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* Update README.md * Update README.md and README_zh-CN.md * Update README.md and README_zh-CN.md * Update README_zh-CN.md Co-authored-by: ToumaKazusa3 <shensanjing@chensanjingdeMacBook-Air.local>
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<div align="center"> | ||
<img src="resources/mmtrack-logo.png" width="600"/> | ||
</div> | ||
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[![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) | ||
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[English](/README.md) | 简体中文 | ||
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文档: https://mmtracking.readthedocs.io/ | ||
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## 简介 | ||
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MMTracking是一款基于PyTorch的视频目标感知开源工具箱,是[OpenMMLab](http://openmmlab.org/)项目的一部分。 | ||
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主分支代码目前支持**PyTorch 1.3以上**的版本。 | ||
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<div align="left"> | ||
<img src="https://user-images.githubusercontent.com/24663779/103343312-c724f480-4ac6-11eb-9c22-b56f1902584e.gif" width="800"/> | ||
</div> | ||
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### 主要特性 | ||
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- **首个开源一体化视频目标感知平台** | ||
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MMTracking是首个开源一体化视频目标感知工具箱,同时支持视频目标检测,单目标跟踪和多目标跟踪等多种任务和算法。 | ||
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- **模块化设计** | ||
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MMTracking将统一的视频目标感知框架解耦成不同的模块组件,通过组合不同模块组件,用户可以便捷地构建自定义视频目标感知模型。 | ||
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- **简洁、高效、强大** | ||
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**简洁**:MMTracking与其他OpenMMLab平台充分交互。MMTracking充分复用[MMDetection](https://github.com/open-mmlab/mmdetection)中的已有模块,我们只需要修改配置文件就可以使用任何检测器。 | ||
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**高效**:MMTracking所有操作都在GPU上运行。相比其他开源库的实现,MMTracking的训练和推理更加高效。 | ||
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**强大**:MMTracking复现了SOTA性能的模型。受益于[MMDetection](https://github.com/open-mmlab/mmdetection)的持续推进,部分实现精度超出官方版本。 | ||
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## 许可 | ||
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该项目遵循[Apache 2.0 license](/LICENSE)开源协议。 | ||
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## 更新记录 | ||
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v0.5.1版本已于2021年2月1日发布,可通过查阅[更新日志](/docs/changelog.md)了解更多细节以及发布历史。 | ||
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## 基准测试与模型库 | ||
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本工具箱支持的各个模型的结果和设置都可以在[模型库](docs/model_zoo.md)页面中查看。 | ||
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支持的视频目标检测算法: | ||
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- [x] [DFF](configs/vid/dff) (CVPR 2017) | ||
- [x] [FGFA](configs/vid/fgfa) (ICCV 2017) | ||
- [x] [SELSA](configs/vid/selsa) (ICCV 2019) | ||
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支持的多目标跟踪算法: | ||
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- [x] [SORT/DeepSORT](configs/mot/deepsort) (ICIP 2016/2017) | ||
- [x] [Tracktor](configs/mot/tracktor) (ICCV 2019) | ||
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支持的单目标跟踪算法: | ||
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- [x] [SiameseRPN++](configs/sot/siamese_rpn) (CVPR 2019) | ||
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## 安装 | ||
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请参考[安装指南](docs/install.md)进行安装。 | ||
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## 教程 | ||
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请参考[数据集](docs/dataset.md)和[快速开始](docs/quick_run.md)了解MMTracking的基本使用。MMTracking也提供了更详细的[教程](docs/tutorials/)。 | ||
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## 引用 | ||
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如果你觉得MMTracking对你的研究有所帮助,可以考虑引用它: | ||
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```latex | ||
@misc{mmtrack2020, | ||
title={{MMTracking: OpenMMLab} video perception toolbox and benchmark}, | ||
author={MMTracking Contributors}, | ||
howpublished = {\url{https://github.com/open-mmlab/mmtracking}}, | ||
year={2020} | ||
} | ||
``` | ||
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## 参与贡献 | ||
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我们非常欢迎用户对于MMTracking做出的任何贡献,可以参考[贡献指南]([/.github/CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md))文件了解更多细节。 | ||
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## 致谢 | ||
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MMTracking是一款开源项目,我们欢迎任何贡献和反馈。我们希望该工具箱和基准测试可以为社区提供灵活的代码工具,供用户复现现有算法并开发自己新的视频目标感知方法。 | ||
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## OpenMMLab 的其他项目 | ||
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- [MMCV](https://github.com/open-mmlab/mmcv):OpenMMLab计算机视觉基础库 | ||
- [MMClassification](https://github.com/open-mmlab/mmclassification):OpenMMLab图像分类工具箱与测试基准 | ||
- [MMDetection](https://github.com/open-mmlab/mmdetection):OpenMMLab检测工具箱与测试基准 | ||
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d):OpenMMLab新一代通用3D目标检测平台 | ||
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation):OpenMMLab语义分割工具箱与测试基准 | ||
- [MMAction2](https://github.com/open-mmlab/mmaction2):OpenMMLab新一代视频理解工具箱与测试基准 | ||
- [MMTracking](https://github.com/open-mmlab/mmtracking):OpenMMLab一体化视频目标感知平台 | ||
- [MMPose](https://github.com/open-mmlab/mmpose):OpenMMLab姿态估计工具箱与测试基准 | ||
- [MMEditing](https://github.com/open-mmlab/mmediting):OpenMMLab图像视频编辑工具箱 | ||
- [MMOCR](https://github.com/open-mmlab/mmocr):OpenMMLab全流程文字检测识别理解工具包 |