572,27.56,224,0.875,bilinear,+5.821,+1.996,-17 mobilevitv2_100.cvnets_in1k,84.894,15.106,96.388,3.612,4.90,256,0.888,bicubic,+6.814,+2.218,+55 hrnet_w44.ms_in1k,84.886,15.114,96.430,3.570,67.06,224,0.875,bilinear,+5.992,+2.066,-8 resnet50s.gluon_in1k,84.877,15.123,96.441,3.559,25.68,224,0.875,bicubic,+6.163,+2.199,+6 regnety_008_tv.tv2_in1k,84.875,15.125,96.637,3.363,6.43,224,0.965,bicubic,+6.209,+2.247,+9 regnetx_080.pycls_in1k,84.865,15.136,96.432,3.568,39.57,224,0.875,bicubic,+5.667,+1.878,-31 levit_conv_128.fb_dist_in1k,84.847,15.153,96.353,3.647,9.21,224,0.900,bicubic,+6.353,+2.345,+16 visformer_tiny.in1k,84.845,15.155,96.509,3.491,10.32,224,0.900,bicubic,+6.685,+2.343,+43 levit_128.fb_dist_in1k,84.845,15.155,96.353,3.647,9.21,224,0.900,bicubic,+6.355,+2.341,+17 res2net50_26w_8s.in1k,84.837,15.163,96.355,3.645,48.40,224,0.875,bilinear,+5.895,+2.061,-19 vit_tiny_patch16_384.augreg_in21k_ft_in1k,84.832,15.168,96.714,3.286,5.79,384,1.000,bicubic,+6.408,+2.172,+22 repghostnet_200.in1k,84.830,15.170,96.413,3.587,9.80,224,0.875,bicubic,+6.024,+2.083,-11 resnet50d.gluon_in1k,84.830,15.170,96.398,3.602,25.58,224,0.875,bicubic,+5.752,+1.932,-30 dla60_res2next.in1k,84.826,15.174,96.407,3.593,17.03,224,0.875,bilinear,+6.386,+2.263,+16 resnet152.tv_in1k,84.826,15.174,96.232,3.768,60.19,224,0.875,bilinear,+6.504,+2.186,+26 resnet26t.ra2_in1k,84.824,15.176,96.390,3.610,16.01,320,1.000,bicubic,+6.496,+2.266,+24 dla60_res2net.in1k,84.818,15.182,96.473,3.527,20.85,224,0.875,bilinear,+6.354,+2.275,+11 mixnet_l.ft_in1k,84.815,15.185,96.323,3.677,7.33,224,0.875,bicubic,+5.849,+2.141,-29 dla102x.in1k,84.807,15.193,96.552,3.448,26.31,224,0.875,bilinear,+6.295,+2.316,+2 xception41.tf_in1k,84.785,15.214,96.411,3.589,26.97,299,0.903,bicubic,+6.281,+2.135,+2 pit_xs_224.in1k,84.783,15.217,96.501,3.499,10.62,224,0.900,bicubic,+6.607,+2.339,+28 hrnet_w18.ms_aug_in1k,84.783,15.217,96.466,3.534,21.30,224,0.950,bilinear,+6.661,+2.412,+34 regnetx_064.pycls_in1k,84.773,15.227,96.494,3.506,26.21,224,0.875,bicubic,+5.707,+2.034,-38 resnet34.a1_in1k,84.762,15.238,96.230,3.771,21.80,288,1.000,bicubic,+6.844,+2.466,+46 poolformerv2_s12.sail_in1k,84.756,15.244,96.373,3.627,11.89,224,1.000,bicubic,+6.754,+2.508,+37 gcresnext26ts.ch_in1k,84.756,15.244,96.293,3.707,10.48,288,1.000,bicubic,+6.342,+2.257,+9 hrnet_w40.ms_in1k,84.745,15.255,96.552,3.448,57.56,224,0.875,bilinear,+5.813,+2.088,-35 repvgg_b2.rvgg_in1k,84.726,15.274,96.471,3.529,89.02,224,0.875,bilinear,+5.934,+2.051,-24 res2net50_26w_6s.in1k,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.158,+2.159,-11 resmlp_12_224.fb_distilled_in1k,84.719,15.281,96.225,3.775,15.35,224,0.875,bicubic,+6.765,+2.665,+37 vit_base_patch32_384.augreg_in1k,84.715,15.285,96.323,3.677,88.30,384,1.000,bicubic,+5.959,+2.097,-25 legacy_seresnet152.in1k,84.713,15.287,96.419,3.580,66.82,224,0.875,bilinear,+6.053,+2.049,-17 cs3darknet_m.c2ns_in1k,84.700,15.300,96.488,3.512,9.31,288,0.950,bicubic,+7.066,+2.472,+50 selecsls60b.in1k,84.662,15.338,96.300,3.700,32.77,224,0.875,bicubic,+6.250,+2.132,+1 bat_resnext26ts.ch_in1k,84.653,15.347,96.268,3.732,10.73,256,0.900,bicubic,+6.401,+2.170,+9 hrnet_w32.ms_in1k,84.651,15.349,96.413,3.587,41.23,224,0.875,bilinear,+6.209,+2.223,-7 seresnext26d_32x4d.bt_in1k,84.642,15.357,96.261,3.739,16.81,288,0.950,bicubic,+5.829,+2.022,-37 tf_efficientnetv2_b0.in1k,84.623,15.377,96.274,3.726,7.14,224,0.875,bicubic,+6.265,+2.260,+1 efficientnet_b1.ft_in1k,84.611,15.389,96.334,3.666,7.79,256,1.000,bicubic,+5.811,+1.992,-36 regnetx_040.pycls_in1k,84.606,15.394,96.379,3.621,22.12,224,0.875,bicubic,+6.114,+2.137,-16 vit_relpos_base_patch32_plus_rpn_256.sw_in1k,84.598,15.402,96.014,3.986,119.42,256,0.900,bicubic,+5.114,+1.876,-94 regnety_032.pycls_in1k,84.593,15.407,96.415,3.585,19.44,224,0.875,bicubic,+5.717,+2.007,-46 seresnext26t_32x4d.bt_in1k,84.589,15.411,96.381,3.619,16.81,288,0.950,bicubic,+5.845,+2.069,-36 hrnet_w30.ms_in1k,84.583,15.417,96.381,3.619,37.71,224,0.875,bilinear,+6.387,+2.159,+4 efficientnet_es.ra_in1k,84.581,15.419,96.308,3.692,5.44,224,0.875,bicubic,+6.523,+2.382,+13 tf_mixnet_l.in1k,84.578,15.422,96.244,3.756,7.33,224,0.875,bicubic,+5.802,+2.242,-41 wide_resnet101_2.tv_in1k,84.546,15.454,96.349,3.651,126.89,224,0.875,bilinear,+5.704,+2.067,-49 hrnet_w18_small_v2.gluon_in1k,84.542,15.458,96.285,3.715,15.60,224,0.875,bicubic,+6.352,+2.383,+1 vit_small_patch16_224.augreg_in1k,84.536,15.464,96.272,3.728,22.05,224,0.900,bicubic,+5.688,+1.984,-52 dla60x.in1k,84.531,15.469,96.293,3.707,17.35,224,0.875,bilinear,+6.295,+2.267,-3 legacy_seresnet101.in1k,84.497,15.503,96.326,3.674,49.33,224,0.875,bilinear,+6.111,+2.064,-15 resnet50.a3_in1k,84.484,15.515,96.003,3.997,25.56,224,0.950,bicubic,+6.436,+2.223,+7 cs3darknet_focus_m.c2ns_in1k,84.482,15.518,96.417,3.583,9.30,288,0.950,bicubic,+7.198,+2.451,+51 seresnext26ts.ch_in1k,84.480,15.520,96.321,3.679,10.39,288,1.000,bicubic,+6.210,+2.229,-11 tf_efficientnet_b1.in1k,84.472,15.528,96.074,3.926,7.79,240,0.882,bicubic,+5.910,+1.980,-36 coat_lite_tiny.in1k,84.459,15.541,96.383,3.617,5.72,224,0.900,bicubic,+6.939,+2.461,+32 tf_efficientnet_em.in1k,84.455,15.545,96.185,3.815,6.90,240,0.882,bicubic,+6.329,+2.137,-3 wide_resnet50_2.tv_in1k,84.429,15.571,96.257,3.743,68.88,224,0.875,bilinear,+5.953,+2.169,-31 repvgg_b1.rvgg_in1k,84.420,15.580,96.217,3.783,57.42,224,0.875,bilinear,+6.052,+2.121,-21 efficientnet_b1_pruned.in1k,84.397,15.603,96.140,3.860,6.33,240,0.882,bicubic,+6.157,+2.306,-14 vit_base_patch16_224.augreg_in1k,84.384,15.616,96.042,3.958,86.57,224,0.900,bicubic,+5.230,+1.952,-83 res2net50_26w_4s.in1k,84.359,15.642,96.091,3.909,25.70,224,0.875,bilinear,+6.409,+2.239,+6 hardcorenas_f.miil_green_in1k,84.324,15.676,96.025,3.975,8.20,224,0.875,bilinear,+6.228,+2.222,-7 res2net50_14w_8s.in1k,84.311,15.688,96.076,3.924,25.06,224,0.875,bilinear,+6.153,+2.230,-11 selecsls60.in1k,84.282,15.718,96.103,3.897,30.67,224,0.875,bicubic,+6.294,+2.273,+1 mobilevit_s.cvnets_in1k,84.277,15.723,96.259,3.741,5.58,256,0.900,bicubic,+5.965,+2.111,-24 regnetx_032.pycls_in1k,84.245,15.755,96.242,3.758,15.30,224,0.875,bicubic,+6.077,+2.160,-16 mobileone_s3.apple_in1k,84.233,15.767,96.135,3.865,10.17,224,0.900,bilinear,+6.241,+2.221,-3 convnextv2_atto.fcmae_ft_in1k,84.226,15.774,96.059,3.941,3.71,288,0.950,bicubic,+6.466,+2.333,+9 eca_resnext26ts.ch_in1k,84.224,15.776,96.191,3.809,10.30,288,1.000,bicubic,+6.224,+2.265,-6 ese_vovnet19b_dw.ra_in1k,84.220,15.780,96.259,3.741,6.54,288,0.950,bicubic,+6.476,+2.475,+7 convnext_atto_ols.a2_in1k,84.220,15.780,96.217,3.783,3.70,288,0.950,bicubic,+7.004,+2.541,+36 resnet50c.gluon_in1k,84.213,15.787,96.163,3.837,25.58,224,0.875,bicubic,+6.207,+2.171,-12 res2next50.in1k,84.213,15.787,96.001,3.999,24.67,224,0.875,bilinear,+5.971,+2.109,-28 mobileone_s2.apple_in1k,84.196,15.804,96.063,3.937,7.88,224,0.900,bilinear,+6.680,+2.395,+15 dla102.in1k,84.190,15.810,96.206,3.794,33.27,224,0.875,bilinear,+6.166,+2.272,-15 densenetblur121d.ra_in1k,84.168,15.832,96.240,3.760,8.00,288,0.950,bicubic,+6.846,+2.452,+22 rexnet_100.nav_in1k,84.158,15.842,96.249,3.751,4.80,224,0.875,bicubic,+6.302,+2.609,-5 fastvit_t8.apple_dist_in1k,84.143,15.857,96.078,3.922,4.03,256,0.900,bicubic,+6.967,+2.780,+30 convnext_atto.d2_in1k,84.141,15.859,96.200,3.800,3.70,288,0.950,bicubic,+7.133,+2.498,+39 inception_v3.tf_in1k,84.141,15.859,95.911,4.089,23.83,299,0.875,bicubic,+6.285,+2.045,-7 res2net50_48w_2s.in1k,84.117,15.883,95.960,4.040,25.29,224,0.875,bilinear,+6.603,+2.410,+9 xcit_tiny_12_p16_224.fb_in1k,84.111,15.889,96.236,3.764,6.72,224,1.000,bicubic,+6.971,+2.520,+29 ghostnetv2_160.in1k,84.098,15.902,96.210,3.790,12.39,224,0.875,bicubic,+6.266,+2.270,-9 tf_efficientnet_lite2.in1k,84.087,15.913,96.074,3.926,6.09,260,0.890,bicubic,+6.625,+2.322,+7 poolformer_s12.sail_in1k,84.049,15.951,96.178,3.822,11.92,224,0.900,bicubic,+6.809,+2.646,+20 resnet34.a2_in1k,84.045,15.955,95.922,4.078,21.80,288,1.000,bicubic,+6.887,+2.648,+24 efficientnet_b0.ra_in1k,84.034,15.966,95.965,4.035,5.29,224,0.875,bicubic,+6.340,+2.433,-8 crossvit_9_dagger_240.in1k,84.019,15.981,96.084,3.916,8.78,240,0.875,bicubic,+7.041,+2.466,+31 tf_efficientnet_cc_b0_8e.in1k,83.972,16.028,96.067,3.933,24.01,224,0.875,bicubic,+6.068,+2.405,-19 regnety_016.pycls_in1k,83.966,16.034,96.005,3.995,11.20,224,0.875,bicubic,+6.098,+2.287,-20 gmixer_24_224.ra3_in1k,83.966,16.034,95.854,4.146,24.72,224,0.875,bicubic,+5.940,+2.186,-31 hardcorenas_e.miil_green_in1k,83.963,16.037,95.903,4.097,8.07,224,0.875,bilinear,+6.173,+2.203,-16 resnext50_32x4d.tv_in1k,83.951,16.049,95.969,4.031,25.03,224,0.875,bilinear,+6.329,+2.273,-10 resnet50.gluon_in1k,83.940,16.060,96.020,3.980,25.56,224,0.875,bicubic,+6.358,+2.300,-8 densenet161.tv_in1k,83.910,16.090,96.022,3.978,28.68,224,0.875,bicubic,+6.552,+2.380,+2 mobilenetv2_120d.ra_in1k,83.902,16.098,95.903,4.097,5.83,224,0.875,bicubic,+6.594,+2.401,+3 inception_v3.tf_adv_in1k,83.897,16.103,95.939,4.061,23.83,299,0.875,bicubic,+6.305,+2.209,-13 resnet101.tv_in1k,83.863,16.137,95.888,4.112,44.55,224,0.875,bilinear,+6.483,+2.342,-2 tinynet_a.in1k,83.825,16.175,95.817,4.183,6.19,192,0.875,bicubic,+6.177,+2.277,-19 resnet26d.bt_in1k,83.791,16.209,95.960,4.040,16.01,288,0.950,bicubic,+6.383,+2.322,-5 dpn68b.mx_in1k,83.786,16.214,95.986,4.014,12.61,224,0.875,bicubic,+6.268,+2.134,-13 inception_v3.tv_in1k,83.756,16.244,95.886,4.114,23.83,299,0.875,bicubic,+6.322,+2.412,-9 hardcorenas_d.miil_green_in1k,83.756,16.244,95.738,4.262,7.50,224,0.875,bilinear,+6.322,+2.248,-9 xcit_nano_12_p8_224.fb_dist_in1k,83.733,16.267,95.963,4.037,3.05,224,1.000,bicubic,+7.401,+2.865,+39 dla60.in1k,83.716,16.284,95.926,4.074,22.04,224,0.875,bilinear,+6.670,+2.608,+11 resnext26ts.ra2_in1k,83.701,16.299,95.984,4.016,10.30,288,1.000,bicubic,+6.523,+2.520,+1 repvgg_b1g4.rvgg_in1k,83.699,16.301,96.027,3.973,39.97,224,0.875,bilinear,+6.111,+2.191,-22 convmixer_1024_20_ks9_p14.in1k,83.682,16.318,95.884,4.116,24.38,224,0.960,bicubic,+6.746,+2.534,+13 legacy_seresnet50.in1k,83.669,16.331,95.984,4.016,28.09,224,0.875,bilinear,+6.025,+2.226,-28 regnetx_008.tv2_in1k,83.667,16.333,95.975,4.025,7.26,224,0.965,bicubic,+6.361,+2.311,-10 tf_efficientnet_b0.ap_in1k,83.652,16.348,95.785,4.215,5.29,224,0.875,bicubic,+6.562,+2.523,+2 skresnet34.ra_in1k,83.645,16.355,95.928,4.072,22.28,224,0.875,bicubic,+6.735,+2.784,+11 tf_efficientnet_cc_b0_4e.in1k,83.641,16.359,95.740,4.260,13.31,224,0.875,bicubic,+6.339,+2.404,-12 repghostnet_150.in1k,83.630,16.369,95.920,4.080,6.58,224,0.875,bicubic,+6.171,+2.410,-22 seresnet50.a3_in1k,83.624,16.376,95.709,4.292,28.09,224,0.950,bicubic,+6.598,+2.636,+2 densenet121.ra_in1k,83.596,16.404,96.054,3.946,7.98,288,0.950,bicubic,+7.096,+2.686,+21 resmlp_12_224.fb_in1k,83.569,16.431,95.760,4.240,15.35,224,0.875,bicubic,+6.921,+2.582,+11 densenet201.tv_in1k,83.554,16.446,95.809,4.191,20.01,224,0.875,bicubic,+6.268,+2.329,-16 mobilenetv3_large_100.miil_in21k_ft_in1k,83.554,16.446,95.448,4.552,5.48,224,0.875,bilinear,+5.634,+2.534,-51 mixnet_m.ft_in1k,83.532,16.468,95.687,4.313,5.01,224,0.875,bicubic,+6.272,+2.269,-16 legacy_seresnext26_32x4d.in1k,83.522,16.478,95.709,4.292,16.79,224,0.875,bicubic,+6.414,+2.395,-9 gernet_s.idstcv_in1k,83.513,16.487,95.798,4.202,8.17,224,0.875,bilinear,+6.603,+2.482,+2 tf_efficientnet_b0.aa_in1k,83.502,16.498,95.704,4.296,5.29,224,0.875,bicubic,+6.658,+2.486,+2 hrnet_w18.ms_in1k,83.490,16.511,95.911,4.089,21.30,224,0.875,bilinear,+6.738,+2.467,+3 resnet34.bt_in1k,83.468,16.532,95.965,4.035,21.80,288,0.950,bicubic,+6.988,+2.611,+13 selecsls42b.in1k,83.451,16.549,95.736,4.264,32.46,224,0.875,bicubic,+6.281,+2.344,-17 efficientvit_m5.r224_in1k,83.449,16.551,95.813,4.187,12.47,224,0.875,bicubic,+6.391,+2.629,-12 efficientformerv2_s0.snap_dist_in1k,83.402,16.598,95.817,4.183,3.60,224,0.950,bicubic,+7.288,+2.959,+19 hardcorenas_c.miil_green_in1k,83.353,16.647,95.713,4.287,5.52,224,0.875,bilinear,+6.287,+2.551,-15 ghostnetv2_130.in1k,83.342,16.658,95.843,4.157,8.96,224,0.875,bicubic,+6.586,+2.481,-4 tf_efficientnet_lite1.in1k,83.338,16.662,95.647,4.353,5.42,240,0.882,bicubic,+6.694,+2.423,-2 fastvit_t8.apple_in1k,83.270,16.730,95.832,4.168,4.03,256,0.900,bicubic,+7.096,+2.780,+13 dpn68.mx_in1k,83.195,16.805,95.617,4.383,12.61,224,0.875,bicubic,+6.849,+2.609,+9 tf_mixnet_m.in1k,83.189,16.811,95.469,4.531,5.01,224,0.875,bicubic,+6.235,+2.315,-14 regnetx_016.pycls_in1k,83.180,16.820,95.740,4.260,9.19,224,0.875,bicubic,+6.256,+2.325,-13 