Awesome

rec-attend-public

Code that implements paper "End-to-End Instance Segmentation with Recurrent Attention".

Dependencies

• Python 2.7
• TensorFlow 0.12 (not compatible with TensorFlow 1.0)
• OpenCV
• NumPy
• SciPy
• PyYaml
• hdf5 and H5Py
• tqdm
• Pillow (required by cityscapes evaluation)

Installation

Compile Hungarian matching module

./hungarian_build.sh

CVPPP Experiments

First modify setup_cvppp.sh with your dataset folder paths.

./setup_cvppp.sh

Run experiments:

./run_cvppp.sh

KITTI Experiments

First modify setup_kitti.sh with your dataset folder paths.

./setup_kitti.sh

Run experiments:

./run_cvppp.sh

Cityscapes Experiments

First modify setup_cityscapes.sh with your dataset folder paths.

./setup_cityscapes.sh

Run experiments:

./run_cityscapes.sh

Citation

If you use our code, please consider cite the following: End-to-End Instance Segmentation with Recurrent Attention. Mengye Ren, Richard S. Zemel. CVPR 2017.

@inproceedings{ren17recattend,
  author    = {Mengye Ren and Richard S. Zemel},
  title     = {End-to-End Instance Segmentation with Recurrent Attention},
  booktitle = {CVPR},
  year      = {2017}
}