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# vit-emotion-results

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: 1.3992
- Accuracy: 0.525

## 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
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0003        | 1.0   | 40   | 1.9729          | 0.3063   |
| 1.7229        | 2.0   | 80   | 1.7334          | 0.3625   |
| 1.6051        | 3.0   | 120  | 1.5905          | 0.425    |
| 1.4902        | 4.0   | 160  | 1.5132          | 0.4313   |
| 1.3939        | 5.0   | 200  | 1.4877          | 0.4437   |
| 1.3373        | 6.0   | 240  | 1.4920          | 0.4125   |
| 1.238         | 7.0   | 280  | 1.3992          | 0.525    |
| 1.1901        | 8.0   | 320  | 1.3982          | 0.4875   |
| 1.0601        | 9.0   | 360  | 1.3512          | 0.5188   |
| 1.0272        | 10.0  | 400  | 1.3698          | 0.5125   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1

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