- Create virtual environment (python <=3.6)
virtualenv venv - Activate environment (bash)
source venv/bin/activate - Install requirements
pip install -r requirements.txt
python segmentation.py
- Pretrained model from https://github.com/qubvel/segmentation_models
- Additional data from https://www.airs-dataset.com/ (I did not have the time to take full advantage of it). Data is too large to put here(~17gb) and possibly proprietary
- https://dataturks.com/ for annotation of validation images (I did not train on them, just for easier model evaluation)
- make more use of large christchurch dataset (prepare/augment data better)
- Try different models/losses (dice loss seems better from intuition since it aligns with IoU score which is very informative in my opinion)