fold_2

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: 1.1294
• Accuracy: 0.9517
• F1 Score: 0.9555
• Recall: 0.9524

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
2.9670	1.0	19	2.9365	0.2310	0.2142	0.2367
2.8214	2.0	38	2.7318	0.3138	0.2945	0.3061
2.5025	3.0	57	2.4670	0.4655	0.4676	0.4694
2.1809	4.0	76	2.1553	0.6448	0.6653	0.6767
1.7435	5.0	95	1.8033	0.8	0.8207	0.8246
1.4272	6.0	114	1.5291	0.8621	0.8763	0.8784
1.2204	7.0	133	1.3853	0.8897	0.8988	0.9015
1.1890	8.0	152	1.3082	0.9241	0.9281	0.9282
1.1161	9.0	171	1.2493	0.9276	0.9321	0.9331
1.0740	10.0	190	1.2355	0.9345	0.9384	0.9405
1.0635	11.0	209	1.2155	0.9379	0.9410	0.9417
1.0404	12.0	228	1.2010	0.9379	0.9418	0.9440
1.0244	13.0	247	1.2017	0.9448	0.9483	0.9526
0.9996	14.0	266	1.1814	0.9414	0.9443	0.9440
1.0016	15.0	285	1.1746	0.9310	0.9346	0.9330
1.0121	16.0	304	1.1656	0.9414	0.9452	0.9489
1.0118	17.0	323	1.1659	0.9379	0.9421	0.9453
0.9903	18.0	342	1.1524	0.9379	0.9417	0.9415
0.9853	19.0	361	1.1513	0.9414	0.9449	0.9452
0.9830	20.0	380	1.1551	0.9448	0.9484	0.9502
0.9801	21.0	399	1.1524	0.9414	0.9450	0.9453
0.9789	22.0	418	1.1515	0.9414	0.9452	0.9465
0.9704	23.0	437	1.1479	0.9448	0.9486	0.9488
0.9875	24.0	456	1.1507	0.9379	0.9416	0.9404
0.9705	25.0	475	1.1499	0.9379	0.9415	0.9442
0.9650	26.0	494	1.1488	0.9345	0.9386	0.9417
0.9642	27.0	513	1.1540	0.9345	0.9389	0.9429
0.9709	28.0	532	1.1403	0.9448	0.9485	0.9513
0.9636	29.0	551	1.1398	0.9379	0.9410	0.9417
0.9617	30.0	570	1.1364	0.9517	0.9549	0.9573
0.9724	31.0	589	1.1384	0.9483	0.9510	0.9539
0.9596	32.0	608	1.1324	0.9414	0.9449	0.9452
0.9802	33.0	627	1.1548	0.9379	0.9419	0.9466
0.9668	34.0	646	1.1408	0.9448	0.9498	0.9536
0.9645	35.0	665	1.1354	0.9483	0.9522	0.9524
0.9646	36.0	684	1.1294	0.9517	0.9555	0.9524
0.9598	37.0	703	1.1203	0.9517	0.9552	0.9574
0.9645	38.0	722	1.1170	0.9517	0.9550	0.9562
0.9643	39.0	741	1.1292	0.9448	0.9489	0.9501
0.9602	40.0	760	1.1349	0.9448	0.9482	0.9464
0.9616	41.0	779	1.1334	0.9483	0.9517	0.9539
0.9576	42.0	798	1.1323	0.9448	0.9491	0.9513
0.9651	43.0	817	1.1420	0.9414	0.9467	0.9500
0.9596	44.0	836	1.1366	0.9414	0.9458	0.9451
0.9606	45.0	855	1.1412	0.9414	0.9462	0.9488
0.9604	46.0	874	1.1442	0.9414	0.9458	0.9489
0.9628	47.0	893	1.1434	0.9379	0.9427	0.9453
0.9597	48.0	912	1.1399	0.9414	0.9460	0.9476

Framework versions

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