libra_rcnn

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.

The extended version of Libra R-CNN is accpeted by IJCV.

@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}
}

@article{pang2021towards,
  title={Towards Balanced Learning for Instance Recognition},
  author={Pang, Jiangmiao and Chen, Kai and Li, Qi and Xu, Zhihai and Feng, Huajun and Shi, Jianping and Ouyang, Wanli and Lin, Dahua},
  journal={International Journal of Computer Vision},
  volume={129},
  number={5},
  pages={1376--1393},
  year={2021},
  publisher={Springer}
}

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