Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Lightweight image classifier for CIFAR-10

  • Received : 2021.09.12
  • Accepted : 2021.09.27
  • Published : 2021.09.30
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Abstract

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

Keywords

Acknowledgement

This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2021-0-02052) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (2020-0-01080, Variable-precision deep learning processor technology for high-speed multiple object tracking).

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