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: 1.1249
Accuracy: 0.6188

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: 5e-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	1.8344	0.3
No log	2.0	80	1.5609	0.4375
No log	3.0	120	1.4819	0.4562
No log	4.0	160	1.3477	0.5188
No log	5.0	200	1.2618	0.5813
No log	6.0	240	1.1946	0.5813
No log	7.0	280	1.1800	0.5875
No log	8.0	320	1.1921	0.5625
No log	9.0	360	1.1274	0.6
No log	10.0	400	1.0886	0.65
No log	11.0	440	1.0750	0.6125
No log	12.0	480	1.1349	0.575
1.0832	13.0	520	1.0841	0.5875
1.0832	14.0	560	1.1195	0.5813
1.0832	15.0	600	1.0865	0.6188

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

malifiahm/emotion_classification

emotion_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

malifiahm/
emotion_classification