wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/gcnet

GCNet

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

Introduction

Official Repo

Code Snippet

Abstract

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. The code and configurations are released at this https URL.

Citation

@inproceedings{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},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={0--0},
  year={2019}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
GCNet	R-50-D8	512x1024	40000	5.8	3.93	77.69	78.56	config	model | log
GCNet	R-101-D8	512x1024	40000	9.2	2.61	78.28	79.34	config	model | log
GCNet	R-50-D8	769x769	40000	6.5	1.67	78.12	80.09	config	model | log
GCNet	R-101-D8	769x769	40000	10.5	1.13	78.95	80.71	config	model | log
GCNet	R-50-D8	512x1024	80000	-	-	78.48	80.01	config	model | log
GCNet	R-101-D8	512x1024	80000	-	-	79.03	79.84	config	model | log
GCNet	R-50-D8	769x769	80000	-	-	78.68	80.66	config	model | log
GCNet	R-101-D8	769x769	80000	-	-	79.18	80.71	config	model | log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
GCNet	R-50-D8	512x512	80000	8.5	23.38	41.47	42.85	config	model | log
GCNet	R-101-D8	512x512	80000	12	15.20	42.82	44.54	config	model | log
GCNet	R-50-D8	512x512	160000	-	-	42.37	43.52	config	model | log
GCNet	R-101-D8	512x512	160000	-	-	43.69	45.21	config	model | log

Pascal VOC 2012 + Aug

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
GCNet	R-50-D8	512x512	20000	5.8	23.35	76.42	77.51	config	model | log
GCNet	R-101-D8	512x512	20000	9.2	14.80	77.41	78.56	config	model | log
GCNet	R-50-D8	512x512	40000	-	-	76.24	77.63	config	model | log
GCNet	R-101-D8	512x512	40000	-	-	77.84	78.59	config	model | log