Abstract

Deep learning has revolutionized image classification tasks, including dog identification. This case study investigates the development of a deep neural network (DNN) for dog breed detection. We use popular models such as VGG16 and ResNet50V2 and use transfer learning and data augmentation to improve the performance of the model. In this study, different models are evaluated and the problems in establishing the classification of different breeds of dogs are discussed. In addition to evaluating the model, this study also examines real-world applications and highlights the importance of accurate identification of dogs in veterinary and animal services. This work also improves the development of the model by providing new ideas for solving problems related to the appearance of different animals. This research contributes to deep learning-based image classification by providing insight into technological advances and practical implications.