guided_anchoring

Region Proposal by Guided Anchoring

Introduction

[ALGORITHM]

We provide config files to reproduce the results in the CVPR 2019 paper for Region Proposal by Guided Anchoring.

@inproceedings{wang2019region,
    title={Region Proposal by Guided Anchoring},
    author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
    year={2019}
}

Results and Models

The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).

Method	Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	AR 1000	Config	Download
GA-RPN	R-50-FPN	caffe	1x	5.3	15.8	68.4	config	model | log
GA-RPN	R-101-FPN	caffe	1x	7.3	13.0	69.5	config	model | log
GA-RPN	X-101-32x4d-FPN	pytorch	1x	8.5	10.0	70.6	config	model | log
GA-RPN	X-101-64x4d-FPN	pytorch	1x	7.1	7.5	71.2	config	model | log

Method	Backbone	Style	Lr schd	Mem (GB)	Inf time (fps)	box AP	Config	Download
GA-Faster RCNN	R-50-FPN	caffe	1x	5.5		39.6	config	model | log
GA-Faster RCNN	R-101-FPN	caffe	1x	7.5		41.5	config	model | log
GA-Faster RCNN	X-101-32x4d-FPN	pytorch	1x	8.7	9.7	43.0	config	model | log
GA-Faster RCNN	X-101-64x4d-FPN	pytorch	1x	11.8	7.3	43.9	config	model | log
GA-RetinaNet	R-50-FPN	caffe	1x	3.5	16.8	36.9	config	model | log
GA-RetinaNet	R-101-FPN	caffe	1x	5.5	12.9	39.0	config	model | log
GA-RetinaNet	X-101-32x4d-FPN	pytorch	1x	6.9	10.6	40.5	config	model | log
GA-RetinaNet	X-101-64x4d-FPN	pytorch	1x	9.9	7.7	41.3	config	model | log

• In the Guided Anchoring paper, score_thr is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.

• Performance on COCO test-dev benchmark are shown as follows.

Method	Backbone	Style	Lr schd	Aug Train	Score thr	AP	AP_50	AP_75	AP_small	AP_medium	AP_large	Download
GA-Faster RCNN	R-101-FPN	caffe	1x	F	0.05
GA-Faster RCNN	R-101-FPN	caffe	1x	F	0.001
GA-RetinaNet	R-101-FPN	caffe	1x	F	0.05
GA-RetinaNet	R-101-FPN	caffe	2x	T	0.05