smids_5x_deit_small_sgd_0001_fold1

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

• Loss: 0.5286
• Accuracy: 0.7913

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: 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
1.0382	1.0	376	1.0714	0.4090
1.0394	2.0	752	1.0356	0.4491
0.9541	3.0	1128	1.0000	0.5042
0.928	4.0	1504	0.9637	0.5559
0.8813	5.0	1880	0.9285	0.5943
0.8344	6.0	2256	0.8967	0.6227
0.7873	7.0	2632	0.8681	0.6361
0.8127	8.0	3008	0.8414	0.6544
0.8077	9.0	3384	0.8166	0.6628
0.7559	10.0	3760	0.7932	0.6811
0.7293	11.0	4136	0.7713	0.6962
0.7184	12.0	4512	0.7510	0.7028
0.6866	13.0	4888	0.7320	0.7078
0.6588	14.0	5264	0.7145	0.7195
0.6425	15.0	5640	0.6984	0.7245
0.6378	16.0	6016	0.6836	0.7312
0.5876	17.0	6392	0.6699	0.7396
0.6379	18.0	6768	0.6573	0.7429
0.6063	19.0	7144	0.6456	0.7479
0.5557	20.0	7520	0.6350	0.7496
0.5709	21.0	7896	0.6253	0.7513
0.5404	22.0	8272	0.6166	0.7563
0.5599	23.0	8648	0.6082	0.7529
0.5567	24.0	9024	0.6008	0.7613
0.5445	25.0	9400	0.5938	0.7646
0.5273	26.0	9776	0.5874	0.7629
0.5187	27.0	10152	0.5814	0.7613
0.4686	28.0	10528	0.5760	0.7629
0.502	29.0	10904	0.5710	0.7629
0.5086	30.0	11280	0.5663	0.7663
0.5383	31.0	11656	0.5621	0.7679
0.5306	32.0	12032	0.5581	0.7696
0.4719	33.0	12408	0.5545	0.7713
0.4733	34.0	12784	0.5512	0.7763
0.4916	35.0	13160	0.5482	0.7796
0.4659	36.0	13536	0.5454	0.7796
0.4447	37.0	13912	0.5429	0.7830
0.5196	38.0	14288	0.5406	0.7830
0.4685	39.0	14664	0.5386	0.7830
0.4526	40.0	15040	0.5367	0.7830
0.4896	41.0	15416	0.5350	0.7863
0.4446	42.0	15792	0.5336	0.7863
0.4328	43.0	16168	0.5323	0.7863
0.5156	44.0	16544	0.5312	0.7880
0.4252	45.0	16920	0.5303	0.7896
0.4576	46.0	17296	0.5296	0.7896
0.4261	47.0	17672	0.5291	0.7913
0.4841	48.0	18048	0.5288	0.7913
0.4563	49.0	18424	0.5286	0.7913
0.4361	50.0	18800	0.5286	0.7913

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2