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# vit-emotion-classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3912
- Accuracy: 0.5687

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.058         | 1.0   | 80   | 1.9682          | 0.3063   |
| 1.7534        | 2.0   | 160  | 1.7016          | 0.3875   |
| 1.5632        | 3.0   | 240  | 1.5568          | 0.4688   |
| 1.2999        | 4.0   | 320  | 1.4694          | 0.5437   |
| 1.1246        | 5.0   | 400  | 1.3912          | 0.5687   |
| 0.9904        | 6.0   | 480  | 1.3551          | 0.5625   |
| 0.8557        | 7.0   | 560  | 1.3209          | 0.5625   |
| 0.7612        | 8.0   | 640  | 1.3006          | 0.5625   |
| 0.6658        | 9.0   | 720  | 1.2911          | 0.5687   |
| 0.6531        | 10.0  | 800  | 1.2854          | 0.5563   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1

anger contempt disgust fear happy neutral sad surprise