README.md

Training RRWNet

This directory contains the code to train the RRWNet model.

Configuration options and hyperparameters can be found in config.py.

The data can be downloaded from the following link:

• https://drive.google.com/drive/folders/1LHOrkaHZh0O3kOIXRVD23904ZtvVzCnV?usp=sharing

Place the data in the _Data/ directory under train/.

To train the model, run the following commands:

# Activate the virtual environment
source ../venv/bin/activate
# Train the model
python3 train.py --dataset RITE-train --model RRWNet

The available datasets for training are RITE-train and HRF-Karlsson-w1024, while the available models are RRWNet, RRWNetAll, RRUNet, WNet, and UNet. See the paper for more details.

Training logs and weights will be saved under the __training/ directory.

Once the model is trained, the predictions can be generated using the following command.

python3 get_predictions.py -p <path_to_the_trained_model> -i <path_to_the_images>

The predictions will be saved under the tests_predictions/ directory in the path specified by the -p flag.

To evaluate the predictions, see eval/README.md.