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 = {Wang, Jiaqi and Zhang, Wenwei and Cao, Yuhang and Chen, Kai and Pang, Jiangmiao and Gong, Tao and Shi, Jianping, Loy, Chen Change and Lin, Dahua},
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 |
Download |
SABL Faster R-CNN |
R-50-FPN |
1x |
N |
39.9 |
model | log |
SABL Faster R-CNN |
R-101-FPN |
1x |
N |
41.7 |
model | log |
SABL Cascade R-CNN |
R-50-FPN |
1x |
N |
41.6 |
model | log |
SABL Cascade R-CNN |
R-101-FPN |
1x |
N |
43.0 |
model | log |
Method |
Backbone |
GN |
Lr schd |
ms-train |
box AP |
Download |
SABL RetinaNet |
R-50-FPN |
N |
1x |
N |
37.7 |
model | log |
SABL RetinaNet |
R-50-FPN |
Y |
1x |
N |
38.8 |
model | log |
SABL RetinaNet |
R-101-FPN |
N |
1x |
N |
39.7 |
model | log |
SABL RetinaNet |
R-101-FPN |
Y |
1x |
N |
40.5 |
model | log |
SABL RetinaNet |
R-101-FPN |
Y |
2x |
Y (640~800) |
42.9 |
model | log |
SABL RetinaNet |
R-101-FPN |
Y |
2x |
Y (480~960) |
43.6 |
model | log |