Dog’s Breed Prediction using Deep Learning

Abstract: Dog Breed Prediction from Images using Deep Learning, in this project, we propose a Convolutional Neural Network (CNN) based approach for predicting dog breeds from images. With the increasing popularity of dogs as pets and the need for proper care and maintenance, it is essential to identify the breed of a dog accurately. However, manual identification can be time-consuming and prone to errors. Therefore, we propose a deep learning-based solution that can predict the breed of a dog with high accuracy. We train a CNN model on a large dataset of dog images, where each image is labeled with the breed of the dog. The model learns to extract features from the images and classify them into one of the 180 breeds. We evaluate the performance of the model using standard metrics and compare it with other machine learning algorithms. Our experimental results show that the proposed CNN model outperforms other algorithms in predicting dog breeds from images. The model achieves an accuracy of 90%, making it a reliable tool for dog breed prediction. Additionally, we develop a web application that allows users to upload an image of a dog and predict its breed using our model. This project demonstrates the effectiveness of deep learning techniques in predicting dog breeds from images. The proposed CNN model can be a valuable tool for veterinarians, pet owners, and breeders, helping them identify the breed of a dog accurately and efficiently.