tf_efficientnet_es.in1k,83.180,16.820,95.580,4.420,5.44,224,0.875,bicubic,+6.582,+2.378,-5 xcit_nano_12_p16_384.fb_dist_in1k,83.178,16.822,95.755,4.245,3.05,384,1.000,bicubic,+7.720,+3.058,+27 mobilenetv2_140.ra_in1k,83.174,16.826,95.687,4.313,6.11,224,0.875,bicubic,+6.658,+2.699,-2 levit_128s.fb_dist_in1k,83.052,16.948,95.533,4.467,7.78,224,0.900,bicubic,+6.526,+2.661,-5 levit_conv_128s.fb_dist_in1k,83.046,16.954,95.536,4.464,7.78,224,0.900,bicubic,+6.526,+2.670,-5 repvgg_a2.rvgg_in1k,82.996,17.004,95.593,4.407,28.21,224,0.875,bilinear,+6.538,+2.591,-2 resnet50.tv_in1k,82.958,17.042,95.469,4.531,25.56,224,0.875,bilinear,+6.830,+2.611,+4 repghostnet_130.in1k,82.922,17.078,95.459,4.541,5.48,224,0.875,bicubic,+6.546,+2.567,-3 resnet26.bt_in1k,82.917,17.083,95.726,4.274,16.00,288,0.950,bicubic,+6.551,+2.546,-3 hardcorenas_b.miil_green_in1k,82.864,17.136,95.395,4.605,5.18,224,0.875,bilinear,+6.316,+2.633,-13 mobileone_s1.apple_in1k,82.853,17.147,95.540,4.460,4.83,224,0.900,bilinear,+7.067,+2.748,+7 mobilevitv2_075.cvnets_in1k,82.796,17.204,95.570,4.430,2.87,256,0.888,bicubic,+7.188,+2.826,+11 densenet169.tv_in1k,82.689,17.311,95.604,4.396,14.15,224,0.875,bicubic,+6.789,+2.576,+4 vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,82.687,17.313,95.847,4.153,6.36,384,1.000,bicubic,+6.727,+2.585,+1 regnety_004.tv2_in1k,82.640,17.360,95.499,4.501,4.34,224,0.965,bicubic,+7.046,+2.799,+9 edgenext_x_small.in1k,82.580,17.420,95.459,4.541,2.34,288,1.000,bicubic,+6.892,+2.693,+4 tf_efficientnet_b0.in1k,82.563,17.437,95.418,4.582,5.29,224,0.875,bicubic,+6.033,+2.410,-19 mixnet_s.ft_in1k,82.520,17.480,95.356,4.644,4.13,224,0.875,bicubic,+6.526,+2.086,-4 vit_small_patch32_224.augreg_in21k_ft_in1k,82.516,17.484,95.664,4.336,22.88,224,0.900,bicubic,+6.522,+2.864,-6 regnety_008.pycls_in1k,82.486,17.514,95.489,4.511,6.26,224,0.875,bicubic,+6.184,+2.427,-11 efficientnet_lite0.ra_in1k,82.371,17.629,95.294,4.706,4.65,224,0.875,bicubic,+6.889,+2.774,+6 resnest14d.gluon_in1k,82.356,17.644,95.335,4.665,10.61,224,0.875,bilinear,+6.848,+2.827,+4 hardcorenas_a.miil_green_in1k,82.322,17.678,95.288,4.712,5.26,224,0.875,bilinear,+6.384,+2.780,-7 efficientnet_es_pruned.in1k,82.294,17.706,95.301,4.699,5.44,224,0.875,bicubic,+7.288,+2.857,+15 mobilenetv3_rw.rmsp_in1k,82.264,17.736,95.234,4.766,5.48,224,0.875,bicubic,+6.644,+2.531,-3 semnasnet_100.rmsp_in1k,82.258,17.742,95.226,4.774,3.89,224,0.875,bicubic,+6.808,+2.628,+4 mobilenetv3_large_100.ra_in1k,82.179,17.821,95.192,4.808,5.48,224,0.875,bicubic,+6.413,+2.654,-8 vit_tiny_patch16_224.augreg_in21k_ft_in1k,82.080,17.920,95.476,4.524,5.72,224,0.900,bicubic,+6.618,+2.632,0 mobilenetv2_110d.ra_in1k,82.068,17.932,95.070,4.930,4.52,224,0.875,bicubic,+7.014,+2.886,+8 tf_mixnet_s.in1k,82.038,17.962,95.126,4.874,4.13,224,0.875,bicubic,+6.386,+2.486,-9 repvgg_b0.rvgg_in1k,81.999,18.001,95.104,4.896,15.82,224,0.875,bilinear,+6.855,+2.688,+2 deit_tiny_distilled_patch16_224.fb_in1k,81.993,18.007,95.134,4.866,5.91,224,0.900,bicubic,+7.489,+3.244,+18 hrnet_w18_small_v2.ms_in1k,81.976,18.024,95.160,4.840,15.60,224,0.875,bilinear,+6.866,+2.744,+2 mixer_b16_224.goog_in21k_ft_in1k,81.976,18.024,94.451,5.549,59.88,224,0.875,bicubic,+5.374,+2.227,-39 tf_efficientnet_lite0.in1k,81.950,18.050,95.160,4.840,4.65,224,0.875,bicubic,+7.118,+2.990,+7 ghostnetv2_100.in1k,81.905,18.095,95.111,4.889,6.16,224,0.875,bicubic,+6.739,+2.757,-4 tinynet_b.in1k,81.880,18.120,94.876,5.124,3.73,188,0.875,bicubic,+6.902,+2.690,+3 tf_mobilenetv3_large_100.in1k,81.848,18.152,95.059,4.941,5.48,224,0.875,bilinear,+6.332,+2.466,-13 pit_ti_distilled_224.in1k,81.779,18.221,95.098,4.902,5.10,224,0.900,bicubic,+7.523,+3.146,+14 repghostnet_111.in1k,81.743,18.257,94.842,5.158,4.54,224,0.875,bicubic,+6.687,+2.650,-4 densenet121.tv_in1k,81.732,18.268,95.036,4.964,7.98,224,0.875,bicubic,+6.968,+2.882,+3 regnety_006.pycls_in1k,81.717,18.283,95.115,4.885,6.06,224,0.875,bicubic,+6.449,+2.589,-11 regnetx_004_tv.tv2_in1k,81.694,18.306,95.057,4.943,5.50,224,0.965,bicubic,+7.094,+2.887,+5 resnet18d.ra2_in1k,81.679,18.321,95.079,4.921,11.71,288,0.950,bicubic,+7.885,+3.241,+19 dla34.in1k,81.664,18.336,94.867,5.133,15.74,224,0.875,bilinear,+7.024,+2.801,+1 xcit_nano_12_p8_224.fb_in1k,81.645,18.355,95.271,4.729,3.05,224,1.000,bicubic,+7.735,+3.103,+15 crossvit_9_240.in1k,81.613,18.387,94.981,5.019,8.55,240,0.875,bicubic,+7.653,+3.019,+11 fbnetc_100.rmsp_in1k,81.559,18.441,94.968,5.032,5.57,224,0.875,bilinear,+6.430,+2.580,-14 mobilevit_xs.cvnets_in1k,81.553,18.447,95.023,4.977,2.32,256,0.900,bicubic,+6.919,+2.675,-2 legacy_seresnet34.in1k,81.538,18.462,94.897,5.103,21.96,224,0.875,bilinear,+6.736,+2.771,-7 efficientvit_m4.r224_in1k,81.498,18.503,95.002,4.998,8.80,224,0.875,bicubic,+7.130,+3.022,+1 regnetx_008.pycls_in1k,81.487,18.513,95.053,4.947,7.26,224,0.875,bicubic,+6.459,+2.715,-14 resnet34.gluon_in1k,81.481,18.520,94.799,5.201,21.80,224,0.875,bicubic,+6.901,+2.817,-4 mnasnet_100.rmsp_in1k,81.446,18.554,94.914,5.086,4.38,224,0.875,bicubic,+6.794,+2.792,-9 vgg19_bn.tv_in1k,81.438,18.562,94.771,5.229,143.68,224,0.875,bilinear,+7.222,+2.927,-1 repvgg_a1.rvgg_in1k,81.256,18.744,94.714,5.286,14.09,224,0.875,bilinear,+6.794,+2.858,-5 vit_base_patch32_224.augreg_in1k,81.143,18.857,94.427,5.572,88.22,224,0.900,bicubic,+6.249,+2.649,-16 convit_tiny.fb_in1k,81.126,18.874,95.036,4.964,5.71,224,0.875,bicubic,+8.014,+3.324,+14 crossvit_tiny_240.in1k,81.098,18.902,94.983,5.017,7.01,240,0.875,bicubic,+7.758,+3.075,+9 resnet18.a1_in1k,81.036,18.964,94.357,5.643,11.69,288,1.000,bicubic,+7.878,+3.331,+11 repghostnet_100.in1k,80.930,19.070,94.543,5.457,4.07,224,0.875,bicubic,+6.724,+3.001,-6 spnasnet_100.rmsp_in1k,80.883,19.117,94.526,5.474,4.42,224,0.875,bilinear,+6.789,+2.706,-6 resnet34.a3_in1k,80.814,19.186,94.353,5.647,21.80,224,0.950,bicubic,+7.844,+3.247,+12 efficientvit_m3.r224_in1k,80.693,19.307,94.556,5.444,6.90,224,0.875,bicubic,+7.319,+3.208,+2 ghostnet_100.in1k,80.678,19.322,94.359,5.641,5.18,224,0.875,bicubic,+6.720,+2.827,-6 regnety_004.pycls_in1k,80.656,19.344,94.682,5.318,4.34,224,0.875,bicubic,+6.630,+2.934,-9 skresnet18.ra_in1k,80.648,19.352,94.378,5.622,11.96,224,0.875,bicubic,+7.614,+3.206,+6 regnetx_006.pycls_in1k,80.639,19.361,94.530,5.470,6.20,224,0.875,bicubic,+6.771,+2.852,-6 pit_ti_224.in1k,80.599,19.401,94.620,5.380,4.85,224,0.900,bicubic,+7.689,+3.216,+8 resnet18.fb_swsl_ig1b_ft_in1k,80.577,19.423,94.741,5.259,11.69,224,0.875,bilinear,+7.289,+3.011,0 vgg16_bn.tv_in1k,80.571,19.429,94.600,5.400,138.37,224,0.875,bilinear,+7.201,+3.086,-4 semnasnet_075.rmsp_in1k,80.481,19.519,94.319,5.681,2.91,224,0.875,bicubic,+7.477,+3.179,+2 hrnet_w18_small.gluon_in1k,80.406,19.593,94.045,5.955,13.19,224,0.875,bicubic,+6.486,+2.851,-13 resnet34.tv_in1k,80.381,19.619,94.430,5.570,21.80,224,0.875,bilinear,+7.075,+3.010,-5 resnet18.a2_in1k,80.310,19.690,94.099,5.901,11.69,288,1.000,bicubic,+7.938,+3.503,+7 mobilenetv2_100.ra_in1k,80.253,19.747,94.188,5.812,3.50,224,0.875,bicubic,+7.285,+3.172,0 xcit_nano_12_p16_224.fb_dist_in1k,80.231,19.769,94.351,5.649,3.05,224,1.000,bicubic,+7.921,+3.491,+7 vit_base_patch32_224.sam_in1k,80.214,19.786,93.823,6.177,88.22,224,0.900,bicubic,+6.520,+2.809,-14 resnet18.fb_ssl_yfcc100m_ft_in1k,80.095,19.905,94.592,5.408,11.69,224,0.875,bilinear,+7.497,+3.176,-1 tf_mobilenetv3_large_075.in1k,80.073,19.927,94.180,5.820,3.99,224,0.875,bilinear,+6.643,+2.828,-15 deit_tiny_patch16_224.fb_in1k,80.011,19.988,94.449,5.551,5.72,224,0.900,bicubic,+7.841,+3.333,+7 hrnet_w18_small.ms_in1k,79.550,20.450,93.906,6.093,13.19,224,0.875,bilinear,+7.214,+3.226,+1 repvgg_a0.rvgg_in1k,79.508,20.492,93.778,6.222,9.11,224,0.875,bilinear,+7.100,+3.286,-4 vgg19.tv_in1k,79.484,20.516,93.868,6.132,143.67,224,0.875,bilinear,+7.106,+2.994,-3 regnetx_004.pycls_in1k,79.420,20.580,93.847,6.153,5.16,224,0.875,bicubic,+7.018,+3.021,-5 tf_mobilenetv3_large_minimal_100.in1k,79.232,20.768,93.702,6.298,3.92,224,0.875,bilinear,+6.968,+3.062,-1 edgenext_xx_small.in1k,79.175,20.825,93.819,6.181,1.33,288,1.000,bicubic,+7.297,+3.267,+4 legacy_seresnet18.in1k,79.164,20.836,93.774,6.226,11.78,224,0.875,bicubic,+7.404,+3.442,+5 resnet14t.c3_in1k,79.143,20.857,93.565,6.435,10.08,224,0.950,bicubic,+6.889,+3.259,-3 repghostnet_080.in1k,79.089,20.911,93.716,6.284,3.28,224,0.875,bicubic,+6.877,+3.233,-3 vgg16.tv_in1k,79.038,20.962,93.644,6.356,138.36,224,0.875,bilinear,+7.446,+3.260,+3 vgg13_bn.tv_in1k,78.995,21.005,93.661,6.339,133.05,224,0.875,bilinear,+7.407,+3.283,+3 vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,78.991,21.009,93.898,6.102,6.34,224,0.900,bicubic,+7.193,+3.074,-1 lcnet_100.ra2_in1k,78.899,21.101,93.550,6.450,2.95,224,0.875,bicubic,+6.797,+3.196,-5 pvt_v2_b0.in1k,78.756,21.244,93.836,6.164,3.67,224,0.900,bicubic,+8.096,+3.640,+6 efficientvit_m2.r224_in1k,78.632,21.368,93.552,6.448,4.19,224,0.875,bicubic,+7.818,+3.410,+4 mobileone_s0.apple_in1k,78.496,21.504,93.322,6.678,5.29,224,0.875,bilinear,+7.094,+3.480,-1 tinynet_c.in1k,78.449,21.551,93.125,6.875,2.46,184,0.875,bicubic,+7.207,+3.393,0 efficientvit_b0.r224_in1k,78.425,21.575,92.801,7.199,3.41,224,0.950,bicubic,+7.027,+3.373,-2 resnet18.gluon_in1k,78.376,21.624,93.129,6.871,11.69,224,0.875,bicubic,+7.542,+3.373,-1 mobilevitv2_050.cvnets_in1k,78.124,21.876,93.565,6.435,1.37,256,0.888,bicubic,+7.976,+3.647,+3 vgg11_bn.tv_in1k,77.956,22.044,93.232,6.768,132.87,224,0.875,bilinear,+7.573,+3.424,0 xcit_nano_12_p16_224.fb_in1k,77.904,22.096,93.437,6.563,3.05,224,1.000,bicubic,+7.942,+3.675,+2 regnety_002.pycls_in1k,77.422,22.578,92.905,7.095,3.16,224,0.875,bicubic,+7.142,+3.375,-1 resnet18.tv_in1k,77.285,22.715,92.756,7.244,11.69,224,0.875,bilinear,+7.525,+3.686,+2 mixer_l16_224.goog_in21k_ft_in1k,77.279,22.721,90.584,9.416,208.20,224,0.875,bicubic,+5.225,+2.910,-16 vgg13.tv_in1k,77.227,22.773,92.692,7.308,133.05,224,0.875,bilinear,+7.295,+3.442,-1 mobilevit_xxs.cvnets_in1k,76.604,23.396,92.681,7.319,1.27,256,0.900,bicubic,+7.686,+3.735,+1 resnet18.a3_in1k,76.457,23.543,92.226,7.774,11.69,224,0.950,bicubic,+8.205,+4.054,+6 efficientvit_m1.r224_in1k,76.388,23.612,92.542,7.458,2.98,224,0.875,bicubic,+8.082,+3.872,+4 vgg11.tv_in1k,76.384,23.616,92.156,7.844,132.86,224,0.875,bilinear,+7.362,+3.532,-3 repghostnet_058.in1k,76.224,23.776,92.117,7.883,2.55,224,0.875,bicubic,+7.310,+3.697,-2 resnet10t.c3_in1k,76.171,23.829,92.226,7.774,5.44,224,0.950,bicubic,+7.806,+4.190,0 regnetx_002.pycls_in1k,76.128,23.872,92.198,7.801,2.68,224,0.875,bicubic,+7.376,+3.656,-2 lcnet_075.ra2_in1k,76.036,23.964,92.060,7.940,2.36,224,0.875,bicubic,+7.254,+3.700,-4 dla60x_c.in1k,75.637,24.363,92.171,7.829,1.32,224,0.875,bilinear,+7.725,+3.739,+1 mobilenetv3_small_100.lamb_in1k,74.921,25.078,91.487,8.512,2.54,224,0.875,bicubic,+7.263,+3.852,+1 tf_mobilenetv3_small_100.in1k,74.725,25.275,91.266,8.735,2.54,224,0.875,bilinear,+6.803,+3.594,-2 tinynet_d.in1k,74.292,25.708,90.917,9.083,2.34,152,0.875,bicubic,+7.320,+3.851,0 repghostnet_050.in1k,74.236,25.764,90.808,9.191,2.31,224,0.875,bicubic,+7.270,+3.888,0 mnasnet_small.lamb_in1k,73.801,26.199,90.732,9.268,2.03,224,0.875,bicubic,+7.605,+4.228,0 dla46x_c.in1k,73.655,26.345,91.097,8.903,1.07,224,0.875,bilinear,+7.663,+4.123,0 mobilenetv2_050.lamb_in1k,73.470,26.530,90.317,9.682,1.97,224,0.875,bicubic,+7.522,+4.233,0 tf_mobilenetv3_small_075.in1k,72.814,27.186,90.051,9.949,2.04,224,0.875,bilinear,+7.088,+3.919,0 dla46_c.in1k,72.626,27.374,90.499,9.501,1.30,224,0.875,bilinear,+7.754,+4.201,+1 mobilenetv3_small_075.lamb_in1k,72.317,27.683,89.666,10.334,2.04,224,0.875,bicubic,+7.081,+4.220,-1 efficientvit_m0.r224_in1k,71.091,28.909,89.589,10.411,2.35,224,0.875,bicubic,+7.821,+4.413,0 lcnet_050.ra2_in1k,70.402,29.598,88.825,11.175,1.88,224,0.875,bicubic,+7.264,+4.443,0 tf_mobilenetv3_small_minimal_100.in1k,70.096,29.904,88.516,11.485,2.04,224,0.875,bilinear,+7.202,+4.278,0 tinynet_e.in1k,66.810,33.190,86.280,13.720,2.04,106,0.875,bicubic,+6.944,+4.518,0 mobilenetv3_small_050.lamb_in1k,64.697,35.303,84.858,15.142,1.59,224,0.875,bicubic,+6.781,+4.678,0
hf_public_repos/pytorch-image-models	hf_public_repos/pytorch-image-models/hfdocs/README.md	# Hugging Face Timm Docs ## Getting Started ``` pip install git+https://github.com/huggingface/doc-builder.git@main#egg=hf-doc-builder pip install watchdog black ``` ## Preview the Docs Locally ``` doc-builder preview timm hfdocs/source ```
