The architecture of CCNN. The input subplots of CCNN from the top to bottom panel are individually the pulse profile, DM curve, the frequency versus phase plot, and the time versus phase plot whose sizes are 64, 200, 64 × 64, and 64 × 64, respectively. The output sizes of the 1D convolutional layer are L × N and the output sizes of the 2D convolutional layer are H × W × N, where L denotes the length of the tensors, H and W are the height and width of the tensors, and N is the number of the feature maps. The output layer of the H-CCNN and V-CCNN are different due to the dissimilar operation in concatenate layer and their subsequent layers. The former model outputs the probability of a candidate being pulsar, while the output layer of the latter model contains two neurons, respectively, representing the probabilities of a candidate being pulsar and non-pulsar. GMP means global max pooling layer, Concat represents the concatenate layer and FC is the abbreviation of fully connected layer.