Update README.md

README.md

@@ -1,31 +1,50 @@
 ---
 license: mit
 language:
-- en
+  - en
+base_model: mobilenetv2
+datasets:
+  - custom
 metrics:
-- accuracy
-- f1
+  - accuracy
+  - f1
+pipeline_tag: image-classification
+library_name: tensorflow
 tags:
-- >-
-  retinal-disease-detection medical-imaging   - fundus-images   - mobilenetv2  
+  - retinal-disease-detection
+  - medical-imaging
+  - fundus-images
+  - mobilenetv2
   - classification
+  - grad-cam
+  - retina
+model_name: RetinaVision-MNet
 ---
-RetinaVision-MNet is a custom-trained MobileNetV2-based deep learning model for multi-class retinal disease detection.
-It predicts 10 retinal conditions from fundus images and generates Grad-CAM heatmaps for interpretable diagnosis.
-The model was trained from scratch and is hosted on Hugging Face due to GitHub size limits.
+# 🧠 RetinaVision-MNet
 
-Key Features
+**RetinaVision-MNet** is a custom-trained MobileNetV2-based deep learning model for multi-class retinal disease detection.  
+It predicts **10 retinal conditions from fundus images** and includes **Grad-CAM heatmaps** to provide interpretable visual explanations for every prediction.
 
-10-class retinal disease classification
+The model is trained entirely from scratch and is hosted on Hugging Face due to GitHub’s file-size limitations.
 
-MobileNetV2 backbone (lightweight + high accuracy)
+---
+
+## 🔥 Key Features
 
-Grad-CAM visual explanations
+- **10-class retinal disease classification**
+- **MobileNetV2 backbone** — lightweight and efficient for medical imaging
+- **Grad-CAM interpretability** for understanding model decisions  
+- **Custom-trained model (.h5)** using Keras / TensorFlow  
+- **Optimized for FastAPI deployment** with async inference  
+- Works seamlessly with secure JWT-protected backend  
+
+---
 
-Trained from scratch on curated dataset
+## 📦 Usage
 
-Optimized for FastAPI deployment
+Download the model file from the **Files and Versions** tab and place it in your project:
 
-Usage
+```python
+from tensorflow.keras.models import load_model
 
-Download the model file from the Files and Versions tab and load it into your application.
+model = load_model("mobile_model.h5")