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SDDNet_ACMMM23

Runmin Cong, Yuchen Guan, Jinpeng Chen, Wei Zhang, Yao Zhao, and Sam Kwong, SDDNet: Style-guided dual-layer disentanglement network for shadow detection, ACM Multimedia (ACM MM), 2023. In Press.

Network

Our overall framework：

Requirement

Pleasure configure the environment according to the given version:

• python 3.6.10
• pytorch 1.10.1
• cudatoolkit 11.1
• torchvision 0.11.2
• tensorboard 2.3.0
• opencv-python 3.4.2
• PIL 7.2.0
• pydensecrf 1.0rc3
• numpy 1.18.5

We also provide ".yaml" files for conda environment configuration, you can use conda env create -f env.yaml to create a required environment.

ResNext101 has been adopted, please put resnext_101_32x4d.pth in the SDDNet/resnext directory. You can download the model from [Link], code: mvpl.

Preparation

Please follow this structure to inspect the code:

├── ISTD_Dataset
    ├── test
    ├── train
├── SBU-shadow
    ├── SBU-Test_rename
    ├── SBUTrain4KRecoveredSmall
├── UCF
    ├── train_A
    ├── train_B
├── SDDNet
    ├── ckpt
    ├── datasets
    ├── logs
    ├── networks
    ├── resnext
    ├── test
    ├── utils
    ├── crf_refine.py
    ├── modelsize_estimate.py
    ├── test.py
    ├── train.py

Training and Testing

Please Note : The input images folder is always named 'train_A' and the GT folder is always named 'train_B' for uniform processing.

Training command :

python train.py

Testing command : The trained model for SDDNet can be download here: [Baidu Netdisk Link], code: mvpl or [Google Drive Link].

python test.py
python crf_refine.py

Results

1. Qualitative results: we provide the saliency maps, you can download them from [Baidu Netdisk Link], code: mvpl or [Google Drive Link].
2. Quantitative results:

Contact Us

If you have any questions, please contact Runmin Cong at rmcong@sdu.edu.cn or Yuchen Guan at yuchenguan@bjtu.edu.cn.