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) | Inf time (fps) | box AP | Download |
|---|---|---|---|---|---|---|---|
| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | model | log |
| Fast R-CNN | R-50-FPN | pytorch | 1x | ||||
| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | model | log |
| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | model | log |
| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | model | log |