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Vision Transformer (ViT) for Facial Expression Recognition Model Card

Model Overview

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 dataset, 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.

Evaluation Metrics

  • Validation set accuracy: 0.7113
  • Test set accuracy: 0.7116

Limitations

  • Data Bias: The model's performance may be influenced by biases present in the training data.
  • Generalization: The model's ability to generalize to unseen data is subject to the diversity of the training dataset.