Deep Learning-Based Lightweight and Efficient Garbage Classification with Two-Phase Fine-Tuning of MobileNetV2

Deep Learning-Based Lightweight and Efficient Garbage Classification with Two-Phase Fine-Tuning of MobileNetV2 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep Learning-Based Lightweight and Efficient Garbage Classification with Two-Phase Fine-Tuning of MobileNetV2 Hermann Djeugoué Nzeuga, Sanjoy Choudhury This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7920543/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid urbanization and population growth have necessitated the development of intelligent and connected solutions integrated into IoT systems to optimize waste collection , reduce processing costs, and improve the quality of the urban environment. In this article, we propose an waste classification model based on the fine-tuning of MobileNetV2, a pre-trained convolutional neural network, optimized using the Adaptive Moment Estimation (Adam) Algorithm. With a size of only 10 MB, the model is compatible with integration into IoT architectures for the detection, collection and automated centralisa-tion of waste-related data. Evaluated on a public Kaggle dataset comprising 12 classes (battery waste, biological, cardboard, clothes, glass, metal, paper, plastic, shoes, trash) the model achieves an high training and validation accuracy of 0.9995%, 0.9858% respectively , accompanied by high precision, recall and F1-score validation a test values. These results highlight the potential of the approach to automate waste sorting, reduce energy consumption and improve the efficiency of recycling systems in smart urban environments. The proposed model thus represents a promising solution to support sustainable waste management initiative. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7920543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546832372,"identity":"ff5e386b-4c00-49ae-ac0a-347bda4b22b2","order_by":0,"name":"Hermann Djeugoué Nzeuga","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACAygtZwAXYiZSizHpWhI3EO0wc/bepxt+7riTvp29/eEDxj029gzsvAfwarHsOW52s/fMs9ydPWeAjnuWltjAzJeA32E30thu8LYdzt1wI4dNguHA4QQGZh4D/FruP2O7+bftcLrBjfTnPxgO/LcnrOUGG9ttoC0JBjcSzBgYDhxgbCCo5Uwa223ZtsOGIL9IJBxITmwjqOX4Mbabb9sOy5sDQ+zDhwN29vz8Z/BrQQUJQMxGgvpRMApGwSgYBTgAAKnnRf06RU8FAAAAAElFTkSuQmCC","orcid":"","institution":"University of yaounde I","correspondingAuthor":true,"prefix":"","firstName":"Hermann","middleName":"Djeugoué","lastName":"Nzeuga","suffix":""},{"id":546832374,"identity":"ce20ceb9-3848-4c3e-b8ae-170ff9f10758","order_by":1,"name":"Sanjoy Choudhury","email":"","orcid":"","institution":"S.N. 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