Libra R-CNN: Towards Balanced Learning for Object Detection

Introduction

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}
}

Results and models

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