trpakov

Vision Transformer (ViT) for Facial Expression Recognition Model Card 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 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. - Validation set accuracy: 0.7113 - Test set accuracy: 0.7116 - 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.