Article
Version 1
Preserved in Portico This version is not peer-reviewed
Deep machine learning of the MobileNet, Efficient and Inception models
Version 1
: Received: 30 November 2023 / Approved: 30 November 2023 / Online: 30 November 2023 (15:39:09 CET)
A peer-reviewed article of this Preprint also exists.
Rybczak, M.; Kozakiewicz, K. Deep Machine Learning of MobileNet, Efficient, and Inception Models. Algorithms 2024, 17, 96. Rybczak, M.; Kozakiewicz, K. Deep Machine Learning of MobileNet, Efficient, and Inception Models. Algorithms 2024, 17, 96.
Abstract
Today, specific CNN models assigned to specific tasks are often used. In this article, the authors have investigated three models MobileNet, EfficientNetB0 and InceptionV3. Artificial intelligence methods for deep networks will be presented but implemented in a limited computer resource. Three types of training bases were investigated, starting with a simple base verifying five colors, followed by recognition of two different orthogonal elements, and then more complex images discriminating between elements. The research aimed to demonstrate the models' capabilities based on training base parameters such as the number of images and epoch types. The 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 different models based on TensorFlow, Keras libraries in the Jupiter environment written based on the Python language. The authors' proposal of the selected AI model enables further work on e.g. image classification, but in a limited computer resource for industrial implementation e.g. on a PLC.
Keywords
artificial intelligence; deep learning; CNN; Mobilenet; EfficientNetB0; Inception
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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