Optimization of Large-Scale Image Recognition Algorithm Based on Deep Learning

Image recognition technology has shown a wide application prospect in many fields. However, the task of largescale image recognition still faces challenges such as high consumption of computing resources and low recognition precision. In order to solve these problems, this article studies the strategy of optimizing the deep learning (DL) algorithm for large-scale image recognition, and designs regularization strategy and learning rate scheduling method by analyzing the model structure, loss function and other factors. DL can automatically learn the high-level abstract features of an image by constructing a multi-layer neural network model. In this study, the performance of several classic DL models, including VGG16, InceptionV3 and ResNet50, in image recognition tasks is compared. In addition, the advantages of multi-node parallel processing in shortening recognition time and improving processing efficiency are also discussed. The results show that the proposed optimization strategy can significantly improve the performance of DL model in large-scale image recognition tasks. In particular, the ResNet50 model has achieved the highest recognition precision after optimization. The research results provide an effective solution for the optimization of large-scale image recognition algorithm based on DL.