Side-Aware Boundary Localization for More Precise Object Detection

Introduction

We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for Side-Aware Boundary Localization for More Precise Object Detection.

@inproceedings{Wang_2020_ECCV,
    title = {Side-Aware Boundary Localization for More Precise Object Detection},
    author = {Jiaqi Wang and Wenwei Zhang and Yuhang Cao and Kai Chen and Jiangmiao Pang and Tao Gong and Jianping Shi and Chen Change Loy and Dahua Lin},
    booktitle = {ECCV},
    year = {2020}
}

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). Single-scale testing (1333x800) is adopted in all results.

Method	Backbone	Lr schd	ms-train	box AP	Config	Download
SABL Faster R-CNN	R-50-FPN	1x	N	39.9	config	model | log
SABL Faster R-CNN	R-101-FPN	1x	N	41.7	config	model | log
SABL Cascade R-CNN	R-50-FPN	1x	N	41.6	config	model | log
SABL Cascade R-CNN	R-101-FPN	1x	N	43.0	config	model | log

Method	Backbone	GN	Lr schd	ms-train	box AP	Config	Download
SABL RetinaNet	R-50-FPN	N	1x	N	37.7	config	model | log
SABL RetinaNet	R-50-FPN	Y	1x	N	38.8	config	model | log
SABL RetinaNet	R-101-FPN	N	1x	N	39.7	config	model | log
SABL RetinaNet	R-101-FPN	Y	1x	N	40.5	config	model | log
SABL RetinaNet	R-101-FPN	Y	2x	Y (640~800)	42.9	config	model | log
SABL RetinaNet	R-101-FPN	Y	2x	Y (480~960)	43.6	config	model | log