This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.2938
Accuracy: 0.5437

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: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.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: 13

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9954	1.0	40	1.9657	0.3625
1.7379	2.0	80	1.7294	0.5
1.521	3.0	120	1.5916	0.5312
1.3358	4.0	160	1.5033	0.475
1.1573	5.0	200	1.4300	0.525
1.0019	6.0	240	1.3785	0.5312
0.8266	7.0	280	1.3402	0.5062
0.6911	8.0	320	1.2938	0.5437
0.5639	9.0	360	1.3023	0.5062
0.4955	10.0	400	1.2990	0.5125
0.4623	11.0	440	1.2864	0.5312
0.4193	12.0	480	1.2813	0.5188
0.4003	13.0	520	1.2815	0.5188

Framework versions

Transformers 4.51.3
Pytorch 2.7.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1

AriqF/emotion-classifier-vit

emotion-classifier-vit

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AriqF/
emotion-classifier-vit