Attention ConvMixer Model and Application for Fish Species Classification
Keywords:Fish species classification, Attention ConvMixer, Priority Channel Attention, Priority Spatial Attention
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.
National Geographic.“Ocean.”National Geographic, n.d.,https://education.nationalgeographic.org
K.V. Ramachandran "The Importance of Fish Taxonomy"(2007).
Peng Zhang , Qingyuan Liu , Yuanming Wang , Kefeng Li , Leilei Qin , Ruifeng Liang , Jiaying Li "Does drifting passage need to be linked to fish habitat assessment? Assessing environmental flow for multiple fish species with different spawning patterns with a framework integrating habitat connectivity" (2022)
A. Krizhevsky, I. Sutskever, and G. Hinton. "ImageNet classification with deep convolutional neural networks." ,In NIPS, (2012)
Karen Simonyan, Andrew Zisserman "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv:1409.1556 (2014)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun "Deep Residual Learning for Image Recognition" ,arXiv:1512.03385 (2015)
Mingxing Tan, Quoc V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" arXiv:1905.11946 (2019)
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le"MnasNet: Platform-Aware Neural Architecture Search for Mobile" arXiv:1807.11626 (2019)
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby, "Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" Proceedings of the 9th International Conference on Learning Representations, (2021)
Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X.,Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J. and Others "MLP-Mixer: An all-mlp architecture for vision." Advances In Neural Information Processing Systems. 34 (2021)
Asher Trockman, J. Zico Kolter, "Patches Are All You Need?" arXiv:2201.09792 (2022)
Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu"Spatial Transformer Networks" arXiv:1506.02025 (2016)
Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon, "CBAM: Convolutional Block Attention Module" arXiv:1807.06521 (2018)
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu "Squeeze-and-Excitation Networks" arXiv:1709.01507 (2017)
Prasetyo, E., Suciati, N., Fatichah, C., "Fishgres Dataset for Fish Species Classification" https://doi.org/http://dx.doi.org/10.17632/76cr3wfhff.1 (2020).
J Jäger, M Simon, J Denzler, V Wolff. . . -Swansea"Croatian Fish Dataset: Fine-grained classification of fish species in their natural habitat"(2015)
Md. Aminul Islam; Md. Rasel Howlader; Umme Habiba; Rahat Hossain Faisal; Md. Mostafijur Rahman "Indigenous Fish Classification of Bangladesh using Hybrid Features with SVM Classifier" IC4ME2 (2019)
Diederik P. Kingma, Jimmy Ba, "Adam: A Method for Stochastic Optimization" arXiv:1412.6980 (2014)
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich"Going deeper with convolutions" (2014)
Hong-Phuc Lai, Thi-Thao Tran, and Van-Truong Pham. "Axial attention mlp-mixer: A new architecture for image segmentation." 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE). IEEE, 2022
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National Foundation for Science and Technology Development
Grant numbers 102.05-2021.34