
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# emotion_classifier

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: 1.6092
- 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

anger contempt disgust fear happy neutral sad surprise