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resnet101-base_tobacco-cnn_tobacco3482_kd_MSE

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metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet101-base_tobacco-cnn_tobacco3482_kd_MSE
    results: []

resnet101-base_tobacco-cnn_tobacco3482_kd_MSE

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0899
  • Accuracy: 0.395
  • Brier Loss: 0.6867
  • Nll: 4.7352
  • F1 Micro: 0.395
  • F1 Macro: 0.2347
  • Ece: 0.2366
  • Aurc: 0.3626

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 1.0 13 1.1202 0.17 0.8964 8.4790 0.17 0.1089 0.2136 0.8244
No log 2.0 26 1.0772 0.165 0.8950 8.2397 0.165 0.0929 0.2120 0.8534
No log 3.0 39 0.9427 0.2 0.8847 7.1036 0.2000 0.0796 0.2384 0.7748
No log 4.0 52 0.7947 0.21 0.8720 6.5481 0.2100 0.0649 0.2432 0.7270
No log 5.0 65 0.5378 0.205 0.8432 6.3064 0.205 0.0544 0.2367 0.6763
No log 6.0 78 0.4557 0.18 0.8402 6.3878 0.18 0.0308 0.2384 0.7467
No log 7.0 91 0.4326 0.18 0.8383 6.3386 0.18 0.0308 0.2385 0.7234
No log 8.0 104 0.2832 0.265 0.8085 6.3561 0.265 0.1012 0.2570 0.6272
No log 9.0 117 0.2672 0.255 0.8124 6.2296 0.255 0.0981 0.2569 0.6567
No log 10.0 130 0.2452 0.29 0.7953 6.3199 0.29 0.1153 0.2717 0.5884
No log 11.0 143 0.2155 0.31 0.7764 6.3618 0.31 0.1231 0.2728 0.4803
No log 12.0 156 0.1315 0.31 0.7371 6.2610 0.31 0.1231 0.2343 0.4419
No log 13.0 169 0.1803 0.3 0.7665 6.1189 0.3 0.1187 0.2587 0.4579
No log 14.0 182 0.1426 0.31 0.7386 6.1115 0.31 0.1236 0.2502 0.4341
No log 15.0 195 0.1431 0.31 0.7334 5.9353 0.31 0.1274 0.2624 0.4233
No log 16.0 208 0.1540 0.32 0.7318 5.7102 0.32 0.1432 0.2493 0.4322
No log 17.0 221 0.2603 0.305 0.7784 5.6776 0.305 0.1361 0.2751 0.5118
No log 18.0 234 0.1000 0.35 0.7074 5.4636 0.35 0.1574 0.2420 0.4027
No log 19.0 247 0.1014 0.33 0.7131 5.5297 0.33 0.1413 0.2439 0.4245
No log 20.0 260 0.2862 0.265 0.8013 5.5041 0.265 0.1126 0.2762 0.6324
No log 21.0 273 0.1224 0.34 0.7183 5.2027 0.34 0.1544 0.2673 0.4222
No log 22.0 286 0.1406 0.345 0.7173 5.1426 0.345 0.1612 0.2710 0.4019
No log 23.0 299 0.1509 0.34 0.7270 5.0281 0.34 0.1565 0.2641 0.4178
No log 24.0 312 0.0994 0.37 0.6996 5.1278 0.37 0.1771 0.2390 0.3930
No log 25.0 325 0.1965 0.35 0.7474 5.0356 0.35 0.1707 0.2774 0.4503
No log 26.0 338 0.1104 0.37 0.7085 5.0275 0.37 0.1984 0.2663 0.3927
No log 27.0 351 0.1674 0.34 0.7299 4.9200 0.34 0.1739 0.2787 0.4257
No log 28.0 364 0.2424 0.335 0.7626 5.0286 0.335 0.1693 0.2905 0.5297
No log 29.0 377 0.1261 0.345 0.7185 5.0591 0.345 0.1730 0.2892 0.4142
No log 30.0 390 0.1574 0.365 0.7213 4.8809 0.3650 0.1951 0.2983 0.4062
No log 31.0 403 0.1227 0.365 0.7098 4.8152 0.3650 0.1996 0.2802 0.3992
No log 32.0 416 0.1114 0.355 0.7010 4.8224 0.3550 0.1915 0.2657 0.3958
No log 33.0 429 0.1027 0.39 0.6934 4.7755 0.39 0.2245 0.2653 0.3695
No log 34.0 442 0.0959 0.385 0.6875 4.8715 0.3850 0.2299 0.2591 0.3699
No log 35.0 455 0.0905 0.395 0.6897 4.8649 0.395 0.2367 0.2519 0.3627
No log 36.0 468 0.0879 0.365 0.6911 4.8472 0.3650 0.2132 0.2437 0.3910
No log 37.0 481 0.0867 0.39 0.6881 4.7379 0.39 0.2335 0.2576 0.3680
No log 38.0 494 0.0934 0.4 0.6916 4.6797 0.4000 0.2490 0.2578 0.3628
0.2032 39.0 507 0.0928 0.38 0.6901 4.6734 0.38 0.2268 0.2432 0.3783
0.2032 40.0 520 0.0995 0.39 0.6875 4.8180 0.39 0.2323 0.2647 0.3730
0.2032 41.0 533 0.0944 0.37 0.6892 4.8193 0.37 0.2174 0.2536 0.3862
0.2032 42.0 546 0.0904 0.415 0.6885 4.5644 0.415 0.2556 0.2729 0.3573
0.2032 43.0 559 0.0951 0.39 0.6899 4.6549 0.39 0.2417 0.2525 0.3692
0.2032 44.0 572 0.0884 0.4 0.6860 4.6572 0.4000 0.2402 0.2587 0.3557
0.2032 45.0 585 0.0867 0.38 0.6874 4.6558 0.38 0.2278 0.2526 0.3738
0.2032 46.0 598 0.0861 0.405 0.6844 4.5777 0.405 0.2537 0.2548 0.3628
0.2032 47.0 611 0.0874 0.385 0.6853 4.4946 0.3850 0.2380 0.2570 0.3743
0.2032 48.0 624 0.0880 0.405 0.6857 4.5605 0.405 0.2500 0.2489 0.3555
0.2032 49.0 637 0.0884 0.4 0.6853 4.6057 0.4000 0.2481 0.2401 0.3616
0.2032 50.0 650 0.0899 0.395 0.6867 4.7352 0.395 0.2347 0.2366 0.3626

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.2.0.dev20231002
  • Datasets 2.7.1
  • Tokenizers 0.13.3