This model is a fine-tuned version of google/vit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0109
Accuracy: 0.9176
Precision: 0.9113
Recall: 0.9176
F1: 0.9140

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: 0.0001
train_batch_size: 24
eval_batch_size: 4
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0016	1.0	759	0.0011	0.9341	0.9261	0.9341	0.9255
0.0003	2.0	1518	0.0015	0.9496	0.9457	0.9496	0.9456
0.0003	3.0	2277	0.0021	0.9309	0.9217	0.9309	0.9209
0.0001	4.0	3036	0.0033	0.9204	0.9194	0.9204	0.9199
0.0001	5.0	3795	0.0048	0.9273	0.9180	0.9273	0.9201
0.0001	6.0	4554	0.0037	0.9168	0.9159	0.9168	0.9164
0.0001	7.0	5313	0.0090	0.9083	0.9158	0.9083	0.9116
0.0	8.0	6072	0.0054	0.9273	0.9185	0.9273	0.9209
0.0	9.0	6831	0.0089	0.9136	0.9118	0.9136	0.9126
0.0	10.0	7590	0.0109	0.9176	0.9113	0.9176	0.9140

Framework versions

Transformers 4.53.0.dev0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.1

goodcasper/vit_itri_2class

vit_itri_2class

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

goodcasper/
vit_itri_2class