# PoseC3D ## 简介 ```BibTeX @misc{duan2021revisiting, title={Revisiting Skeleton-based Action Recognition}, author={Haodong Duan and Yue Zhao and Kai Chen and Dian Shao and Dahua Lin and Bo Dai}, year={2021}, eprint={2104.13586}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
姿态估计结果


关键点热图三维体可视化


肢体热图三维体可视化


## 模型库 ### FineGYM | 配置文件 | 热图类型 | GPU 数量 | 主干网络 | Mean Top-1 | ckpt | log | json | | :---------------------------------------------------------------------------------------------------- | :------: | :------: | :----------: | :--------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: | | [slowonly_r50_u48_240e_gym_keypoint](/configs/skeleton/posec3d/slowonly_r50_u48_240e_gym_keypoint.py) | 关键点 | 8 x 2 | SlowOnly-R50 | 93.7 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_gym_keypoint/slowonly_r50_u48_240e_gym_keypoint-b07a98a0.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_gym_keypoint/slowonly_r50_u48_240e_gym_keypoint.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_gym_keypoint/slowonly_r50_u48_240e_gym_keypoint.json) | | [slowonly_r50_u48_240e_gym_limb](/configs/skeleton/posec3d/slowonly_r50_u48_240e_gym_limb.py) | 肢体 | 8 x 2 | SlowOnly-R50 | 94.0 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_gym_limb/slowonly_r50_u48_240e_gym_limb-c0d7b482.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_gym_limb/slowonly_r50_u48_240e_gym_limb.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_gym_limb/slowonly_r50_u48_240e_gym_limb.json) | | 融合预测结果 | | | | 94.3 | | | | ### NTU60_XSub | 配置文件 | 热图类型 | GPU 数量 | 主干网络 | Top-1 | ckpt | log | json | | :------------------------------------------------------------------------------------------------------------------ | :------: | :------: | :----------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | | [slowonly_r50_u48_240e_ntu60_xsub_keypoint](/configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_keypoint.py) | 关键点 | 8 x 2 | SlowOnly-R50 | 93.7 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_keypoint/slowonly_r50_u48_240e_ntu60_xsub_keypoint-f3adabf1.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_keypoint/slowonly_r50_u48_240e_ntu60_xsub_keypoint.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_keypoint/slowonly_r50_u48_240e_ntu60_xsub_keypoint.json) | | [slowonly_r50_u48_240e_ntu60_xsub_limb](/configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_limb.py) | 肢体 | 8 x 2 | SlowOnly-R50 | 93.4 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_limb/slowonly_r50_u48_240e_ntu60_xsub_limb-1d69006a.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_limb/slowonly_r50_u48_240e_ntu60_xsub_limb.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu60_xsub_limb/slowonly_r50_u48_240e_ntu60_xsub_limb.json) | | 融合预测结果 | | | | 94.1 | | | | ### NTU120_XSub | 配置文件 | 热图类型 | GPU 数量 | 主干网络 | Top-1 | ckpt | log | json | | :-------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :----------: | :---: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------: | | [slowonly_r50_u48_240e_ntu120_xsub_keypoint](/configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py) | 关键点 | 8 x 2 | SlowOnly-R50 | 86.3 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint/slowonly_r50_u48_240e_ntu120_xsub_keypoint-6736b03f.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint/slowonly_r50_u48_240e_ntu120_xsub_keypoint.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint/slowonly_r50_u48_240e_ntu120_xsub_keypoint.json) | | [slowonly_r50_u48_240e_ntu120_xsub_limb](/configs/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_limb.py) | 肢体 | 8 x 2 | SlowOnly-R50 | 85.7 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_limb/slowonly_r50_u48_240e_ntu120_xsub_limb-803c2317.pth?) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_limb/slowonly_r50_u48_240e_ntu120_xsub_limb.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_r50_u48_240e_ntu120_xsub_limb/slowonly_r50_u48_240e_ntu120_xsub_limb.json) | | 融合预测结果 | | | | 86.9 | | | | ### UCF101 | 配置文件 | 热图类型 | GPU 数量 | 主干网络 | Top-1 | ckpt | log | json | | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :----------: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint](/configs/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint.py) | 关键点 | 8 | SlowOnly-R50 | 87.0 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint-cae8aa4a.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint/slowonly_kinetics400_pretrained_r50_u48_120e_ucf101_split1_keypoint.json) | ### HMDB51 | 配置文件 | 热图类型 | GPU 数量 | 主干网络 | Top-1 | ckpt | log | json | | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | :------: | :----------: | :---: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint](/configs/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint.py) | 关键点 | 8 | SlowOnly-R50 | 69.3 | [ckpt](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint-76ffdd8b.pth) | [log](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint.log) | [json](https://download.openmmlab.com/mmaction/skeleton/posec3d/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint/slowonly_kinetics400_pretrained_r50_u48_120e_hmdb51_split1_keypoint.json) | 注： 1. 这里的 **GPU 数量** 指的是得到模型权重文件对应的 GPU 个数。默认地，MMAction2 所提供的配置文件对应使用 8 块 GPU 进行训练的情况。 依据 [线性缩放规则](https://arxiv.org/abs/1706.02677)，当用户使用不同数量的 GPU 或者每块 GPU 处理不同视频个数时，需要根据批大小等比例地调节学习率。 如，lr=0.2 对应 8 GPUs x 16 video/gpu，以及 lr=0.4 对应 16 GPUs x 16 video/gpu。 2. 用户可以参照 [准备骨骼数据集](https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README_zh-CN.md) 来获取以上配置文件使用的骨骼标注。 ## 如何训练 用户可以使用以下指令进行模型训练。 ```shell python tools/train.py ${CONFIG_FILE} [optional arguments] ``` Example: 以确定性的训练，加以定期的验证过程进行 PoseC3D 模型在 FineGYM 数据集上的训练。 ```shell python tools/train.py configs/skeleton/posec3d/slowonly_r50_u48_240e_gym_keypoint.py \ --work-dir work_dirs/slowonly_r50_u48_240e_gym_keypoint \ --validate --seed 0 --deterministic ``` 有关自定义数据集上的训练，可以参考 [Custom Dataset Training](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md)。 更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。 ## 如何测试 用户可以使用以下指令进行模型测试。 ```shell python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments] ``` Example: 在 FineGYM 数据集上测试 PoseC3D 模型，并将结果导出为一个 pickle 文件。 ```shell python tools/test.py configs/skeleton/posec3d/slowonly_r50_u48_240e_gym_keypoint.py \ checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \ --out result.pkl ``` 更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。