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UDP-Pose

Official code of 《The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation》 Official code of 《AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation》 Illustrating the performance of the proposed UDP

News

  • [2021/1/12] A new version of UDP paper is provided with more clear and more detailed methodology explaination, extra experimental results, and more discoveries. ArXiv.
  • [2020/12/14] AID for mmpose is provided in HuangJunJie2017/mmpose alone with pretrained models in BaiduDisk(dsa9).
  • [2020/11/23] UDP for mmpose is provided in HuangJunJie2017/mmpose alone with pretrained models in BaiduDisk(dsa9). Examples for both top-down paradigm and bottom-up paradigm are provided in this branch.
  • [2020/11/04] We propose UDPv1 with LOSS.KPD=3.5. The performance of UDPv1 is superior when compared with UDP in coco dataset.
  • [2020/10/26] We get a better tradeoff between speed and precision by applying UDP to the state-of-the-art Bottom-Up methods.
  • [2020/8/23] We win the 2020 COCO Keypoint Detection Challenge with UDP!
  • [2020/6/12] UDP for hrnet and UDP for RSN are provided.
  • [2020/2/24] Paper has been accepted by CVPR2020!
  • [2019/11/10] Project page is created.
  • [2019/11/7] UDP is now on ArXiv. demo image

Top-Down

Results on MPII val dataset

Method--- Head Sho. Elb. Wri. Hip Kne. Ank. Mean Mean 0.1
HRNet32 97.1 95.9 90.3 86.5 89.1 87.1 83.3 90.3 37.7
+Dark 97.2 95.9 91.2 86.7 89.7 86.7 84.0 90.6 42.0
+UDP 97.4 96.0 91.0 86.5 89.1 86.6 83.3 90.4 42.1

Results on COCO val2017 with detector having human AP of 65.1 on COCO val2017 dataset

Arch Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR
pose_resnet_50 256x192 34.0M 8.90 71.3 89.9 78.9 68.3 77.4 76.9
+UDP 256x192 34.2M 8.96 72.9 90.0 80.2 69.7 79.3 78.2
pose_resnet_50 384x288 34.0M 20.0 73.2 90.7 79.9 69.4 80.1 78.2
+UDP 384x288 34.2M 20.1 74.0 90.3 80.0 70.2 81.0 79.0
pose_resnet_152 256x192 68.6M 15.7 72.9 90.6 80.8 69.9 79.0 78.3
+UDP 256x192 68.8M 15.8 74.3 90.9 81.6 71.2 80.6 79.6
pose_resnet_152 384x288 68.6M 35.6 75.3 91.0 82.3 71.9 82.0 80.4
+UDP 384x288 68.8M 35.7 76.2 90.8 83.0 72.8 82.9 81.2
pose_hrnet_w32 256x192 28.5M 7.10 75.6 91.9 83.0 72.2 81.6 80.5
+UDP 256x192 28.7M 7.16 76.8 91.9 83.7 73.1 83.3 81.6
+UDPv1 256x192 28.7M 7.16 77.2 91.6 84.2 73.7 83.7 82.5
+UDPv1+AID 256x192 28.7M 7.16 77.9 92.1 84.5 74.1 84.1 82.8
RSN18+UDP 256x192 - 2.5 74.7 - - - - -
pose_hrnet_w32 384x288 28.5M 16.0 76.7 91.9 83.6 73.2 83.2 81.6
+UDP 384x288 28.7M 16.1 77.8 91.7 84.5 74.2 84.3 82.4
pose_hrnet_w48 256x192 63.6M 14.6 75.9 91.9 83.5 72.6 82.1 80.9
+UDP 256x192 63.8M 14.7 77.2 91.8 83.7 73.8 83.7 82.0
pose_hrnet_w48 384x288 63.6M 32.9 77.1 91.8 83.8 73.5 83.5 81.8
+UDP 384x288 63.8M 33.0 77.8 92.0 84.3 74.2 84.5 82.5

Note:

  • Flip test is used.
  • Person detector has person AP of 65.1 on COCO val2017 dataset.
  • GFLOPs is for convolution and linear layers only.
  • UDPv1: v0:LOSS.KPD=4.0, v1:LOSS.KPD=3.5

Results on COCO test-dev with detector having human AP of 65.1 on COCO val2017 dataset

