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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.