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/training_script.mdx	# Scripts A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release. The training and validation scripts evolved from early versions of the [PyTorch Imagenet Examples](https://github.com/pytorch/examples). I have added significant functionality over time, including CUDA specific performance enhancements based on [NVIDIA's APEX Examples](https://github.com/NVIDIA/apex/tree/master/examples). ## Training Script The variety of training args is large and not all combinations of options (or even options) have been fully tested. For the training dataset folder, specify the folder to the base that contains a `train` and `validation` folder. To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value: ```bash ./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4 ``` <Tip> It is recommended to use PyTorch 1.9+ w/ PyTorch native AMP and DDP instead of APEX AMP. --amp defaults to native AMP as of timm ver 0.4.3. --apex-amp will force use of APEX components if they are installed. </Tip> ## Validation / Inference Scripts Validation and inference scripts are similar in usage. One outputs metrics on a validation set and the other outputs topk class ids in a csv. Specify the folder containing validation images, not the base as in training script. To validate with the model's pretrained weights (if they exist): ```bash python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained ``` To run inference from a checkpoint: ```bash python inference.py /imagenet/validation/ --model mobilenetv3_large_100 --checkpoint ./output/train/model_best.pth.tar ``` ## Training Examples ### EfficientNet-B2 with RandAugment - 80.4 top-1, 95.1 top-5 These params are for dual Titan RTX cards with NVIDIA Apex installed: ```bash ./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .016 ``` ### MixNet-XL with RandAugment - 80.5 top-1, 94.9 top-5 This params are for dual Titan RTX cards with NVIDIA Apex installed: ```bash ./distributed_train.sh 2 /imagenet/ --model mixnet_xl -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .969 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.3 --amp --lr .016 --dist-bn reduce ``` ### SE-ResNeXt-26-D and SE-ResNeXt-26-T These hparams (or similar) work well for a wide range of ResNet architecture, generally a good idea to increase the epoch # as the model size increases... ie approx 180-200 for ResNe(X)t50, and 220+ for larger. Increase batch size and LR proportionally for better GPUs or with AMP enabled. These params were for 2 1080Ti cards: ```bash ./distributed_train.sh 2 /imagenet/ --model seresnext26t_32x4d --lr 0.1 --warmup-epochs 5 --epochs 160 --weight-decay 1e-4 --sched cosine --reprob 0.4 --remode pixel -b 112 ``` ### EfficientNet-B3 with RandAugment - 81.5 top-1, 95.7 top-5 The training of this model started with the same command line as EfficientNet-B2 w/ RA above. After almost three weeks of training the process crashed. The results weren't looking amazing so I resumed the training several times with tweaks to a few params (increase RE prob, decrease rand-aug, increase ema-decay). Nothing looked great. I ended up averaging the best checkpoints from all restarts. The result is mediocre at default res/crop but oddly performs much better with a full image test crop of 1.0. ### EfficientNet-B0 with RandAugment - 77.7 top-1, 95.3 top-5 [Michael Klachko](https://github.com/michaelklachko) achieved these results with the command line for B2 adapted for larger batch size, with the recommended B0 dropout rate of 0.2. ```bash ./distributed_train.sh 2 /imagenet/ --model efficientnet_b0 -b 384 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .048 ``` ### ResNet50 with JSD loss and RandAugment (clean + 2x RA augs) - 79.04 top-1, 94.39 top-5 Trained on two older 1080Ti cards, this took a while. Only slightly, non statistically better ImageNet validation result than my first good AugMix training of 78.99. However, these weights are more robust on tests with ImageNetV2, ImageNet-Sketch, etc. Unlike my first AugMix runs, I've enabled SplitBatchNorm, disabled random erasing on the clean split, and cranked up random erasing prob on the 2 augmented paths. ```bash ./distributed_train.sh 2 /imagenet -b 64 --model resnet50 --sched cosine --epochs 200 --lr 0.05 --amp --remode pixel --reprob 0.6 --aug-splits 3 --aa rand-m9-mstd0.5-inc1 --resplit --split-bn --jsd --dist-bn reduce ``` ### EfficientNet-ES (EdgeTPU-Small) with RandAugment - 78.066 top-1, 93.926 top-5 Trained by [Andrew Lavin](https://github.com/andravin) with 8 V100 cards. Model EMA was not used, final checkpoint is the average of 8 best checkpoints during training. ```bash ./distributed_train.sh 8 /imagenet --model efficientnet_es -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 ``` ### MobileNetV3-Large-100 - 75.766 top-1, 92,542 top-5 ```bash ./distributed_train.sh 2 /imagenet/ --model mobilenetv3_large_100 -b 512 --sched step --epochs 600 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 --lr-noise 0.42 0.9 ``` ### ResNeXt-50 32x4d w/ RandAugment - 79.762 top-1, 94.60 top-5 These params will also work well for SE-ResNeXt-50 and SK-ResNeXt-50 and likely 101. I used them for the SK-ResNeXt-50 32x4d that I trained with 2 GPU using a slightly higher LR per effective batch size (lr=0.18, b=192 per GPU). The cmd line below are tuned for 8 GPU training. ```bash ./distributed_train.sh 8 /imagenet --model resnext50_32x4d --lr 0.6 --warmup-epochs 5 --epochs 240 --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 192 -j 6 --amp --dist-bn reduce ```
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/installation.mdx	# Installation Before you start, you'll need to setup your environment and install the appropriate packages. `timm` is tested on Python 3+. ## Virtual Environment You should install `timm` in a [virtual environment](https://docs.python.org/3/library/venv.html) to keep things tidy and avoid dependency conflicts. 1. Create and navigate to your project directory: ```bash mkdir ~/my-project cd ~/my-project ``` 2. Start a virtual environment inside your directory: ```bash python -m venv .env ``` 3. Activate and deactivate the virtual environment with the following commands: ```bash # Activate the virtual environment source .env/bin/activate # Deactivate the virtual environment source .env/bin/deactivate ``` ` Once you've created your virtual environment, you can install `timm` in it. ## Using pip The most straightforward way to install `timm` is with pip: ```bash pip install timm ``` Alternatively, you can install `timm` from GitHub directly to get the latest, bleeding-edge version: ```bash pip install git+https://github.com/rwightman/pytorch-image-models.git ``` Run the following command to check if `timm` has been properly installed: ```bash python -c "from timm import list_models; print(list_models(pretrained=True)[:5])" ``` This command lists the first five pretrained models available in `timm` (which are sorted alphebetically). You should see the following output: ```python ['adv_inception_v3', 'bat_resnext26ts', 'beit_base_patch16_224', 'beit_base_patch16_224_in22k', 'beit_base_patch16_384'] ``` ## From Source Building `timm` from source lets you make changes to the code base. To install from the source, clone the repository and install with the following commands: ```bash git clone https://github.com/rwightman/pytorch-image-models.git cd timm pip install -e . ``` Again, you can check if `timm` was properly installed with the following command: ```bash python -c "from timm import list_models; print(list_models(pretrained=True)[:5])" ```
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/hf_hub.mdx	# Sharing and Loading Models From the Hugging Face Hub The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub. In this short guide, we'll see how to: 1. Share a `timm` model on the Hub 2. How to load that model back from the Hub ## Authenticating First, you'll need to make sure you have the `huggingface_hub` package installed. ```bash pip install huggingface_hub ``` Then, you'll need to authenticate yourself. You can do this by running the following command: ```bash huggingface-cli login ``` Or, if you're using a notebook, you can use the `notebook_login` helper: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Sharing a Model ```py >>> import timm >>> model = timm.create_model('resnet18', pretrained=True, num_classes=4) ``` Here is where you would normally train or fine-tune the model. We'll skip that for the sake of this tutorial. Let's pretend we've now fine-tuned the model. The next step would be to push it to the Hub! We can do this with the `timm.models.hub.push_to_hf_hub` function. ```py >>> model_cfg = dict(labels=['a', 'b', 'c', 'd']) >>> timm.models.hub.push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg) ``` Running the above would push the model to `<your-username>/resnet18-random` on the Hub. You can now share this model with your friends, or use it in your own code! ## Loading a Model Loading a model from the Hub is as simple as calling `timm.create_model` with the `pretrained` argument set to the name of the model you want to load. In this case, we'll use [`nateraw/resnet18-random`](https://huggingface.co/nateraw/resnet18-random), which is the model we just pushed to the Hub. ```py >>> model_reloaded = timm.create_model('hf_hub:nateraw/resnet18-random', pretrained=True) ```
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/index.mdx	# timm <img class="float-left !m-0 !border-0 !dark:border-0 !shadow-none !max-w-lg w-[150px]" src="https://huggingface.co/front/thumbnails/docs/timm.png"/> `timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation scripts. It comes packaged with >700 pretrained models, and is designed to be flexible and easy to use. Read the [quick start guide](quickstart) to get up and running with the `timm` library. You will learn how to load, discover, and use pretrained models included in the library. <div class="mt-10"> <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5"> <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./feature_extraction" ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div> <p class="text-gray-700">Learn the basics and become familiar with timm. Start here if you are using timm for the first time!</p> </a> <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./reference/models" ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div> <p class="text-gray-700">Technical descriptions of how timm classes and methods work.</p> </a> </div> </div>
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/models.mdx	# Model Summaries The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below. Most included models have pretrained weights. The weights are either: 1. from their original sources 2. ported by myself from their original impl in a different framework (e.g. Tensorflow models) 3. trained from scratch using the included training script The validation results for the pretrained weights are [here](results) A more exciting view (with pretty pictures) of the models within `timm` can be found at [paperswithcode](https://paperswithcode.com/lib/timm). ## Big Transfer ResNetV2 (BiT) * Implementation: [resnetv2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnetv2.py) * Paper: `Big Transfer (BiT): General Visual Representation Learning` - https://arxiv.org/abs/1912.11370 * Reference code: https://github.com/google-research/big_transfer ## Cross-Stage Partial Networks * Implementation: [cspnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/cspnet.py) * Paper: `CSPNet: A New Backbone that can Enhance Learning Capability of CNN` - https://arxiv.org/abs/1911.11929 * Reference impl: https://github.com/WongKinYiu/CrossStagePartialNetworks ## DenseNet * Implementation: [densenet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/densenet.py) * Paper: `Densely Connected Convolutional Networks` - https://arxiv.org/abs/1608.06993 * Code: https://github.com/pytorch/vision/tree/master/torchvision/models ## DLA * Implementation: [dla.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dla.py) * Paper: https://arxiv.org/abs/1707.06484 * Code: https://github.com/ucbdrive/dla ## Dual-Path Networks * Implementation: [dpn.