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SiamMask

NEW: now including code for both training and inference!

This is the official implementation with training 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)
CVPR 2019
[Paper] [Video] [Project Page]

Bibtex

If you find this code useful, please consider citing:

@inproceedings{wang2019fast,
    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},
    booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
    year={2019}
}

Contents

1. Environment Setup
2. Demo
3. Testing
4. Training

Environment setup

This 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 your PYTHONPATH

export PYTHONPATH=$PWD:$PYTHONPATH

Demo

• Setup your environment
• Download the SiamMask model

cd $SiamMask/experiments/siammask_sharp
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 $SiamMask/experiments/siammask_sharp
export PYTHONPATH=$PWD:$PYTHONPATH
python ../../tools/demo.py --resume SiamMask_DAVIS.pth --config config_davis.json

Testing

• 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_sharp
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

• Evaluate performance on Youtube-VOS (need download data from website)

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 dataset (ytb-bb+ytb-vos+vid+coco+det).

Training

Training Data

• Download the Youtube-VOS, COCO, ImageNet-DET, and ImageNet-VID.
• Preprocess each datasets according the readme files.

Download the pre-trained model (174 MB)

(This model was trained on the ImageNet-1k Dataset)

cd $SiamMask/experiments
wget http://www.robots.ox.ac.uk/~qwang/resnet.model
ls | grep siam | xargs -I {} cp resnet.model {}

Training SiamMask base model

• Setup your environment
• From the experiment directory, run

cd $SiamMask/experiments/siammask_base/
bash run.sh

• Training takes about 10 hours in our 4 Tesla V100 GPUs.
• If you experience out-of-memory errors, you can reduce the batch size in run.sh.
• You can view progress on Tensorboard (logs are at <experiment_dir>/logs/)
• After training, you can test checkpoints on VOT dataset.

bash test_all.sh -s 1 -e 20 -d VOT2018 -g 4  # test all snapshots with 4 GPUs

• Select best model for hyperparametric search.

#bash test_all.sh -m [best_test_model] -d VOT2018 -n [thread_num] -g [gpu_num] # 8 threads with 4 GPUS
bash test_all.sh -m snapshot/checkpoint_e12.pth -d VOT2018 -n 8 -g 4 # 8 threads with 4 GPUS

Training SiamMask model with the Refine module

• Setup your environment
• In the experiment file, train with the best SiamMask base model

cd $SiamMask/experiments/siammask_sharp
bash run.sh <best_base_model>
bash run.sh checkpoint_e12.pth

• You can view progress on Tensorboard (logs are at <experiment_dir>/logs/)
• After training, you can test checkpoints on VOT dataset

bash test_all.sh -s 1 -e 20 -d VOT2018 -g 4

Training SiamRPN++ model (unofficial)

• Setup your environment
• From the experiment directory, run

cd $SiamMask/experiments/siamrpn_resnet
bash run.sh

• You can view progress on Tensorboard (logs are at <experiment_dir>/logs/)
• After training, you can test checkpoints on VOT dataset

bash test_all.sh -h
bash test_all.sh -s 1 -e 20 -d VOT2018 -g 4

License

Licensed under an MIT license.