DEEP LEARNING BASED ON DOG BREED CLASSIFICATION SYSTEM

DEEP LEARNING BASED ON DOG BREED CLASSIFICATION SYSTEM

VIKAS M S1, SINDHU S L2

1 STUDENT, DEPARTMENT OF MASTER OF COMPUTER APPLICATIONS, BIET, DAVANGERE

2 ASSISTANT PROFESSOR, DEPARTMENT OF MCA, BIET, DAVANGERE

ABSTRACT

This paper introduces a sophisticated deep learning-based system for automated dog breed classification, utilizing convolutional neural networks (CNNs) to accurately identify and categorize various dog breeds from images. The proposed system addresses the need for efficient and objective breed recognition methods in fields such as veterinary medicine, animal breeding, and pet services. By harnessing the power of CNNs and leveraging a comprehensive dataset of annotated dog images, the system achieves remarkable accuracy and reliability in breed classification tasks. Through rigorous experimentation and evaluation, including performance testing and real-world validation, the system demonstrates its efficacy in diverse scenarios, showcasing its potential to streamline breed identification processes and enhance productivity in relevant domains. The findings of this study contribute to the advancement of automated dog breed classification systems and underscore the transformative impact of deep learThis paper presents a deep learning- based approach for dog breed classification, leveraging convolutional neural networks (CNNs) to accurately identify and classify different dog breeds from images. The proposed system offers a reliable and efficient solution for dog breed recognition, with potential applications in veterinary medicine, animal welfare, and pet services. Through extensive experimentation and evaluation, the system demonstrates high accuracy and robustness in breed classification, highlighting its effectiveness in real-world scenarios.ning technologies in animal-related industries.

Keywords: Machine Learning, Tensor Flow lite, deep learning.