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}
}
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 |