We provide config files to reproduce the results in the CVPR 2019 paper Libra R-CNN.
@inproceedings{pang2019libra,
title={Libra R-CNN: Towards Balanced Learning for Object Detection},
author={Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Dahua Lin},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)
| Architecture | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|---|---|---|---|---|---|---|---|---|
| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.2 | 0.375 | 12.0 | 38.5 | model |
| Fast R-CNN | R-50-FPN | pytorch | 1x | 3.7 | 0.272 | 16.3 | 38.5 | model |
| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.0 | 0.495 | 10.4 | 40.3 | model |
| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.1 | 1.050 | 6.8 | 42.7 | model |
| RetinaNet | R-50-FPN | pytorch | 1x | 3.7 | 0.328 | 11.8 | 37.7 | model |