vit_fold_1

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

• Loss: 1.0952
• Accuracy: 0.9690
• F1 Score: 0.9709
• Recall: 0.9694

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
2.9282	1.0	19	2.7689	0.3310	0.2729	0.2884
2.7470	2.0	38	2.5989	0.4448	0.3540	0.3755
2.4449	3.0	57	2.3265	0.5931	0.5856	0.5613
2.0224	4.0	76	2.0013	0.7379	0.7505	0.7314
1.6546	5.0	95	1.6556	0.8276	0.8368	0.8288
1.3650	6.0	114	1.3947	0.9	0.9044	0.9030
1.1791	7.0	133	1.2692	0.9207	0.9228	0.9237
1.1519	8.0	152	1.2026	0.9414	0.9428	0.9398
1.1252	9.0	171	1.1660	0.9448	0.9465	0.9448
1.0789	10.0	190	1.1463	0.9483	0.9501	0.9497
1.0771	11.0	209	1.1329	0.9517	0.9533	0.9509
1.0519	12.0	228	1.1264	0.9552	0.9569	0.9533
1.0340	13.0	247	1.1138	0.9586	0.9610	0.9619
1.0224	14.0	266	1.1073	0.9655	0.9666	0.9644
0.9936	15.0	285	1.1274	0.9586	0.9593	0.9558
0.9975	16.0	304	1.1038	0.9655	0.9666	0.9644
0.9908	17.0	323	1.1107	0.9621	0.9631	0.9595
0.9870	18.0	342	1.1084	0.9655	0.9663	0.9631
0.9862	19.0	361	1.1079	0.9655	0.9663	0.9631
0.9739	20.0	380	1.0912	0.9655	0.9666	0.9644
0.9810	21.0	399	1.0898	0.9690	0.9698	0.9680
0.9865	22.0	418	1.0878	0.9655	0.9668	0.9656
0.9694	23.0	437	1.0842	0.9655	0.9668	0.9656
0.9649	24.0	456	1.0958	0.9655	0.9666	0.9644
0.9710	25.0	475	1.0934	0.9690	0.9698	0.9680
0.9672	26.0	494	1.0923	0.9655	0.9666	0.9644
0.9653	27.0	513	1.0932	0.9690	0.9698	0.9680
0.9622	28.0	532	1.0820	0.9655	0.9669	0.9668
0.9677	29.0	551	1.0928	0.9655	0.9666	0.9644
0.9639	30.0	570	1.0834	0.9655	0.9666	0.9644
0.9671	31.0	589	1.0952	0.9690	0.9709	0.9694
0.9635	32.0	608	1.0909	0.9655	0.9682	0.9694
0.9710	33.0	627	1.0919	0.9690	0.9709	0.9694
0.9614	34.0	646	1.0951	0.9655	0.9682	0.9694
0.9637	35.0	665	1.0940	0.9690	0.9709	0.9694
0.9618	36.0	684	1.0847	0.9690	0.9700	0.9693
0.9578	37.0	703	1.0871	0.9655	0.9668	0.9656
0.9646	38.0	722	1.0910	0.9655	0.9671	0.9680

Framework versions

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