Skip to content

Latest commit

 

History

History
131 lines (104 loc) · 6.79 KB

README.md

File metadata and controls

131 lines (104 loc) · 6.79 KB
Raw

Introduction

This is the official code of Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation. This paper has been accepted to CVPR2021.

This repo is built on Bottom-up-Higher-HRNet.

Main Results

Results on COCO val2017 without multi-scale test

Method Backbone Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L)
HigherHRNet HRNet-w32 512 28.6M 47.9 67.1 86.2 73.0 61.5 76.1
HigherHRNet + SWAHR HRNet-w32 512 28.6M 48.0 68.9 87.8 74.9 63.0 77.4
HigherHRNet HRNet-w48 640 63.8M 154.3 69.9 87.2 76.1 65.4 76.4
HigherHRNet + SWAHR HRNet-w48 640 63.8M 154.6 70.8 88.5 76.8 66.3 77.4

Results on COCO val2017 with multi-scale test

Method Backbone Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L)
HigherHRNet HRNet-w32 512 28.6M 47.9 69.9 87.1 76.0 65.3 77.0
HigherHRNet + SWAHR HRNet-w32 512 28.6M 48.0 71.4 88.9 77.8 66.3 78.9
HigherHRNet HRNet-w48 640 63.8M 154.3 72.1 88.4 78.2 67.8 78.3
HigherHRNet + SWAHR HRNet-w48 640 63.8M 154.6 73.2 89.8 79.1 69.1 79.3

Results on COCO test-dev2017 without multi-scale test

Method Backbone Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L)
OpenPose* - - - - 61.8 84.9 67.5 57.1 68.2
Hourglass Hourglass 512 277.8M 206.9 56.6 81.8 61.8 49.8 67.0
PersonLab ResNet-152 1401 68.7M 405.5 66.5 88.0 72.6 62.4 72.3
PifPaf - - - - 66.7 - - 62.4 72.9
Bottom-up HRNet HRNet-w32 512 28.5M 38.9 64.1 86.3 70.4 57.4 73.9
HigherHRNet HRNet-w32 512 28.6M 47.9 66.4 87.5 72.8 61.2 74.2
HigherHRNet + SWAHR HRNet-w32 512 28.6M 48.0 67.9 88.9 74.5 62.4 75.5
HigherHRNet HRNet-w48 640 63.8M 154.3 68.4 88.2 75.1 64.4 74.2
HigherHRNet + SWAHR HRNet-w48 640 63.8M 154.6 70.2 89.9 76.9 65.2 77.0

Results on COCO test-dev2017 with multi-scale test

Method Backbone Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L)
Hourglass Hourglass 512 277.8M 206.9 63.0 85.7 68.9 58.0 70.4
Hourglass* Hourglass 512 277.8M 206.9 65.5 86.8 72.3 60.6 72.6
PersonLab ResNet-152 1401 68.7M 405.5 68.7 89.0 75.4 64.1 75.5
HigherHRNet HRNet-w48 640 63.8M 154.3 70.5 89.3 77.2 66.6 75.8
HigherHRNet + SWAHR HRNet-w48 640 63.8M 154.6 72.0 90.7 78.8 67.8 77.7

Results on CrowdPose test

Method AP Ap .5 AP .75 AP (E) AP (M) AP (H)
Mask-RCNN 57.2 83.5 60.3 69.4 57.9 45.8
AlphaPose 61.0 81.3 66.0 71.2 61.4 51.1
SPPE 66.0. 84.2 71.5 75.5 66.3 57.4
OpenPose - - - 62.7 48.7 32.3
HigherHRNet 65.9 86.4 70.6 73.3 66.5 57.9
HigherHRNet + SWAHR 71.6 88.5 77.6 78.9 72.4 63.0
HigherHRNet* 67.6 87.4 72.6 75.8 68.1 58.9
HigherHRNet + SWAHR* 73.8 90.5 79.9 81.2 74.7 64.7

'*' indicates multi-scale test

Installation

The details about preparing the environment and datasets can be referred to README.md.

Downlaod our pretrained weights from BaidunYun(Password: 8weh) or GoogleDrive to ./models.

Training and Testing

Testing on COCO val2017 dataset using pretrained weights

For single-scale testing:

python tools/dist_valid.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pose_coco/pose_higher_hrnet_w32_512.pth

By default, we use horizontal flip. To test without flip:

python tools/dist_valid.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pose_coco/pose_higher_hrnet_w32_512.pth \
    TEST.FLIP_TEST False

Multi-scale testing is also supported, although we do not report results in our paper:

python tools/dist_valid.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pose_coco/pose_higher_hrnet_w32_512.pth \
    TEST.SCALE_FACTOR '[0.5, 1.0, 2.0]'

Training on COCO train2017 dataset

python tools/dist_train.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml

By default, it will use all available GPUs on the machine for training. To specify GPUs, use

CUDA_VISIBLE_DEVICES=0,1 python tools/dist_train.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml

Testing on your own images

python tools/dist_inference.py \
    --img_dir path/to/your/directory/of/images \
    --save_dir path/where/results/are/saved \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pose_coco/pose_higher_hrnet_w32_512.pth \
    TEST.SCALE_FACTOR '[0.5, 1.0, 2.0]'

Citation

If you find this work or code is helpful in your research, please cite:

@inproceedings{LuoSWAHR,
  title={Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation},
  author={Zhengxiong Luo and Zhicheng Wang and Yan Huang and Liang Wang and Tieniu Tan and Erjin Zhou},
  booktitle={CVPR},
  year={2021}
}