Research on the problem of image multi-classification

Zelin Song, Xiaoyu Wu, Jiayao Qian
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引用次数: 1

Abstract

Image classification is an important research topic in the computer vision area. The classification task of small data volume, large number of classes, smaller differences between classes and larger differences within classes has always been a challenge. And this article aims to study and discuss such problems, using three methods: ResNets, EfficientNet and MoCo V2 for experiment and continuous optimization, and got the following results: The accuracy rate of ResNets classification is 0.43, of EfficientNet classification is 0.47, and of MoCo V2 classification, it is 0.62.
图像多分类问题的研究
图像分类是计算机视觉领域的一个重要研究课题。数据量小、类数多、类间差异小、类内差异大的分类任务一直是一个挑战。本文针对这些问题进行研究和讨论,采用ResNets、EfficientNet和MoCo V2三种方法进行实验和持续优化,得到如下结果:ResNets分类准确率为0.43,EfficientNet分类准确率为0.47,MoCo V2分类准确率为0.62。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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