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/dpn.py) * Paper: `Dual Path Networks` - https://arxiv.org/abs/1707.01629 * My PyTorch code: https://github.com/rwightman/pytorch-dpn-pretrained * Reference code: https://github.com/cypw/DPNs ## GPU-Efficient Networks * Implementation: [byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py) * Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090 * Reference code: https://github.com/idstcv/GPU-Efficient-Networks ## HRNet * Implementation: [hrnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/hrnet.py) * Paper: `Deep High-Resolution Representation Learning for Visual Recognition` - https://arxiv.org/abs/1908.07919 * Code: https://github.com/HRNet/HRNet-Image-Classification ## Inception-V3 * Implementation: [inception_v3.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v3.py) * Paper: `Rethinking the Inception Architecture for Computer Vision` - https://arxiv.org/abs/1512.00567 * Code: https://github.com/pytorch/vision/tree/master/torchvision/models ## Inception-V4 * Implementation: [inception_v4.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_v4.py) * Paper: `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning` - https://arxiv.org/abs/1602.07261 * Code: https://github.com/Cadene/pretrained-models.pytorch * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets ## Inception-ResNet-V2 * Implementation: [inception_resnet_v2.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/inception_resnet_v2.py) * Paper: `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning` - https://arxiv.org/abs/1602.07261 * Code: https://github.com/Cadene/pretrained-models.pytorch * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets ## NASNet-A * Implementation: [nasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/nasnet.py) * Papers: `Learning Transferable Architectures for Scalable Image Recognition` - https://arxiv.org/abs/1707.07012 * Code: https://github.com/Cadene/pretrained-models.pytorch * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet ## PNasNet-5 * Implementation: [pnasnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/pnasnet.py) * Papers: `Progressive Neural Architecture Search` - https://arxiv.org/abs/1712.00559 * Code: https://github.com/Cadene/pretrained-models.pytorch * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/nasnet ## EfficientNet * Implementation: [efficientnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/efficientnet.py) * Papers: * EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252 * EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665 * EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946 * EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html * MixNet - https://arxiv.org/abs/1907.09595 * MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626 * MobileNet-V2 - https://arxiv.org/abs/1801.04381 * FBNet-C - https://arxiv.org/abs/1812.03443 * Single-Path NAS - https://arxiv.org/abs/1904.02877 * My PyTorch code: https://github.com/rwightman/gen-efficientnet-pytorch * Reference code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet ## MobileNet-V3 * Implementation: [mobilenetv3.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mobilenetv3.py) * Paper: `Searching for MobileNetV3` - https://arxiv.org/abs/1905.02244 * Reference code: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet ## RegNet * Implementation: [regnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/regnet.py) * Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678 * Reference code: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py ## RepVGG * Implementation: [byobnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/byobnet.py) * Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697 * Reference code: https://github.com/DingXiaoH/RepVGG ## ResNet, ResNeXt * Implementation: [resnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py) * ResNet (V1B) * Paper: `Deep Residual Learning for Image Recognition` - https://arxiv.org/abs/1512.03385 * Code: https://github.com/pytorch/vision/tree/master/torchvision/models * ResNeXt * Paper: `Aggregated Residual Transformations for Deep Neural Networks` - https://arxiv.org/abs/1611.05431 * Code: https://github.com/pytorch/vision/tree/master/torchvision/models * 'Bag of Tricks' / Gluon C, D, E, S ResNet variants * Paper: `Bag of Tricks for Image Classification with CNNs` - https://arxiv.org/abs/1812.01187 * Code: https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnetv1b.py * Instagram pretrained / ImageNet tuned ResNeXt101 * Paper: `Exploring the Limits of Weakly Supervised Pretraining` - https://arxiv.org/abs/1805.00932 * Weights: https://pytorch.org/hub/facebookresearch_WSL-Images_resnext (NOTE: CC BY-NC 4.0 License, NOT commercial friendly) * Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet and ResNeXts * Paper: `Billion-scale semi-supervised learning for image classification` - https://arxiv.org/abs/1905.00546 * Weights: https://github.com/facebookresearch/semi-supervised-ImageNet1K-models (NOTE: CC BY-NC 4.0 License, NOT commercial friendly) * Squeeze-and-Excitation Networks * Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507 * Code: Added to ResNet base, this is current version going forward, old `senet.py` is being deprecated * ECAResNet (ECA-Net) * Paper: `ECA-Net: Efficient Channel Attention for Deep CNN` - https://arxiv.org/abs/1910.03151v4 * Code: Added to ResNet base, ECA module contributed by @VRandme, reference https://github.com/BangguWu/ECANet ## Res2Net * Implementation: [res2net.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/res2net.py) * Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169 * Code: https://github.com/gasvn/Res2Net ## ResNeSt * Implementation: [resnest.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnest.py) * Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955 * Code: https://github.com/zhanghang1989/ResNeSt ## ReXNet * Implementation: [rexnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/rexnet.py) * Paper: `ReXNet: Diminishing Representational Bottleneck on CNN` - https://arxiv.org/abs/2007.00992 * Code: https://github.com/clovaai/rexnet ## Selective-Kernel Networks * Implementation: [sknet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/sknet.py) * Paper: `Selective-Kernel Networks` - https://arxiv.org/abs/1903.06586 * Code: https://github.com/implus/SKNet, https://github.com/clovaai/assembled-cnn ## SelecSLS * Implementation: [selecsls.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/selecsls.py) * Paper: `XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera` - https://arxiv.org/abs/1907.00837 * Code: https://github.com/mehtadushy/SelecSLS-Pytorch ## Squeeze-and-Excitation Networks * Implementation: [senet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/senet.py) NOTE: I am deprecating this version of the networks, the new ones are part of `resnet.py` * Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507 * Code: https://github.com/Cadene/pretrained-models.pytorch ## TResNet * Implementation: [tresnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tresnet.py) * Paper: `TResNet: High Performance GPU-Dedicated Architecture` - https://arxiv.org/abs/2003.13630 * Code: https://github.com/mrT23/TResNet ## VGG * Implementation: [vgg.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vgg.py) * Paper: `Very Deep Convolutional Networks For Large-Scale Image Recognition` - https://arxiv.org/pdf/1409.1556.pdf * Reference code: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py ## Vision Transformer * Implementation: [vision_transformer.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) * Paper: `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 * Reference code and pretrained weights: https://github.com/google-research/vision_transformer ## VovNet V2 and V1 * Implementation: [vovnet.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vovnet.py) * Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667 * Reference code: https://github.com/youngwanLEE/vovnet-detectron2 ## Xception * Implementation: [xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/xception.py) * Paper: `Xception: Deep Learning with Depthwise Separable Convolutions` - https://arxiv.org/abs/1610.02357 * Code: https://github.com/Cadene/pretrained-models.pytorch ## Xception (Modified Aligned, Gluon) * Implementation: [gluon_xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/gluon_xception.py) * Paper: `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation` - https://arxiv.org/abs/1802.02611 * Reference code: https://github.com/dmlc/gluon-cv/tree/master/gluoncv/model_zoo, https://github.com/jfzhang95/pytorch-deeplab-xception/ ## Xception (Modified Aligned, TF) * Implementation: [aligned_xception.py](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/aligned_xception.py) * Paper: `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation` - https://arxiv.org/abs/1802.02611 * Reference code: https://github.com/tensorflow/models/tree/master/research/deeplab
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/feature_extraction.mdx	# Feature Extraction All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification. ## Penultimate Layer Features (Pre-Classifier Features) The features from the penultimate model layer can be obtained in several ways without requiring model surgery (although feel free to do surgery). One must first decide if they want pooled or un-pooled features. ### Unpooled There are three ways to obtain unpooled features. Without modifying the network, one can call `model.forward_features(input)` on any model instead of the usual `model(input)`. This will bypass the head classifier and global pooling for networks. If one wants to explicitly modify the network to return unpooled features, they can either create the model without a classifier and pooling, or remove it later. Both paths remove the parameters associated with the classifier from the network. #### forward_features() ```py >>> import torch >>> import timm >>> m = timm.create_model('xception41', pretrained=True) >>> o = m(torch.randn(2, 3, 299, 299)) >>> print(f'Original shape: {o.shape}') >>> o = m.forward_features(torch.randn(2, 3, 299, 299)) >>> print(f'Unpooled shape: {o.shape}') ``` Output: ```text Original shape: torch.Size([2, 1000]) Unpooled shape: torch.Size([2, 2048, 10, 10]) ``` #### Create with no classifier and pooling ```py >>> import torch >>> import timm >>> m = timm.create_model('resnet50', pretrained=True, num_classes=0, global_pool='') >>> o = m(torch.randn(2, 3, 224, 224)) >>> print(f'Unpooled shape: {o.shape}') ``` Output: ```text Unpooled shape: torch.Size([2, 2048, 7, 7]) ``` #### Remove it later ```py >>> import torch >>> import timm >>> m = timm.create_model('densenet121', pretrained=True) >>> o = m(torch.randn(2, 3, 224, 224)) >>> print(f'Original shape: {o.shape}') >>> m.reset_classifier(0, '') >>> o = m(torch.randn(2, 3, 224, 224)) >>> print(f'Unpooled shape: {o.shape}') ``` Output: ```text Original shape: torch.Size([2, 1000]) Unpooled shape: torch.Size([2, 1024, 7, 7]) ``` ### Pooled To modify the network to return pooled features, one can use `forward_features()` and pool/flatten the result themselves, or modify the network like above but keep pooling intact. #### Create with no classifier ```py >>> import torch >>> import timm >>> m = timm.create_model('resnet50', pretrained=True, num_classes=0) >>> o = m(torch.randn(2, 3, 224, 224)) >>> print(f'Pooled shape: {o.shape}') ``` Output: ```text Pooled shape: torch.Size([2, 2048]) ``` #### Remove it later ```py >>> import torch >>> import timm >>> m = timm.create_model('ese_vovnet19b_dw', pretrained=True) >>> o = m(torch.randn(2, 3, 224, 224)) >>> print(f'Original shape: {o.shape}') >>> m.reset_classifier(0) >>> o = m(torch.randn(2, 3, 224, 224)) >>> print(f'Pooled shape: {o.shape}') ``` Output: ```text Original shape: torch.Size([2, 1000]) Pooled shape: torch.Size([2, 1024]) ``` ## Multi-scale Feature Maps (Feature Pyramid) Object detection, segmentation, keypoint, and a variety of dense pixel tasks require access to feature maps from the backbone network at multiple scales. This is often done by modifying the original classification network. Since each network varies quite a bit in structure, it's not uncommon to see only a few backbones supported in any given obj detection or segmentation library. `timm` allows a consistent interface for creating any of the included models as feature backbones that output feature maps for selected levels. A feature backbone can be created by adding the argument `features_only=True` to any `create_model` call. By default 5 strides