Pre-trained CNNs: Evaluating Emergency Vehicle Image Classification

Ali Omari Alaoui, Omaima El Bahi, Mohamed Rida Fethi, Othmane Farhaoui, Ahmad El Allaoui and Yousef Farhaoui

Data and Metadata, 2023, vol. 2, 153

Abstract: In this paper, we aim to provide a comprehensive analysis of image classification, specifically in the context of emergency vehicle classification. We have conducted an in-depth investigation, exploring the effectiveness of six pre-trained Convolutional Neural Network (CNN) models. These models, namely VGG19, VGG16, MobileNetV3Large, MobileNetV3Small, MobileNetV2, and MobileNetV1, have been thoroughly examined and evaluated within the domain of emergency vehicle classification. The research methodology utilized in this study is carefully designed with a systematic approach. It includes the thorough preparation of datasets, deliberate modifications to the model architecture, careful selection of layer operations, and fine-tuning of the model compilation. To gain a comprehensive understanding of the performance, we conducted a detailed series of experiments. We analyzed nuanced performance metrics such as accuracy, loss, and training time, considering important factors in the evaluation process. The results obtained from this study provide a comprehensive understanding of the advantages and disadvantages of each model. Moreover, they emphasize the crucial significance of carefully choosing a suitable pre-trained Convolutional Neural Network (CNN) model for image classification tasks. Essentially, this article provides a comprehensive overview of image classification, highlighting the crucial significance of pre-trained CNN models in achieving precise outcomes, especially in the demanding field of emergency vehicle classification

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:2:y:2023:i::p:153:id:1056294dm2023153

DOI: 10.56294/dm2023153

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