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README.md

Dual Attention Network for Scene Segmentation(CVPR2019)

Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu

Introduction

We propose a Dual Attention Network (DANet) to adaptively integrate local features with their global dependencies based on the self-attention mechanism. And we achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff-10k dataset.

image

Cityscapes testing set result

We train our DANet-101 with only fine annotated data and submit our test results to the official evaluation server.

image

Updates

2020/9Renew the code, which supports Pytorch 1.4.0 or later!

2020/8:The new TNNLS version DRANet achieves 82.9% on Cityscapes test set (submit the result on August, 2019), which is a new state-of-the-arts performance with only using fine annotated dataset and Resnet-101. The code will be released in DRANet.

2020/7:DANet is supported on MMSegmentation, in which DANet achieves 80.47% with single scale testing and 82.02% with multi-scale testing on Cityscapes val set.

2018/9:DANet released. The trained model with ResNet101 achieves 81.5% on Cityscapes test set.

Usage

  1. Install pytorch

    • The code is tested on python3.6 and torch 1.4.0.
    • The code is modified from PyTorch-Encoding.
  2. Clone the resposity

    git clone https://github.com/junfu1115/DANet.git 
    cd DANet 
    python setup.py install
  3. Dataset

    • Download the Cityscapes dataset and convert the dataset to 19 categories.
    • Please put dataset in folder ./datasets
  4. Evaluation for DANet

    • Download trained model DANet101 and put it in folder ./experiments/segmentation/models/

    • cd ./experiments/segmentation/

    • For single scale testing, please run:

    • CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py --dataset citys --model danet --backbone resnet101 --resume  models/DANet101.pth.tar --eval --base-size 2048 --crop-size 768 --workers 1 --multi-grid --multi-dilation 4 8 16 --os 8 --aux --no-deepstem
    • Evaluation Result

      The expected scores will show as follows: DANet101 on cityscapes val set (mIoU/pAcc): 79.93/95.97(ss)

  5. Evaluation for DRANet

    • Download trained model DRANet101 and put it in folder ./experiments/segmentation/models/

    • Evaluation code is in folder ./experiments/segmentation/

    • cd ./experiments/segmentation/

    • For single scale testing, please run:

    • CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py --dataset citys --model dran --backbone resnet101 --resume  models/dran101.pth.tar --eval --base-size 2048 --crop-size 768 --workers 1 --multi-grid --multi-dilation 4 8 16 --os 8 --aux
    • Evaluation Result

      The expected scores will show as follows: DRANet101 on cityscapes val set (mIoU/pAcc): 81.63/96.62 (ss)

Citation

if you find DANet and DRANet useful in your research, please consider citing:

@article{fu2020scene,
  title={Scene Segmentation With Dual Relation-Aware Attention Network},
  author={Fu, Jun and Liu, Jing and Jiang, Jie and Li, Yong and Bao, Yongjun and Lu, Hanqing},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2020},
  publisher={IEEE}
}
@inproceedings{fu2019dual,
  title={Dual attention network for scene segmentation},
  author={Fu, Jun and Liu, Jing and Tian, Haijie and Li, Yong and Bao, Yongjun and Fang, Zhiwei and Lu, Hanqing},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3146--3154},
  year={2019}
}

Acknowledgement

Thanks PyTorch-Encoding, especially the Synchronized BN!

项目简介

🚀 Github 镜像仓库 🚀

源项目地址

https://github.com/junfu1115/danet

发行版本

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贡献者 9

开发语言

  • Python 77.1 %
  • Cuda 13.7 %
  • C++ 8.7 %
  • Shell 0.5 %
  • Makefile 0.0 %