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# Vision Transformer (ViT) for Facial Expression Recognition Model Card |
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## Model Overview |
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- **Model Name:** [trpakov/vit-face-expression](https://huggingface.co/trpakov/vit-face-expression) |
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- **Task:** Facial Expression/Emotion Recognition |
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- **Dataset:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013) |
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- **Model Architecture:** [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit) |
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- **Finetuned from model:** [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) |
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## Model Description |
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The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition. |
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It is trained on the FER2013 dataset, which consists of facial images categorized into seven different emotions: |
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- Angry |
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- Disgust |
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- Fear |
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- Happy |
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- Sad |
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- Surprise |
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- Neutral |
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## Data Preprocessing |
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The input images are preprocessed before being fed into the model. The preprocessing steps include: |
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- **Resizing:** Images are resized to the specified input size. |
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- **Normalization:** Pixel values are normalized to a specific range. |
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- **Data Augmentation:** Random transformations such as rotations, flips, and zooms are applied to augment the training dataset. |
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## Evaluation Metrics |
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- **Validation set accuracy:** 0.7113 |
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- **Test set accuracy:** 0.7116 |
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## Limitations |
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- **Data Bias:** The model's performance may be influenced by biases present in the training data. |
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- **Generalization:** The model's ability to generalize to unseen data is subject to the diversity of the training dataset. |
