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author:

Li, Sixu (Li, Sixu.) [1] | Xiao, Boyang (Xiao, Boyang.) [2] | Xie, Sixiong (Xie, Sixiong.) [3]

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EI

Abstract:

Wildlife recognition is the task of matching and outputting similar probabilities given two images, with twin networks being a popular framework in the field. In this work, we use the Siamese architecture as the main framework. As for the backbone, we compare two popular backbone networks, including VGGNet and ResNet, respectively. We compare those two networks with respect to the convergence speed, robustness and maximum accuracy to exploit the effectiveness of our method. We test those networks on AFHQ dataset. Our method with ResNet achieves 98.86% accuracy. Our experiments have confirmed that, to a certain extent, the larger the number of layers in the network, the higher the stability and accuracy of model training. © 2022 SPIE.

Keyword:

Animals Convolutional neural networks Network layers Statistical tests

Community:

  • [ 1 ] [Li, Sixu]International School Beijing University of Technology (BJUT), Beijing, China
  • [ 2 ] [Xiao, Boyang]International School Beijing University of Post and Telecommunications (BUPT), Beijing, China
  • [ 3 ] [Xie, Sixiong]International School Fuzhou University (FZU), Fuzhou, China

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ISSN: 0277-786X

Year: 2022

Volume: 12456

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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