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Weight Standardization

Introduction

@article{weightstandardization,
  author    = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
  title     = {Weight Standardization},
  journal   = {arXiv preprint arXiv:1903.10520},
  year      = {2019},
}

Results and Models

Faster R-CNN

Backbone Style Normalization Lr schd Mem (GB) Inf time (fps) box AP mask AP Download
R-50-FPN pytorch GN+WS 1x 5.9 11.7 39.7 - model | log
R-101-FPN pytorch GN+WS 1x 8.9 9.0 41.7 - model | log
X-50-32x4d-FPN pytorch GN+WS 1x 7.0 10.3 40.7 - model | log
X-101-32x4d-FPN pytorch GN+WS 1x 10.8 7.6 42.1 - model | log

Mask R-CNN

Backbone Style Normalization Lr schd Mem (GB) Inf time (fps) box AP mask AP Download
R-50-FPN pytorch GN+WS 2x 7.3 10.5 40.6 36.6 model | log
R-101-FPN pytorch GN+WS 2x 10.3 8.6 42.0 37.7 model | log
X-50-32x4d-FPN pytorch GN+WS 2x 8.4 9.3 41.1 37.0 model | log
X-101-32x4d-FPN pytorch GN+WS 2x 12.2 7.1 42.1 37.9 model | log
R-50-FPN pytorch GN+WS 20-23-24e 7.3 - 41.1 37.1 model | log
R-101-FPN pytorch GN+WS 20-23-24e 10.3 - 43.1 38.6 model | log
X-50-32x4d-FPN pytorch GN+WS 20-23-24e 8.4 - 42.1 38.0 model | log
X-101-32x4d-FPN pytorch GN+WS 20-23-24e 12.2 - 42.7 38.5 model | log

Note:

  • GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN.
  • In the paper, a 20-23-24e lr schedule is used instead of 2x.
  • The X-50-GN and X-101-GN pretrained models are also shared by the authors.