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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.