Imaged Based Species Recognition (Dogs & Cats)

Image-based species recognition has significant applications in the identification and classification of animals, including dogs and cats, two of the most commonly kept domesticated animals worldwide. In this research paper, we explore the effectiveness of different deep learning models for the task of dog and cat species recognition from images. We compare the performance of various CNN architectures and investigate the impact of different training strategies on the accuracy of classification. Our results demonstrate that deep learning models can achieve high accuracy in dog and cat species recognition, with ResNet-50 outperforming other architectures. These findings have important applications in animal welfare, conservation biology, and veterinary medicine, including monitoring and managing animal populations in the wild, identifying lost or stray pets, and aiding in the diagnosis and treatment of animal diseases.