Channel Pruning Method for Anchor-Free Detector

Abstract

Aiming at the problems of large redundant parameters, high computational cost and slow detection speed of the anchor-free detector, a channel pruning method guided by double attention modules (CPDAM) is proposed to compress the anchor-free object detectors. The performance of the channel attention and spatial attention submodules is further improved using pooling and group normalization. The improved channel attention and spatial attention submodules are fused using a channel grouping strategy and are continuously trained to generate a scale value for each channel indicating the importance of the channel on the classification task. A global scale value is calculated using the scale values and the channel pruning of the backbone network is performed based on the evaluation of channel importance by this value. The improved anchor-free object detector is experimentally validated on PASCAL VOC, ImageNet and CIFAR-100 datasets, and the experimental results show that the number of parameters of CenterNet-ResNet101 before and after pruning is decreased from 6.995×107 to 2.238×107, and the FPS is increased from 27 to 46, with only 0.6 percentage points mAP loss.

Keywords

• anchor-free; object detector; attention module; channel pruning