Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal images

Code for the IROS 2020 publication "Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal images" by Johan Vertens, Jannik Zürn, and Wolfram Burgard

Citation

If you use this code in your research, please cite our paper:

@inproceedings{vertens2020heatnet,
  title={Heatnet: Bridging the day-night domain gap in semantic segmentation with thermal images},
  author={Vertens, Johan and Z{\"u}rn, Jannik and Burgard, Wolfram},
  booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={8461--8468},
  year={2020},
  organization={IEEE}
}

Installation

Dataset Download

Please download our dataset from the project website and extract it somewhere on your hard drive. Please download the training and validation data and extract them into separate folders.

Usage

In the following, we describe how to use the code for training and evaluating the Heatnet model.

Model Training

To train the model, run the following command:

python scripts/main.py --data <path_to_training_data> --valdata <path_to_validation_data>

Model Evaluation

python scripts/main.py --data <path_to_training_data> --valdata <path_to_validation_data> --resume <path_to_model> --evaluate

Configuration

Model architecture definitions, training parameters, and other settings can be found in the heatnet_conf.json file.

jzuern/heatnet-pub