vit_fold_3

nqvii/vit_fold_3

vit_fold_3 is a image classification model from nqvii. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Aug 8, 2026
Updated: Aug 8, 2026
SHA: ed745dd1

Base models

google/vit-base-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

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

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Aug 8, 2026
Updated: Aug 8, 2026