%global _empty_manifest_terminate_build 0
Name: python-mmtrack
Version: 0.14.0
Release: 1
Summary: OpenMMLab Unified Video Perception Platform
License: Apache License 2.0
URL: https://github.com/open-mmlab/mmtracking
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/54/ab/4f702809260dfe754bd6cb9f62c440fa32ba41b327aa896a62d21912678d/mmtrack-0.14.0.tar.gz
BuildArch: noarch
Requires: python3-attributee
Requires: python3-dotty-dict
Requires: python3-lap
Requires: python3-matplotlib
Requires: python3-mmcls
Requires: python3-motmetrics
Requires: python3-packaging
Requires: python3-pandas
Requires: python3-pycocotools
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-terminaltables
Requires: python3-tqdm
Requires: python3-cython
Requires: python3-numpy
Requires: python3-attributee
Requires: python3-dotty-dict
Requires: python3-lap
Requires: python3-matplotlib
Requires: python3-mmcls
Requires: python3-motmetrics
Requires: python3-packaging
Requires: python3-pandas
Requires: python3-pycocotools
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-terminaltables
Requires: python3-tqdm
Requires: python3-asynctest
Requires: python3-codecov
Requires: python3-flake8
Requires: python3-interrogate
Requires: python3-isort
Requires: python3-kwarray
Requires: python3-pytest
Requires: python3-ubelt
Requires: python3-xdoctest
Requires: python3-yapf
Requires: python3-cython
Requires: python3-numpy
Requires: python3-mmcls
Requires: python3-mmcv-full
Requires: python3-mmdet
Requires: python3-asynctest
Requires: python3-codecov
Requires: python3-flake8
Requires: python3-interrogate
Requires: python3-isort
Requires: python3-kwarray
Requires: python3-pytest
Requires: python3-ubelt
Requires: python3-xdoctest
Requires: python3-yapf
%description
[](https://pypi.org/project/mmtrack/)
[](https://pypi.org/project/mmtrack)
[](https://mmtracking.readthedocs.io/en/latest/)
[](https://github.com/open-mmlab/mmtracking/actions)
[](https://codecov.io/gh/open-mmlab/mmtracking)
[](https://github.com/open-mmlab/mmtracking/blob/master/LICENSE)
[📘Documentation](https://mmtracking.readthedocs.io/) |
[🛠️Installation](https://mmtracking.readthedocs.io/en/latest/install.html) |
[👀Model Zoo](https://mmtracking.readthedocs.io/en/latest/model_zoo.html) |
[🆕Update News](https://mmtracking.readthedocs.io/en/latest/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmtracking/issues/new/choose)
English | [简体中文](README_zh-CN.md)
## Introduction
MMTracking is an open source video perception toolbox by PyTorch. It is a part of [OpenMMLab](https://openmmlab.com) 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.
## What's New
We release MMTracking 1.0.0rc0, the first version of MMTracking 1.x.
Built upon the new [training engine](https://github.com/open-mmlab/mmengine), MMTracking 1.x unifies the interfaces of datasets, models, evaluation, and visualization.
We also support more methods in MMTracking 1.x, such as [StrongSORT](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/mot/strongsort) for MOT, [Mask2Former](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/vis/mask2former) for VIS, [PrDiMP](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/sot/prdimp) for SOT.
Please refer to [dev-1.x](https://github.com/open-mmlab/mmtracking/tree/dev-1.x) branch for the using of MMTracking 1.x.
## 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.
A Colab tutorial is provided. You may preview the notebook [here](./demo/MMTracking_Tutorial.ipynb) or directly run it on [Colab](https://colab.research.google.com/github/open-mmlab/mmtracking/blob/master/demo/MMTracking_Tutorial.ipynb).
There are also 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).
## Benchmark and model zoo
Results and models are available in the [model zoo](docs/en/model_zoo.md).
