Paper - BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments (ICCV Workshops 2021)

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

Watch the video here

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

Dataset	Dataloader	List of images
CityScapes	dataset/cityscapes.py	dataset/cityscapes_list
India Driving Dataset	dataset/idd_dataset.py,idd_openset.py	dataset/idd_list
GTA	dataset/gta_dataset.py	dataset/gta_list
SynScapes	dataset/synscapes.py	dataset/synscapes_list
Berkeley Deep Drive	dataset/bdd/bdd_source.py	dataset/bdd_list

Our network

Training your own model

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

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.

Datasets

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

Dependencies

PyTorch
NumPy
SciPy
Matplotlib

Acknowledgements

This code is heavily borrowed from AdaptSegNet.