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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. |