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README.md

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) 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