File size: 1,364 Bytes
98187fe 7cd2a73 98187fe 7cd2a73 98187fe 7cd2a73 98187fe 7cd2a73 98187fe 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 c3f3dd4 7cd2a73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
---
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")
|