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# vit-Facial-Expression-Recognition

This model is a fine-tuned version of [carlosleao/vit-Facial-Expression-Recognition](https://huggingface.co/carlosleao/vit-Facial-Expression-Recognition) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2687
- Accuracy: 0.4177

## 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: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9372        | 0.8959  | 100  | 1.5720          | 0.4417   |
| 0.9147        | 1.7917  | 200  | 1.6084          | 0.4364   |
| 0.8393        | 2.6876  | 300  | 1.7268          | 0.4169   |
| 0.7882        | 3.5834  | 400  | 1.7604          | 0.4227   |
| 0.6916        | 4.4793  | 500  | 1.8619          | 0.4124   |
| 0.6367        | 5.3751  | 600  | 1.9493          | 0.4261   |
| 0.5848        | 6.2710  | 700  | 2.0511          | 0.4046   |
| 0.5183        | 7.1669  | 800  | 2.1316          | 0.4230   |
| 0.4788        | 8.0627  | 900  | 2.2210          | 0.4026   |
| 0.4586        | 8.9586  | 1000 | 2.2687          | 0.4177   |
| 0.4079        | 9.8544  | 1100 | 2.4038          | 0.3747   |
| 0.3797        | 10.7503 | 1200 | 2.3664          | 0.4046   |
| 0.2957        | 11.6461 | 1300 | 2.4534          | 0.4068   |
| 0.2622        | 12.5420 | 1400 | 2.5413          | 0.3956   |
| 0.2202        | 13.4378 | 1500 | 2.5601          | 0.4127   |
| 0.2112        | 14.3337 | 1600 | 2.6560          | 0.3920   |
| 0.1769        | 15.2296 | 1700 | 2.8006          | 0.3909   |
| 0.161         | 16.1254 | 1800 | 2.8011          | 0.3928   |
| 0.155         | 17.0213 | 1900 | 2.9518          | 0.3856   |
| 0.1309        | 17.9171 | 2000 | 2.9363          | 0.3727   |
| 0.1001        | 18.8130 | 2100 | 2.9187          | 0.3998   |
| 0.0816        | 19.7088 | 2200 | 3.0563          | 0.3842   |
| 0.0672        | 20.6047 | 2300 | 2.9358          | 0.4205   |
| 0.0567        | 21.5006 | 2400 | 3.1118          | 0.3970   |
| 0.0524        | 22.3964 | 2500 | 3.2147          | 0.4054   |
| 0.0413        | 23.2923 | 2600 | 3.1928          | 0.3951   |
| 0.0368        | 24.1881 | 2700 | 3.1599          | 0.4141   |
| 0.0275        | 25.0840 | 2800 | 3.1720          | 0.4166   |
| 0.029         | 25.9798 | 2900 | 3.1924          | 0.4012   |
| 0.0231        | 26.8757 | 3000 | 3.2031          | 0.4088   |
| 0.0226        | 27.7716 | 3100 | 3.2125          | 0.4113   |
| 0.0205        | 28.6674 | 3200 | 3.2122          | 0.4118   |
| 0.0197        | 29.5633 | 3300 | 3.2126          | 0.4116   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1

neutral happiness surprise sadness anger disgust fear contempt