entry_points.md

Data and keras-retinanet setup

(assumes the Kaggle API is installed and configured)

Run the following shell commands from the top level directory

cd data
kaggle competitions download -c rsna-pneumonia-detection-challenge
unzip stage_2_test_images.zip -d stage_2_test_images
unzip stage_2_train_images.zip -d stage_2_train_images
cd ../keras-retinanet
python setup.py build_ext --inplace
cd ..
python prepare_data.py

Note that settings.json is configured for creating training and validation sets from the Stage 1 training labels, which are what I used to train the models for the competition. I did not perform any training on the Stage 2 training set, which included Stage 1 test images.

Model training

There are two RetinaNets used in my solution, which are trained by the following scripts.

./train50.sh
./train101.sh

Snapshots after each epoch of training are saved in snapshots. For the competition, I manually selected the best snapshots from several runs based on the precision score and maximum Youden index reported during training.

Bounding box prediction

python predict.py

Output .csv is saved to submissions folder