Convolution layers are a series of filters that distill features as the input of the next layers. As for the pooling layer, it is a down-sampling method that comes with convolution layers. It reduces data dimensions by combining the outputs of neuron clusters at one layer into a single neuron and usually follows the convolution layers. Therefore, it can not only decrease the calculation cost increase the generalization performance of the network but also accelerate the convergence speed of the model training.