# rec-attend-public

**Repository Path**: zhuang_shuo/rec-attend-public

## Basic Information

- **Project Name**: rec-attend-public
- **Description**: Code that implements paper "End-to-End Instance Segmentation with Recurrent Attention"
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-31
- **Last Updated**: 2021-03-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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
```bash
./hungarian_build.sh
```

## CVPPP Experiments
First modify `setup_cvppp.sh` with your dataset folder paths.
```bash
./setup_cvppp.sh
```

Run experiments:
```bash
./run_cvppp.sh
```

## KITTI Experiments
First modify `setup_kitti.sh` with your dataset folder paths.
```bash
./setup_kitti.sh
```

Run experiments:
```bash
./run_cvppp.sh
```

## Cityscapes Experiments
First modify `setup_cityscapes.sh` with your dataset folder paths.
```bash
./setup_cityscapes.sh
```

Run experiments:
```bash
./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}
}
```