Application of deep learning in image classification

Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2024-01-06

asL

Abstract： With the rapid development of deep learning technology, its wide application in the field of image classification has brought revolutionary changes to the field of computer vision. This paper focuses on the application of deep learning in image classification. Based on CIFAR-10 data set, different models are constructed by convolutional neural network (CNN) to evaluate the classification accuracy of images. I also took well-known models such as ResNet and evaluated their classification performance on the CIFAR-10 dataset. In order to further study the characteristics of the model, we introduce different activation functions in the experiment, and reveal the effect of activation functions on the model performance by comparing their performance in the classification task. In addition, we systematically study the classification accuracy of the model under different training rounds by adjusting hyperparameters such as training rounds. In the experiment, I found that the number of training rounds is not always better, and there may be overfitting phenomenon. However, I solved the overfitting problem to some extent by adjusting the model and adding the Dropout layer. At the same time, through the optimization and adjustment of the model, my classification accuracy has also been greatly improved, up to 0.7509. In the process of continuous optimization, I am more and more confident about the prospect of deep learning in image classification, and I will continue to further study in this direction.
 

Peer Review Status:Awaiting Review

 Hits 634  Downloads 254  Comment 0