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High-resolution networks (HRNets) for object detection

Introduction

@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}
}

@article{SunZJCXLMWLW19,
  title={High-Resolution Representations for Labeling Pixels and Regions},
  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao 
  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
  journal   = {CoRR},
  volume    = {abs/1904.04514},
  year={2019}
}

Results and Models

Faster R-CNN

Backbone Style Lr schd box AP Download
HRNetV2p-W18 pytorch 1x 36.1 model
HRNetV2p-W18 pytorch 2x 38.3 model
HRNetV2p-W32 pytorch 1x 39.5 model
HRNetV2p-W32 pytorch 2x 40.6 model
HRNetV2p-W48 pytorch 1x 40.9 model
HRNetV2p-W48 pytorch 2x 41.5 model

Mask R-CNN

Backbone Style Lr schd box AP mask AP Download
HRNetV2p-W18 pytorch 1x 37.3 34.2 model
HRNetV2p-W18 pytorch 2x 39.2 35.7 model
HRNetV2p-W32 pytorch 1x 40.7 36.8 model
HRNetV2p-W32 pytorch 2x 41.7 37.5 model
HRNetV2p-W48 pytorch 1x 42.4 38.1 model
HRNetV2p-W48 pytorch 2x 42.9 38.3 model

Cascade R-CNN

Backbone Style Lr schd box AP Download
HRNetV2p-W18 pytorch 20e 41.2 model
HRNetV2p-W32 pytorch 20e 43.7 model
HRNetV2p-W48 pytorch 20e 44.6 model

Cascade Mask R-CNN

Backbone Style Lr schd box AP mask AP Download
HRNetV2p-W18 pytorch 20e 41.9 36.4 model
HRNetV2p-W32 pytorch 20e 44.5 38.5 model
HRNetV2p-W48 pytorch 20e 46.0 39.5 model

Hybrid Task Cascade (HTC)

Backbone Style Lr schd box AP mask AP Download
HRNetV2p-W18 pytorch 20e 43.1 37.9 model
HRNetV2p-W32 pytorch 20e 45.3 39.6 model
HRNetV2p-W48 pytorch 20e 46.8 40.7 model
HRNetV2p-W48 pytorch 28e 47.0 41.0 model
X-101-64x4d-FPN pytorch 28e 46.8 40.7 model

FCOS

Backbone Style GN MS train Lr schd box AP Download
HRNetV2p-W18 pytorch Y N 1x 35.2 model
HRNetV2p-W18 pytorch Y N 2x 38.2 model
HRNetV2p-W32 pytorch Y N 1x 37.7 model
HRNetV2p-W32 pytorch Y N 2x 40.3 model
HRNetV2p-W18 pytorch Y Y 2x 38.1 model
HRNetV2p-W32 pytorch Y Y 2x 41.4 model
HRNetV2p-W48 pytorch Y Y 2x 42.9 model

Note:

  • The 28e schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs.
  • HRNetV2 ImageNet pretrained models are in HRNets for Image Classification.