Skip to content

Latest commit

 

History

History
 
 

fcos

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

FCOS: Fully Convolutional One-Stage Object Detection

Introduction

@article{tian2019fcos,
  title={FCOS: Fully Convolutional One-Stage Object Detection},
  author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
  journal={arXiv preprint arXiv:1904.01355},
  year={2019}
}

Results and Models

Backbone Style GN MS train Tricks DCN Lr schd Mem (GB) Inf time (fps) box AP Download
R-50 caffe N N N N 1x 5.2 22.9 36.2 model | log
R-50 caffe Y N N N 1x 6.5 22.7 36.6 model | log
R-50 caffe Y N Y N 1x - - 38.6 model | log
R-50 caffe Y N Y Y 1x - - 42.5 model | log
R-50 caffe Y N N N 2x - - 36.9 model | log
R-101 caffe Y N N N 1x 10.2 17.3 39.2 model | log
R-101 caffe Y N N N 2x - - 39.1 model | log
Backbone Style GN MS train Lr schd Mem (GB) Inf time (fps) box AP Download
R-50 caffe Y Y 2x 6.5 22.9 38.7 model | log
R-101 caffe Y Y 2x 10.2 17.3 40.9 model | log
X-101 pytorch Y Y 2x 10.0 9.3 42.5 model | log

Notes:

  • To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU for R-50 and R-101 models, and 8 GPUs with 2 image/GPU for X-101 models.
  • The X-101 backbone is X-101-64x4d.
  • Tricks means setting norm_on_bbox, centerness_on_reg, center_sampling as True.
  • DCN means using DCNv2 in both backbone and head.