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: 1.1010
Accuracy: 0.9647
F1 Score: 0.9660
Recall: 0.9685

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
2.6854	1.0	20	2.6942	0.3462	0.3547	0.3478
2.4323	2.0	40	2.5258	0.4519	0.4425	0.4417
2.1096	3.0	60	2.2691	0.5769	0.5725	0.5725
1.8003	4.0	80	1.9493	0.7308	0.7337	0.7288
1.5149	5.0	100	1.6536	0.8045	0.8100	0.8110
1.3394	6.0	120	1.4559	0.8622	0.8670	0.8727
1.2386	7.0	140	1.3304	0.8942	0.8957	0.8996
1.1850	8.0	160	1.2781	0.9135	0.9144	0.9176
1.1177	9.0	180	1.2214	0.9263	0.9283	0.9313
1.0805	10.0	200	1.1939	0.9359	0.9370	0.9386
1.0630	11.0	220	1.1824	0.9423	0.9437	0.9461
1.0597	12.0	240	1.1674	0.9423	0.9432	0.9448
1.0277	13.0	260	1.1526	0.9551	0.9563	0.9579
1.0241	14.0	280	1.1404	0.9551	0.9555	0.9546
1.0154	15.0	300	1.1305	0.9583	0.9592	0.9596
1.0142	16.0	320	1.1313	0.9487	0.9505	0.9542
1.0062	17.0	340	1.1250	0.9583	0.9599	0.9623
0.9936	18.0	360	1.1225	0.9583	0.9593	0.9584
0.9816	19.0	380	1.1186	0.9551	0.9557	0.9553
1.0086	20.0	400	1.1110	0.9583	0.9596	0.9597
0.9837	21.0	420	1.1251	0.9519	0.9533	0.9567
0.9834	22.0	440	1.1054	0.9551	0.9561	0.9566
0.9692	23.0	460	1.1031	0.9615	0.9620	0.9628
0.9973	24.0	480	1.1013	0.9551	0.9561	0.9566
0.9689	25.0	500	1.0996	0.9583	0.9592	0.9603
0.9655	26.0	520	1.0984	0.9551	0.9561	0.9566
0.9619	27.0	540	1.1071	0.9583	0.9598	0.9610
0.9746	28.0	560	1.1076	0.9519	0.9532	0.9554
0.9667	29.0	580	1.1114	0.9551	0.9562	0.9579
0.9629	30.0	600	1.1132	0.9487	0.9502	0.9530
0.9680	31.0	620	1.1108	0.9551	0.9565	0.9604
0.9747	32.0	640	1.1019	0.9551	0.9561	0.9566
0.9577	33.0	660	1.1087	0.9583	0.9593	0.9616
0.9670	34.0	680	1.1024	0.9551	0.9561	0.9566
0.9740	35.0	700	1.0980	0.9551	0.9562	0.9579
0.9668	36.0	720	1.0987	0.9615	0.9629	0.9647
0.9746	37.0	740	1.0923	0.9647	0.9659	0.9672
0.9585	38.0	760	1.1002	0.9615	0.9629	0.9647
0.9794	39.0	780	1.1101	0.9615	0.9630	0.9660
0.9628	40.0	800	1.0931	0.9647	0.9659	0.9672
0.9693	41.0	820	1.1010	0.9647	0.9660	0.9685
0.9635	42.0	840	1.1025	0.9615	0.9629	0.9647
0.9680	43.0	860	1.1042	0.9615	0.9629	0.9647
0.9670	44.0	880	1.0979	0.9615	0.9628	0.9634
0.9575	45.0	900	1.1008	0.9583	0.9592	0.9603
0.9705	46.0	920	1.1026	0.9583	0.9593	0.9616
0.9632	47.0	940	1.1008	0.9583	0.9593	0.9616

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/vit_fold_3_v3

vit_fold_3_v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
vit_fold_3_v3