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: 0.3568
Accuracy: 0.9022

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: 3e-05
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
1.6815	1.0	275	0.9075	0.7738
0.9759	2.0	550	0.5867	0.8501
0.7955	3.0	825	0.5191	0.8549
0.7056	4.0	1100	0.4548	0.8755
0.6455	5.0	1375	0.4256	0.8855
0.6249	6.0	1650	0.4114	0.8847
0.5742	7.0	1925	0.4026	0.8875
0.5782	8.0	2200	0.3943	0.8903
0.5383	9.0	2475	0.3929	0.8883
0.5495	10.0	2750	0.3921	0.8879

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

vuongnhathien/vit-base-3e-5-randaug

vit-base-3e-5-randaug

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vuongnhathien/
vit-base-3e-5-randaug