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PoseC3D

Introduction

@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}
}
Pose Estimation Results


Keypoint Heatmap Volume Visualization


Limb Heatmap Volume Visualization


Model Zoo

FineGYM

config pseudo heatmap gpus backbone Mean Top-1 ckpt log json
slowonly_r50_u48_240e_gym_keypoint keypoint 8 x 2 SlowOnly-R50 93.7 ckpt log json
slowonly_r50_u48_240e_gym_limb limb 8 x 2 SlowOnly-R50 94.0 ckpt log json
Fusion 94.3

NTU60_XSub

config pseudo heatmap gpus backbone Top-1 ckpt log json
slowonly_r50_u48_240e_ntu60_xsub_keypoint keypoint 8 x 2 SlowOnly-R50 93.7 ckpt log json
slowonly_r50_u48_240e_ntu60_xsub_limb limb 8 x 2 SlowOnly-R50 93.4 ckpt log json
Fusion 94.1

NTU120_XSub

config pseudo heatmap gpus backbone Top-1 ckpt log json
slowonly_r50_u48_240e_ntu120_xsub_keypoint keypoint 8 x 2 SlowOnly-R50 86.3 ckpt log json
slowonly_r50_u48_240e_ntu120_xsub_limb limb 8 x 2 SlowOnly-R50 85.7 ckpt log json
Fusion 86.9

Notes:

  1. The gpus indicates the number of gpu we used to get the checkpoint. It is noteworthy that the configs we provide are used for 8 gpus as default. According to the Linear Scaling Rule, you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e.g., lr=0.01 for 8 GPUs x 8 videos/gpu and lr=0.04 for 16 GPUs x 16 videos/gpu.
  2. The values in columns named after "reference" are the results got by testing on our dataset, using the checkpoints provided by the author with same model settings. The checkpoints for reference repo can be downloaded here.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train PoseC3D model on FineGYM dataset in a deterministic option with periodic validation.

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

For more details, you can refer to Training setting part in getting_started.

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test PoseC3D model on FineGYM dataset and dump the result to a pickle file.

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

For more details, you can refer to Test a dataset part in getting_started.