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# Vision Transformer (ViT) for Facial Expression Recognition Model Card | |
## Model Overview | |
- **Model Name:** [trpakov/vit-face-expression](https://huggingface.co/trpakov/vit-face-expression) | |
- **Task:** Facial Expression/Emotion Recognition | |
- **Dataset:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013) | |
- **Model Architecture:** [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit) | |
- **Finetuned from model:** [vit-base-patch16-224-in21k](https://huggingface.co/google/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. |