will be output from most models (not all have that many), with the first starting at 2 (some start at 1 or 4). ### Create a feature map extraction model ```py >>> import torch >>> import timm >>> m = timm.create_model('resnest26d', features_only=True, pretrained=True) >>> o = m(torch.randn(2, 3, 224, 224)) >>> for x in o: ... print(x.shape) ``` Output: ```text torch.Size([2, 64, 112, 112]) torch.Size([2, 256, 56, 56]) torch.Size([2, 512, 28, 28]) torch.Size([2, 1024, 14, 14]) torch.Size([2, 2048, 7, 7]) ``` ### Query the feature information After a feature backbone has been created, it can be queried to provide channel or resolution reduction information to the downstream heads without requiring static config or hardcoded constants. The `.feature_info` attribute is a class encapsulating the information about the feature extraction points. ```py >>> import torch >>> import timm >>> m = timm.create_model('regnety_032', features_only=True, pretrained=True) >>> print(f'Feature channels: {m.feature_info.channels()}') >>> o = m(torch.randn(2, 3, 224, 224)) >>> for x in o: ... print(x.shape) ``` Output: ```text Feature channels: [32, 72, 216, 576, 1512] torch.Size([2, 32, 112, 112]) torch.Size([2, 72, 56, 56]) torch.Size([2, 216, 28, 28]) torch.Size([2, 576, 14, 14]) torch.Size([2, 1512, 7, 7]) ``` ### Select specific feature levels or limit the stride There are two additional creation arguments impacting the output features. * `out_indices` selects which indices to output * `output_stride` limits the feature output stride of the network (also works in classification mode BTW) `out_indices` is supported by all models, but not all models have the same index to feature stride mapping. Look at the code or check feature_info to compare. The out indices generally correspond to the `C(i+1)th` feature level (a `2^(i+1)` reduction). For most models, index 0 is the stride 2 features, and index 4 is stride 32. `output_stride` is achieved by converting layers to use dilated convolutions. Doing so is not always straightforward, some networks only support `output_stride=32`. ```py >>> import torch >>> import timm >>> m = timm.create_model('ecaresnet101d', features_only=True, output_stride=8, out_indices=(2, 4), pretrained=True) >>> print(f'Feature channels: {m.feature_info.channels()}') >>> print(f'Feature reduction: {m.feature_info.reduction()}') >>> o = m(torch.randn(2, 3, 320, 320)) >>> for x in o: ... print(x.shape) ``` Output: ```text Feature channels: [512, 2048] Feature reduction: [8, 8] torch.Size([2, 512, 40, 40]) torch.Size([2, 2048, 40, 40]) ```
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/_toctree.yml	- sections: - local: index title: Home - local: quickstart title: Quickstart - local: installation title: Installation title: Get started - sections: - local: feature_extraction title: Using Pretrained Models as Feature Extractors - local: training_script title: Training With The Official Training Script - local: hf_hub title: Share and Load Models from the 🤗 Hugging Face Hub title: Tutorials - sections: - local: models title: Model Summaries - local: results title: Results - local: models/adversarial-inception-v3 title: Adversarial Inception v3 - local: models/advprop title: AdvProp (EfficientNet) - local: models/big-transfer title: Big Transfer (BiT) - local: models/csp-darknet title: CSP-DarkNet - local: models/csp-resnet title: CSP-ResNet - local: models/csp-resnext title: CSP-ResNeXt - local: models/densenet title: DenseNet - local: models/dla title: Deep Layer Aggregation - local: models/dpn title: Dual Path Network (DPN) - local: models/ecaresnet title: ECA-ResNet - local: models/efficientnet title: EfficientNet - local: models/efficientnet-pruned title: EfficientNet (Knapsack Pruned) - local: models/ensemble-adversarial title: Ensemble Adversarial Inception ResNet v2 - local: models/ese-vovnet title: ESE-VoVNet - local: models/fbnet title: FBNet - local: models/gloun-inception-v3 title: (Gluon) Inception v3 - local: models/gloun-resnet title: (Gluon) ResNet - local: models/gloun-resnext title: (Gluon) ResNeXt - local: models/gloun-senet title: (Gluon) SENet - local: models/gloun-seresnext title: (Gluon) SE-ResNeXt - local: models/gloun-xception title: (Gluon) Xception - local: models/hrnet title: HRNet - local: models/ig-resnext title: Instagram ResNeXt WSL - local: models/inception-resnet-v2 title: Inception ResNet v2 - local: models/inception-v3 title: Inception v3 - local: models/inception-v4 title: Inception v4 - local: models/legacy-se-resnet title: (Legacy) SE-ResNet - local: models/legacy-se-resnext title: (Legacy) SE-ResNeXt - local: models/legacy-senet title: (Legacy) SENet - local: models/mixnet title: MixNet - local: models/mnasnet title: MnasNet - local: models/mobilenet-v2 title: MobileNet v2 - local: models/mobilenet-v3 title: MobileNet v3 - local: models/nasnet title: NASNet - local: models/noisy-student title: Noisy Student (EfficientNet) - local: models/pnasnet title: PNASNet - local: models/regnetx title: RegNetX - local: models/regnety title: RegNetY - local: models/res2net title: Res2Net - local: models/res2next title: Res2NeXt - local: models/resnest title: ResNeSt - local: models/resnet title: ResNet - local: models/resnet-d title: ResNet-D - local: models/resnext title: ResNeXt - local: models/rexnet title: RexNet - local: models/se-resnet title: SE-ResNet - local: models/selecsls title: SelecSLS - local: models/seresnext title: SE-ResNeXt - local: models/skresnet title: SK-ResNet - local: models/skresnext title: SK-ResNeXt - local: models/spnasnet title: SPNASNet - local: models/ssl-resnet title: SSL ResNet - local: models/swsl-resnet title: SWSL ResNet - local: models/swsl-resnext title: SWSL ResNeXt - local: models/tf-efficientnet title: (Tensorflow) EfficientNet - local: models/tf-efficientnet-condconv title: (Tensorflow) EfficientNet CondConv - local: models/tf-efficientnet-lite title: (Tensorflow) EfficientNet Lite - local: models/tf-inception-v3 title: (Tensorflow) Inception v3 - local: models/tf-mixnet title: (Tensorflow) MixNet - local: models/tf-mobilenet-v3 title: (Tensorflow) MobileNet v3 - local: models/tresnet title: TResNet - local: models/wide-resnet title: Wide ResNet - local: models/xception title: Xception title: Model Pages isExpanded: false - sections: - local: reference/models title: Models - local: reference/data title: Data - local: reference/optimizers title: Optimizers - local: reference/schedulers title: Learning Rate Schedulers title: Reference
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/results.mdx	# Results CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository [results folder](https://github.com/rwightman/pytorch-image-models/tree/master/results). ## Self-trained Weights The table below includes ImageNet-1k validation results of model weights that I've trained myself. It is not updated as frequently as the csv results outputs linked above. \|Model \| Acc@1 (Err) \| Acc@5 (Err) \| Param # (M) \| Interpolation \| Image Size \| \|---\|---\|---\|---\|---\|---\| \| efficientnet_b3a \| 82.242 (17.758) \| 96.114 (3.886) \| 12.23 \| bicubic \| 320 (1.0 crop) \| \| efficientnet_b3 \| 82.076 (17.924) \| 96.020 (3.980) \| 12.23 \| bicubic \| 300 \| \| regnet_32 \| 82.002 (17.998) \| 95.906 (4.094) \| 19.44 \| bicubic \| 224 \| \| skresnext50d_32x4d \| 81.278 (18.722) \| 95.366 (4.634) \| 27.5 \| bicubic \| 288 (1.0 crop) \| \| seresnext50d_32x4d \| 81.266 (18.734) \| 95.620 (4.380) \| 27.6 \| bicubic \| 224 \| \| efficientnet_b2a \| 80.608 (19.392) \| 95.310 (4.690) \| 9.11 \| bicubic \| 288 (1.0 crop) \| \| resnet50d \| 80.530 (19.470) \| 95.160 (4.840) \| 25.6 \| bicubic \| 224 \| \| mixnet_xl \| 80.478 (19.522) \| 94.932 (5.068) \| 11.90 \| bicubic \| 224 \| \| efficientnet_b2 \| 80.402 (19.598) \| 95.076 (4.924) \| 9.11 \| bicubic \| 260 \| \| seresnet50 \| 80.274 (19.726) \| 95.070 (4.930) \| 28.1 \| bicubic \| 224 \| \| skresnext50d_32x4d \| 80.156 (19.844) \| 94.642 (5.358) \| 27.5 \| bicubic \| 224 \| \| cspdarknet53 \| 80.058 (19.942) \| 95.084 (4.916) \| 27.6 \| bicubic \| 256 \| \| cspresnext50 \| 80.040 (19.960) \| 94.944 (5.056) \| 20.6 \| bicubic \| 224 \| \| resnext50_32x4d \| 79.762 (20.238) \| 94.600 (5.400) \| 25 \| bicubic \| 224 \| \| resnext50d_32x4d \| 79.674 (20.326) \| 94.868 (5.132) \| 25.1 \| bicubic \| 224 \| \| cspresnet50 \| 79.574 (20.426) \| 94.712 (5.288) \| 21.6 \| bicubic \| 256 \| \| ese_vovnet39b \| 79.320 (20.680) \| 94.710 (5.290) \| 24.6 \| bicubic \| 224 \| \| resnetblur50 \| 79.290 (20.710) \| 94.632 (5.368) \| 25.6 \| bicubic \| 224 \| \| dpn68b \| 79.216 (20.784) \| 94.414 (5.586) \| 12.6 \| bicubic \| 224 \| \| resnet50 \| 79.038 (20.962) \| 94.390 (5.610) \| 25.6 \| bicubic \| 224 \| \| mixnet_l \| 78.976 (21.024 \| 94.184 (5.816) \| 7.33 \| bicubic \| 224 \| \| efficientnet_b1 \| 78.692 (21.308) \| 94.086 (5.914) \| 7.79 \| bicubic \| 240 \| \| efficientnet_es \| 78.066 (21.934) \| 93.926 (6.074) \| 5.44 \| bicubic \| 224 \| \| seresnext26t_32x4d \| 77.998 (22.002) \| 93.708 (6.292) \| 16.8 \| bicubic \| 224 \| \| seresnext26tn_32x4d \| 77.986 (22.014) \| 93.746 (6.254) \| 16.8 \| bicubic \| 224 \| \| efficientnet_b0 \| 77.698 (22.302) \| 93.532 (6.468) \| 5.29 \| bicubic \| 224 \| \| seresnext26d_32x4d \| 77.602 (22.398) \| 93.608 (6.392) \| 16.8 \| bicubic \| 224 \| \| mobilenetv2_120d \| 77.294 (22.706 \| 93.502 (6.498) \| 5.8 \| bicubic \| 224 \| \| mixnet_m \| 77.256 (22.744) \| 93.418 (6.582) \| 5.01 \| bicubic \| 224 \| \| resnet34d \| 77.116 (22.884) \| 93.382 (6.618) \| 21.8 \| bicubic \| 224 \| \| seresnext26_32x4d \| 77.104 (22.896) \| 93.316 (6.684) \| 16.8 \| bicubic \| 224 \| \| skresnet34 \| 76.912 (23.088) \| 93.322 (6.678) \| 22.2 \| bicubic \| 224 \| \| ese_vovnet19b_dw \| 76.798 (23.202) \| 93.268 (6.732) \| 6.5 \| bicubic \| 224 \| \| resnet26d \| 76.68 (23.32) \| 93.166 (6.834) \| 16 \| bicubic \| 224 \| \| densenetblur121d \| 76.576 (23.424) \| 93.190 (6.810) \| 8.0 \| bicubic \| 224 \| \| mobilenetv2_140 \| 76.524 (23.476) \| 92.990 (7.010) \| 6.1 \| bicubic \| 224 \| \| mixnet_s \| 75.988 (24.012) \| 92.794 (7.206) \| 4.13 \| bicubic \| 224 \| \| mobilenetv3_large_100 \| 75.766 (24.234) \| 92.542 (7.458) \| 5.5 \| bicubic \| 224 \| \| mobilenetv3_rw \| 75.634 (24.366) \| 92.708 (7.292) \| 5.5 \| bicubic \| 224 \| \| mnasnet_a1 \| 75.448 (24.552) \| 92.604 (7.396) \| 3.89 \| bicubic \| 224 \| \| resnet26 \| 75.292 (24.708) \| 92.57 (7.43) \| 16 \| bicubic \| 224 \| \| fbnetc_100 \| 75.124 (24.876) \| 92.386 (7.614) \| 5.6 \| bilinear \| 224 \| \| resnet34 \| 75.110 (24.890) \| 92.284 (7.716) \| 22 \| bilinear \| 224 \| \| mobilenetv2_110d \| 75.052 (24.948) \| 92.180 (7.820) \| 4.5 \| bicubic \| 224 \| \| seresnet34 \| 74.808 (25.192) \| 92.124 (7.876) \| 22 \| bilinear \| 224 \| \| mnasnet_b1 \| 74.658 (25.342) \| 92.114 (7.886) \| 4.38 \| bicubic \| 224 \| \| spnasnet_100 \| 74.084 (25.916) \| 91.818 (8.182) \| 4.42 \| bilinear \| 224 \| \| skresnet18 \| 73.038 (26.962) \| 91.168 (8.832) \| 11.9 \| bicubic \| 224 \| \| mobilenetv2_100 \| 72.978 (27.022) \| 91.016 (8.984) \| 3.5 \| bicubic \| 224 \| \| resnet18d \| 72.260 (27.740) \| 90.696 (9.304) \| 11.7 \| bicubic \| 224 \| \| seresnet18 \| 71.742 (28.258) \| 90.334 (9.666) \| 11.8 \| bicubic \| 224 \| ## Ported and Other Weights For weights ported from other deep learning frameworks (Tensorflow, MXNet GluonCV) or copied from other PyTorch sources, please see the full results tables for ImageNet and various OOD test sets at in the [results tables](https://github.com/rwightman/pytorch-image-models/tree/master/results). Model code .py files contain links to original sources of models and weights.
