Treffer: Performance Analysis of Deep Learning Algorithms Implemented Using PyTorch in Image Recognition.

With the rapid development of artificial intelligence technology, image recognition has become a key technology in many fields. This paperaims to analyze the performance of image recognition tasks through the implementation of deep learning algorithms using PyTorch. This paperintroduces the selection and preprocessing techniques of datasets, including normalization, data augmentation, size adjustment, and batch processing, which provide high-quality input data for model training. In terms of deep learning model design, Convolutional Neural Network was adopted, CNN was used as the infrastructure and the cross entropy loss function and Adam optimizer were selected for model training. The implementation details of PyTorch demonstrate the model construction, training process, and debugging, as well as how to use PyTorch's DataLoader class to improve data loading efficiency. The Results and Discussion section presents the training results of the model, where the CNN model achieves the highest accuracy of 99% in image recognition tasks, and the loss function value ultimately stabilizes at 0.02, indicating that the model has efficient learning and generalization capabilities. Performance comparison experiments show that, the CNN implemented by PyTorch outperforms MXNet and Caffe in terms of frame rate and processing delay, with a frame rate between 51FPS-79FPS and a minimum processing delay of 308ms. The analysis of practical application scenarios further explores the potential application of the model in fields such as medical image analysis, autonomous driving, retail industry, and safety monitoring, and points out challenges such as data privacy, model generalization ability, and computational resource limitations. [ABSTRACT FROM AUTHOR]