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DSC-PyTorch

This is a PyTorch implementation of "Direction-Aware Spatial Context Features for Shadow Detection, CVPR'18" and detection part of "Direction-Aware Spatial Context Features for Shadow Detection and Removal, T-PAMI'19" based on Xiaowei's DSC (Caffe) written by Tianyu Wang.

The Spacial IRNN is implemented by using CUDA 11.x. The backbone is ResNeXt101 pre-trained on ImageNet and the implementation of loss is from Quanlong Zheng.

Results

We use two GTX 1080Ti to train the DSC on SBU dataset.

SBU

Methods BER Accuracy
DSC (Caffe) 5.59 0.97
DSC (Our) 5.19 0.95

Pre-trained model is available. You can download from OneDrive and put it into SBU_model folder.

  • You can download the ResNeXt101 model from Google Drive and put it in main folder.

Requirements

  • PyTorch == 1.8.1 (training and testing)
  • Cupy (Installation Guide)
  • TensorBoardX
  • Python
  • progressbar2
  • scikit-image
  • pydensecrf

Train/Test

  1. Clone this repository
git clone https://github.com/stevewongv/DSC-PyTorch.git
  1. Train
python3 main.py -a train
  1. Test
python3 main.py -a test

Citations

@InProceedings{Hu_2018_CVPR,      
    author = {Hu, Xiaowei and Zhu, Lei and Fu, Chi-Wing and Qin, Jing and Heng, Pheng-Ann},      
    title = {Direction-Aware Spatial Context Features for Shadow Detection},      
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},      
    pages={7454--7462},        
    year = {2018}
}

@article{hu2019direction,   
    author = {Hu, Xiaowei and Fu, Chi-Wing and Zhu, Lei and Qin, Jing and Heng, Pheng-Ann},    
    title = {Direction-Aware Spatial Context Features for Shadow Detection and Removal},    
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},    
    year  = {2019},          
    note={to appear}                  
}

Modified DSC module is used in SPANet:

@InProceedings{Wang_2019_CVPR,
  author = {Wang, Tianyu and Yang, Xin and Xu, Ke and Chen, Shaozhe and Zhang, Qiang and Lau, Rynson W.H.},
  title = {Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}

TODO List

  • ResNext101 Backbone
  • Test on SBU Test Set
  • VGG19 Backbone
  • Test on ISTD Test Set
  • Test on UCF Test Set
  • ...