Skip to content
forked from STVIR/pysot

SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.

License

Notifications You must be signed in to change notification settings

hawking-npu/pysot

 
 

Repository files navigation

PySOT

PySOT is a software system designed by SenseTime Video Intelligence Research team. It implements state-of-the-art single object tracking algorithms, including SiamRPN and SiamMask. It is written in Python and powered by the PyTorch deep learning framework. This project also contains a Python port of toolkit for evaluating trackers.

PySOT has enabled research projects, including: SiamRPNDaSiamRPNSiamRPN++, and SiamMask.

Example SiamFC, SiamRPN and SiamMask outputs.

Introduction

The goal of PySOT is to provide a high-quality, high-performance codebase for visaul tracking research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research. PySOT includes implementations of the following visual tracking algorithms:

using the following backbone network architectures:

Additional backbone architectures may be easily implemented. For more details about these models, please see References below.

Evaluation toolkit can support the following datasets:

📎 OTB2015 📎 VOT16/18/19 📎 VOT18-LT 📎 LaSOT 📎 UAV123

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the PySOT Model Zoo.

Installation

Please find installation instructions for PyTorch and PySOT in INSTALL.md.

Quick Start: Using PySOT

Add PySOT to your PYTHONPATH

export PYTHONPATH=/path/to/PySOT:$PYTHONPATH

Download models

Download models in PySOT Model Zoo and put the model.pth in the correct directory in experiments

Webcam demo

python tools/demo.py \
    --config experiments/siamrpn_r50_l234_dwxcorr/config.yaml \
    --snapshot experiments/siamrpn_r50_l234_dwxcorr/model.pth \
    # --video demo/bag.avi # (in case you don't have webcam)

Download testing datasets

Download datasets and put them into testing_dataset directory, please refer to pysot-toolkit to setting testing_dataset

Test tracker

cd experiments/siamrpn_r50_l234_dwxcorr
python -u ../../tools/test.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file

The testing results will in the current directory(results/dataset/model_name/)

Eval tracker

assume still in experiments/siamrpn_r50_l234_dwxcorr

python ../../tools/eval.py 	 \
	--tracker_path ./results \ # result path
	--dataset VOT2018        \ # dataset name
	--num 1 		 \ # number thread to eval
	--tracker_prefix 'model'   # tracker_name

References

Contributors

License

PySOT is released under the Apache 2.0 license.

About

SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 84.3%
  • C 15.3%
  • Shell 0.4%