BiMix

The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation arxiv

Framework: visualization results:

Requirements

• scipy==1.2.2
• kornia
• scikit-image

Datasets

Cityscapes: Please follow the instructions in Cityscape to download the training set.

Dark-Zurich: Please follow the instructions in Dark-Zurich to download the training/val/test set.

Nightdriving：Please follow the instructions in Nightdriving to download the training/val/test set.

Training

If you want to train your own models, please follow these steps:

Step1: download the [pre-trained models](https://www.dropbox.com/s/3n1212kxuv82uua/pretrained_models.zip?dl=0) and put it in the root.
Step2: change the data and model paths in configs/train_config.py
Step3: run "python train.py"

Evaluating

To reproduce the reported results in our paper (on Dark-Zurich val or Nightdriving), please follow these steps:

Step1: change the data and model paths in configs/evaluate_config.py
Step2: run "python eva_ep.py"

Testing

To reproduce the reported results in our paper (on Dark-Zurich test), please follow these steps:

Step1: change the data and model paths in configs/test_config.py
Step2: run "python test.py"

To evaluate your methods on the test set, please visit this challenge for more details.

Acknowledgments

The code is based on DANNet and Zero-DCE.

Related works

• MGCDA
• GCMA
• DANNet
• AdaptSegNet

Citation

If you think this paper is useful for your research, please cite our paper:

Contact

• Guanglei Yang ([email protected])