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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: 1.4782
- 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0776        | 1.0   | 10   | 2.0731          | 0.1437   |
| 2.0526        | 2.0   | 20   | 2.0567          | 0.1688   |
| 1.9975        | 3.0   | 30   | 2.0160          | 0.2      |
| 1.8977        | 4.0   | 40   | 1.9550          | 0.3      |
| 1.778         | 5.0   | 50   | 1.8805          | 0.3625   |
| 1.6549        | 6.0   | 60   | 1.8073          | 0.375    |
| 1.5379        | 7.0   | 70   | 1.7428          | 0.4125   |
| 1.4241        | 8.0   | 80   | 1.6957          | 0.4062   |
| 1.3212        | 9.0   | 90   | 1.6550          | 0.45     |
| 1.2245        | 10.0  | 100  | 1.6271          | 0.4437   |
| 1.1336        | 11.0  | 110  | 1.5928          | 0.4562   |
| 1.0483        | 12.0  | 120  | 1.5695          | 0.4688   |
| 0.9669        | 13.0  | 130  | 1.5452          | 0.4875   |
| 0.8889        | 14.0  | 140  | 1.5248          | 0.4875   |
| 0.815         | 15.0  | 150  | 1.5063          | 0.5062   |
| 0.7466        | 16.0  | 160  | 1.4909          | 0.4938   |
| 0.6852        | 17.0  | 170  | 1.4782          | 0.525    |
| 0.6308        | 18.0  | 180  | 1.4615          | 0.5      |
| 0.5819        | 19.0  | 190  | 1.4541          | 0.5      |
| 0.5392        | 20.0  | 200  | 1.4458          | 0.5125   |
| 0.503         | 21.0  | 210  | 1.4393          | 0.5      |
| 0.4718        | 22.0  | 220  | 1.4289          | 0.5188   |
| 0.4458        | 23.0  | 230  | 1.4238          | 0.5188   |
| 0.4234        | 24.0  | 240  | 1.4211          | 0.5125   |
| 0.405         | 25.0  | 250  | 1.4182          | 0.5      |
| 0.3905        | 26.0  | 260  | 1.4157          | 0.5062   |
| 0.379         | 27.0  | 270  | 1.4125          | 0.5062   |
| 0.3706        | 28.0  | 280  | 1.4119          | 0.5062   |
| 0.3649        | 29.0  | 290  | 1.4115          | 0.5062   |
| 0.3618        | 30.0  | 300  | 1.4111          | 0.5062   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

anger contempt disgust fear happy neutral sad surprise