Deep Machine Learning of MobileNet, Efficient, and Inception Models

Today, specific convolution neural network (CNN) models assigned to specific tasks are often used. In this article, the authors explored three models: MobileNet, EfficientNetB0, and InceptionV3 combined. The authors were interested in investigating how quickly an artificial intelligence model can be...

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Published inAlgorithms Vol. 17; no. 3; p. 96
Main Authors Rybczak, Monika, Kozakiewicz, Krystian
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2024
Subjects
Accuracy
Algorithms
Anemia
Artificial intelligence
Artificial neural networks
Brain cancer
Cameras
Classification
deep learning
EfficientNetB0
Equipment and supplies
Image classification
Inception
Linux
Machine learning
Mathematical models
MobileNet
Neural networks
Parameters
Programming languages
Python
Teaching
Virtual environments
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ISSN1999-4893
1999-4893
DOI10.3390/a17030096

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Summary:Today, specific convolution neural network (CNN) models assigned to specific tasks are often used. In this article, the authors explored three models: MobileNet, EfficientNetB0, and InceptionV3 combined. The authors were interested in investigating how quickly an artificial intelligence model can be taught with limited computer resources. Three types of training bases were investigated, starting with a simple base verifying five colours, then recognizing two different orthogonal elements, followed by more complex images from different families. This research aimed to demonstrate the capabilities of the models based on training base parameters such as the number of images and epoch types. Architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of the different models based on the TensorFlow and Keras libraries in the Jupiter environment based on the Python programming language. An artificial intelligence model with a combination of MobileNet, proposed by Siemens, and Efficient and Inception, selected by the authors, allows for further work to be conducted on image classification, but with limited computer resources for industrial implementation on a programmable logical controller (PLC). The study showed a 90% success rate, with a learning time of 180 s.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a17030096