README.md

AutoAssign: Differentiable Label Assignment for Dense Object Detection

Introduction

@article{zhu2020autoassign,
  title={AutoAssign: Differentiable Label Assignment for Dense Object Detection},
  author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2007.03496},
  year={2020}
}

Results and Models

Backbone	Style	Lr schd	Mem (GB)	box AP	Config	Download
R-50	pytorch	1x	4.08	40.4	config	model | log

Note:

1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation.
2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch.