mmdetection/configs/sparse_rcnn

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

Introduction

@article{peize2020sparse,
  title   =  {{SparseR-CNN}: End-to-End Object Detection with Learnable Proposals},
  author  =  {Peize Sun and Rufeng Zhang and Yi Jiang and Tao Kong and Chenfeng Xu and Wei Zhan and Masayoshi Tomizuka and Lei Li and Zehuan Yuan and Changhu Wang and Ping Luo},
  journal =  {arXiv preprint arXiv:2011.12450},
  year    =  {2020}
}

Results and Models

Model	Backbone	Style	Lr schd	Number of Proposals	Multi-Scale	RandomCrop	box AP	Config	Download
Sparse R-CNN	R-50-FPN	pytorch	1x	100	False	False	37.9	config	model | log
Sparse R-CNN	R-50-FPN	pytorch	3x	100	True	False	42.8	config	model | log
Sparse R-CNN	R-50-FPN	pytorch	3x	300	True	True	45.0	config	model | log
Sparse R-CNN	R-101-FPN	pytorch	3x	100	True	False	44.2	config	model | log
Sparse R-CNN	R-101-FPN	pytorch	3x	300	True	True	46.2	config	model | log

Notes

We observe about 0.3 AP noise especially when using ResNet-101 as the backbone.