A Small Sample Image Recognition Method Based on ResNet and Transfer Learning

With the vigorous development of artificial intelligence big data era and the advent of 5G era, the amount of network information shows a blowout-like growth. As a result, the accurate query of information is faced with unprecedented challenges. The image as a material reproduction of visual perception, has a large number of retrieval requests all the time, but the traditional image target recognition annotation is mainly based on pixel-level supervised learning. In the face of massive high-quality image recognition, it is very difficult for users to query the target content accurately and quickly. Therefore, this paper studies the animal classification model based on Convolutional Neural Network (CNN), using transfer learning to pre-train the characteristics of the network and combined with the hybrid classification model of CNN. In the experiment, CATS/DOGS were used as the data set, and PyTorch was used to train the network model. Experimental research shows that the accuracy of 96.43% is achieved by using CNN+ transfer learning algorithm, which is significantly higher than that of traditional methods. For small-scale data sets, it effectively solves the non-transferability of manual feature extraction, and improves the accuracy and robustness.