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30 November 2022 Animal recognition using Siamese network with two kinds of backbone networks
Sixu Li, Boyang Xiao, Sixiong Xie
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Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124562W (2022) https://doi.org/10.1117/12.2659594
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China
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.
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Sixu Li, Boyang Xiao, and Sixiong Xie "Animal recognition using Siamese network with two kinds of backbone networks", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124562W (30 November 2022); https://doi.org/10.1117/12.2659594
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KEYWORDS
Neural networks
Image classification
Analytical research
Performance modeling
Animal model studies
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