Pet Classification Using Transfer Learning

Abstract: This study delves into applying transfer learning to pet classification using pre-trained neural networks, specifically convolutional neural networks (CNNs). It adapts models originally trained on ImageNet to distinguish between different pet species. By fine-tuning these models on a custom pet dataset, the study aims to leverage previous knowledge to improve classification accuracy, even with limited labeled data. Various transfer learning strategies, model architectures, and hyper parameters are analyzed to identify the most effective configuration for accurate and efficient pet classification. The research has implications for pet monitoring, identification, and healthcare, contributing to computer vision and deep learning in specialized image recognition.