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Birds Classification Based on Deep Transfer Learning

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Smart Computing and Communication (SmartCom 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12608))

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Abstract

At present, there is no huge data set on bird classification, and the classic CUB-200-2011 data set has only 11 788 images, which are still unable to train a generalized classification and recognition model compared with ImageNet and other large data sets with millions of data. Therefore, using deep transfer learning, after tuning for bird recognition, is very valuable with large data sets training model parameters. In this paper, by comparing the training effect of common benchmark models in CUB-200-2011 dataset, ResNeXt model is selected as the transfer learning benchmark model for its well performance. Through optimizing the loss function and reducing the learning rate, the proposed model provides better performance for the data augmentation and adding the full connection layer. Compared with the benchmark model, the recognition rate of the proposed model can reach 84.43%.

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Acknowledgements

This work is supported the Fundamental Research Funds for the Central Universities (TD2014-02).

Author information

Authors and Affiliations

  1. School of Information, Beijing Forestry University, Beijing, 100083, China

    Peng Wu & Gang Wu

  2. School of Information Engineering, Zhejiang A & F University, Lin’an, 311300, Zhejiang, China

    Peng Wu & Xiaomei Yi

  3. School of Information Engineering, Huzhou University, Huzhou, 313000, Zhejiang, China

    Xiaoping Wu

  4. Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK, 74464, USA

    Naixue Xiong

Authors
  1. Peng Wu
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  2. Gang Wu
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  3. Xiaoping Wu
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  4. Xiaomei Yi
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  5. Naixue Xiong
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Corresponding author

Correspondence to Gang Wu .

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Editors and Affiliations

  1. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Wu, P., Wu, G., Wu, X., Yi, X., Xiong, N. (2021). Birds Classification Based on Deep Transfer Learning. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2020. Lecture Notes in Computer Science(), vol 12608. Springer, Cham. https://doi.org/10.1007/978-3-030-74717-6_19

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  • DOI: https://doi.org/10.1007/978-3-030-74717-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74716-9

  • Online ISBN: 978-3-030-74717-6

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