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# vit-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: 1.3506
- 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 2.0656          | 0.1938   |
| No log        | 2.0   | 40   | 2.0408          | 0.2625   |
| No log        | 3.0   | 60   | 1.9845          | 0.275    |
| No log        | 4.0   | 80   | 1.8774          | 0.35     |
| 1.9717        | 5.0   | 100  | 1.7409          | 0.45     |
| 1.9717        | 6.0   | 120  | 1.6349          | 0.4437   |
| 1.9717        | 7.0   | 140  | 1.5541          | 0.4437   |
| 1.9717        | 8.0   | 160  | 1.5007          | 0.5188   |
| 1.9717        | 9.0   | 180  | 1.4531          | 0.525    |
| 1.4968        | 10.0  | 200  | 1.4263          | 0.5312   |
| 1.4968        | 11.0  | 220  | 1.3975          | 0.5188   |
| 1.4968        | 12.0  | 240  | 1.3915          | 0.525    |
| 1.4968        | 13.0  | 260  | 1.3270          | 0.5375   |
| 1.4968        | 14.0  | 280  | 1.3360          | 0.575    |
| 1.2146        | 15.0  | 300  | 1.3185          | 0.5437   |
| 1.2146        | 16.0  | 320  | 1.3288          | 0.55     |
| 1.2146        | 17.0  | 340  | 1.3262          | 0.5563   |
| 1.2146        | 18.0  | 360  | 1.3142          | 0.55     |
| 1.2146        | 19.0  | 380  | 1.2982          | 0.5625   |
| 1.0644        | 20.0  | 400  | 1.2704          | 0.5625   |
| 1.0644        | 21.0  | 420  | 1.2862          | 0.55     |
| 1.0644        | 22.0  | 440  | 1.2941          | 0.55     |
| 1.0644        | 23.0  | 460  | 1.2876          | 0.5312   |
| 1.0644        | 24.0  | 480  | 1.3066          | 0.5625   |
| 1.0161        | 25.0  | 500  | 1.2734          | 0.55     |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0

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