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}
}
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) | Train time (s/iter) | Inf time (fps) | AR 1000 | Download |
|---|---|---|---|---|---|---|---|---|
| GA-RPN | R-50-FPN | caffe | 1x | 5.0 | 0.55 | 13.3 | 68.5 | model |
| GA-RPN | R-101-FPN | caffe | 1x | 7.1 | 0.66 | 9.8 | 69.6 | model |
| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 0.88 | 8.5 | 70.0 | model |
| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 11.4 | 1.24 | 6.5 | 70.5 | model |
| Method | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|---|---|---|---|---|---|---|---|---|
| GA-Fast RCNN | R-50-FPN | caffe | 1x | 3.3 | 0.23 | 14.9 | 39.5 | model |
| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.1 | 0.64 | 9.6 | 39.9 | model |
| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.3 | 0.75 | 8.0 | 41.5 | model |
| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 0.97 | 7.1 | 42.9 | model |
| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.6 | 1.33 | 5.7 | 43.9 | model |
| GA-RetinaNet | R-50-FPN | caffe | 1x | 3.2 | 0.50 | 10.7 | 37.0 | model |
| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.3 | 0.63 | 8.5 | 38.9 | model |
| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.7 | 0.87 | 7.5 | 40.3 | model |
| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.6 | 1.22 | 5.8 | 40.8 | model |
- In the Guided Anchoring paper,
score_thris set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.