Research on Image Recognition Technology Based on Convolutional Neural Network and Deep Learning

In order to cope with the massive image processing tasks, the structure of convolutional neural networks is becoming more and more complex, and the scale of parameters is becoming larger and larger. The redundant design in which reduces the image recognition processing speed, affects the accuracy of image recognition, and restricts the further development of convolutional neural networks in the application of image recognition technology. Therefore, this article proposes taking the AlexNet model as the basic architecture, using the VGG16 model to pre-train the data set to obtain a soft target, and then transfering the learned prior knowledge to the AlexNet model for distillation training. It is hoped that better model weights can be obtained through this combination, which can greatly reduce the parameter scale of the network while improving the recognition accuracy.