Which network is stronger? Le Net, Alex Net and VGG on image classification

Research Article
Open access

Which network is stronger? Le Net, Alex Net and VGG on image classification

Ruotong Ding 1*
  • 1 University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom    
  • *corresponding author Ruotong.ding@student.manchester.ac.uk
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

Image Classification has become an important and focused area in computer vision. It is widely used and needed in E-commerce area, social media platforms, robotics, etc. The image classification system is aimed to classify the category of the images by associating labels with the image. Being an important area in machine learning, in recent years, with the deep learning technology developing at a fast pace, the convolutional neural network (CNN) is proven that it has great performance in many image classification tasks. This article will mainly focus on three classic convolutional neural networks applied to the image classification tasks. The three networks are LeNet, AlexNet, and VGG. By summarizing their frameworks and usage in the image classification tasks, the analysis of the performance of the three networks will be given.

Keywords:

Image Classification, CNN, LeNet, AlexNet, VGG, Performance, Analysis.

Ding,R. (2023). Which network is stronger? Le Net, Alex Net and VGG on image classification. Applied and Computational Engineering,4,294-300.
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References

[1]. D. Lowe, 2004, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110.

[2]. S. Albawi, T. A. Mohammed and S. Al-Zawi, 2017, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186.

[3]. El-Sawy, A., EL-Bakry, H., Loey, M. 2017, CNN for Handwritten Arabic Digits Recognition Based on LeNet-5. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_54

[4]. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, 1998, Gradient-based learning applied to document recognition, in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, doi: 10.1109/5.726791

[5]. X. Zhang, The AlexNet, LeNet-5 and VGG NET applied to CIFAR-10, 2021, International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 414-419, doi: 10.1109/ICBASE53849.2021.00083

[6]. Dave Gershgorn, 2017, The data that transformed AI research—and possibly the world, [online] Available: https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/

[7]. Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey, 2012, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, vol 60, no. 6, pp. 84-90.

[8]. Simonyan, K. and Zisserman, A. 2015, Very Deep Convolutional Networks for Large-Scale Image Recognition. The 3rd International Conference on Learning Representations (ICLR2015). https://arxiv.org/abs/1409.1556

[9]. Hassan, M., 2022. VGG16 - Convolutional Network for Classification and Detection. [online] Neurohive.io. Available at: <https://neurohive.io/en/popular-networks/vgg16/> [Accessed 12 September 2022].

[10]. M. Kayed, A. Anter and H. Mohamed, 2020, Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture, International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), pp. 238-243, doi: 10.1109/ITCE48509.2020.9047776.

Cite this article

Ding,R. (2023). Which network is stronger? Le Net, Alex Net and VGG on image classification. Applied and Computational Engineering,4,294-300.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. D. Lowe, 2004, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110.

[2]. S. Albawi, T. A. Mohammed and S. Al-Zawi, 2017, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186.

[3]. El-Sawy, A., EL-Bakry, H., Loey, M. 2017, CNN for Handwritten Arabic Digits Recognition Based on LeNet-5. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_54

[4]. Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, 1998, Gradient-based learning applied to document recognition, in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, doi: 10.1109/5.726791

[5]. X. Zhang, The AlexNet, LeNet-5 and VGG NET applied to CIFAR-10, 2021, International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 414-419, doi: 10.1109/ICBASE53849.2021.00083

[6]. Dave Gershgorn, 2017, The data that transformed AI research—and possibly the world, [online] Available: https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/

[7]. Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey, 2012, ImageNet Classification with Deep Convolutional Neural Networks, Communications of the ACM, vol 60, no. 6, pp. 84-90.

[8]. Simonyan, K. and Zisserman, A. 2015, Very Deep Convolutional Networks for Large-Scale Image Recognition. The 3rd International Conference on Learning Representations (ICLR2015). https://arxiv.org/abs/1409.1556

[9]. Hassan, M., 2022. VGG16 - Convolutional Network for Classification and Detection. [online] Neurohive.io. Available at: <https://neurohive.io/en/popular-networks/vgg16/> [Accessed 12 September 2022].

[10]. M. Kayed, A. Anter and H. Mohamed, 2020, Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture, International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), pp. 238-243, doi: 10.1109/ITCE48509.2020.9047776.

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