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Paper - BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments

Project Page - https://gamma.umd.edu/researchdirections/autonomousdriving/bomuda/

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Please cite our paper if you find it useful.

@article{kothandaraman2020bomuda,
  title={BoMuDA: Boundless Multi-Source Domain Adaptive Segmentation in Unconstrained Environments},
  author={Kothandaraman, Divya and Chandra, Rohan and Manocha, Dinesh},
  journal={arXiv preprint arXiv:2010.03523},
  year={2020}
}

Table of Contents

• Paper - <a href="link to paper" rel="nofollow"><strong>BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments</strong></a>
• Repo Details and Contents
  • Code structure
  • Training your own model
  • Datasets
  • Dependencies
• Our network
• Acknowledgements

Repo Details and Contents

Python version: 3.7

Code structure

Dataloaders <br>

Our network

Training your own model

Stage 1: Train networks for single source domain adaptation on various source-target pairs. <br>

python train_singlesourceDA.py

Stage 2: Use the trained single-source networks, and the corresponding domain discriminators for multi-source domain adaptation.

python train_bddbase_multi3source_furtheriterations.py

Evaluation (closed-set DA):

python eval_idd_BoMuDA.py

Evaluation (open-set DA):

python eval_idd_openset.py

Make sure to set appropriate paths to the folders containing the datasets, and the models in the training and evaluation files. <br>

Datasets

• Clear weather: CityScapes
• India Driving Dataset
• GTA5
• SynScapes
• Berkeley Deep Drive

Dependencies

PyTorch <br> NumPy <br> SciPy <br> Matplotlib <br>

Acknowledgements

This code is heavily borrowed from AdaptSegNet.