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Predicting the Breed of Dogs and Cats with Fine-Tuned Keras Applications

I.-Hung Wang1, Mahardi2, Kuang-Chyi Lee2,*, Shinn-Liang Chang1

1 Department of Power Mechanical Engineering, National Formosa University
2 Department of Automation Engineering, National Formosa University, Huwei Township, Taiwan

* Corresponding Author: Kuang-Chyi Lee. Email: email

(This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)

Intelligent Automation & Soft Computing 2021, 30(3), 995-1005. https://doi.org/10.32604/iasc.2021.019020

Abstract

The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general image datasets from ImageNet. In this paper, the ResNet-152 v2, Inception-ResNet v2, and Xception models, adopted from Keras application, are retrained to predict the breed among the 21 classes of dogs and cats. Our results indicate that the Xception model has produced the highest prediction accuracy. The training accuracy is 99.49%, the validation accuracy is 99.21%, and the testing accuracy is 91.24%. Besides, the training time is about 14 hours and the predicting time is about 18.41 seconds.

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Cite This Article

I. Wang, M. , K. Lee and S. Chang, "Predicting the breed of dogs and cats with fine-tuned keras applications," Intelligent Automation & Soft Computing, vol. 30, no.3, pp. 995–1005, 2021.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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