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 9.0. The backbone is ResNeXt101 pre-trained on ImageNet and the implementation of loss is from Quanlong Zheng.

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

• You can download the pre-trained model from Google Drive and put it into SBU_model folder.

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

• PyTorch == 0.4.1 (1.0.x may not work for training)
• Cupy (Installation Guide)
• TensorBoardX
• Python3.6
• progressbar2
• scikit-image
• pydensecrf

1. Clone this repository

git clone https://github.com/stevewongv/DSC-PyTorch.git

2. Train

python3 main.py -a train

3. Test

python3 main.py -a test

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