Update README.md

README.md

@@ -28,16 +28,42 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# vit-base-patch16-224-in21k-finetuned
+# Vision Transformer (ViT) for Facial Expression Recognition Model Card
+
+## Model Overview
+
+- **Model Name:** [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition)
+
+- **Task:** Facial Expression/Emotion Recognition
+
+- **Datasets:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013), [MMI Facial Expression Database](https://mmifacedb.eu)
+
+- **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)
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the FER 2013 and MMI datasets.
-It achieves the following results on the evaluation set:
 - Loss: 0.4353
 - Accuracy: 0.8571
 
 ## Model description
 
-More information needed
+The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition. 
+
+It is trained on the FER2013 and MMI facial Expression datasets , 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.
 
 ## Intended uses & limitations