# Diabetic Retinopathy Detection with AI

## Setup

### Cloning the repo

Install git LFS via [this instruction](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage).
```bash
git clone https://github.com/SDAIA-KAUST-AI/diabetic-retinopathy-detection.git
git lfs install # to make sure LFS is enabled
git lfs pull # to bring in demo images and pretrained models
```

### Gradio app environment

Install from pip requirements file:

```bash
conda create -y -n retinopathy_app python=3.10
conda activate retinopathy_app
pip install -r requirements.txt
python app.py
```

Install manually:

```bash
pip install pytorch --index-url  https://download.pytorch.org/whl/cpu
pip install gradio
pip install transformers
```

### Training environment

Create conda environment from YAML:
```bash
mamba env create -n retinopathy_train -f environment.yml
```

Download the data from [Kaggle](https://www.kaggle.com/competitions/diabetic-retinopathy-detection/data) or use kaggle API:

```bash
pip install kaggle
kaggle competitions download -c diabetic-retinopathy-detection
mkdir retinopathy_data/
unzip diabetic-retinopathy-detection.zip -d retinopathy_data/
```

Launch training:
```bash
conda activate retinopathy_train
python train.py
```
The trained model will be put into `lightning_logs/`.