metadata
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:
from tensorflow.keras.models import load_model
model = load_model("mobile_model.h5")