vit_fold_3

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: 1.0572
• Accuracy: 0.9828
• F1 Score: 0.9840
• Recall: 0.9841

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
2.9536	1.0	19	2.9528	0.3	0.2473	0.3174
2.7882	2.0	38	2.6926	0.3690	0.3570	0.3867
2.4219	3.0	57	2.3561	0.5793	0.5998	0.5921
2.0092	4.0	76	1.9862	0.7586	0.7827	0.7875
1.6129	5.0	95	1.6200	0.8724	0.8863	0.8884
1.3723	6.0	114	1.3893	0.9138	0.9220	0.9240
1.2462	7.0	133	1.2759	0.9276	0.9335	0.9325
1.1739	8.0	152	1.2234	0.9379	0.9427	0.9411
1.1189	9.0	171	1.1838	0.9448	0.9486	0.9460
1.1044	10.0	190	1.1554	0.9517	0.9557	0.9545
1.0877	11.0	209	1.1483	0.9483	0.9529	0.9533
1.0598	12.0	228	1.1355	0.9552	0.9591	0.9595
1.0458	13.0	247	1.1253	0.9586	0.9624	0.9645
1.0242	14.0	266	1.0983	0.9724	0.9742	0.9730
1.0151	15.0	285	1.0982	0.9621	0.9644	0.9606
1.0158	16.0	304	1.0982	0.9621	0.9656	0.9682
1.0131	17.0	323	1.0913	0.9655	0.9685	0.9707
1.0065	18.0	342	1.0878	0.9655	0.9685	0.9707
0.9920	19.0	361	1.0783	0.9690	0.9708	0.9680
0.9799	20.0	380	1.0697	0.9793	0.9808	0.9791
0.9830	21.0	399	1.0664	0.9793	0.9808	0.9791
0.9798	22.0	418	1.0630	0.9793	0.9810	0.9816
0.9896	23.0	437	1.0678	0.9724	0.9750	0.9767
0.9769	24.0	456	1.0585	0.9793	0.9809	0.9803
0.9736	25.0	475	1.0572	0.9828	0.9840	0.9841
0.9697	26.0	494	1.0557	0.9793	0.9809	0.9803
0.9685	27.0	513	1.0585	0.9793	0.9808	0.9791
0.9706	28.0	532	1.0609	0.9724	0.9749	0.9754
0.9833	29.0	551	1.0702	0.9724	0.9750	0.9767
0.9717	30.0	570	1.0728	0.9759	0.9776	0.9753
0.9727	31.0	589	1.0614	0.9759	0.9779	0.9779
0.9645	32.0	608	1.0531	0.9793	0.9809	0.9803
0.9730	33.0	627	1.0505	0.9828	0.9839	0.9828
0.9642	34.0	646	1.0454	0.9828	0.9839	0.9828
0.9687	35.0	665	1.0516	0.9759	0.9779	0.9779
0.9650	36.0	684	1.0544	0.9724	0.9748	0.9754
0.9651	37.0	703	1.0512	0.9793	0.9809	0.9803
0.9675	38.0	722	1.0516	0.9793	0.9808	0.9791
0.9622	39.0	741	1.0499	0.9759	0.9780	0.9792
0.9641	40.0	760	1.0488	0.9828	0.9839	0.9828
0.9640	41.0	779	1.0479	0.9828	0.9839	0.9828
0.9619	42.0	798	1.0464	0.9828	0.9839	0.9828
0.9602	43.0	817	1.0481	0.9793	0.9809	0.9803
0.9670	44.0	836	1.0482	0.9828	0.9839	0.9828

Framework versions

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