Shallow convolutional neural network for image classification

Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST.