Survey of image classification algorithms based on deep learning

Abstract

The traditional image classification methods have defects, which can not process massive image data, and can not meet the needs of image classification in speed and accuracy. The performance of deep learning in the field of computer vision is better than the traditional machine learning technology, and it has become the mainstream method of image classification. Based on the deep learning method, this paper summarizes the commonly used algorithm models in the field of image classification, analyzes the error rate, architecture design, application scenarios and other aspects of the models, and then compares the differences between the current network models with outstanding classification effect through experiments, and further verifies the advantages and disadvantages of various models. Finally, the development trend of deep learning in image classification is summarized, and the possible research directions in the future are discussed.