Our work is based on the open source code library pysot, so the readme document has not been changed, and the related operations of the environment configuration are exactly the same. Thanks to the contributors of pysot. Our main contributions to original PySOT are GC block and CiSK, their codes can be find at ACSiamRPN/pysot/models. The main code of ACSiamRPN is model_builder.py at ACSiamRPN/pysot/models, in which it used GC block and CiSK. At line 92, GC block is introduced; at line 102, RPN head module is introduced. RPN head contains CiSK blocks, and its code is given in SKNet2 folder.
- [Xiaofei Qin]
- Yipeng Zhang
The following is the original text of pysot's readme
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: SiamRPN, DaSiamRPN, SiamRPN++, and SiamMask.
Example SiamFC, SiamRPN and SiamMask outputs.
The goal of PySOT is to provide a high-quality, high-performance codebase for visual 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
We provide a large set of baseline results and trained models available for download in the PySOT Model Zoo.
Please find installation instructions for PyTorch and PySOT in INSTALL.md.
export PYTHONPATH=/path/to/pysot:$PYTHONPATHDownload models in PySOT Model Zoo and put the model.pth in the correct directory in experiments
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 datasets and put them into testing_dataset directory. Jsons of commonly used datasets can be downloaded from Google Drive or BaiduYun. If you want to test tracker on new dataset, please refer to pysot-toolkit to setting testing_dataset.
cd experiments/siamrpn_alex_dwxcorr
python -u ../../tools/test.py \
--snapshot model.pth \ # model path
--dataset VOT2018 \ # dataset name
--config config.yaml # config fileThe testing results will in the current directory(results/dataset/model_name/)
assume still in experiments/siamrpn_alex_dwxcorr_16gpu
python ../../tools/eval.py \
--tracker_path ./results \ # result path
--dataset VOT2018 \ # dataset name
--num 1 \ # number thread to eval
--tracker_prefix 'model' # tracker_nameSee TRAIN.md for detailed instruction.
If you meet problem, try searching our GitHub issues first. We intend the issues page to be a forum in which the community collectively troubleshoots problems. But please do not post duplicate issues. If you have similar issue that has been closed, you can reopen it.
ModuleNotFoundError: No module named 'pysot'
🎯Solution: Run export PYTHONPATH=path/to/pysot first before you run the code.
ImportError: cannot import name region
🎯Solution: Build region by python setup.py build_ext —-inplace as decribled in INSTALL.md.
PySOT is released under the Apache 2.0 license.