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: 1.6092
Accuracy: 0.4125

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: 2e-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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	40	2.0750	0.15
No log	2.0	80	2.0046	0.1875
No log	3.0	120	1.8909	0.3063
No log	4.0	160	1.7726	0.3563
No log	5.0	200	1.6970	0.3438
No log	6.0	240	1.6562	0.3937
No log	7.0	280	1.6269	0.4062
No log	8.0	320	1.6092	0.4125
No log	9.0	360	1.6012	0.4125
No log	10.0	400	1.5955	0.4125

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cpu
Datasets 3.2.0
Tokenizers 0.21.0

ayooke97/emotion_classifier

emotion_classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ayooke97/
emotion_classifier