Arch Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR
pose_resnet_50 256x192 34.0M 8.90 70.2 90.9 78.3 67.1 75.9 75.8
+UDP 256x192 34.2M 8.96 71.7 91.1 79.6 68.6 77.5 77.2
pose_resnet_50 384x288 34.0M 20.0 71.3 91.0 78.5 67.3 77.9 76.6
+UDP 384x288 34.2M 20.1 72.5 91.1 79.7 68.8 79.1 77.9
pose_resnet_152 256x192 68.6M 15.7 71.9 91.4 80.1 68.9 77.4 77.5
+UDP 256x192 68.8M 15.8 72.9 91.6 80.9 70.0 78.5 78.4
pose_resnet_152 384x288 68.6M 35.6 73.8 91.7 81.2 70.3 80.0 79.1
+UDP 384x288 68.8M 35.7 74.7 91.8 82.1 71.5 80.8 80.0
pose_hrnet_w32 256x192 28.5M 7.10 73.5 92.2 82.0 70.4 79.0 79.0
+UDP 256x192 28.7M 7.16 75.2 92.4 82.9 72.0 80.8 80.4
pose_hrnet_w32 384x288 28.5M 16.0 74.9 92.5 82.8 71.3 80.9 80.1
+UDP 384x288 28.7M 16.1 76.1 92.5 83.5 72.8 82.0 81.3
pose_hrnet_w48 256x192 63.6M 14.6 74.3 92.4 82.6 71.2 79.6 79.7
+UDP 256x192 63.8M 14.7 75.7 92.4 83.3 72.5 81.4 80.9
pose_hrnet_w48 384x288 63.6M 32.9 75.5 92.5 83.3 71.9 81.5 80.5
+UDP 384x288 63.8M 33.0 76.5 92.7 84.0 73.0 82.4 81.6

Note:

  • Flip test is used.
  • Person detector has person AP of 65.1 on COCO val2017 dataset.
  • GFLOPs is for convolution and linear layers only.

Bottom-Up

HRNet

Arch P2I Input size Speed(task/s) AP Ap .5 AP .75 AP (M) AP (L) AR
HRNet(ori) T 512x512 - 64.4 - - 57.1 75.6 -
HRNet(mmpose) F 512x512 39.5 65.8 86.3 71.8 59.2 76.0 70.7
HRNet(mmpose) T 512x512 6.8 65.3 86.2 71.5 58.6 75.7 70.9
HRNet+UDP T 512x512 5.8 65.9 86.2 71.8 59.4 76.0 71.4
HRNet+UDP F 512x512 37.2 67.0 86.2 72.0 60.7 76.7 71.6
HRNet+UDP+AID F 512x512 37.2 68.4 88.1 74.9 62.7 77.1 73.0

HigherHRNet

Arch P2I Input size Speed(task/s) AP Ap .5 AP .75 AP (M) AP (L) AR
HigherHRNet(ori) T 512x512 - 67.1 - - 61.5 76.1 -
HigherHRNet T 512x512 9.4 67.2 86.1 72.9 61.8 76.1 72.2
HigherHRNet+UDP T 512x512 9.0 67.6 86.1 73.7 62.2 76.2 72.4
HigherHRNet F 512x512 24.1 67.1 86.1 73.6 61.7 75.9 72.0
HigherHRNet+UDP F 512x512 23.0 67.6 86.2 73.8 62.2 76.2 72.4
HigherHRNet+UDP+AID F 512x512 23.0 69.0 88.0 74.9 64.0 76.9 73.8

Note:

  • ori : Result from original HigherHrnet
  • mmpose : Pretrained models from mmpose
  • P2I : PROJECT2IMAGE
  • we use mmpose for codebase
  • the configurations of the baseline are HRNet-W32-512x512-batch16-lr0.001
  • Speed is tested with dist_test in mmpose codebase and 8 Gpus + 16 batchsize

Quick Start

Please refer to Install / Data Preparation / Get Start

Data preparation For coco, we provide the human detection result and pretrained model at BaiduDisk(dsa9)

Citation

If you use our code or models in your research, please cite with:

@InProceedings{Huang_2020_CVPR,
author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
@article{huang2020aid,
  title={AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation,
  author={Huang, Junjie and Zhu, Zheng and Huang, Guan and Du, Dalong},
  journal={arXiv preprint arXiv:2008.07139},
  year={2020}
}