MMDetection/configs/gcnet

• GCNet for Object Detection
• • Introduction
  • Citing GCNet
  • Results and models

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	model | log
X-101-FPN	Cascade Mask	GC(c3-c5, r4)	1x	10.6		46.4	40.1	model | log
X-101-FPN	DCN Cascade Mask	-	1x			44.9	38.9	model | log
X-101-FPN	DCN Cascade Mask	GC(c3-c5, r16)	1x			44.6		model | log
X-101-FPN	DCN Cascade Mask	GC(c3-c5, r4)	1x			45.7	39.5	model | 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.