Open AccessResearch on Image Classification Algorithm Based on Pytorch
Author(s) -
Lixiang Jiang,
Zizhao Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012009
Subject(s) - mnist database , computer science , convolutional neural network , artificial intelligence , deep learning , contextual image classification , image (mathematics) , construct (python library) , artificial neural network , feature (linguistics) , pattern recognition (psychology) , set (abstract data type) , machine learning , linguistics , philosophy , programming language
With the development of artificial intelligence and deep learning, image classification technology has ushered in new opportunities and challenges. The so-called image classification problem is the problem that the user passes in the image, and then the computer sends out the classification and description of the content of the image. Feature description and detection as a traditional image classification method have many drawbacks, such as low accuracy and long time-consuming problems. Among the existing deep learning frameworks, Pytorch is relatively effective. Researchers can use the Pytorch framework to construct convolutional neural network models quickly and easily, and train them on massive data sets. This paper briefly introduces the image classification algorithm, neural network and Pytorch. The classification experiment is carried out on the Fashion MNIST data set, and the accuracy of the model reaches 88.39%. The future research direction is discussed.