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GCNet for Object Detection

By Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu.

We provide config files to reproduce the results in the paper for "GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond" on COCO object detection.

Introduction

GCNet is initially described in arxiv. Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks.

Citing GCNet

@article{cao2019GCNet,
  title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond},
  author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han},
  journal={arXiv preprint arXiv:1904.11492},
  year={2019}
}

Results and models

The results on COCO 2017val are shown in the below table.

Backbone Model Context Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN Mask GC(c3-c5, r16) 1x 4.5 0.533 10.1 38.5 35.1 model
R-50-FPN Mask GC(c3-c5, r4) 1x 4.6 0.533 9.9 38.9 35.5 model
R-101-FPN Mask GC(c3-c5, r16) 1x 7.0 0.731 8.6 40.8 37.0 model
R-101-FPN Mask GC(c3-c5, r4) 1x 7.1 0.747 8.6 40.8 36.9 model
Backbone Model Context Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN Mask - 1x 3.9 0.543 10.2 37.2 33.8 model
R-50-FPN Mask GC(c3-c5, r16) 1x 4.5 0.547 9.9 39.4 35.7 model
R-50-FPN Mask GC(c3-c5, r4) 1x 4.6 0.603 9.4 39.9 36.2 model
R-101-FPN Mask - 1x 5.8 0.665 9.2 39.8 36.0 model
R-101-FPN Mask GC(c3-c5, r16) 1x 7.0 0.778 9.0 41.1 37.4 model
R-101-FPN Mask GC(c3-c5, r4) 1x 7.1 0.786 8.9 41.7 37.6 model
X-101-FPN Mask - 1x 7.1 0.912 8.5 41.2 37.3 model
X-101-FPN Mask GC(c3-c5, r16) 1x 8.2 1.055 7.7 42.4 38.0 model
X-101-FPN Mask GC(c3-c5, r4) 1x 8.3 1.037 7.6 42.9 38.5 model
X-101-FPN Cascade Mask - 1x - - - 44.7 38.3 model
X-101-FPN Cascade Mask GC(c3-c5, r16) 1x - - - 45.9 39.3 model
X-101-FPN Cascade Mask GC(c3-c5, r4) 1x - - - 46.5 39.7 model
X-101-FPN DCN Cascade Mask - 1x - - - 47.1 40.4 model
X-101-FPN DCN Cascade Mask GC(c3-c5, r16) 1x - - - 47.9 40.9 model
X-101-FPN DCN Cascade Mask GC(c3-c5, r4) 1x - - - 47.9 40.8 model

Notes:

  • The SyncBN is added in the backbone for all models in Table 2.
  • GC denotes Global Context (GC) block is inserted after 1x1 conv of backbone.
  • DCN denotes replace 3x3 conv with 3x3 Deformable Convolution in c3-c5 stages of backbone.
  • r4 and r16 denote ratio 4 and ratio 16 in GC block respectively.