Recently, the training time of deep neural network has been greatly shortened because of the rapid development of computer technology especially the improvement of hardware conditions. The deep residual network has rapidly become a new research hotspot. The architecture exhibits good features in terms of precision and convergence. Researchers have delved into its nature and proposed many improvements on deep residual networks, such as wide residual networks, deep pyramidal residual networks, densely residual networks, attention residual network, etc. This study analyzes the construction of different residual units from the design of residual network, and introduces different variants of deep residual network. From different aspects, We compare the differences between different networks and the performance of these network architectures on common image classification datasets. Finally, we summarize these networks and discuss some research directions of future deep residual networks in the field of image classification.