---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: Facial Expression Recognition
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8571428571428571
---

<!-- 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. -->

# Vision Transformer (ViT) for Facial Expression Recognition Model Card

## Model Overview

- **Model Name:** [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition)

- **Task:** Facial Expression/Emotion Recognition

- **Datasets:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013), [MMI Facial Expression Database](https://mmifacedb.eu)

- **Model Architecture:** [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)

- **Finetuned from model:** [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)

- Loss: 0.4353
- Accuracy: 0.8571

## Model description

The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition. 

It is trained on the FER2013 and MMI facial Expression datasets , which consists of facial images categorized into seven different emotions:
- Angry
- Disgust
- Fear
- Happy
- Sad
- Surprise
- Neutral

## Data Preprocessing

The input images are preprocessed before being fed into the model. The preprocessing steps include:
- **Resizing:** Images are resized to the specified input size.
- **Normalization:** Pixel values are normalized to a specific range.
- **Data Augmentation:** Random transformations such as rotations, flips, and zooms are applied to augment the training dataset.

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.7964        | 1.0   | 798  | 0.7271   | 0.7869          |
| 0.6567        | 2.0   | 1596 | 0.7380   | 0.7539          |
| 0.6842        | 3.0   | 2394 | 0.7837   | 0.6287          |
| 0.5242        | 4.0   | 3192 | 0.7839   | 0.6282          |
| 0.4321        | 5.0   | 3990 | 0.7823   | 0.6423          |
| 0.3129        | 6.0   | 4788 | 0.7838   | 0.6533          |
| 0.4245        | 7.0   | 5586 | 0.8542   | 0.4382          |
| 0.3806        | 8.0   | 6384 | 0.8531   | 0.4375          |
| 0.3112        | 9.0   | 7182 | 0.8557   | 0.4372          |
| 0.2692        | 10.0  | 7980 | 0.8571   | 0.4353          |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0