Deep separable convolutional neural networks based on structural reparameterization

A new lightweight convolutional neural network (CNN) model called the deep separable convolutional neural network (DSCNN) based on structural reparameterization is proposed, aiming at the single-branch deep convolutional approach used in the majority of the current CNN models, which not only affects the expressive ability of the model but also occupies a large number of parameters and Flops. Firstly, the feature extraction module (reparameterization MiXer, RepMiX) of the model can fuse information between different channels and spatial locations, realizing multi-scale feature fusion. Secondly, the reparameterized asymmetric spatial operator (RepASO) in the DSCNN learns different channel feature information through a multi-branch structure with different functions, which improves the model feature learning ability; meanwhile, both RepMiX and RepASO combine the structural reparameterization technique and the idea of depth wise separable convolution (DS-Conv) to realize structural decoupling in the training and inference phases, which accelerates the model inference while reducing the model parameters and Flops. Finally, comparative experiments are carried out on the Tiny-imagenet-200, CIFAR-10, and CIFAR-100 datasets in addition to a self-constructed dataset for the classification of aluminum ingot surface defects. The experimental results demonstrate that the DSCNN maintains competitive accuracy while achieving higher floating-point speeds.