This is a PyTorch implementation of Deep GrabCut, for object segmentation. We use DeepLab-v2 instead of DeconvNet in this repository.

The code was tested with Python 3.5. To use this code, please do:

0. Clone the repo:

   git clone https://github.com/jfzhang95/DeepGrabCut-PyTorch
   cd DeepGrabCut-PyTorch

1. Install dependencies:

   pip install pytorch torchvision -c pytorch
   pip install matplotlib opencv pillow

2. You can download pretrained model from GoogleDrive, and then put the model into models/

3. To try the demo of Deep GrabCut, please run:

   python demo.py

If installed correctly, the result should look like this:

To train Deep GrabCut on PASCAL (or PASCAL + SBD), please follow these additional steps:

4. Download the pre-trained PSPNet model for semantic segmentation, taken from this repository.

   cd models/
   chmod +x download_pretrained_psp_model.sh
   ./download_pretrained_psp_model.sh
   cd ..

5. Set the paths in mypath.py, so that they point to the location of PASCAL/SBD dataset.

6. Run python train.py to train Deep Grabcut.