### Video Object Detection
Supported Methods
- [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 Datasets
- [x] [ILSVRC](http://image-net.org/challenges/LSVRC/2017/)
### Single Object Tracking
Supported Methods
- [x] [SiameseRPN++](configs/sot/siamese_rpn) (CVPR 2019)
- [x] [STARK](configs/sot/stark) (ICCV 2021)
- [ ] [PrDiMP](https://arxiv.org/abs/2003.12565) (CVPR2020) (WIP)
Supported Datasets
- [x] [LaSOT](http://vision.cs.stonybrook.edu/~lasot/)
- [x] [UAV123](https://cemse.kaust.edu.sa/ivul/uav123/)
- [x] [TrackingNet](https://tracking-net.org/)
- [x] [OTB100](http://www.visual-tracking.net/)
- [x] [GOT10k](http://got-10k.aitestunion.com/)
- [x] [VOT2018](https://www.votchallenge.net/vot2018/)
### Multi-Object Tracking
Supported Methods
- [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) (ECCV 2022)
- [x] [OC-SORT](configs/mot/ocsort) (arXiv 2022)
Supported Datasets
- [x] [MOT Challenge](https://motchallenge.net/)
- [x] [CrowdHuman](https://www.crowdhuman.org/)
- [x] [LVIS](https://www.lvisdataset.org/)
- [x] [TAO](https://taodataset.org/)
- [x] [DanceTrack](https://arxiv.org/abs/2111.14690)
### Video Instance Segmentation
Supported Methods
- [x] [MaskTrack R-CNN](configs/vis/masktrack_rcnn) (ICCV 2019)
Supported Datasets
- [x] [YouTube-VIS](https://youtube-vos.org/dataset/vis/)
## 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 and [this discussion](https://github.com/open-mmlab/mmtracking/issues/73) for development roadmap.
## 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}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## 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.
%package -n python3-mmtrack
Summary: OpenMMLab Unified Video Perception Platform
Provides: python-mmtrack
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-mmtrack
[](https://pypi.org/project/mmtrack/)
[](https://pypi.org/project/mmtrack)
[](https://mmtracking.readthedocs.io/en/latest/)
[](https://github.com/open-mmlab/mmtracking/actions)
[](https://codecov.io/gh/open-mmlab/mmtracking)
[](https://github.com/open-mmlab/mmtracking/blob/master/LICENSE)
[📘Documentation](https://mmtracking.readthedocs.io/) |
[🛠️Installation](https://mmtracking.readthedocs.io/en/latest/install.html) |
[👀Model Zoo](https://mmtracking.readthedocs.io/en/latest/model_zoo.html) |
[🆕Update News](https://mmtracking.readthedocs.io/en/latest/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmtracking/issues/new/choose)
English | [简体中文](README_zh-CN.md)
## Introduction
MMTracking is an open source video perception toolbox by PyTorch. It is a part of [OpenMMLab](https://openmmlab.com) 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.
## What's New
We release MMTracking 1.0.0rc0, the first version of MMTracking 1.x.
Built upon the new [training engine](https://github.com/open-mmlab/mmengine), MMTracking 1.x unifies the interfaces of datasets, models, evaluation, and visualization.
We also support more methods in MMTracking 1.x, such as [StrongSORT](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/mot/strongsort) for MOT, [Mask2Former](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/vis/mask2former) for VIS, [PrDiMP](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/sot/prdimp) for SOT.
Please refer to [dev-1.x](https://github.com/open-mmlab/mmtracking/tree/dev-1.x) branch for the using of MMTracking 1.x.
## 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.
A Colab tutorial is provided. You may preview the notebook [here](./demo/MMTracking_Tutorial.ipynb) or directly run it on [Colab](https://colab.research.google.com/github/open-mmlab/mmtracking/blob/master/demo/MMTracking_Tutorial.ipynb).
There are also 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).
## Benchmark and model zoo
Results and models are available in the [model zoo](docs/en/model_zoo.md).
### Video Object Detection
Supported Methods
- [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 Datasets
- [x] [ILSVRC](http://image-net.org/challenges/LSVRC/2017/)
### Single Object Tracking
Supported Methods
- [x] [SiameseRPN++](configs/sot/siamese_rpn) (CVPR 2019)
- [x] [STARK](configs/sot/stark) (ICCV 2021)
- [ ] [PrDiMP](https://arxiv.org/abs/2003.12565) (CVPR2020) (WIP)
Supported Datasets
- [x] [LaSOT](http://vision.cs.stonybrook.edu/~lasot/)
- [x] [UAV123](https://cemse.kaust.edu.sa/ivul/uav123/)
- [x] [TrackingNet](https://tracking-net.org/)
- [x] [OTB100](http://www.visual-tracking.net/)
- [x] [GOT10k](http://got-10k.aitestunion.com/)
- [x] [VOT2018](https://www.votchallenge.net/vot2018/)
### Multi-Object Tracking
Supported Methods
- [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) (ECCV 2022)
- [x] [OC-SORT](configs/mot/ocsort) (arXiv 2022)
Supported Datasets
- [x] [MOT Challenge](https://motchallenge.net/)
- [x] [CrowdHuman](https://www.crowdhuman.org/)
- [x] [LVIS](https://www.lvisdataset.org/)
- [x] [TAO](https://taodataset.org/)
- [x] [DanceTrack](https://arxiv.org/abs/2111.14690)
### Video Instance Segmentation
Supported Methods
- [x] [MaskTrack R-CNN](configs/vis/masktrack_rcnn) (ICCV 2019)
Supported Datasets
- [x] [YouTube-VIS](https://youtube-vos.org/dataset/vis/)
## 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 and [this discussion](https://github.com/open-mmlab/mmtracking/issues/73) for development roadmap.
