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Author
Contributors
Last Commit
May. 23, 2019
Created
Mar. 4, 2019

SiamMask

PWC

This is the official inference code for SiamMask (CVPR2019). For technical details, please refer to:

Fast Online Object Tracking and Segmentation: A Unifying Approach
Qiang Wang*, Li Zhang*, Luca Bertinetto*, Weiming Hu, Philip H.S. Torr (* denotes equal contribution)
CVPR2019
[Paper] [Video] [Project Page]

Contents

  1. Environment Setup
  2. Demo
  3. Testing Models

Environment Setup

All the code has been tested on Ubuntu 16.04, Python 3.6, Pytorch 0.4.1, CUDA 9.2, RTX 2080 GPUs

  • Clone the repository
git clone https://github.com/foolwood/SiamMask.git && cd SiamMask
export SiamMask=$PWD
  • Setup python environment
conda create -n siammask python=3.6
source activate siammask
pip install -r requirements.txt
bash make.sh
  • Add the project to PYTHONPATH
export PYTHONPATH=$PWD:$PYTHONPATH

Demo

  • Setup your environment
  • Download the SiamMask model
cd $SiamMask/experiments/siammask
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT.pth
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth
  • Run demo.py
cd $SiamMask/experiments/siammask
export PYTHONPATH=$PWD:$PYTHONPATH
python ../../tools/demo.py --resume SiamMask_DAVIS.pth --config config_davis.json

Testing Models

  • Setup your environment
  • Download test data
cd $SiamMask/data
sudo apt-get install jq
bash get_test_data.sh
  • Download pretrained models
cd $SiamMask/experiments/siammask
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT.pth
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT_LD.pth
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth
  • Evaluate performance on VOT
bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2016 0
bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2018 0
bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2019 0
bash test_mask_refine.sh config_vot18.json SiamMask_VOT_LD.pth VOT2016 0
bash test_mask_refine.sh config_vot18.json SiamMask_VOT_LD.pth VOT2018 0
python ../../tools/eval.py --dataset VOT2016 --tracker_prefix C --result_dir ./test/VOT2016
python ../../tools/eval.py --dataset VOT2018 --tracker_prefix C --result_dir ./test/VOT2018
python ../../tools/eval.py --dataset VOT2019 --tracker_prefix C --result_dir ./test/VOT2019
  • Evaluate performance on DAVIS (less than 50s)
bash test_mask_refine.sh config_davis.json SiamMask_DAVIS.pth DAVIS2016 0
bash test_mask_refine.sh config_davis.json SiamMask_DAVIS.pth DAVIS2017 0
bash test_mask_refine.sh config_davis.json SiamMask_DAVIS.pth ytb_vos 0

Results

These are the reproduction results from this repository. All results can be downloaded from our project page.

Tracker VOT2016
EAO / A / R
VOT2018
EAO / A / R
DAVIS2016
J / F
DAVIS2017
J / F
Youtube-VOS
J_s / J_u / F_s / F_u
Speed
SiamMask-box 0.412/0.623/0.233 0.363/0.584/0.300 - / - - / - - / - / - / - 77 FPS
SiamMask 0.433/0.639/0.214 0.380/0.609/0.276 0.713/0.674 0.543/0.585 0.602/0.451/0.582/0.477 56 FPS
SiamMask-LD 0.455/0.634/0.219 0.423/0.615/0.248 - / - - / - - / - / - / - 56 FPS

Note:

  • Speed are tested on a NVIDIA RTX 2080.
  • -box reports an axis-aligned bounding box from the box branch.
  • -LD means training with large datasets (ytb-bb+ytb-vos+vid+coco+det).

License

Licensed under an MIT license.

Citing SiamMask

If you use this code, please cite:

@article{Wang2019SiamMask,
    title={Fast Online Object Tracking and Segmentation: A Unifying Approach},
    author={Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip HS},
    journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2019}
}