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‎README.md‎

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## 0.x / 1.x Migration
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MMPose v1.0.0 is a major update, including many API and config file changes. Currently, a part of the algorithms have been migrated to v1.0.0, and the remaining algorithms will be completed in subsequent versions. We will show the migration progress in the following list.
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<details close>
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<summary><b>Migration Progress</b></summary>
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| Algorithm | Status |
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| :-------------------------------- | :---------: |
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| MTUT (CVPR 2019) | |
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| MSPN (ArXiv 2019) | done |
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| InterNet (ECCV 2020) | |
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| DEKR (CVPR 2021) | done |
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| HigherHRNet (CVPR 2020) | |
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| DeepPose (CVPR 2014) | done |
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| RLE (ICCV 2021) | done |
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| SoftWingloss (TIP 2021) | done |
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| VideoPose3D (CVPR 2019) | done |
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| Hourglass (ECCV 2016) | done |
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| LiteHRNet (CVPR 2021) | done |
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| AdaptiveWingloss (ICCV 2019) | done |
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| SimpleBaseline2D (ECCV 2018) | done |
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| PoseWarper (NeurIPS 2019) | |
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| SimpleBaseline3D (ICCV 2017) | done |
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| HMR (CVPR 2018) | |
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| UDP (CVPR 2020) | done |
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| VIPNAS (CVPR 2021) | done |
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| Wingloss (CVPR 2018) | done |
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| DarkPose (CVPR 2020) | done |
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| Associative Embedding (NIPS 2017) | in progress |
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| VoxelPose (ECCV 2020) | |
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| RSN (ECCV 2020) | done |
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| CID (CVPR 2022) | done |
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| CPM (CVPR 2016) | done |
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| HRNet (CVPR 2019) | done |
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| HRNetv2 (TPAMI 2019) | done |
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| SCNet (CVPR 2020) | done |
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</details>
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MMPose v1.0.0 is a major update, including many API and config file changes. Currently, a part of the algorithms have been migrated to v1.0.0, and the remaining algorithms will be completed in subsequent versions. We will show the migration progress in this [Roadmap](https://github.com/open-mmlab/mmpose/issues/2258).
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If your algorithm has not been migrated, you can continue to use the [0.x branch](https://github.com/open-mmlab/mmpose/tree/0.x) and [old documentation](https://mmpose.readthedocs.io/en/0.x/).
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- [Configs](https://mmpose.readthedocs.io/en/latest/user_guides/configs.html)
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- [Prepare Datasets](https://mmpose.readthedocs.io/en/latest/user_guides/prepare_datasets.html)
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- [Train and Test](https://mmpose.readthedocs.io/en/latest/user_guides/train_and_test.html)
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- [Deployment](https://mmpose.readthedocs.io/en/dev-1.x/user_guides/how_to_deploy.html)
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- [Model Analysis](https://mmpose.readthedocs.io/en/dev-1.x/user_guides/model_analysis.html)
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- [Dataset Annotation and Preprocessing](https://mmpose.readthedocs.io/en/dev-1.x/user_guides/dataset_tools.html)
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2. For developers who wish to develop based on MMPose:
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‎README_CN.md‎

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## 0.x / 1.x 迁移
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MMPose v1.0.0 是一个重大更新，包括了大量的 API 和配置文件的变化。目前 v1.0.0 中已经完成了一部分算法的迁移工作，剩余的算法将在后续的版本中陆续完成，我们将在下面的列表中展示迁移进度。
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<details close>
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<summary><b>迁移进度</b></summary>
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| 算法名称 | 迁移进度 |
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| :-------------------------------- | :---------: |
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| MTUT (CVPR 2019) | |
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| MSPN (ArXiv 2019) | done |
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| InterNet (ECCV 2020) | |
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| DEKR (CVPR 2021) | done |
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| HigherHRNet (CVPR 2020) | |
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| DeepPose (CVPR 2014) | done |
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| RLE (ICCV 2021) | done |
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| SoftWingloss (TIP 2021) | done |
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| VideoPose3D (CVPR 2019) | done |
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| Hourglass (ECCV 2016) | done |
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| LiteHRNet (CVPR 2021) | done |
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| AdaptiveWingloss (ICCV 2019) | done |
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| SimpleBaseline2D (ECCV 2018) | done |
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| PoseWarper (NeurIPS 2019) | |
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| SimpleBaseline3D (ICCV 2017) | done |
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| HMR (CVPR 2018) | |
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| UDP (CVPR 2020) | done |
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| VIPNAS (CVPR 2021) | done |
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| Wingloss (CVPR 2018) | done |
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| DarkPose (CVPR 2020) | done |
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| Associative Embedding (NIPS 2017) | in progress |
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| VoxelPose (ECCV 2020) | |
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| RSN (ECCV 2020) | done |
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| CID (CVPR 2022) | done |
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| CPM (CVPR 2016) | done |
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| HRNet (CVPR 2019) | done |
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| HRNetv2 (TPAMI 2019) | done |
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| SCNet (CVPR 2020) | done |
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</details>
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MMPose v1.0.0 是一个重大更新，包括了大量的 API 和配置文件的变化。目前 v1.0.0 中已经完成了一部分算法的迁移工作，剩余的算法将在后续的版本中陆续完成，我们将在这个 [Issue 页面](https://github.com/open-mmlab/mmpose/issues/2258) 中展示迁移进度。
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如果您使用的算法还没有完成迁移，您也可以继续使用访问 [0.x 分支](https://github.com/open-mmlab/mmpose/tree/0.x) 和 [旧版文档](https://mmpose.readthedocs.io/zh_CN/0.x/)
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- [配置文件](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/configs.html)
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- [准备数据集](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/prepare_datasets.html)
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- [训练与测试](https://mmpose.readthedocs.io/zh_CN/latest/user_guides/train_and_test.html)
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- [模型部署](https://mmpose.readthedocs.io/zh_CN/dev-1.x/user_guides/how_to_deploy.html)
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- [模型分析工具](https://mmpose.readthedocs.io/zh_CN/dev-1.x/user_guides/model_analysis.html)
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- [数据集标注与预处理脚本](https://mmpose.readthedocs.io/zh_CN/dev-1.x/user_guides/dataset_tools.html)
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2. 对于希望基于 MMPose 进行开发的研究者和开发者：
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- [自定义数据变换](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_transforms.html)
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- [自定义优化器](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_optimizer.html)
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- [自定义日志](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/customize_logging.html)
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- [模型部署](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/how_to_deploy.html)
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- [模型分析工具](https://mmpose.readthedocs.io/zh_CN/latest/advanced_guides/model_analysis.html)
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- [迁移指南](https://mmpose.readthedocs.io/zh_CN/latest/migration.html)
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3. 对于希望加入开源社区，向 MMPose 贡献代码的研究者和开发者：

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