Open AccessDog Breed Classification Using Convolutional Neural Network
Author(s) -
B Suyash,
P. P. Rishikesh,
W Rohit,
J Kaustubh,
Balaji Bodhke
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1473
Subject(s) - convolutional neural network , breed , artificial intelligence , pattern recognition (psychology) , computer science , spotting , population , representation (politics) , machine learning , medicine , biology , zoology , environmental health , politics , political science , law
Dogs are one of the most common domestic animals. Due to a large number of dogs, there are several issues such as population control, decreased outbreak such as Rabies, vaccination control, and legal ownership. At present, there are over 180 dog breeds. Each dog breed has specific characteristics and health conditions. In order to provide appropriate treatments and training, it is essential to identify individuals and their breeds. Machine learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks ( CNNs ) gives single often used methods for image classification and detection. In this exertion , we define a CNN based approach for spotting dogs in perchance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm ( CNN ) gives good analysis accuracy for all the tested datasets.