VGG and InceptionV3 model based on CIFAR data contrast analysis

Abstract

This paper introduces in detail the performance comparative analysis of VGG and InceptionV3 based on CIFAR-100 data set in image classification tasks. The experimental results show that the InceptionV3 model performs best on the CIFAR-100 dataset, and its high accuracy and balanced classification effect are impressive. In contrast, the VGG model, while simple in structure, is slightly less accurate. Further analysis shows that InceptionV3 model has more advantages in feature extraction and fusion design, which makes it perform well in image classification tasks. Additionally, the paper explores the broader applications and future prospects of the studied models. By doing so, it provides valuable insights into potential research directions for model comparison. This comprehensive analysis serves as a benchmark, shedding light on the strengths and weaknesses of VGG and InceptionV3 models in image classification. It stands as a valuable reference for future developments in comparative model research.