---
library_name: transformers
base_model: carlosleao/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: RAFDB-Facial-Expression-Recognition
  results: []
---

<!-- 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. -->

# RAFDB-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: 1.5906
- Accuracy: 0.4159

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7235        | 2.0833  | 100  | 1.6571          | 0.3875   |
| 1.5738        | 4.1667  | 200  | 1.5756          | 0.3967   |
| 1.4811        | 6.25    | 300  | 1.5389          | 0.4166   |
| 1.3178        | 8.3333  | 400  | 1.5501          | 0.4003   |
| 1.095         | 10.4167 | 500  | 1.5906          | 0.4159   |
| 0.8081        | 12.5    | 600  | 1.7889          | 0.3641   |
| 0.594         | 14.5833 | 700  | 1.9123          | 0.3833   |
| 0.434         | 16.6667 | 800  | 2.1054          | 0.3638   |
| 0.3201        | 18.75   | 900  | 2.3273          | 0.3455   |
| 0.2472        | 20.8333 | 1000 | 2.3722          | 0.3510   |
| 0.1879        | 22.9167 | 1100 | 2.4218          | 0.3611   |
| 0.1255        | 25.0    | 1200 | 2.4329          | 0.3817   |
| 0.0749        | 27.0833 | 1300 | 2.5189          | 0.3585   |
| 0.0538        | 29.1667 | 1400 | 2.5638          | 0.3686   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0