---
library_name: transformers
base_model: mo-thecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-facial-expression-fatigue
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9363636363636364
---

<!-- 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-fatigue

This model is a fine-tuned version of [mo-thecreator/vit-Facial-Expression-Recognition](https://huggingface.co/mo-thecreator/vit-Facial-Expression-Recognition) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3145
- Accuracy: 0.9364

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2255        | 1.0   | 110  | 0.1784          | 0.9409   |
| 0.0959        | 2.0   | 220  | 0.2311          | 0.9364   |
| 0.0372        | 3.0   | 330  | 0.2092          | 0.9409   |
| 0.0056        | 4.0   | 440  | 0.3145          | 0.9364   |


### Framework versions

- Transformers 5.6.2
- Pytorch 2.9.1
- Datasets 4.8.4
- Tokenizers 0.22.2