Performance analysis and comparison of cat and dog image classification based on different models

Abstract

Image classification has widespread applications in computer vision, with significant advancements in performance due to deep learning models. Cat and dog image classification, as a classic problem, has attracted considerable research interest. This study aims to conduct a comprehensive analysis and comparison of deep learning models, including LeNet, ResNet, and VGG, in the context of cat and dog image classification. This paper employed two datasets: traditional cat and dog images and non-traditional, diverse images. Data preprocessing and augmentation were applied, and various model architectures were constructed. Through training and testing, this paper assessed the performance of these models under different conditions. The research findings indicate that ResNet excels in handling various datasets and different dataset sizes, demonstrating outstanding image classification performance. LeNet performs well on traditional datasets but experiences performance degradation when dealing with non-traditional datasets and smaller dataset sizes. VGG performs reasonably well on the original dataset but needs help processing non-traditional datasets. These results provide valuable insights for guiding model selection and optimization in image classification tasks.