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: 1.0725
Accuracy: 0.9793
F1 Score: 0.9805
Recall: 0.9804

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
2.9237	1.0	19	2.7962	0.3138	0.2811	0.3322
2.7282	2.0	38	2.5914	0.4172	0.3767	0.4048
2.4043	3.0	57	2.3162	0.6034	0.5791	0.5798
2.0554	4.0	76	1.9844	0.7552	0.7492	0.7470
1.6234	5.0	95	1.6522	0.8276	0.8322	0.8284
1.3940	6.0	114	1.4163	0.9069	0.9144	0.9180
1.2166	7.0	133	1.2960	0.9345	0.9396	0.9426
1.1550	8.0	152	1.2153	0.9586	0.9616	0.9608
1.1312	9.0	171	1.1790	0.9621	0.9641	0.9632
1.0838	10.0	190	1.1564	0.9690	0.9706	0.9706
1.0738	11.0	209	1.1289	0.9655	0.9673	0.9669
1.0487	12.0	228	1.1151	0.9690	0.9711	0.9706
1.0299	13.0	247	1.1217	0.9724	0.9745	0.9767
1.0164	14.0	266	1.1016	0.9759	0.9774	0.9779
0.9999	15.0	285	1.1021	0.9621	0.9647	0.9632
0.9976	16.0	304	1.0900	0.9724	0.9744	0.9755
0.9823	17.0	323	1.0973	0.9586	0.9614	0.9596
0.9854	18.0	342	1.0897	0.9621	0.9644	0.9620
0.9863	19.0	361	1.0825	0.9759	0.9774	0.9779
0.9700	20.0	380	1.0744	0.9759	0.9774	0.9779
0.9759	21.0	399	1.0824	0.9655	0.9677	0.9657
0.9796	22.0	418	1.0725	0.9793	0.9805	0.9804
0.9706	23.0	437	1.0655	0.9724	0.9731	0.9729
0.9635	24.0	456	1.0727	0.9759	0.9759	0.9742
0.9672	25.0	475	1.0723	0.9724	0.9731	0.9729
0.9662	26.0	494	1.0667	0.9724	0.9731	0.9729
0.9655	27.0	513	1.0770	0.9724	0.9730	0.9717
0.9641	28.0	532	1.0689	0.9759	0.9762	0.9766
0.9675	29.0	551	1.0696	0.9690	0.9698	0.9680
0.9609	30.0	570	1.0618	0.9690	0.9700	0.9693
0.9661	31.0	589	1.0660	0.9724	0.9731	0.9729
0.9630	32.0	608	1.0684	0.9724	0.9731	0.9729
0.9706	33.0	627	1.0765	0.9724	0.9730	0.9717
0.9575	34.0	646	1.0753	0.9655	0.9669	0.9668
0.9607	35.0	665	1.0766	0.9690	0.9700	0.9693
0.9589	36.0	684	1.0685	0.9759	0.9761	0.9754
0.9569	37.0	703	1.0672	0.9759	0.9761	0.9754
0.9572	38.0	722	1.0670	0.9724	0.9732	0.9742
0.9606	39.0	741	1.0680	0.9690	0.9700	0.9693
0.9552	40.0	760	1.0706	0.9724	0.9732	0.9729

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/fold_1

fold_1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
fold_1