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: 1.1587
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: 7e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	54	1.6922	0.2875
No log	2.0	108	1.4183	0.4688
No log	3.0	162	1.3431	0.4437
No log	4.0	216	1.1979	0.5437
No log	5.0	270	1.1368	0.6188
No log	6.0	324	1.1457	0.5875
No log	7.0	378	1.1509	0.575
No log	8.0	432	1.1037	0.5938
No log	9.0	486	1.1060	0.575
1.1174	10.0	540	1.1083	0.5938

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

grhaputra/emotion_image_classification

emotion_image_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

grhaputra/
emotion_image_classification