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: 1.5074
Accuracy: 0.5125

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	40	1.3274	0.5687
No log	2.0	80	1.4828	0.5188
No log	3.0	120	1.2860	0.5875
No log	4.0	160	1.3801	0.5375
No log	5.0	200	1.3808	0.55
No log	6.0	240	1.4464	0.525
No log	7.0	280	1.5266	0.5188
No log	8.0	320	1.4280	0.5188
No log	9.0	360	1.3953	0.5687
No log	10.0	400	1.4902	0.5312
No log	11.0	440	1.3965	0.5625
No log	12.0	480	1.4328	0.55
0.1776	13.0	520	1.5172	0.5188
0.1776	14.0	560	1.6457	0.5062
0.1776	15.0	600	1.4402	0.5375

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Dricz/emotion_recognition

emotion_recognition

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dricz/
emotion_recognition