https://github.com/tusimple/tusimple-duc

Understanding Convolution for Semantic Segmentation
https://github.com/tusimple/tusimple-duc

cityscapes convolution deep-learning deep-neural-networks mxnet python semantic-segmentation

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Understanding Convolution for Semantic Segmentation

• Host: GitHub
• URL: https://github.com/tusimple/tusimple-duc
• Owner: TuSimple
• License: apache-2.0
• Created: 2017-11-07T02:19:43.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2021-10-26T04:02:46.000Z (about 3 years ago)
• Last Synced: 2025-01-11T22:11:45.861Z (11 days ago)
• Topics: cityscapes, convolution, deep-learning, deep-neural-networks, mxnet, python, semantic-segmentation
• Language: Python
• Homepage: https://arxiv.org/abs/1702.08502
• Size: 35.2 KB
• Stars: 613
• Watchers: 25
• Forks: 118
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TuSimple-DUC

by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell.

## Introduction

This repository is for [Understanding Convolution for Semantic Segmentation](https://arxiv.org/abs/1702.08502) (WACV 2018), which achieved state-of-the-art result on the CityScapes, PASCAL VOC 2012, and Kitti Road benchmark.

## Requirement

We tested our code on:

Ubuntu 16.04, Python 2.7 with

[MXNet (0.11.0)](https://github.com/TuSimple/mxnet), numpy(1.13.1), cv2(3.2.0), PIL(4.2.1), and cython(0.25.2)

## Usage

1. Clone the repository:

   ```shell

   git clone [email protected]:TuSimple/TuSimple-DUC.git

   python setup.py develop --user

   ```

2. Download the pretrained model from [Google Drive](https://drive.google.com/drive/folders/0B72xLTlRb0SoREhISlhibFZTRmM?resourcekey=0-g2Fr58Nn591bs5ZvZ0Vlwg&usp=sharing).

3. Build MXNet (only tested on the TuSimple version):

   ```shell

   git clone --recursive [email protected]:TuSimple/mxnet.git

   vim make/config.mk (we should have USE_CUDA = 1, modify USE_CUDA_PATH, and have USE_CUDNN = 1 to enable GPU usage.)

   make -j

   cd python

   python setup.py develop --user

   ```

   For more MXNet tutorials, please refer to the [official documentation](https://mxnet.incubator.apache.org/install/index.html).

3. Training:

   ```shell

   cd train

   python train_model.py ../configs/train/train_cityscapes.cfg

   ```

   The paths/dirs in the ``.cfg`` file need to be specified by the user.

4. Testing

   ```

   cd test

   python predict_full_image.py ../configs/test/test_full_image.cfg

   ```

   The paths/dirs in the ``.cfg`` file need to be specified by the user.

5. Results:

   Modify the ``result_dir`` path in the config file to save the label map and visualizations. The expected scores are:

   (single scale testing denotes as 'ss' and multiple scale testing denotes as 'ms')

   - ResNet101-DUC-HDC on CityScapes testset (mIoU): 79.1(ss) / 80.1(ms)

   - ResNet152-DUC on VOC2012 (mIoU): 83.1(ss)

## Citation

If you find the repository is useful for your research, please consider citing:

    @article{wang2017understanding,

      title={Understanding convolution for semantic segmentation},

      author={Wang, Panqu and Chen, Pengfei and Yuan, Ye and Liu, Ding and Huang, Zehua and Hou, Xiaodi and Cottrell, Garrison},

      journal={arXiv preprint arXiv:1702.08502},

      year={2017}

    }

## Questions

Please contact [email protected] or [email protected] .