Research on Residual Networks for Image Classification.

In recent years, with the continuous expansion of data sets and the continuous improvement of computer performance, the traditional image classification methods always lead to low accuracy. Because of their high accuracy and great convergence, the residual networks have become a key technical in the field of image classification, they are worthy of studying in detail. Each variant improves network performance by improving classification accuracy, reducing model complexity or reducing calculation amount. Firstly, This paper analyzes the advantages and disadvantages of each variant and the suggestions are given for the application of them. Then, the performance of each variant is compared intuitively from the three aspects of accuracy rate, parameter amount and calculation amount. Finally, the challenges and future development of residual networks are put forward. [ABSTRACT FROM AUTHOR]