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README.md

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

Introduction

[ALGORITHM]

@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.