The goal of this project is to build a high accuracy/real time model able to classify Diabetic degree. The model takes as an input an image and predicts its label.
- No DR
- Mild DR
- Moderate DR
- Severe DR
- Proliferate DR
The Dataset contains 3662 training images and 1928 for testing.
A CSV file containing the labels is also available.
The Dataset can be downloaded here: https://www.kaggle.com/c/aptos2019-blindness-detection/data.
To train the model I used transfer learning for Resnet50.
The Resnet50_model.py trains the model and stores the results (accuracy and loss functions, confusion matrix ...).
python Resnet50_model.py
Finally, to ensure a real time detection, I used both integer and full integer quatization to convert weights and activation functions from 32bit floats to 8bit integers.
