  The Residual Networks of Residual Networks (RoR) exhibits excellent
performance in the image classification task, but sharply increasing the number
of feature map channels makes the characteristic information transmission
incoherent, which losses a certain of information related to classification
prediction, limiting the classification performance. In this paper, a Pyramidal
RoR network model is proposed by analysing the performance characteristics of
RoR and combining with the PyramidNet. Firstly, based on RoR, the Pyramidal RoR
network model with channels gradually increasing is designed. Secondly, we
analysed the effect of different residual block structures on performance, and
chosen the residual block structure which best favoured the classification
performance. Finally, we add an important principle to further optimize
Pyramidal RoR networks, drop-path is used to avoid over-fitting and save
training time. In this paper, image classification experiments were performed
on CIFAR-10/100 and SVHN datasets, and we achieved the current lowest
classification error rates were 2.96%, 16.40% and 1.59%, respectively.
Experiments show that the Pyramidal RoR network optimization method can improve
the network performance for different data sets and effectively suppress the
gradient disappearance problem in DCNN training.
