Attention ConvMixer Model and Application for  Fish Species Classification

Thanh Viet Le; Hoang-Minh-Quang Le; Van Yem Vu; Thi-Thao Tran; Van-Truong Pham

doi:10.4108/eetinis.v10i3.3562

Authors

Thanh Viet Le Hanoi University of Science and Technology
Hoang-Minh-Quang Le Hanoi University of Science and Technology
Van Yem Vu Hanoi University of Science and Technology
Thi-Thao Tran Hanoi University of Science and Technology
Van-Truong Pham Hanoi University of Science and Technology

DOI:

https://doi.org/10.4108/eetinis.v10i3.3562

Keywords:

Fish species classification, Attention ConvMixer, Priority Channel Attention, Priority Spatial Attention

Abstract

Exploring the ocean has always been one of the foremost challenges for humankind, and fish classification is one of the crucial tasks in this endeavor. Manual fish classification methods, although accurate, consume significant time, money, and effort, while computer-based methods such as image processing and traditional machine learning often fall short of achieving high accuracy. Recently, deep convolutional neural networks have demonstrated their capability to ensure both time efficiency and accuracy in this task. However, deep convolutional networks typically have a large number of parameters, requiring substantial training time, and the convolutional operations lack attentional mechanisms. Therefore, in this paper, we propose the AttentionConvMixer neural network with Priority Channel Attention (PCA) and Priority Spatial Attention (PSA). The proposed approach exhibits good performance across all three fish classification datasets without introducing any additional parameters, thus demonstrating the effectiveness of our proposed method.

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