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MobileACNet: ACNet-Based Lightweight Model for Image Classification

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Image and Vision Computing (IVCNZ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13836))

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Abstract

Lightweight CNN models aim to extend the application of deep learning from conventional image classification to mobile edge device-based image classification. However, the accuracy of lightweight CNN models currently is not as comparable as traditional large CNN models. To improve the accuracy of mobile platform-based image classification, we propose MobileACNet, a novel ACNet-based lightweight model based on MobileNetV3 (a popular lightweight CNN for image classification on mobile platforms). Our model adopts a similar idea to ACNet: consider global inference and local inference adaptively to improve the classification accuracy. We improve the MobileNetV3 by replacing the inverted residual block with our proposed adaptive inverted residual module (AIR). Experimental results show that our proposed MobileACNet can effectively improve the image classification accuracy by providing additional adaptive global inference on three public datasets, i.e., Cifar-100 dataset, Tiny ImageNet dataset, and a large-scale dataset ImageNet, for mobile-platform-based image classification.

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Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand

    Tao Jiang, Yujun Ma, Feng Hou & Ruili Wang

  2. School of Computer Science, Peking University, Beijing, China

    Ming Zong

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  1. Tao Jiang
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  2. Ming Zong
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  3. Yujun Ma
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  4. Feng Hou
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  5. Ruili Wang
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Corresponding authors

Correspondence to Ming Zong or Feng Hou .

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Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Martin Stommel

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Jiang, T., Zong, M., Ma, Y., Hou, F., Wang, R. (2023). MobileACNet: ACNet-Based Lightweight Model for Image Classification. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-25825-1_26

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