Vision Transformer (ViT) for Facial Expression Recognition Model Card

Model Overview

• Model Name: trpakov/vit-face-expression

• Task: Facial Expression/Emotion Recognition

• Dataset: FER2013

• Model Architecture: Vision Transformer (ViT)

• Finetuned from model: vit-base-patch16-224-in21k

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.