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# emotion_classification_v1

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: 1.1905
- Accuracy: 0.575

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 10   | 2.0278          | 0.2437   |
| No log        | 2.0   | 20   | 1.8875          | 0.3875   |
| No log        | 3.0   | 30   | 1.6890          | 0.4313   |
| No log        | 4.0   | 40   | 1.5484          | 0.5      |
| No log        | 5.0   | 50   | 1.4799          | 0.5125   |
| No log        | 6.0   | 60   | 1.4148          | 0.5375   |
| No log        | 7.0   | 70   | 1.3529          | 0.5375   |
| No log        | 8.0   | 80   | 1.3120          | 0.5312   |
| No log        | 9.0   | 90   | 1.2790          | 0.5813   |
| No log        | 10.0  | 100  | 1.2498          | 0.575    |
| No log        | 11.0  | 110  | 1.2610          | 0.525    |
| No log        | 12.0  | 120  | 1.1896          | 0.5938   |
| No log        | 13.0  | 130  | 1.2251          | 0.5312   |
| No log        | 14.0  | 140  | 1.2019          | 0.575    |
| No log        | 15.0  | 150  | 1.1797          | 0.5563   |
| No log        | 16.0  | 160  | 1.2484          | 0.5437   |
| No log        | 17.0  | 170  | 1.1766          | 0.5875   |
| No log        | 18.0  | 180  | 1.2401          | 0.4938   |
| No log        | 19.0  | 190  | 1.1977          | 0.5312   |
| No log        | 20.0  | 200  | 1.1839          | 0.5875   |
| No log        | 21.0  | 210  | 1.2028          | 0.5687   |
| No log        | 22.0  | 220  | 1.2048          | 0.5625   |
| No log        | 23.0  | 230  | 1.2637          | 0.5375   |
| No log        | 24.0  | 240  | 1.2371          | 0.5375   |
| No log        | 25.0  | 250  | 1.2777          | 0.5687   |
| No log        | 26.0  | 260  | 1.2544          | 0.525    |
| No log        | 27.0  | 270  | 1.2104          | 0.5625   |
| No log        | 28.0  | 280  | 1.1372          | 0.5938   |
| No log        | 29.0  | 290  | 1.2405          | 0.575    |
| No log        | 30.0  | 300  | 1.1624          | 0.6062   |
| No log        | 31.0  | 310  | 1.2376          | 0.5875   |
| No log        | 32.0  | 320  | 1.1794          | 0.5875   |
| No log        | 33.0  | 330  | 1.2156          | 0.5563   |
| No log        | 34.0  | 340  | 1.1725          | 0.55     |
| No log        | 35.0  | 350  | 1.2394          | 0.55     |
| No log        | 36.0  | 360  | 1.1886          | 0.5938   |
| No log        | 37.0  | 370  | 1.1760          | 0.6188   |
| No log        | 38.0  | 380  | 1.2757          | 0.525    |
| No log        | 39.0  | 390  | 1.1703          | 0.6062   |
| No log        | 40.0  | 400  | 1.2734          | 0.575    |
| No log        | 41.0  | 410  | 1.2265          | 0.5563   |
| No log        | 42.0  | 420  | 1.2651          | 0.5687   |
| No log        | 43.0  | 430  | 1.2419          | 0.5813   |
| No log        | 44.0  | 440  | 1.1871          | 0.6      |
| No log        | 45.0  | 450  | 1.2542          | 0.575    |
| No log        | 46.0  | 460  | 1.1910          | 0.5813   |
| No log        | 47.0  | 470  | 1.1990          | 0.6      |
| No log        | 48.0  | 480  | 1.2097          | 0.5813   |
| No log        | 49.0  | 490  | 1.2226          | 0.5875   |
| 0.699         | 50.0  | 500  | 1.2793          | 0.5375   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

anger contempt disgust fear happy neutral sad surprise