## 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}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## 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.
%package help
Summary: Development documents and examples for mmtrack
Provides: python3-mmtrack-doc
%description help
[](https://pypi.org/project/mmtrack/)
[](https://pypi.org/project/mmtrack)
[](https://mmtracking.readthedocs.io/en/latest/)
[](https://github.com/open-mmlab/mmtracking/actions)
[](https://codecov.io/gh/open-mmlab/mmtracking)
[](https://github.com/open-mmlab/mmtracking/blob/master/LICENSE)
[📘Documentation](https://mmtracking.readthedocs.io/) |
[🛠️Installation](https://mmtracking.readthedocs.io/en/latest/install.html) |
[👀Model Zoo](https://mmtracking.readthedocs.io/en/latest/model_zoo.html) |
[🆕Update News](https://mmtracking.readthedocs.io/en/latest/changelog.html) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmtracking/issues/new/choose)
English | [简体中文](README_zh-CN.md)
## Introduction
MMTracking is an open source video perception toolbox by PyTorch. It is a part of [OpenMMLab](https://openmmlab.com) 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.
## What's New
We release MMTracking 1.0.0rc0, the first version of MMTracking 1.x.
Built upon the new [training engine](https://github.com/open-mmlab/mmengine), MMTracking 1.x unifies the interfaces of datasets, models, evaluation, and visualization.
We also support more methods in MMTracking 1.x, such as [StrongSORT](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/mot/strongsort) for MOT, [Mask2Former](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/vis/mask2former) for VIS, [PrDiMP](https://github.com/open-mmlab/mmtracking/tree/dev-1.x/configs/sot/prdimp) for SOT.
Please refer to [dev-1.x](https://github.com/open-mmlab/mmtracking/tree/dev-1.x) branch for the using of MMTracking 1.x.
## 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.
A Colab tutorial is provided. You may preview the notebook [here](./demo/MMTracking_Tutorial.ipynb) or directly run it on [Colab](https://colab.research.google.com/github/open-mmlab/mmtracking/blob/master/demo/MMTracking_Tutorial.ipynb).
There are also 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).
## Benchmark and model zoo
Results and models are available in the [model zoo](docs/en/model_zoo.md).
### Video Object Detection
Supported Methods
- [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 Datasets
- [x] [ILSVRC](http://image-net.org/challenges/LSVRC/2017/)
### Single Object Tracking
Supported Methods
- [x] [SiameseRPN++](configs/sot/siamese_rpn) (CVPR 2019)
- [x] [STARK](configs/sot/stark) (ICCV 2021)
- [ ] [PrDiMP](https://arxiv.org/abs/2003.12565) (CVPR2020) (WIP)
Supported Datasets
- [x] [LaSOT](http://vision.cs.stonybrook.edu/~lasot/)
- [x] [UAV123](https://cemse.kaust.edu.sa/ivul/uav123/)
- [x] [TrackingNet](https://tracking-net.org/)
- [x] [OTB100](http://www.visual-tracking.net/)
- [x] [GOT10k](http://got-10k.aitestunion.com/)
- [x] [VOT2018](https://www.votchallenge.net/vot2018/)
### Multi-Object Tracking
Supported Methods
- [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) (ECCV 2022)
- [x] [OC-SORT](configs/mot/ocsort) (arXiv 2022)
Supported Datasets
- [x] [MOT Challenge](https://motchallenge.net/)
- [x] [CrowdHuman](https://www.crowdhuman.org/)
- [x] [LVIS](https://www.lvisdataset.org/)
- [x] [TAO](https://taodataset.org/)
- [x] [DanceTrack](https://arxiv.org/abs/2111.14690)
### Video Instance Segmentation
Supported Methods
- [x] [MaskTrack R-CNN](configs/vis/masktrack_rcnn) (ICCV 2019)
Supported Datasets
- [x] [YouTube-VIS](https://youtube-vos.org/dataset/vis/)
## 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 and [this discussion](https://github.com/open-mmlab/mmtracking/issues/73) for development roadmap.
## 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}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## 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.
%prep
%autosetup -n mmtrack-0.14.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-mmtrack -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed Apr 12 2023 Python_Bot - 0.14.0-1
- Package Spec generated