Animal Image Classifier Based on Convolutional Neural Network

Abstract

In modern society, there are dogs and cats around people, as well as rare wild animals living in nature. The relationship between human beings and animals is getting closer and closer. The rapid development of machine learning and deep learning technology has been widely used in the academic field. Aiming at the problem of animal image classification, this paper uses Pytorch to learn about 10,000 pictures containing cats, dogs, and wild animals (tiger, lion, etc.) based on the research algorithm of convolutional neural network in the field of image classification. And a convolutional neural network model that can realize the animal image classifier is established and optimized, so that the model can efficiently classify cats, dogs and wildlife pictures. The results show that the accuracy of the two models is above 90%, and the model loss ranges from 0.706 to 0.061, and 0.807 to 0.051, respectively, showing the characteristics of good model fitting effect and strong optimization ability. Meanwhile, The accuracy of the model can be increased by properly increasing the number of full connection layers. Therefore, by constructing the convolutional neural network, the accurate detection of national ecological protection animal images can be realized.