smids_10x_deit_small_sgd_001_fold3

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: 0.2811
• Accuracy: 0.9083

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
0.545	1.0	750	0.5587	0.785
0.4133	2.0	1500	0.4211	0.8467
0.358	3.0	2250	0.3782	0.8633
0.3237	4.0	3000	0.3490	0.87
0.3443	5.0	3750	0.3305	0.8767
0.2928	6.0	4500	0.3200	0.8817
0.2686	7.0	5250	0.3122	0.8867
0.2534	8.0	6000	0.3123	0.885
0.2251	9.0	6750	0.2946	0.8933
0.1954	10.0	7500	0.2908	0.9
0.2504	11.0	8250	0.2911	0.8967
0.2172	12.0	9000	0.2849	0.905
0.2089	13.0	9750	0.2810	0.905
0.2631	14.0	10500	0.2804	0.905
0.2076	15.0	11250	0.2751	0.915
0.1833	16.0	12000	0.2763	0.9067
0.2051	17.0	12750	0.2775	0.905
0.1927	18.0	13500	0.2752	0.9083
0.1896	19.0	14250	0.2722	0.9117
0.193	20.0	15000	0.2720	0.905
0.1978	21.0	15750	0.2723	0.905
0.193	22.0	16500	0.2691	0.91
0.1867	23.0	17250	0.2706	0.9133
0.1588	24.0	18000	0.2753	0.9083
0.1896	25.0	18750	0.2771	0.8983
0.1697	26.0	19500	0.2708	0.9133
0.1259	27.0	20250	0.2702	0.9117
0.152	28.0	21000	0.2731	0.9083
0.1891	29.0	21750	0.2747	0.9117
0.1716	30.0	22500	0.2723	0.9083
0.1252	31.0	23250	0.2778	0.905
0.1227	32.0	24000	0.2742	0.9083
0.166	33.0	24750	0.2738	0.9017
0.1299	34.0	25500	0.2772	0.9083
0.1287	35.0	26250	0.2752	0.91
0.1172	36.0	27000	0.2784	0.9033
0.1292	37.0	27750	0.2763	0.9033
0.1686	38.0	28500	0.2772	0.9067
0.1469	39.0	29250	0.2777	0.9067
0.1673	40.0	30000	0.2785	0.9083
0.1244	41.0	30750	0.2779	0.9067
0.149	42.0	31500	0.2782	0.9067
0.1031	43.0	32250	0.2799	0.905
0.1374	44.0	33000	0.2832	0.9067
0.1179	45.0	33750	0.2818	0.905
0.1282	46.0	34500	0.2810	0.905
0.1603	47.0	35250	0.2819	0.9067
0.1237	48.0	36000	0.2811	0.9083
0.1333	49.0	36750	0.2808	0.9067
0.1344	50.0	37500	0.2811	0.9083

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2