Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification
The image classification is a classical problem of image processing, computer vision, and machine learning. This paper presents an analysis of the performance using Convolutional Neural Network (CNN) for image classifying using deep learning. MiniVGGNet is CNN architecture used in this paper to trai...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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UTHM Publisher
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/80197/ http://psasir.upm.edu.my/id/eprint/80197/1/Improving%20Convolutional%20Neural%20Network%20%28CNN%29%20architecture%20%28miniVGGNet%29%20with%20Batch%20Normalization%20and%20Learning%20Rate%20Decay%20Factor%20for%20Image%20Classification.pdf |
| _version_ | 1848858854032408576 |
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| author | Ismail, Asmida Ahmad, Siti Anom Che Soh, Azura Hassan, Mohd Khair Harith, Hazreen Haizi |
| author_facet | Ismail, Asmida Ahmad, Siti Anom Che Soh, Azura Hassan, Mohd Khair Harith, Hazreen Haizi |
| author_sort | Ismail, Asmida |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The image classification is a classical problem of image processing, computer vision, and machine learning. This paper presents an analysis of the performance using Convolutional Neural Network (CNN) for image classifying using deep learning. MiniVGGNet is CNN architecture used in this paper to train a network for image classification, and CIFAR-10 is selected dataset used for this purpose. The performance of the network was improved by hyper parameter tuning techniques using batch normalization and learning rate decay factor. This paper compares the performance of the trained network by adding batch normalization layer and adjusting the value of learning rate decay factor for the network architecture. Based on the experimental results, adding batch normalization layer allow the networks to improve classification accuracy from 80% to 82%. Applying learning rate decay factor will improve classification accuracy to 83% and reduce the effects of overfitting in learning plot. Performance analysis shows that applying hyper parameter tuning can improve the performance of the network and increasing the ability of the model to generalize. |
| first_indexed | 2025-11-15T12:20:03Z |
| format | Article |
| id | upm-80197 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:20:03Z |
| publishDate | 2019 |
| publisher | UTHM Publisher |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-801972020-10-02T00:14:08Z http://psasir.upm.edu.my/id/eprint/80197/ Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification Ismail, Asmida Ahmad, Siti Anom Che Soh, Azura Hassan, Mohd Khair Harith, Hazreen Haizi The image classification is a classical problem of image processing, computer vision, and machine learning. This paper presents an analysis of the performance using Convolutional Neural Network (CNN) for image classifying using deep learning. MiniVGGNet is CNN architecture used in this paper to train a network for image classification, and CIFAR-10 is selected dataset used for this purpose. The performance of the network was improved by hyper parameter tuning techniques using batch normalization and learning rate decay factor. This paper compares the performance of the trained network by adding batch normalization layer and adjusting the value of learning rate decay factor for the network architecture. Based on the experimental results, adding batch normalization layer allow the networks to improve classification accuracy from 80% to 82%. Applying learning rate decay factor will improve classification accuracy to 83% and reduce the effects of overfitting in learning plot. Performance analysis shows that applying hyper parameter tuning can improve the performance of the network and increasing the ability of the model to generalize. UTHM Publisher 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80197/1/Improving%20Convolutional%20Neural%20Network%20%28CNN%29%20architecture%20%28miniVGGNet%29%20with%20Batch%20Normalization%20and%20Learning%20Rate%20Decay%20Factor%20for%20Image%20Classification.pdf Ismail, Asmida and Ahmad, Siti Anom and Che Soh, Azura and Hassan, Mohd Khair and Harith, Hazreen Haizi (2019) Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification. International Journal of Integrated Engineering, 11 (4). pp. 51-59. ISSN 2229-838X; ESSN: 2600-7916 10.30880/ijie.2019.11.04.006 |
| spellingShingle | Ismail, Asmida Ahmad, Siti Anom Che Soh, Azura Hassan, Mohd Khair Harith, Hazreen Haizi Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification |
| title | Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification |
| title_full | Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification |
| title_fullStr | Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification |
| title_full_unstemmed | Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification |
| title_short | Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with batch normalization and learning rate decay factor for image classification |
| title_sort | improving convolutional neural network (cnn) architecture (minivggnet) with batch normalization and learning rate decay factor for image classification |
| url | http://psasir.upm.edu.my/id/eprint/80197/ http://psasir.upm.edu.my/id/eprint/80197/ http://psasir.upm.edu.my/id/eprint/80197/1/Improving%20Convolutional%20Neural%20Network%20%28CNN%29%20architecture%20%28miniVGGNet%29%20with%20Batch%20Normalization%20and%20Learning%20Rate%20Decay%20Factor%20for%20Image%20Classification.pdf |