mt-cly/itis

Iteratively Trained Interactive Segmentation

This is an official TensorFlow implementation for

Sabarinath Mahadevan, Paul Voigtlaender, and Bastian Leibe,
"Iteratively Trained Interactive Segmentation",
British Machine Vision Conference, 2018.

Requirements

• Python 3
• Tensorflow
• Scipy
• scikit-image
• numpy
• pillow

Citation

If you use this code or models, please cite the following:

@inproceedings{mahadevanitis,
  author={Sabarinath Mahadevan and Paul Voigtlaender and Bastian Leibe},
  title={Iteratively Trained Interactive Segmentation},
  booktitle={British Machine Vision Conference (BMVC)},
  year={2018},
}

Pre-trained Models

You can download the pre-trained models from our internal server. All available models are in a single tar.gz file. Currently it contains models that can be used to reproduce the results for iFCN and ITIS in Table 1, and for the ablation study in Figure 5 (see paper for details.

Usage

• Download PascalVOC dataset (http://host.robots.ox.ac.uk/pascal/VOC/)
• Create a folder data within the source root directory, and copy the Pascal VOC dataset files to it. Alternatively, add a parameter "data_dir: <path to pascal voc root>" in the respective config files.
• Download the weights as explained in the previous section, and place them under 'models' directory. Alternatively, you could change the paramter "load" to point it to the required path.
• Run the following to evaluate the given models

iFCN:         python main.py configs/pascal_ifcn
iFCN + gauss: python main.py configs/pascal_gauss
ITIS:         python main.py configs/pascal_itis