GSnet: combine Ghostnet and Shufflenetv2 to get better performance

Zecong Ye; Xiaolong Cui; Xinyuan Qiu; Rongqi Jiang; Yanli Fu

doi:10.1117/12.2604552

5 October 2021 GSnet: combine Ghostnet and Shufflenetv2 to get better performance

Zecong Ye, Xiaolong Cui, Xinyuan Qiu, Rongqi Jiang, Yanli Fu

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119111J (2021) https://doi.org/10.1117/12.2604552
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

In computer vision technology based on deep learning, the backbone network is very important, and its performance can usually affect vision-related tasks such as target detection and target segmentation. This article proposes a Ghostnet improvement strategy combined with Shufflenetv2 named GSnet. This paper uses Shufflenetv2's concatenate and shuffle operations to further improve Ghostnet. And the attention module in Ghostnet has been improved and optimized. This paper uses long-distance non-local features to further improve SA attention and embed it in Ghostnet. Experiments are carried out with the data set of cifar100, and after experimental verification, this method reduces the amount of parameters and calculations on the basis of the original method and improves the accuracy of TOP1 by 1.26%. Code is available at https://github.com/Yipzcc later.

Citation Download Citation

Zecong Ye, Xiaolong Cui, Xinyuan Qiu, Rongqi Jiang, and Yanli Fu "GSnet: combine Ghostnet and Shufflenetv2 to get better performance", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119111J (5 October 2021); https://doi.org/10.1117/12.2604552

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KEYWORDS

Target detection

Computer vision technology

Convolution

Image segmentation

Machine vision

Computer networks

Data processing

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