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: 1.3327
Accuracy: 0.4875

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
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8526	1.0	10	1.8929	0.3563
1.7464	2.0	20	1.7105	0.3625
1.6096	3.0	30	1.5898	0.4625
1.4988	4.0	40	1.5056	0.5188
1.4218	5.0	50	1.4349	0.4938
1.3439	6.0	60	1.4127	0.525
1.2799	7.0	70	1.3780	0.55
1.2037	8.0	80	1.3463	0.5
1.1637	9.0	90	1.3236	0.55
1.1361	10.0	100	1.2950	0.5437
1.0836	11.0	110	1.3059	0.525
1.046	12.0	120	1.2707	0.525
1.0277	13.0	130	1.2686	0.5563
1.0236	14.0	140	1.2790	0.5062
0.9926	15.0	150	1.2763	0.5687

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

aditira/emotion_classification

emotion_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

aditira/
emotion_classification