Skip to content

Latest commit

 

History

History
32 lines (25 loc) · 4.32 KB

README.md

File metadata and controls

32 lines (25 loc) · 4.32 KB

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Introduction

We provide config files to reproduce the object detection results in the paper Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

@article{li2020generalized,
  title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection},
  author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian},
  journal={arXiv preprint arXiv:2006.04388},
  year={2020}
}

Results and Models

Backbone Style Lr schd Multi-scale Training Inf time (fps) box AP Config Download
R-50 pytorch 1x No 19.5 40.2 config model | log
R-50 pytorch 2x Yes 19.5 42.9 config model | log
R-101 pytorch 2x Yes 14.7 44.7 config model | log
R-101-dcnv2 pytorch 2x Yes 12.9 47.1 config model | log
X-101-32x4d pytorch 2x Yes 12.1 45.9 config model | log
X-101-32x4d-dcnv2 pytorch 2x Yes 10.7 48.1 config model | log

[1] 1x and 2x mean the model is trained for 90K and 180K iterations, respectively.
[2] All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..
[3] dcnv2 denotes deformable convolutional networks v2.
[4] FPS is tested with a single GeForce RTX 2080Ti GPU, using a batch size of 1.