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: 0.3257
Accuracy: 0.9143

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.678	1.0	275	0.5015	0.8624
0.4558	2.0	550	0.4184	0.8859
0.3474	3.0	825	0.3892	0.8907
0.2925	4.0	1100	0.3692	0.8974
0.2376	5.0	1375	0.3615	0.9034
0.1907	6.0	1650	0.3533	0.9046
0.1605	7.0	1925	0.3385	0.9133
0.138	8.0	2200	0.3296	0.9165
0.1288	9.0	2475	0.3323	0.9149
0.1415	10.0	2750	0.3319	0.9165

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

vuongnhathien/vit-base-augment

vit-base-augment

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vuongnhathien/
vit-base-augment