Skip to content

Latest commit

 

History

History
 
 

double_heads

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 

Double Heads

Rethinking Classification and Localization for Object Detection

Abstract

Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.

Results and Models

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP Config Download
R-50-FPN pytorch 1x 6.8 9.5 40.0 config model | log

Citation

@article{wu2019rethinking,
    title={Rethinking Classification and Localization for Object Detection},
    author={Yue Wu and Yinpeng Chen and Lu Yuan and Zicheng Liu and Lijuan Wang and Hongzhi Li and Yun Fu},
    year={2019},
    eprint={1904.06493},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}