Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)

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Deep Learning S2VD
Overview

S2VD

Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021)

Requirements and Dependencies

  • Ubuntu 16.04, cuda 10.0
  • Python 3.6.10, Pytorch 1.6.0
  • More detail (See environment.yml)

Training pipelines

  1. Download the NTURain dataset from here or Baidu Cloud(Passwd:q067), and prepare the training data as follows:

    • Labled synthetic data:

          python makedata/preparedata_NTU.py  --ntu_path your_downloaded_synthetic_path --train_path your_saved_train_path

    • Unlabled real data:

          python makedata/preparedata_NTU_semi.py  --ntu_path_semi your_downloaded_real_path --train_path your_saved_train_path

                Note that you should better put the synthetic and real training data sets into two different training folders.

  1. Modify the configured file options_derain.json according to your own training and testing path.

  2. Begin training:

        python main_NTURain.py

Testing pipelines

You need firstly download the testing dataset of NTURain and MSCSC into the folder testsets.

  • NTURain synthetic data set:

        python test_NTURain_synthetic.py

    This manuscript will re-produce the paper results in Table 1.

  • NTURain real data set:

        python test_NTURain_real.py

  • MSCSC real data set:

        python test_MSCSC_real.py

Citation

@incollection{CVPR2021_2429,
title = {Semi-supervised video deraining with dynamical rain generator},
author = {Yue, Zongsheng and Xie, Jianwen and Zhao, Qian and Meng, Deyu},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2021}
}
Comments
  • about the deraining result

    about the deraining result

    you claim "Such a gap between synthetic and real data sets results in poor performance when applying them to real scenarios. To address these issues, this paper proposes a new semi-supervised video deraining method " in your paper, but i can't found it is suitable for real scenarios from the results attaching on your paper. Why is this happening? thank you.

    opened by whyandbecause 2
  • Question about latent_dir_name

    Question about latent_dir_name

    FileNotFoundError: [Errno 2] No such file or directory: ///**/latent_eps6_rho05_tv112 I can't find latent_eps6_rho05_tv112, and don't know how to generate it.

    opened by LuPaoPao 2
  • About the derainer network

    About the derainer network

    Hello! Thanks for your excellent work. I wonder about the network architecture of the derainer f(-;w) in Figure 2. of your paper: Why does it need to minus original image after the Pixel-Shuffle layer ? Thank you.

    opened by sfwang20 1
  • How to implement baseline1 in the experiment？

    How to implement baseline1 in the experiment？

    Hi, your work is fantastic, if I want to implement the baseline1 experiment in your ablation studies, i.e. ""train the derainer with the MSE loss on labeled dataset ", how can I do it exactly? Thanks.

    opened by booker-max 0
  • about network

    about network

    Hello, your work is excellent, and I was reading your paper when I had a question: In Fig.3, which stage of the network do the Transition Model and Emission Model belong to? What I don't understand is what role these two modules play in the whole network. Do they play a role in the process of Fig.2? Looking forward to your reply.

    opened by yuru-S 0
  • The question of downloading NTURain dataset

    The question of downloading NTURain dataset

    By using VPN , I try a lot of times to download the NTURain dataset in China ,but failure. Do you have another way to download this dataset ? Expect your reply!

    opened by CV-Rookie 6
Owner
Zongsheng Yue
Zongsheng Yue
GitHub
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