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* add logo * draft for the main doc * README.md * update contribute.md
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‎.github/CONTRIBUTING.md

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# Contributing to mmtracking
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All kinds of contributions are welcome, including but not limited to the following.
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- Fixes (typo, bugs)
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- New features and components
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## Workflow
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1. fork and pull the latest mmtracking
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2. checkout a new branch (do not use master branch for PRs)
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3. commit your changes
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4. create a PR
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Note
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- If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
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- If you are the author of some papers and would like to include your method to mmtracking, please let us know (open an issue or contact the maintainers). We will much appreciate your contribution.
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- For new features and new modules, unit tests are required to improve the code's robustness.
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## Code style
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### Python
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We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
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We use the following tools for linting and formatting:
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- [flake8](http://flake8.pycqa.org/en/latest/): linter
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- [yapf](https://github.com/google/yapf): formatter
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- [isort](https://github.com/timothycrosley/isort): sort imports
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Style configurations of yapf and isort can be found in [setup.cfg](../setup.cfg).
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We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`,
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fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.
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The config for a pre-commit hook is stored in [.pre-commit-config](../.pre-commit-config.yaml).
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After you clone the repository, you will need to install initialize pre-commit hook.
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```shell
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pip install -U pre-commit
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```
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From the repository folder
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```shell
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pre-commit install
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```
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If you are facing issue when installing markdown lint, you may install ruby for markdown lint by following
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```shell
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# install rvm
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curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail
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# set up environment
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# Note that you might need to edit ~/.bashrc, ~/.bash_profile, and ~/.profile
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source ~/.profile
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rvm autolibs disable
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# install ruby
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rvm install 2.7.1
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```
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After this on every commit check code linters and formatter will be enforced.
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>Before you create a PR, make sure that your code lints and is formatted by yapf.
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### C++ and CUDA
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We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).

‎LICENSE

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‎README.md

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# MMTracking
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<div align="center">
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<img src="resources/mmtrack-logo.png" width="600"/>
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</div>
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[![PyPI](https://img.shields.io/pypi/v/mmtracking)](https://pypi.org/project/mmtracking)
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[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmtracking.readthedocs.io/en/latest/)
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[![badge](https://github.com/open-mmlab/mmtracking/workflows/build/badge.svg)](https://github.com/open-mmlab/mmtracking/actions)
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[![codecov](https://codecov.io/gh/open-mmlab/mmtracking/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmtracking)
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[![license](https://img.shields.io/github/license/open-mmlab/mmtracking.svg)](https://github.com/open-mmlab/mmtracking/blob/master/LICENSE)
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Documentation: https://mmtracking.readthedocs.io/
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## Introduction
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MMTracking is an open source video perception toolbox based on PyTorch.
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It is a part of the OpenMMLab project.
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The master branch works with PyTorch 1.3 to 1.6.
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[DEMO]
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### Major features
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- **First Unified Video Perception Platform**
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We are the first open source toolbox that unifies versatile video perception tasks include video object detection, single object tracking, and multiple object tracking.
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- **Modular Design**
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We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules.
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- **Simpler, Faster and Stronger**
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**Simpler**: MMTracking is built upon [MMDetection](https://github.com/open-mmlab/mmdetection) that we can capitalize any detector only through modifying the configs.
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**Faster**: All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations.
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**Stronger**: We reproduce state-of-the-art models and some of them even outperform the offical implementations.
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## License
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This project is released under the [Apache 2.0 license](LICENSE).
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## Changelog
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v0.5.0 was released in -.
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Please refer to [changelog.md](docs/changelog.md) for details and release history.
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## Benchmark and model zoo
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Results and models are available in the [model zoo](docs/model_zoo.md).
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## Installation
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Please refer to [install.md](docs/install.md) for installation and dataset preparation.
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## Get Started
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Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMTracking.
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There are also tutorials.
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## Contributing
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We appreciate all contributions to improve MMTracking. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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## Acknowledgement
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MMTracking is an open source project that welcome any contribution and feedback.
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We wish that the toolbox and benchmark could serve the growing research
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community by providing a flexible as well as standardized toolkit to reimplement existing methods
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and develop their own new semantic segmentation methods.

‎resources/mmtrack-logo.png

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