A lightweight convolutional network based on pruning algorithm for YOLO

Guanyu Liu; Yuzhao Li; Yuanchen Song; Yumeng Liu; Xiaofeng Xu; Zhen Zhao; Ruiheng Zhang

doi:10.1117/12.2680414

27 June 2023 A lightweight convolutional network based on pruning algorithm for YOLO

Guanyu Liu, Yuzhao Li, Yuanchen Song, Yumeng Liu, Xiaofeng Xu, Zhen Zhao, Ruiheng Zhang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127050Z (2023) https://doi.org/10.1117/12.2680414
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

With the rapid development of deep learning, neural network models have become increasingly complicated, leading to larger storage space requirements and slower reasoning speed. These factors make it difficult to be deployed on resourcelimited platforms. To alleviate this problem, network pruning, an effective model compression method, is commonly performed in a deep neural network. However, traditional pruning methods simply set redundant weights to zero, thus failing to achieve the acceleration effect. In this paper, a channel-wise model scaling method is proposed to reduce the model size and speed up reasoning by structurally removing the redundant filters in convolutional layers. To make the residual block more sparse, we develop a pruning method for residual cells. Experimental results on the YOLOv3 detector show that our proposed approach achieves a 70.6% parameter compression ratio without compromising accuracy.

Citation Download Citation

Guanyu Liu, Yuzhao Li, Yuanchen Song, Yumeng Liu, Xiaofeng Xu, Zhen Zhao, and Ruiheng Zhang "A lightweight convolutional network based on pruning algorithm for YOLO", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127050Z (27 June 2023); https://doi.org/10.1117/12.2680414

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KEYWORDS

Education and training

Tunable filters

Object detection

Batch normalization

Convolution

Deep learning

Matrices

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