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

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Deep Learning BiMix
Overview

BiMix

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

Framework: image visualization results: image

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

Citation

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

Contact

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Comments
  • DarkZurich在线测试

    DarkZurich在线测试

    您好，我最近希望能够利用DarkZurich数据集进行网络测试。但是在网站提交后，都报错Missing prediction for ground truth file GOPR0364_frame_000001_gt_labelTrainIds.png这个问题。浏览网站之前的论坛，感觉应该是文件结构的问题，但我根据网站的描述也没有发现问题，请问您对于这个问题有什么看法吗？ 我的文件结构： test.zip |---confidence |---labelTrainIds(GOPR0364_frame_000001.png,......) |---labelTrainIds_invalid

    opened by RyanQR 2
  • 模型的选择

    模型的选择

    • 大佬，您好，我跑了你的代码，采用RefineNet，lightnet=enhance_net_nopool()；
    • 最后选择RefineNet_mixbf/dannet48000.pth'和RefineNet_mixbf/dannet_light48000.pth'在Dark Zurich val上进行验证；
    • 验证结果如下：
    road:	88.18
sidewalk:	58.12
building:	74.97
wall:	34.62
fence:	33.81
pole:	23.25
light:	8.03
sign:	13.37
vegetation:	58.67
terrain:	23.5
sky:	77.15
person:	17.58
rider:	1.15
car:	42.31
truck:	0.0
bus:	0.0
train:	0.0
motocycle:	11.55
bicycle:	20.36
mIoU: 30.88
    • 由于这个结果和您在论文中提到的不太一致，所以想问一下，您的论文中所用的lightnet是采用了zeroDCE的还是DANNet的，以及最终所用的pth是哪一个i_iter的？
    • 谢谢~
    opened by creater-zq 2
Owner
stanley
stanley
GitHub
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