Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net
In this paper, we compare three different models of multilayer neural networks in terms of their success in the classification phase. These models were designed for EmguCV, ML.NET and Tensorflow.Net libraries, which are currently among the most widely used libraries in the implementation of an autom...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3730 |
| _version_ | 1797437018843643904 |
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| author | Martin Magdin Juraj Benc Štefan Koprda Zoltán Balogh Daniel Tuček |
| author_facet | Martin Magdin Juraj Benc Štefan Koprda Zoltán Balogh Daniel Tuček |
| author_sort | Martin Magdin |
| collection | DOAJ |
| description | In this paper, we compare three different models of multilayer neural networks in terms of their success in the classification phase. These models were designed for EmguCV, ML.NET and Tensorflow.Net libraries, which are currently among the most widely used libraries in the implementation of an automatic recognition system. Using the EmguCV library, we achieved a success rate in the classification of human faces of 81.95% and with ML.NET, which was based on the pre-trained ResNet50 model using convolution layers, up to 91.15% accuracy. The result of the success of the classification process was influenced by the time required for training and also the time required for the classification itself. The Tensorflow.Net model did not show sufficient classification ability when classifying using vector distances; the highest success rate of classification was only 13.31%. Neural networks were trained on a dataset with 1454 photographs of faces involving 43 people. At a time when neural networks are becoming more and more used for applications of different natures, it is necessary to choose the right model in the classification process that will be able to achieve the required accuracy with the minimum time required for training. The application created by us allows the insertion of images and the creation of their own datasets, on the basis of which the user can train a model with its own parameters. Models can then be saved and integrated into other applications. |
| first_indexed | 2024-03-09T11:12:52Z |
| format | Article |
| id | doaj.art-5499b8e5812d4b16b532f628192677ce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:12:52Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5499b8e5812d4b16b532f628192677ce2023-12-01T00:37:30ZengMDPI AGApplied Sciences2076-34172022-04-01128373010.3390/app12083730Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.NetMartin Magdin0Juraj Benc1Štefan Koprda2Zoltán Balogh3Daniel Tuček4Department of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, 949 74 Nitra-Chrenová, SlovakiaDepartment of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, 949 74 Nitra-Chrenová, SlovakiaDepartment of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, 949 74 Nitra-Chrenová, SlovakiaDepartment of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, 949 74 Nitra-Chrenová, SlovakiaDepartment of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, 949 74 Nitra-Chrenová, SlovakiaIn this paper, we compare three different models of multilayer neural networks in terms of their success in the classification phase. These models were designed for EmguCV, ML.NET and Tensorflow.Net libraries, which are currently among the most widely used libraries in the implementation of an automatic recognition system. Using the EmguCV library, we achieved a success rate in the classification of human faces of 81.95% and with ML.NET, which was based on the pre-trained ResNet50 model using convolution layers, up to 91.15% accuracy. The result of the success of the classification process was influenced by the time required for training and also the time required for the classification itself. The Tensorflow.Net model did not show sufficient classification ability when classifying using vector distances; the highest success rate of classification was only 13.31%. Neural networks were trained on a dataset with 1454 photographs of faces involving 43 people. At a time when neural networks are becoming more and more used for applications of different natures, it is necessary to choose the right model in the classification process that will be able to achieve the required accuracy with the minimum time required for training. The application created by us allows the insertion of images and the creation of their own datasets, on the basis of which the user can train a model with its own parameters. Models can then be saved and integrated into other applications.https://www.mdpi.com/2076-3417/12/8/3730classificationconvolution netsneural netsdetectionface recognition |
| spellingShingle | Martin Magdin Juraj Benc Štefan Koprda Zoltán Balogh Daniel Tuček Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net Applied Sciences classification convolution nets neural nets detection face recognition |
| title | Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net |
| title_full | Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net |
| title_fullStr | Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net |
| title_full_unstemmed | Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net |
| title_short | Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net |
| title_sort | comparison of multilayer neural network models in terms of success of classifications based on emgucv ml net and tensorflow net |
| topic | classification convolution nets neural nets detection face recognition |
| url | https://www.mdpi.com/2076-3417/12/8/3730 |
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