hf_public_repos/pytorch-image-models/hfdocs	hf_public_repos/pytorch-image-models/hfdocs/source/quickstart.mdx	# Quickstart This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate `timm` into their model training workflow. First, you'll need to install `timm`. For more information on installation, see [Installation](installation). ```bash pip install timm ``` ## Load a Pretrained Model Pretrained models can be loaded using [`create_model`]. Here, we load the pretrained `mobilenetv3_large_100` model. ```py >>> import timm >>> m = timm.create_model('mobilenetv3_large_100', pretrained=True) >>> m.eval() ``` <Tip> Note: The returned PyTorch model is set to train mode by default, so you must call .eval() on it if you plan to use it for inference. </Tip> ## List Models with Pretrained Weights To list models packaged with `timm`, you can use [`list_models`]. If you specify `pretrained=True`, this function will only return model names that have associated pretrained weights available. ```py >>> import timm >>> from pprint import pprint >>> model_names = timm.list_models(pretrained=True) >>> pprint(model_names) [ 'adv_inception_v3', 'cspdarknet53', 'cspresnext50', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'densenetblur121d', 'dla34', 'dla46_c', ] ``` You can also list models with a specific pattern in their name. ```py >>> import timm >>> from pprint import pprint >>> model_names = timm.list_models('resnet') >>> pprint(model_names) [ 'cspresnet50', 'cspresnet50d', 'cspresnet50w', 'cspresnext50', ... ] ``` ## Fine-Tune a Pretrained Model You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('mobilenetv3_large_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To fine-tune on your own dataset, you have to write a PyTorch training loop or adapt `timm`'s [training script](training_script) to use your dataset. ## Use a Pretrained Model for Feature Extraction Without modifying the network, one can call model.forward_features(input) on any model instead of the usual model(input). This will bypass the head classifier and global pooling for networks. For a more in depth guide to using `timm` for feature extraction, see [Feature Extraction](feature_extraction). ```py >>> import timm >>> import torch >>> x = torch.randn(1, 3, 224, 224) >>> model = timm.create_model('mobilenetv3_large_100', pretrained=True) >>> features = model.forward_features(x) >>> print(features.shape) torch.Size([1, 960, 7, 7]) ``` ## Image Augmentation To transform images into valid inputs for a model, you can use [`timm.data.create_transform`], providing the desired `input_size` that the model expects. This will return a generic transform that uses reasonable defaults. ```py >>> timm.data.create_transform((3, 224, 224)) Compose( Resize(size=256, interpolation=bilinear, max_size=None, antialias=None) CenterCrop(size=(224, 224)) ToTensor() Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250])) ) ``` Pretrained models have specific transforms that were applied to images fed into them while training. If you use the wrong transform on your image, the model won't understand what it's seeing! To figure out which transformations were used for a given pretrained model, we can start by taking a look at its `pretrained_cfg` ```py >>> model.pretrained_cfg {'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth', 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), 'crop_pct': 0.875, 'interpolation': 'bicubic', 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'first_conv': 'conv_stem', 'classifier': 'classifier', 'architecture': 'mobilenetv3_large_100'} ``` We can then resolve only the data related configuration by using [`timm.data.resolve_data_config`]. ```py >>> timm.data.resolve_data_config(model.pretrained_cfg) {'input_size': (3, 224, 224), 'interpolation': 'bicubic', 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'crop_pct': 0.875} ``` We can pass this data config to [`timm.data.create_transform`] to initialize the model's associated transform. ```py >>> data_cfg = timm.data.resolve_data_config(model.pretrained_cfg) >>> transform = timm.data.create_transform(data_cfg) >>> transform Compose( Resize(size=256, interpolation=bicubic, max_size=None, antialias=None) CenterCrop(size=(224, 224)) ToTensor() Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250])) ) ``` <Tip> Note: Here, the pretrained model's config happens to be the same as the generic config we made earlier. This is not always the case. So, it's safer to use the data config to create the transform as we did here instead of using the generic transform. </Tip> ## Using Pretrained Models for Inference Here, we will put together the above sections and use a pretrained model for inference. First we'll need an image to do inference on. Here we load a picture of a leaf from the web: ```py >>> import requests >>> from PIL import Image >>> from io import BytesIO >>> url = 'https://datasets-server.huggingface.co/assets/imagenet-1k/--/default/test/12/image/image.jpg' >>> image = Image.open(requests.get(url, stream=True).raw) >>> image ``` Here's the image we loaded: <img src="https://datasets-server.huggingface.co/assets/imagenet-1k/--/default/test/12/image/image.jpg" alt="An Image from a link" width="300"/> Now, we'll create our model and transforms again. This time, we make sure to set our model in evaluation mode. ```py >>> model = timm.create_model('mobilenetv3_large_100', pretrained=True).eval() >>> transform = timm.data.create_transform( *timm.data.resolve_data_config(model.pretrained_cfg) ) ``` We can prepare this image for the model by passing it to the transform. ```py >>> image_tensor = transform(image) >>> image_tensor.shape torch.Size([3, 224, 224]) ``` Now we can pass that image to the model to get the predictions. We use `unsqueeze(0)` in this case, as the model is expecting a batch dimension. ```py >>> output = model(image_tensor.unsqueeze(0)) >>> output.shape torch.Size([1, 1000]) ``` To get the predicted probabilities, we apply softmax to the output. This leaves us with a tensor of shape `(num_classes,)`. ```py >>> probabilities = torch.nn.functional.softmax(output[0], dim=0) >>> probabilities.shape torch.Size([1000]) ``` Now we'll find the top 5 predicted class indexes and values using `torch.topk`. ```py >>> values, indices = torch.topk(probabilities, 5) >>> indices tensor([162, 166, 161, 164, 167]) ``` If we check the imagenet labels for the top index, we can see what the model predicted... ```py >>> IMAGENET_1k_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt' >>> IMAGENET_1k_LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n') >>> [{'label': IMAGENET_1k_LABELS[idx], 'value': val.item()} for val, idx in zip(values, indices)] [{'label': 'beagle', 'value': 0.8486220836639404}, {'label': 'Walker_hound, Walker_foxhound', 'value': 0.03753996267914772}, {'label': 'basset, basset_hound', 'value': 0.024628572165966034}, {'label': 'bluetick', 'value': 0.010317106731235981}, {'label': 'English_foxhound', 'value': 0.006958036217838526}] ```
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/reference/data.mdx	# Data [[autodoc]] timm.data.create_dataset [[autodoc]] timm.data.create_loader [[autodoc]] timm.data.create_transform [[autodoc]] timm.data.resolve_data_config
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/reference/models.mdx	# Models [[autodoc]] timm.create_model [[autodoc]] timm.list_models
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/reference/optimizers.mdx	# Optimization This page contains the API reference documentation for learning rate optimizers included in `timm`. ## Optimizers ### Factory functions [[autodoc]] timm.optim.optim_factory.create_optimizer [[autodoc]] timm.optim.optim_factory.create_optimizer_v2 ### Optimizer Classes [[autodoc]] timm.optim.adabelief.AdaBelief [[autodoc]] timm.optim.adafactor.Adafactor [[autodoc]] timm.optim.adahessian.Adahessian [[autodoc]] timm.optim.adamp.AdamP [[autodoc]] timm.optim.adamw.AdamW [[autodoc]] timm.optim.lamb.Lamb [[autodoc]] timm.optim.lars.Lars [[autodoc]] timm.optim.lookahead.Lookahead [[autodoc]] timm.optim.madgrad.MADGRAD [[autodoc]] timm.optim.nadam.Nadam [[autodoc]] timm.optim.nvnovograd.NvNovoGrad [[autodoc]] timm.optim.radam.RAdam [[autodoc]] timm.optim.rmsprop_tf.RMSpropTF [[autodoc]] timm.optim.sgdp.SGDP
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/reference/schedulers.mdx	# Learning Rate Schedulers This page contains the API reference documentation for learning rate schedulers included in `timm`. ## Schedulers ### Factory functions [[autodoc]] timm.scheduler.scheduler_factory.create_scheduler [[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2 ### Scheduler Classes [[autodoc]] timm.scheduler.cosine_lr.CosineLRScheduler [[autodoc]] timm.scheduler.multistep_lr.MultiStepLRScheduler [[autodoc]] timm.scheduler.plateau_lr.PlateauLRScheduler [[autodoc]] timm.scheduler.poly_lr.PolyLRScheduler [[autodoc]] timm.scheduler.step_lr.StepLRScheduler [[autodoc]] timm.scheduler.tanh_lr.TanhLRScheduler
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx	# (Tensorflow) EfficientNet EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a principled way. The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image. The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block). The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('tf_efficientnet_b0', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b0`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('tf_efficientnet_b0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @misc{tan2020efficientnet, title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, author={Mingxing Tan and Quoc V. Le}, year={2020}, eprint={1905.11946}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` <!-- Type: model-index Collections: - Name: TF EfficientNet Paper: Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks' URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for Models: - Name: tf_efficientnet_b0 In Collection: TF EfficientNet Metadata: FLOPs: 488688572 Parameters: 5290000 File Size: 21383997 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet Training Resources: TPUv3 Cloud TPU ID: tf_efficientnet_b0 LR: 0.256 Epochs: 350 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 2048 Image Size: '224' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1241 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 76.85% Top 5 Accuracy: 93.23% - Name: tf_efficientnet_b1 In Collection: TF EfficientNet Metadata: FLOPs: 883633200 Parameters: 7790000 File Size: 31512534 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b1 LR: 0.256 Epochs: 350 Crop Pct: '0.882' Momentum: 0.9 Batch Size: 2048 Image Size: '240' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1251 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.84% Top 5 Accuracy: 94.2% - Name: tf_efficientnet_b2 In Collection: TF EfficientNet Metadata: FLOPs: 1234321170 Parameters: 9110000 File Size: 36797929 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b2 LR: 0.256 Epochs: 350 Crop Pct: '0.89' Momentum: 0.9 Batch Size: 2048 Image Size: '260' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1261 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.07% Top 5 Accuracy: 94.9% - Name: tf_efficientnet_b3 In Collection: TF EfficientNet Metadata: FLOPs: 2275247568 Parameters: 12230000 File Size: 49381362 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b3 LR: 0.256 Epochs: 350 Crop Pct: '0.904' Momentum: 0.9 Batch Size: 2048 Image Size: '300' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1271 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 81.65% Top 5 Accuracy: 95.72% - Name: tf_efficientnet_b4 In Collection: TF EfficientNet Metadata: FLOPs: 5749638672 Parameters: 19340000 File Size: 77989689 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet Training Resources: TPUv3 Cloud TPU ID: tf_efficientnet_b4 LR: 0.256 Epochs: 350 Crop Pct: '0.922' Momentum: 0.9 Batch Size: 2048 Image Size: '380' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1281 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 83.03% Top 5 Accuracy: 96.3% - Name: tf_efficientnet_b5 In Collection: TF EfficientNet Metadata: FLOPs: 13176501888 Parameters: 30390000 File Size: 122403150 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b5 LR: 0.256 Epochs: 350 Crop Pct: '0.934' Momentum: 0.9 Batch Size: 2048 Image Size: '456' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1291 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 83.81% Top 5 Accuracy: 96.75% - Name: tf_efficientnet_b6 In Collection: TF EfficientNet Metadata: FLOPs: 24180518488 Parameters: 43040000 File Size: 173232007 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b6 LR: 0.256 Epochs: 350 Crop Pct: '0.942' Momentum: 0.9 Batch Size: 2048 Image Size: '528' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1301 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 84.11% Top 5 Accuracy: 96.89% - Name: tf_efficientnet_b7 In Collection: TF EfficientNet Metadata: FLOPs: 48205304880 Parameters: 66349999 File Size: 266850607 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b7 LR: 