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	Lr schd	Mem (GB)	Train time (s/iter)	Inf time (fps)	box AP	Download
R-50	caffe	N	N	1x	5.5	0.373	13.7	35.7	model
R-50	caffe	Y	N	1x	6.9	0.396	13.6	36.7	model
R-50	caffe	Y	N	2x	-	-	-	36.9	model
R-101	caffe	Y	N	1x	10.4	0.558	11.6	39.1	model
R-101	caffe	Y	N	2x	-	-	-	39.1	model

Backbone	Style	GN	MS train	Lr schd	Mem (GB)	Train time (s/iter)	Inf time (fps)	box AP	Download
R-50	caffe	Y	Y	2x	-	-	-	38.7	model
R-101	caffe	Y	Y	2x	-	-	-	40.8	model
X-101	caffe	Y	Y	2x	9.7	0.892	7.0	42.8	model

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.