code_test/configs/fpg

• Feature Pyramid Grids
• • Introduction
  • Results and Models

Feature Pyramid Grids

Introduction

@article{chen2020feature,
  title={Feature pyramid grids},
  author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph},
  journal={arXiv preprint arXiv:2004.03580},
  year={2020}
}

Results and Models

We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN.
All backbones are Resnet-50 in pytorch style.

Method	Neck	Lr schd	Mem (GB)	Inf time (fps)	box AP	mask AP	Config	Download
Faster R-CNN	FPG	50e	20.0	-	42.2	-	config	model | log
Faster R-CNN	FPG-chn128	50e	11.9	-	41.2	-	config	model | log
Mask R-CNN	FPG	50e	23.2	-	42.7	37.8	config	model | log
Mask R-CNN	FPG-chn128	50e	15.3	-	41.7	36.9	config	model | log
RetinaNet	FPG	50e	20.8	-	40.5	-	config	model | log
RetinaNet	FPG-chn128	50e	19.9	-	40.3	-	config	model | log

Note: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in
Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision.