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http://dx.doi.org/10.5909/JBE.2022.27.1.133

Analysis of Transfer Learning Effect for Automatic Dog Breed Classification  

Lee, Dongsu (Graduate School of Nano IT Design Fusion, Seoul National University of Science and Technology)
Park, Gooman (Graduate School of Nano IT Design Fusion, Seoul National University of Science and Technology)
Publication Information
Journal of Broadcast Engineering / v.27, no.1, 2022 , pp. 133-145 More about this Journal
Abstract
Compared to the continuously increasing dog population and industry size in Korea, systematic analysis of related data and research on breed classification methods are very insufficient. In this paper, an automatic breed classification method is proposed using deep learning technology for 14 major dog breeds domestically raised. To do this, dog images are collected for deep learning training and a dataset is built, and a breed classification algorithm is created by performing transfer learning based on VGG-16 and Resnet-34 as backbone networks. In order to check the transfer learning effect of the two models on dog images, we compared the use of pre-trained weights and the experiment of updating the weights. When fine tuning was performed based on VGG-16 backbone network, in the final model, the accuracy of Top 1 was about 89% and that of Top 3 was about 94%, respectively. The domestic dog breed classification method and data construction proposed in this paper have the potential to be used for various application purposes, such as classification of abandoned and lost dog breeds in animal protection centers or utilization in pet-feed industry.
Keywords
Deep Learning; Transfer Learning; Resnet; VGGNet; Dog Breed;
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