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 diver…
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