---
library_name: transformers
base_model: carlosleao/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-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. -->

# 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: 1.5831
- Accuracy: 0.4234

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7711        | 2.0833  | 100  | 1.7038          | 0.3915   |
| 1.5964        | 4.1667  | 200  | 1.5836          | 0.3957   |
| 1.5002        | 6.25    | 300  | 1.5438          | 0.4113   |
| 1.3591        | 8.3333  | 400  | 1.5690          | 0.4016   |
| 1.1821        | 10.4167 | 500  | 1.5831          | 0.4234   |
| 0.9323        | 12.5    | 600  | 1.7256          | 0.3960   |
| 0.7237        | 14.5833 | 700  | 1.8623          | 0.3954   |
| 0.5376        | 16.6667 | 800  | 2.0238          | 0.3742   |
| 0.363         | 18.75   | 900  | 2.1839          | 0.3462   |
| 0.238         | 20.8333 | 1000 | 2.2616          | 0.3602   |
| 0.1686        | 22.9167 | 1100 | 2.3515          | 0.3758   |
| 0.0952        | 25.0    | 1200 | 2.4730          | 0.3836   |
| 0.0526        | 27.0833 | 1300 | 2.5531          | 0.3898   |
| 0.0399        | 29.1667 | 1400 | 2.5901          | 0.3778   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1