Research on cat and dog recognition based on several CNN models

CNN (convolutional neural network) is a classical research method of Deep Learning. It can obtain the characteristics of a picture through the information transmission between convolution layers and pooling layers, and generate some output (such as image classification) after final processing. Since the end of the 20th century, scholars have proposed various convolutional neural networks, which have their own characteristics. In this paper, we choose LeNet, AlexNet, VGG, ResNet and GoogLeNet to complete the cat and dog recognition task on kaggle, so as to identify and explore the performance of different networks in different situations. The results show that these classical algorithms have generally become more and more advanced over time, but this cognition is not completely correct. For example, when the number of samples is limited, the more advanced ResNet did not perform as well as the relatively primitive VGG network. Such characteristics help us choose the right algorithm in different situations and guide us refine the algorithm in the future.