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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) Inf time (fps) box AP mask AP Download
R-50-FPN Mask GC(c3-c5, r16) 1x 5.0 39.7 35.9 model | log
R-50-FPN Mask GC(c3-c5, r4) 1x 5.1 15.0 39.9 36.0 model | log
R-101-FPN Mask GC(c3-c5, r16) 1x 7.6 11.4 41.3 37.2 model | log
R-101-FPN Mask GC(c3-c5, r4) 1x 7.8 11.6 42.2 37.8 model | log
Backbone Model Context Lr schd Mem (GB) Inf time (fps) box AP mask AP Download
R-50-FPN Mask - 1x 4.4 16.6 38.4 34.6 model | log
R-50-FPN Mask GC(c3-c5, r16) 1x 5.0 15.5 40.4 36.2 model | log
R-50-FPN Mask GC(c3-c5, r4) 1x 5.1 15.1 40.7 36.5 model | log
R-101-FPN Mask - 1x 6.4 13.3 40.5 36.3 model | log
R-101-FPN Mask GC(c3-c5, r16) 1x 7.6 12.0 42.2 37.8 model | log
R-101-FPN Mask GC(c3-c5, r4) 1x 7.8 11.8 42.2 37.8 model | log
X-101-FPN Mask - 1x 7.6 11.3 42.4 37.7 model | log
X-101-FPN Mask GC(c3-c5, r16) 1x 8.8 9.8 43.5 38.6 model | log
X-101-FPN Mask GC(c3-c5, r4) 1x 9.0 9.7 43.9 39.0 model | log
X-101-FPN Cascade Mask - 1x 9.2 8.4 44.7 38.6 model | log
X-101-FPN Cascade Mask GC(c3-c5, r16) 1x 10.3 7.7 46.2 39.7 modle | log
X-101-FPN Cascade Mask GC(c3-c5, r4) 1x 10.6 46.4 40.1 modle | log
X-101-FPN DCN Cascade Mask - 1x 44.9 38.9 modle | log
X-101-FPN DCN Cascade Mask GC(c3-c5, r16) 1x 44.6 modle | log
X-101-FPN DCN Cascade Mask GC(c3-c5, r4) 1x 45.7 39.5 modle | log

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.