# mmtracking **Repository Path**: wolverineq/mmtracking ## Basic Information - **Project Name**: mmtracking - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 13 - **Created**: 2022-04-21 - **Last Updated**: 2022-04-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
 
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[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmtrack)](https://pypi.org/project/mmtrack/) [![PyPI](https://img.shields.io/pypi/v/mmtrack)](https://pypi.org/project/mmtrack) [![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmtracking.readthedocs.io/en/latest/) [![badge](https://github.com/open-mmlab/mmtracking/workflows/build/badge.svg)](https://github.com/open-mmlab/mmtracking/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmtracking/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmtracking) [![license](https://img.shields.io/github/license/open-mmlab/mmtracking.svg)](https://github.com/open-mmlab/mmtracking/blob/master/LICENSE) English | [简体中文](/README_zh-CN.md) Documentation: https://mmtracking.readthedocs.io/ ## Introduction MMTracking is an open source video perception toolbox based on PyTorch. It is a part of the OpenMMLab project. The master branch works with **PyTorch1.5+**.
### Major features - **The First Unified Video Perception Platform** We are the first open source toolbox that unifies versatile video perception tasks include video object detection, multiple object tracking, single object tracking and video instance segmentation. - **Modular Design** We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules. - **Simple, Fast and Strong** **Simple**: MMTracking interacts with other OpenMMLab projects. It is built upon [MMDetection](https://github.com/open-mmlab/mmdetection) that we can capitalize any detector only through modifying the configs. **Fast**: All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations. **Strong**: We reproduce state-of-the-art models and some of them even outperform the official implementations. ## License This project is released under the [Apache 2.0 license](LICENSE). ## Changelog Release [QDTrack](configs/mot/qdtrack) pretrained models. v0.12.0 was released in 01/04/2022. Please refer to [changelog.md](docs/en/changelog.md) for details and release history. ## Benchmark and model zoo Results and models are available in the [model zoo](docs/en/model_zoo.md). Supported methods of video object detection: - [x] [DFF](configs/vid/dff) (CVPR 2017) - [x] [FGFA](configs/vid/fgfa) (ICCV 2017) - [x] [SELSA](configs/vid/selsa) (ICCV 2019) - [x] [Temporal RoI Align](configs/vid/temporal_roi_align) (AAAI 2021) Supported methods of multi object tracking: - [x] [SORT/DeepSORT](configs/mot/deepsort) (ICIP 2016/2017) - [x] [Tracktor](configs/mot/tracktor) (ICCV 2019) - [x] [QDTrack](configs/mot/qdtrack) (CVPR 2021) - [x] [ByteTrack](configs/mot/bytetrack) (arXiv 2021) Supported methods of single object tracking: - [x] [SiameseRPN++](configs/sot/siamese_rpn) (CVPR 2019) - [x] [STARK](configs/sot/stark) (ICCV 2021) Supported methods of video instance segmentation: - [x] [MaskTrack R-CNN](configs/vis/masktrack_rcnn) (ICCV 2019) ## Installation Please refer to [install.md](docs/en/install.md) for install instructions. ## Getting Started Please see [dataset.md](docs/en/dataset.md) and [quick_run.md](docs/en/quick_run.md) for the basic usage of MMTracking. We also provide usage [tutorials](docs/en/tutorials/), such as [learning about configs](docs/en/tutorials/config.md), [an example about detailed description of vid config](docs/en/tutorials/config_vid.md), [an example about detailed description of mot config](docs/en/tutorials/config_mot.md), [an example about detailed description of sot config](docs/en/tutorials/config_sot.md), [customizing dataset](docs/en/tutorials/customize_dataset.md), [customizing data pipeline](docs/en/tutorials/customize_data_pipeline.md), [customizing vid model](docs/en/tutorials/customize_vid_model.md), [customizing mot model](docs/en/tutorials/customize_mot_model.md), [customizing sot model](docs/en/tutorials/customize_sot_model.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [useful tools](docs/en/useful_tools_scripts.md). ## Contributing We appreciate all contributions to improve MMTracking. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) for the contributing guideline. ## Acknowledgement MMTracking is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new video perception methods. ## Citation If you find this project useful in your research, please consider cite: ```latex @misc{mmtrack2020, title={{MMTracking: OpenMMLab} video perception toolbox and benchmark}, author={MMTracking Contributors}, howpublished = {\url{https://github.com/open-mmlab/mmtracking}}, year={2020} } ``` ## Projects in OpenMMLab - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark. - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition and understanding toolbox. - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark. - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning Toolbox and Benchmark. - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab Model Compression Toolbox and Benchmark. - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab FewShot Learning Toolbox and Benchmark. - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab Generative Model toolbox and benchmark. - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMlab deep learning model deployment toolset.