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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.