Deep Machine Learning of MobileNet, Efficient, and Inception Models

Abstract

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.

Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges

“…MobileNets are a class of highly efficient CNN models built upon a streamlined architecture that leverages depth-wise separable convolutions, being a deep neural network with significantly reduced computational demand [34]. The model was chosen to be suitable for development on devices with limited resources, making it easier to apply in projects without requiring more sophisticated computational resources [44][45][46]. The model was implemented using the open-source libraries of TensorFlow and Sklearn and is available in the Supplementary Materials (Code S2).…”