---
license: mit
language:
  - en
base_model: mobilenetv2
datasets:
  - custom
metrics:
  - accuracy
  - f1
pipeline_tag: image-classification
library_name: tensorflow
tags:
  - retinal-disease-detection
  - medical-imaging
  - fundus-images
  - mobilenetv2
  - classification
  - grad-cam
  - retina
model_name: RetinaVision-MNet
---
# 🧠 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 includes **Grad-CAM heatmaps** to provide interpretable visual explanations for every prediction.

The model is trained entirely from scratch and is hosted on Hugging Face due to GitHub’s file-size limitations.

---

## 🔥 Key Features

- **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  

---

## 📦 Usage

Download the model file from the **Files and Versions** tab and place it in your project:

```python
from tensorflow.keras.models import load_model

model = load_model("mobile_model.h5")