Treffer: Identification of Dog Breeds Using Deep Learning Based on CNN

The Dog's Breed Detector is an innovative project that utilizes computer vision and machine learning techniques to identify dog breeds from images. This project aims to provide a user-friendly platform for dog owners, enthusiasts, and breeders to identify and learn about different dog breeds. The system uses a deep learning-based approach to analyze images of dogs and predict their breed, providing accurate and reliable results. The Dog's Breed Detector system consists of a web-based and a mobile application. The web application, built with HTML, CSS, and JavaScript, allows users to upload images of dogs for breed identification. The system also includes a database of dog breeds, providing information on breed characteristics, temperament, and health. The software components of the Dog's Breed Detector system include Python modules. These technologies enable the system to analyze images, predict dog breeds, and provide user friendly interface for web application. The dog breed detector achieved an accuracy of 81% when tested on a dataset of different breeds under varied lighting and image quality conditions. The Dog's Breed Detector project has the potential to revolutionize the way we identify and learn about dog breeds. With its user-friendly interface and accurate breed identification results, this project can be a valuable resource for dog owners, enthusiasts, and breeders. Index Terms: Dog Breed Identification, Deep Learning, ComputerVision, ImageAnalysis, MachineLearning, Breed Detection, Artificial Intelligence, Convolutional Neural Networks (CNNs), image Classification.