hushem_1x_deit_tiny_rms_lr001_fold5

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.0599
• Accuracy: 0.5366

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: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• 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
No log	1.0	6	2.6067	0.2439
4.0909	2.0	12	1.8085	0.2439
4.0909	3.0	18	1.7809	0.2439
2.0948	4.0	24	1.7586	0.2439
1.6719	5.0	30	1.5135	0.2439
1.6719	6.0	36	1.7849	0.2683
1.5694	7.0	42	1.4636	0.3902
1.5694	8.0	48	1.4809	0.2683
1.519	9.0	54	1.3587	0.3415
1.5241	10.0	60	1.3823	0.2439
1.5241	11.0	66	1.3645	0.3415
1.4557	12.0	72	1.2525	0.3659
1.4557	13.0	78	1.2955	0.3171
1.3674	14.0	84	1.3174	0.3415
1.3868	15.0	90	1.2787	0.3415
1.3868	16.0	96	1.6408	0.2683
1.3152	17.0	102	1.2750	0.3171
1.3152	18.0	108	1.0560	0.5366
1.2693	19.0	114	1.3256	0.4878
1.2554	20.0	120	1.3190	0.3902
1.2554	21.0	126	1.2498	0.3902
1.1813	22.0	132	1.2514	0.3902
1.1813	23.0	138	1.0907	0.5366
1.1113	24.0	144	1.2821	0.3415
1.1728	25.0	150	1.1433	0.4878
1.1728	26.0	156	1.0143	0.5366
1.1037	27.0	162	0.9542	0.5854
1.1037	28.0	168	1.1443	0.5122
1.0914	29.0	174	1.0904	0.4878
1.1385	30.0	180	1.1995	0.4146
1.1385	31.0	186	0.9746	0.6098
1.0636	32.0	192	1.1104	0.4634
1.0636	33.0	198	0.9890	0.6098
1.0129	34.0	204	1.2113	0.3902
0.999	35.0	210	1.0001	0.6098
0.999	36.0	216	1.0972	0.5122
0.9802	37.0	222	1.1639	0.4390
0.9802	38.0	228	1.0730	0.5122
0.9625	39.0	234	1.0471	0.4878
0.9424	40.0	240	1.0692	0.5366
0.9424	41.0	246	1.0654	0.5366
0.9521	42.0	252	1.0599	0.5366
0.9521	43.0	258	1.0599	0.5366
0.9184	44.0	264	1.0599	0.5366
0.9335	45.0	270	1.0599	0.5366
0.9335	46.0	276	1.0599	0.5366
0.9251	47.0	282	1.0599	0.5366
0.9251	48.0	288	1.0599	0.5366
0.9168	49.0	294	1.0599	0.5366
0.8964	50.0	300	1.0599	0.5366

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1