Title: Dog breed classification using convolution neural network

Authors: Amit Kumar Jakhar; Mrityunjay Singh; Anjani Kumar Shukla

Addresses: Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, India ' Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, India ' Department of Applied Science, Bundelkhand Institute of Engineering and Technology, Jhansi, India

Abstract: At present, there are numerous applications of machine learning that have been identified. The identification and recognition of the object is very popular and significant among them. This paper presents a model for dog breed classification using convolution neural network (CNN) that is a very popular model for image processing. In this work, a variant CNN model is used to classify different breeds of dogs which uses advanced filters to detect the patterns that exists within a given image and fixing randomised filter on an image to figure out the available patterns. The proposed model is constructed to calibrate the weights by associating data in front of the Xception model. The object identification task gets superhuman potential in the Xception model and it is trained over 15 millions of good resolution images with around 22,000 different object classifications. The experimental result reveals that the proposed model performs well in terms of accuracy, i.e., 98%.

Keywords: dog breed classification; machine learning; convolution neural network; CNN.

DOI: 10.1504/IJSI.2021.118622

International Journal of Swarm Intelligence, 2021 Vol.6 No.2, pp.130 - 142

Received: 20 Jul 2020
Accepted: 27 Nov 2020
Published online: 29 Oct 2021 *

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