| --- |
| 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") |
| |
