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: 1.1330
Accuracy: 0.9552
F1 Score: 0.9591
Recall: 0.9611

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
3.0764	1.0	19	3.0505	0.2069	0.1685	0.1989
2.8915	2.0	38	2.8592	0.3276	0.2738	0.2986
2.5571	3.0	57	2.5948	0.4724	0.4113	0.4311
2.1836	4.0	76	2.2633	0.6241	0.6056	0.6021
1.7834	5.0	95	1.9104	0.7759	0.7743	0.7669
1.4444	6.0	114	1.6022	0.8448	0.8531	0.8439
1.2156	7.0	133	1.4031	0.8724	0.8811	0.8695
1.1415	8.0	152	1.3280	0.8793	0.8863	0.8743
1.0863	9.0	171	1.2605	0.9069	0.9147	0.9123
1.0580	10.0	190	1.2364	0.9207	0.9275	0.9257
1.0411	11.0	209	1.2385	0.9172	0.9225	0.9171
1.0299	12.0	228	1.1877	0.9310	0.9368	0.9355
1.0096	13.0	247	1.1798	0.9345	0.9406	0.9428
0.9902	14.0	266	1.1914	0.9241	0.9291	0.9233
0.9951	15.0	285	1.1893	0.9241	0.9291	0.9233
1.0118	16.0	304	1.1582	0.9414	0.9465	0.9477
1.0025	17.0	323	1.1507	0.9483	0.9525	0.9539
0.9899	18.0	342	1.1432	0.9448	0.9490	0.9478
0.9766	19.0	361	1.1452	0.9448	0.9490	0.9478
0.9757	20.0	380	1.1502	0.9448	0.9497	0.9515
0.9742	21.0	399	1.1370	0.9483	0.9522	0.9515
0.9779	22.0	418	1.1409	0.9414	0.9459	0.9442
0.9699	23.0	437	1.1304	0.9448	0.9490	0.9478
0.9857	24.0	456	1.1345	0.9414	0.9459	0.9442
0.9649	25.0	475	1.1319	0.9483	0.9524	0.9539
0.9665	26.0	494	1.1383	0.9448	0.9495	0.9514
0.9725	27.0	513	1.1625	0.9414	0.9468	0.9502
0.9801	28.0	532	1.1431	0.9483	0.9525	0.9539
0.9628	29.0	551	1.1296	0.9483	0.9524	0.9527
0.9617	30.0	570	1.1306	0.9448	0.9495	0.9503
0.9642	31.0	589	1.1330	0.9552	0.9591	0.9611
0.9632	32.0	608	1.1253	0.9483	0.9527	0.9525
0.9768	33.0	627	1.1471	0.9483	0.9528	0.9551
0.9642	34.0	646	1.1479	0.9483	0.9528	0.9551
0.9688	35.0	665	1.1393	0.9483	0.9527	0.9525
0.9639	36.0	684	1.1510	0.9483	0.9531	0.9562
0.9625	37.0	703	1.1417	0.9448	0.9497	0.9489
0.9624	38.0	722	1.1501	0.9517	0.9561	0.9586
0.9635	39.0	741	1.1514	0.9448	0.9498	0.9513
0.9616	40.0	760	1.1471	0.9414	0.9466	0.9478
0.9619	41.0	779	1.1508	0.9414	0.9468	0.9491
0.9585	42.0	798	1.1411	0.9448	0.9499	0.9502

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/vit_fold_2

vit_fold_2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
vit_fold_2