- Python 3
- Tensorflow
- Scipy
- scikit-image
- numpy
- pillow
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},
}
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.
- 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