0.256 Epochs: 350 Crop Pct: '0.949' Momentum: 0.9 Batch Size: 2048 Image Size: '600' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1312 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 84.93% Top 5 Accuracy: 97.2% - Name: tf_efficientnet_b8 In Collection: TF EfficientNet Metadata: FLOPs: 80962956270 Parameters: 87410000 File Size: 351379853 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - Label Smoothing - RMSProp - Stochastic Depth - Weight Decay Training Data: - ImageNet ID: tf_efficientnet_b8 LR: 0.256 Epochs: 350 Crop Pct: '0.954' Momentum: 0.9 Batch Size: 2048 Image Size: '672' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1323 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 85.35% Top 5 Accuracy: 97.39% - Name: tf_efficientnet_el In Collection: TF EfficientNet Metadata: FLOPs: 9356616096 Parameters: 10590000 File Size: 42800271 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_el Crop Pct: '0.904' Image Size: '300' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1551 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.45% Top 5 Accuracy: 95.17% - Name: tf_efficientnet_em In Collection: TF EfficientNet Metadata: FLOPs: 3636607040 Parameters: 6900000 File Size: 27933644 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_em Crop Pct: '0.882' Image Size: '240' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1541 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.71% Top 5 Accuracy: 94.33% - Name: tf_efficientnet_es In Collection: TF EfficientNet Metadata: FLOPs: 2057577472 Parameters: 5440000 File Size: 22008479 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: tf_efficientnet_es Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1531 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.28% Top 5 Accuracy: 93.6% - Name: tf_efficientnet_l2_ns_475 In Collection: TF EfficientNet Metadata: FLOPs: 217795669644 Parameters: 480310000 File Size: 1925950424 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - AutoAugment - FixRes - Label Smoothing - Noisy Student - RMSProp - RandAugment - Weight Decay Training Data: - ImageNet - JFT-300M Training Resources: TPUv3 Cloud TPU ID: tf_efficientnet_l2_ns_475 LR: 0.128 Epochs: 350 Dropout: 0.5 Crop Pct: '0.936' Momentum: 0.9 Batch Size: 2048 Image Size: '475' Weight Decay: 1.0e-05 Interpolation: bicubic RMSProp Decay: 0.9 Label Smoothing: 0.1 BatchNorm Momentum: 0.99 Stochastic Depth Survival: 0.8 Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1509 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 88.24% Top 5 Accuracy: 98.55% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/gloun-senet.mdx	# (Gluon) SENet A SENet is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration. The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('gluon_senet154', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `gluon_senet154`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('gluon_senet154', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @misc{hu2019squeezeandexcitation, title={Squeeze-and-Excitation Networks}, author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu}, year={2019}, eprint={1709.01507}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- Type: model-index Collections: - Name: Gloun SENet Paper: Title: Squeeze-and-Excitation Networks URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks Models: - Name: gluon_senet154 In Collection: Gloun SENet Metadata: FLOPs: 26681705136 Parameters: 115090000 File Size: 461546622 Architecture: - Convolution - Dense Connections - Global Average Pooling - Max Pooling - Softmax - Squeeze-and-Excitation Block Tasks: - Image Classification Training Data: - ImageNet ID: gluon_senet154 Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L239 Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 81.23% Top 5 Accuracy: 95.35% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/mixnet.mdx	# MixNet MixNet is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('mixnet_l', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `mixnet_l`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('mixnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @misc{tan2019mixconv, title={MixConv: Mixed Depthwise Convolutional Kernels}, author={Mingxing Tan and Quoc V. Le}, year={2019}, eprint={1907.09595}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- Type: model-index Collections: - Name: MixNet Paper: Title: 'MixConv: Mixed Depthwise Convolutional Kernels' URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels Models: - Name: mixnet_l In Collection: MixNet Metadata: FLOPs: 738671316 Parameters: 7330000 File Size: 29608232 Architecture: - Batch Normalization - Dense Connections - Dropout - Global Average Pooling - Grouped Convolution - MixConv - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - MNAS Training Data: - ImageNet ID: mixnet_l Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1669 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.98% Top 5 Accuracy: 94.18% - Name: mixnet_m In Collection: MixNet Metadata: FLOPs: 454543374 Parameters: 5010000 File Size: 20298347 Architecture: - Batch Normalization - Dense Connections - Dropout - Global Average Pooling - Grouped Convolution - MixConv - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - MNAS Training Data: - ImageNet ID: mixnet_m Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1660 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.27% Top 5 Accuracy: 93.42% - Name: mixnet_s In Collection: MixNet Metadata: FLOPs: 321264910 Parameters: 4130000 File Size: 16727982 Architecture: - Batch Normalization - Dense Connections - Dropout - Global Average Pooling - Grouped Convolution - MixConv - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - MNAS Training Data: - ImageNet ID: mixnet_s Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1651 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 75.99% Top 5 Accuracy: 92.79% - Name: mixnet_xl In Collection: MixNet Metadata: FLOPs: 1195880424 Parameters: 11900000 File Size: 48001170 Architecture: - Batch Normalization - Dense Connections - Dropout - Global Average Pooling - Grouped Convolution - MixConv - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Techniques: - MNAS Training Data: - ImageNet ID: mixnet_xl Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1678 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.47% Top 5 Accuracy: 94.93% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/regnetx.mdx	# RegNetX RegNetX is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure): \\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\) For RegNetX we have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('regnetx_002', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `regnetx_002`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('regnetx_002', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @misc{radosavovic2020designing, title={Designing Network Design Spaces}, author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, year={2020}, eprint={2003.13678}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- Type: model-index Collections: - Name: RegNetX Paper: Title: Designing Network Design Spaces URL: https://paperswithcode.com/paper/designing-network-design-spaces Models: - Name: regnetx_002 In Collection: RegNetX Metadata: FLOPs: 255276032 Parameters: 2680000 File Size: 10862199 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_002 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L337 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 68.75% Top 5 Accuracy: 88.56% - Name: regnetx_004 In Collection: RegNetX Metadata: FLOPs: 510619136 Parameters: 5160000 File Size: 20841309 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_004 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L343 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 72.39% Top 5 Accuracy: 90.82% - Name: regnetx_006 In Collection: RegNetX Metadata: FLOPs: 771659136 Parameters: 6200000 File Size: 24965172 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_006 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L349 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 73.84% Top 5 Accuracy: 91.68% - Name: regnetx_008 In Collection: RegNetX Metadata: FLOPs: 1027038208 Parameters: 7260000 File Size: 29235944 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_008 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L355 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 75.05% Top 5 Accuracy: 92.34% - Name: regnetx_016 In Collection: RegNetX Metadata: FLOPs: 2059337856 Parameters: 9190000 File Size: 36988158 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_016 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 1024 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L361 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 76.95% Top 5 Accuracy: 93.43% - Name: regnetx_032 In Collection: RegNetX Metadata: FLOPs: 4082555904 Parameters: 15300000 File Size: 61509573 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_032 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L367 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.15% Top 5 Accuracy: 94.09% - Name: regnetx_040 In Collection: RegNetX Metadata: FLOPs: 5095167744 Parameters: 22120000 File Size: 88844824 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_040 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L373 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.48% Top 5 Accuracy: 94.25% - Name: regnetx_064 In Collection: RegNetX Metadata: FLOPs: 8303405824 Parameters: 26210000 File Size: 105184854 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_064 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L379 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.06% Top 5 Accuracy: 94.47% - Name: regnetx_080 In Collection: RegNetX Metadata: FLOPs: 10276726784 Parameters: 39570000 File Size: 158720042 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_080 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L385 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.21% Top 5 Accuracy: 94.55% - Name: regnetx_120 In Collection: RegNetX Metadata: FLOPs: 15536378368 Parameters: 46110000 File Size: 184866342 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_120 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L391 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.61% Top 5 Accuracy: 94.73% - Name: regnetx_160 In Collection: RegNetX Metadata: FLOPs: 20491740672 Parameters: 54280000 File Size: 217623862 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_160 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 512 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L397 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.84% Top 5 Accuracy: 94.82% - Name: regnetx_320 In Collection: RegNetX Metadata: FLOPs: 40798958592 Parameters: 107810000 File Size: 431962133 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Dense Connections - Global Average Pooling - Grouped Convolution - ReLU Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - ImageNet Training Resources: 8x NVIDIA V100 GPUs ID: regnetx_320 Epochs: 100 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 5.0e-05 Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L403 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.25% Top 5 Accuracy: 95.03% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/efficientnet.mdx	# EfficientNet EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a principled way. The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image. The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('efficientnet_b0', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `efficientnet_b0`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('efficientnet_b0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @misc{tan2020efficientnet, title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, author={Mingxing Tan and Quoc V. Le}, year={2020}, eprint={1905.11946}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` <!