deit_fold_1_v3

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: 1.1628
• Accuracy: 0.9551
• F1 Score: 0.9570
• Recall: 0.9581

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: 1e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 100
• label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.8330	1.0	20	2.7860	0.2372	0.2351	0.2394
2.6641	2.0	40	2.6204	0.3942	0.3682	0.3639
2.4039	3.0	60	2.3714	0.5705	0.5479	0.5415
2.0152	4.0	80	2.0046	0.7308	0.7211	0.7286
1.6639	5.0	100	1.6679	0.8013	0.8035	0.8072
1.4446	6.0	120	1.5218	0.8301	0.8312	0.8445
1.3294	7.0	140	1.4034	0.8814	0.8808	0.8851
1.2539	8.0	160	1.3367	0.9071	0.9055	0.9059
1.1603	9.0	180	1.2977	0.9167	0.9155	0.9151
1.1220	10.0	200	1.2600	0.9167	0.9148	0.9169
1.1301	11.0	220	1.2411	0.9263	0.9260	0.9256
1.0514	12.0	240	1.2276	0.9167	0.9162	0.9200
1.0528	13.0	260	1.2156	0.9359	0.9364	0.9360
1.0386	14.0	280	1.2090	0.9327	0.9324	0.9317
1.0265	15.0	300	1.1935	0.9295	0.9303	0.9317
1.0239	16.0	320	1.1888	0.9391	0.9397	0.9392
1.0195	17.0	340	1.2041	0.9295	0.9304	0.9311
1.0003	18.0	360	1.1992	0.9423	0.9425	0.9409
1.0130	19.0	380	1.1703	0.9455	0.9478	0.9483
1.0041	20.0	400	1.1874	0.9391	0.9393	0.9384
1.0013	21.0	420	1.1704	0.9359	0.9367	0.9360
0.9889	22.0	440	1.1805	0.9423	0.9447	0.9483
0.9880	23.0	460	1.1663	0.9423	0.9439	0.9427
0.9856	24.0	480	1.1758	0.9455	0.9474	0.9458
0.9739	25.0	500	1.1704	0.9455	0.9474	0.9458
0.9741	26.0	520	1.1529	0.9519	0.9542	0.9563
0.9671	27.0	540	1.1938	0.9455	0.9472	0.9449
0.9772	28.0	560	1.1926	0.9423	0.9444	0.9434
0.9697	29.0	580	1.1817	0.9359	0.9374	0.9362
0.9611	30.0	600	1.1628	0.9551	0.9570	0.9581
0.9648	31.0	620	1.1926	0.9455	0.9476	0.9471
0.9619	32.0	640	1.1731	0.9519	0.9535	0.9550
0.9647	33.0	660	1.1810	0.9519	0.9537	0.9562
0.9632	34.0	680	1.1731	0.9551	0.9566	0.9586
0.9644	35.0	700	1.1879	0.9455	0.9474	0.9471
0.9615	36.0	720	1.1760	0.9519	0.9537	0.9562

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2