vit_fold_5

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: 1.1458
• Accuracy: 0.9550
• F1 Score: 0.9567
• Recall: 0.9557

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
2.8317	1.0	19	2.9424	0.1869	0.1888	0.1952
2.6800	2.0	38	2.7703	0.2907	0.2883	0.2921
2.3917	3.0	57	2.5114	0.4844	0.4838	0.4968
2.0233	4.0	76	2.1502	0.6817	0.6816	0.6898
1.6263	5.0	95	1.8205	0.7855	0.7889	0.7957
1.3543	6.0	114	1.5849	0.8304	0.8379	0.8417
1.2245	7.0	133	1.4177	0.8824	0.8883	0.8835
1.1327	8.0	152	1.3330	0.9135	0.9194	0.9130
1.0708	9.0	171	1.2656	0.9308	0.9354	0.9339
1.0489	10.0	190	1.2391	0.9343	0.9378	0.9363
1.0324	11.0	209	1.2209	0.9343	0.9376	0.9376
1.0292	12.0	228	1.2015	0.9308	0.9347	0.9351
1.0188	13.0	247	1.1989	0.9308	0.9347	0.9351
1.0151	14.0	266	1.1872	0.9412	0.9447	0.9436
1.0016	15.0	285	1.1827	0.9308	0.9354	0.9327
1.0162	16.0	304	1.1782	0.9343	0.9386	0.9365
0.9979	17.0	323	1.1789	0.9308	0.9351	0.9315
0.9800	18.0	342	1.1789	0.9446	0.9477	0.9472
0.9783	19.0	361	1.1749	0.9412	0.9444	0.9423
0.9747	20.0	380	1.1614	0.9412	0.9447	0.9447
0.9754	21.0	399	1.1630	0.9412	0.9444	0.9434
0.9707	22.0	418	1.1598	0.9412	0.9447	0.9447
0.9679	23.0	437	1.1559	0.9481	0.9507	0.9496
0.9729	24.0	456	1.1515	0.9481	0.9507	0.9508
0.9643	25.0	475	1.1538	0.9481	0.9507	0.9508
0.9687	26.0	494	1.1609	0.9481	0.9507	0.9508
0.9671	27.0	513	1.1557	0.9446	0.9474	0.9459
0.9706	28.0	532	1.1522	0.9446	0.9477	0.9483
0.9671	29.0	551	1.1504	0.9516	0.9537	0.9532
0.9693	30.0	570	1.1503	0.9481	0.9507	0.9508
0.9629	31.0	589	1.1485	0.9446	0.9477	0.9483
0.9611	32.0	608	1.1480	0.9516	0.9536	0.9521
0.9631	33.0	627	1.1458	0.9550	0.9567	0.9557
0.9606	34.0	646	1.1436	0.9481	0.9507	0.9508
0.9643	35.0	665	1.1477	0.9516	0.9537	0.9532
0.9655	36.0	684	1.1432	0.9516	0.9537	0.9532
0.9656	37.0	703	1.1460	0.9481	0.9507	0.9508
0.9595	38.0	722	1.1457	0.9481	0.9506	0.9485
0.9595	39.0	741	1.1481	0.9481	0.9506	0.9485
0.9623	40.0	760	1.1455	0.9481	0.9507	0.9508
0.9562	41.0	779	1.1487	0.9516	0.9536	0.9521
0.9585	42.0	798	1.1591	0.9481	0.9506	0.9485
0.9638	43.0	817	1.1490	0.9481	0.9507	0.9508
0.9611	44.0	836	1.1544	0.9516	0.9537	0.9532
0.9663	45.0	855	1.1483	0.9516	0.9536	0.9521
0.9619	46.0	874	1.1527	0.9516	0.9534	0.9519

Framework versions

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