-- Type: model-index Collections: - Name: EfficientNet Paper: Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks' URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for Models: - Name: efficientnet_b0 In Collection: EfficientNet Metadata: FLOPs: 511241564 Parameters: 5290000 File Size: 21376743 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_b0 Layers: 18 Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1002 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.71% Top 5 Accuracy: 93.52% - Name: efficientnet_b1 In Collection: EfficientNet Metadata: FLOPs: 909691920 Parameters: 7790000 File Size: 31502706 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_b1 Crop Pct: '0.875' Image Size: '240' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1011 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.71% Top 5 Accuracy: 94.15% - Name: efficientnet_b2 In Collection: EfficientNet Metadata: FLOPs: 1265324514 Parameters: 9110000 File Size: 36788104 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_b2 Crop Pct: '0.875' Image Size: '260' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1020 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.38% Top 5 Accuracy: 95.08% - Name: efficientnet_b2a In Collection: EfficientNet Metadata: FLOPs: 1452041554 Parameters: 9110000 File Size: 49369973 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_b2a Crop Pct: '1.0' Image Size: '288' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1029 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 80.61% Top 5 Accuracy: 95.32% - Name: efficientnet_b3 In Collection: EfficientNet Metadata: FLOPs: 2327905920 Parameters: 12230000 File Size: 49369973 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_b3 Crop Pct: '0.904' Image Size: '300' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1038 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 82.08% Top 5 Accuracy: 96.03% - Name: efficientnet_b3a In Collection: EfficientNet Metadata: FLOPs: 2600628304 Parameters: 12230000 File Size: 49369973 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_b3a Crop Pct: '1.0' Image Size: '320' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1047 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 82.25% Top 5 Accuracy: 96.11% - Name: efficientnet_em In Collection: EfficientNet Metadata: FLOPs: 3935516480 Parameters: 6900000 File Size: 27927309 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_em Crop Pct: '0.882' Image Size: '240' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1118 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.26% Top 5 Accuracy: 94.79% - Name: efficientnet_es In Collection: EfficientNet Metadata: FLOPs: 2317181824 Parameters: 5440000 File Size: 22003339 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_es Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1110 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.09% Top 5 Accuracy: 93.93% - Name: efficientnet_lite0 In Collection: EfficientNet Metadata: FLOPs: 510605024 Parameters: 4650000 File Size: 18820005 Architecture: - 1x1 Convolution - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inverted Residual Block - Squeeze-and-Excitation Block - Swish Tasks: - Image Classification Training Data: - ImageNet ID: efficientnet_lite0 Crop Pct: '0.875' Image Size: '224' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1163 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 75.5% Top 5 Accuracy: 92.51% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-inception-v3.mdx	# (Tensorflow) Inception v3 Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module). The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('tf_inception_v3', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `tf_inception_v3`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('tf_inception_v3', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @article{DBLP:journals/corr/SzegedyVISW15, author = {Christian Szegedy and Vincent Vanhoucke and Sergey Ioffe and Jonathon Shlens and Zbigniew Wojna}, title = {Rethinking the Inception Architecture for Computer Vision}, journal = {CoRR}, volume = {abs/1512.00567}, year = {2015}, url = {http://arxiv.org/abs/1512.00567}, archivePrefix = {arXiv}, eprint = {1512.00567}, timestamp = {Mon, 13 Aug 2018 16:49:07 +0200}, biburl = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <!-- Type: model-index Collections: - Name: TF Inception v3 Paper: Title: Rethinking the Inception Architecture for Computer Vision URL: https://paperswithcode.com/paper/rethinking-the-inception-architecture-for Models: - Name: tf_inception_v3 In Collection: TF Inception v3 Metadata: FLOPs: 7352418880 Parameters: 23830000 File Size: 95549439 Architecture: - 1x1 Convolution - Auxiliary Classifier - Average Pooling - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inception-v3 Module - Max Pooling - ReLU - Softmax Tasks: - Image Classification Training Techniques: - Gradient Clipping - Label Smoothing - RMSProp - Weight Decay Training Data: - ImageNet Training Resources: 50x NVIDIA Kepler GPUs ID: tf_inception_v3 LR: 0.045 Dropout: 0.2 Crop Pct: '0.875' Momentum: 0.9 Image Size: '299' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L449 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 77.87% Top 5 Accuracy: 93.65% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx	# # Ensemble Adversarial Inception ResNet v2 Inception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture). This particular model was trained for study of adversarial examples (adversarial training). The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models). ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('ens_adv_inception_resnet_v2', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `ens_adv_inception_resnet_v2`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('ens_adv_inception_resnet_v2', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @article{DBLP:journals/corr/abs-1804-00097, author = {Alexey Kurakin and Ian J. Goodfellow and Samy Bengio and Yinpeng Dong and Fangzhou Liao and Ming Liang and Tianyu Pang and Jun Zhu and Xiaolin Hu and Cihang Xie and Jianyu Wang and Zhishuai Zhang and Zhou Ren and Alan L. Yuille and Sangxia Huang and Yao Zhao and Yuzhe Zhao and Zhonglin Han and Junjiajia Long and Yerkebulan Berdibekov and Takuya Akiba and Seiya Tokui and Motoki Abe}, title = {Adversarial Attacks and Defences Competition}, journal = {CoRR}, volume = {abs/1804.00097}, year = {2018}, url = {http://arxiv.org/abs/1804.00097}, archivePrefix = {arXiv}, eprint = {1804.00097}, timestamp = {Thu, 31 Oct 2019 16:31:22 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1804-00097.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <!-- Type: model-index Collections: - Name: Ensemble Adversarial Paper: Title: Adversarial Attacks and Defences Competition URL: https://paperswithcode.com/paper/adversarial-attacks-and-defences-competition Models: - Name: ens_adv_inception_resnet_v2 In Collection: Ensemble Adversarial Metadata: FLOPs: 16959133120 Parameters: 55850000 File Size: 223774238 Architecture: - 1x1 Convolution - Auxiliary Classifier - Average Pooling - Average Pooling - Batch Normalization - Convolution - Dense Connections - Dropout - Inception-v3 Module - Max Pooling - ReLU - Softmax Tasks: - Image Classification Training Data: - ImageNet ID: ens_adv_inception_resnet_v2 Crop Pct: '0.897' Image Size: '299' Interpolation: bicubic Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_resnet_v2.py#L351 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ens_adv_inception_resnet_v2-2592a550.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 1.0% Top 5 Accuracy: 17.32% -->
hf_public_repos/pytorch-image-models/hfdocs/source	hf_public_repos/pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx	# SWSL ResNeXt A ResNeXt repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, cardinality (the size of the set of transformations) \\( C \\), as an essential factor in addition to the dimensions of depth and width. The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification. Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only. ## How do I use this model on an image? To load a pretrained model: ```py >>> import timm >>> model = timm.create_model('swsl_resnext101_32x16d', pretrained=True) >>> model.eval() ``` To load and preprocess the image: ```py >>> import urllib >>> from PIL import Image >>> from timm.data import resolve_data_config >>> from timm.data.transforms_factory import create_transform >>> config = resolve_data_config({}, model=model) >>> transform = create_transform(**config) >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") >>> urllib.request.urlretrieve(url, filename) >>> img = Image.open(filename).convert('RGB') >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```py >>> import torch >>> with torch.no_grad(): ... out = model(tensor) >>> probabilities = torch.nn.functional.softmax(out[0], dim=0) >>> print(probabilities.shape) >>> # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```py >>> # Get imagenet class mappings >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") >>> urllib.request.urlretrieve(url, filename) >>> with open("imagenet_classes.txt", "r") as f: ... categories = [s.strip() for s in f.readlines()] >>> # Print top categories per image >>> top5_prob, top5_catid = torch.topk(probabilities, 5) >>> for i in range(top5_prob.size(0)): ... print(categories[top5_catid[i]], top5_prob[i].item()) >>> # prints class names and probabilities like: >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `swsl_resnext101_32x16d`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```py >>> model = timm.create_model('swsl_resnext101_32x16d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](../scripts) for training a new model afresh. ## Citation ```BibTeX @article{DBLP:journals/corr/abs-1905-00546, author = {I. Zeki Yalniz and Herv{\'{e}} J{\'{e}}gou and Kan Chen and Manohar Paluri and Dhruv Mahajan}, title = {Billion-scale semi-supervised learning for image classification}, journal = {CoRR}, volume = {abs/1905.00546}, year = {2019}, url = {http://arxiv.org/abs/1905.00546}, archivePrefix = {arXiv}, eprint = {1905.00546}, timestamp = {Mon, 28 Sep 2020 08:19:37 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <!-- Type: model-index Collections: - Name: SWSL ResNext Paper: Title: Billion-scale semi-supervised learning for image classification URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for Models: - Name: swsl_resnext101_32x16d In Collection: SWSL ResNext Metadata: FLOPs: 46623691776 Parameters: 194030000 File Size: 777518664 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - IG-1B-Targeted - ImageNet Training Resources: 64x GPUs ID: swsl_resnext101_32x16d LR: 0.0015 Epochs: 30 Layers: 101 Crop Pct: '0.875' Batch Size: 1536 Image Size: '224' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L1009 Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 83.34% Top 5 Accuracy: 96.84% - Name: swsl_resnext101_32x4d In Collection: SWSL ResNext Metadata: FLOPs: 10298145792 Parameters: 44180000 File Size: 177341913 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - IG-1B-Targeted - ImageNet Training Resources: 64x GPUs ID: swsl_resnext101_32x4d LR: 0.0015 Epochs: 30 Layers: 101 Crop Pct: '0.875' Batch Size: 1536 Image Size: '224' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L987 Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 83.22% Top 5 Accuracy: 96.77% - Name: swsl_resnext101_32x8d In Collection: SWSL ResNext Metadata: FLOPs: 21180417024 Parameters: 88790000 File Size: 356056638 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - IG-1B-Targeted - ImageNet Training Resources: 64x GPUs ID: swsl_resnext101_32x8d LR: 0.0015 Epochs: 30 Layers: 101 Crop Pct: '0.875' Batch Size: 1536 Image Size: '224' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L998 Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 84.27% Top 5 Accuracy: 97.17% - Name: swsl_resnext50_32x4d In Collection: SWSL ResNext Metadata: FLOPs: 5472648192 Parameters: 25030000 File Size: 100428550 Architecture: - 1x1 Convolution - Batch Normalization - Convolution - Global Average Pooling - Grouped Convolution - Max Pooling - ReLU - ResNeXt Block - Residual Connection - Softmax Tasks: - Image Classification Training Techniques: - SGD with Momentum - Weight Decay Training Data: - IG-1B-Targeted - ImageNet Training Resources: 64x GPUs ID: swsl_resnext50_32x4d LR: 0.0015 Epochs: 30 Layers: 50 Crop Pct: '0.875' Batch Size: 1536 Image Size: '224' Weight Decay: 0.0001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L976 Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 82.17% Top 5 Accuracy: 96.23% -->