| # 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/`. | |