zindi_trees_9th_place

Install dependencies

create virtualenv with python3.8

pip install --upgrade pip

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

pip install -r requirements.txt

Data preparation

Download all the images to ./data.

Modify wandb user and data path in src/configs/default_config.py.

Train models

Train five efficientnetv2_rw_s models with 5 fold CV.

cd src ./train_models.sh

Create submission

Just blend the predictions.

python submit.py

Submit subs/e5_25.csv to Zindi it should give 1.608xxx on the private LB.

Acknowledgements

Thanks for Pascal Pfeiffer and Philipp Singer for sharing their solution https://github.com/pascal-pfeiffer/kaggle-rsna-2022-5th-place Their framework was really useful and it was easy to simplify to this image regression problem.