code_test/configs/gfl

• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
• • Introduction
  • Results and Models

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.