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Performance Evaluation of a Vegetable Recognition System Using Caffe and Chainer

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Complex, Intelligent, and Software Intensive Systems (CISIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 611))

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Abstract

Deep neural network has a deep hierarchy that connect multiple internal layers for feature detection and recognition learning. In our previous work, we proposed a vegetable recognition system which was based on Caffe framework. In this paper, we evaluate the performance of learning accuracy and loss for vegetable category recognition system which is based on Caffe and Chainer frameworks. We evaluate the performance of recognition rate for different categories of vegetables with different pixel sizes.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Makoto Ikeda & Leonard Barolli

  2. Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka, 811-0295, Japan

    Yuki Sakai

  3. Department of Information and Computer Engineering, Okayama University of Science, 1-1 Ridai-cho, Kita-ku, Okayama, 700-0005, Japan

    Tetsuya Oda

Authors
  1. Makoto Ikeda
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  2. Yuki Sakai
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  3. Tetsuya Oda
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  4. Leonard Barolli
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Corresponding author

Correspondence to Makoto Ikeda .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Politècnico di Torino, Istituto Superiore Mario Boella, Turin, Italy

    Olivier Terzo

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Ikeda, M., Sakai, Y., Oda, T., Barolli, L. (2018). Performance Evaluation of a Vegetable Recognition System Using Caffe and Chainer. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-61566-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61565-3

  • Online ISBN: 978-3-319-61566-0

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