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HRNet

Deep High-Resolution Representation Learning for Human Pose Estimation

Introduction

Official Repo

Code Snippet

Abstract

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions \emph{in series} (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams \emph{in parallel}; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at this https URL.

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 512x1024 40000 1.7 23.74 V100 73.86 75.91 config model | log
FCN HRNetV2p-W18 512x1024 40000 2.9 12.97 V100 77.19 78.92 config model | log
FCN HRNetV2p-W48 512x1024 40000 6.2 6.42 V100 78.48 79.69 config model | log
FCN HRNetV2p-W18-Small 512x1024 80000 - - V100 75.31 77.48 config model | log
FCN HRNetV2p-W18 512x1024 80000 - - V100 78.65 80.35 config model | log
FCN HRNetV2p-W48 512x1024 80000 - - V100 79.93 80.72 config model | log
FCN HRNetV2p-W18-Small 512x1024 160000 - - V100 76.31 78.31 config model | log
FCN HRNetV2p-W18 512x1024 160000 - - V100 78.80 80.74 config model | log
FCN HRNetV2p-W48 512x1024 160000 - - V100 80.65 81.92 config model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 512x512 80000 3.8 38.66 V100 31.38 32.45 config model | log
FCN HRNetV2p-W18 512x512 80000 4.9 22.57 V100 36.27 37.28 config model | log
FCN HRNetV2p-W48 512x512 80000 8.2 21.23 V100 41.90 43.27 config model | log
FCN HRNetV2p-W18-Small 512x512 160000 - - V100 33.07 34.56 config model | log
FCN HRNetV2p-W18 512x512 160000 - - V100 36.79 38.58 config model | log
FCN HRNetV2p-W48 512x512 160000 - - V100 42.02 43.86 config model | log

Pascal VOC 2012 + Aug

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 512x512 20000 1.8 43.36 V100 65.5 68.89 config model | log
FCN HRNetV2p-W18 512x512 20000 2.9 23.48 V100 72.30 74.71 config model | log
FCN HRNetV2p-W48 512x512 20000 6.2 22.05 V100 75.87 78.58 config model | log
FCN HRNetV2p-W18-Small 512x512 40000 - - V100 66.61 70.00 config model | log
FCN HRNetV2p-W18 512x512 40000 - - V100 72.90 75.59 config model | log
FCN HRNetV2p-W48 512x512 40000 - - V100 76.24 78.49 config model | log

Pascal Context

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W48 480x480 40000 6.1 8.86 V100 45.14 47.42 config model | log
FCN HRNetV2p-W48 480x480 80000 - - V100 45.84 47.84 config model | log

Pascal Context 59

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W48 480x480 40000 - - V100 50.33 52.83 config model | log
FCN HRNetV2p-W48 480x480 80000 - - V100 51.12 53.56 config model | log

LoveDA

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 512x512 80000 1.59 24.87 V100 49.28 49.42 config model | log
FCN HRNetV2p-W18 512x512 80000 2.76 12.92 V100 50.81 50.95 config model | log
FCN HRNetV2p-W48 512x512 80000 6.20 9.61 V100 51.42 51.64 config model | log

Potsdam

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 512x512 80000 1.58 36.00 V100 77.64 78.8 config model | log
FCN HRNetV2p-W18 512x512 80000 2.76 19.25 V100 78.26 79.24 config model | log
FCN HRNetV2p-W48 512x512 80000 6.20 16.42 V100 78.39 79.34 config model | log

Vaihingen

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 512x512 80000 1.58 38.11 V100 71.81 73.1 config model | log
FCN HRNetV2p-W18 512x512 80000 2.76 19.55 V100 72.57 74.09 config model | log
FCN HRNetV2p-W48 512x512 80000 6.20 17.25 V100 72.50 73.52 config model | log

iSAID

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
FCN HRNetV2p-W18-Small 896x896 80000 4.95 13.84 V100 62.30 62.97 config model | log
FCN HRNetV2p-W18 896x896 80000 8.30 7.71 V100 65.06 65.60 config model | log
FCN HRNetV2p-W48 896x896 80000 16.89 7.34 V100 67.80 68.53 config model | log

Note:

Citation

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}