Author: Zen and the Art of Computer Programming
1 Introduction
Deep learning technology has made great progress in the field of image classification, and various deep network models such as VGG and ResNet have become mainstream solutions. The PyTorch framework provides an easy-to-use and high-performance implementation. Compared with other frameworks, PyTorch has richer API support and more powerful features. This article will introduce in detail how to use PyTorch to implement image classification tasks. First of all, two important concepts need to be clarified: data set and training set. The dataset here includes all image files used for training, validation, and testing. The training set contains pictures used in the training process, the verification set contains a collection of pictures used to compare the training effect and tuning parameters, and the test set is a collection of pictures used to evaluate the model effect. Second, PyTorch is based on the Dynamic Computational Graph, that is, the calculation graph will be rebuilt every time the forward propagation is run, which can provide flexibility and high efficiency. Therefore, the input data dimension or structure can be adjusted as needed, and multiple model combinations and hyperparameter searches can be easily realized. Third, PyTorch also provides a series of pre-trained models for users to directly call to meet different needs. For image classification tasks, commonly used pre-training models are AlexNet, VGG, GoogLeNet, ResNet, etc. Finally, it should be noted that due to time constraints, we only briefly introduce the application of PyTorch in image classification from the perspective of principle and operation, and do not involve code detail optimization and actual project application. In the following chapters, we introduce relevant knowledge points step by step.
2. Overview of PyTorch
2.1 Introduction to PyTorch
2.1.1 Overview of PyTorch
PyTorch is an open-source Python library for scientific computing, deep learning, and machine learning. It provides an automatic derivation mechanism to efficiently process tensors (Tensor), and has powerful GPU acceleration capabilities. Its unique programming model &