Application Research of Image Classification Based on Pytorch and Convolutional Neural Network
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Keywords
Image classification, neural network, PyTorch, Model, Training
Abstract
The image classification problem of convolutional neural network (CNN) on CIFAR-10 dataset is studied, and the model is implemented and experimented with PyTorch. Through the training and testing of the model, we analyze the performance of the model and explore the possibility of improvement. The experimental results show that the model achieves a certain accuracy in the image classification task, but there is space for improvement in some categories.
References
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He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 770-778.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, vol. 25, pp. 1097-1105.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 770-778.
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Published
Oct 9, 2024
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Section
Research Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Li, Z. . (2024). Application Research of Image Classification Based on Pytorch and Convolutional Neural Network. Computers and Artificial Intelligence, 1(1), 28-39. https://doi.org/10.70267/eta2tt72