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: 1.1597
Accuracy: 0.6

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: 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	1.5881	0.4813
No log	2.0	80	1.4495	0.4188
No log	3.0	120	1.3173	0.525
No log	4.0	160	1.2644	0.5375
No log	5.0	200	1.1238	0.6125
No log	6.0	240	1.3448	0.5563
No log	7.0	280	1.3241	0.5938
No log	8.0	320	1.4283	0.5625
No log	9.0	360	1.3231	0.6062
No log	10.0	400	1.4146	0.5938

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

luthfi507/emotion-classification

emotion-classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

luthfi507/
emotion-classification