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: 1.9455
Accuracy: 0.3

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
gradient_accumulation_steps: 4
total_train_batch_size: 64
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	10	2.0636	0.1187
No log	2.0	20	2.0568	0.1437
No log	3.0	30	2.0321	0.1812
No log	4.0	40	2.0247	0.2
No log	5.0	50	1.9975	0.3125
No log	6.0	60	1.9793	0.2875
No log	7.0	70	1.9746	0.275
No log	8.0	80	1.9530	0.3063
No log	9.0	90	1.9487	0.3438
No log	10.0	100	1.9513	0.2812

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Mangara01/emotion_classification

emotion_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Mangara01/
emotion_classification