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Managing Household Waste Through Transfer Learning

by Suman Kunwar
Faculty of Computer Science, Selinus University of Sciences and Literature, Ragusa, Italy

SUBMITTED: 27 January 2024; ACCEPTED: 21 March 2024; PUBLISHED: 25 March 2024

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Abstract

Abstract

As the world continues to face the challenges of climate change, it is crucial to consider the environmental impact of the technologies we use. In this study, we investigate the performance and computational carbon emissions of various transfer learning models for garbage classification. We examine the MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models. Our findings indicate that the EfficientNetV2 family achieves the highest accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model requires more time and produces higher carbon emissions. ResNet50 outperforms ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger carbon footprint. We conclude that EfficientNetV2S is the most sustainable and accurate model with 96.41% accuracy. Our research highlights the significance of considering the ecological impact of machine learning models in garbage classification. 

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Keywords: Garbage Classification; Transfer Learning; Deep Learning; Waste Management; Carbon Emission

Creative Commons Attribution 4.0 International (CC BY 4.0) License
© 2024 Suman Kunwar. This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Kunwar, S. (2024). Managing Household Waste Through Transfer Learning . Industrial and Domestic Waste Management, 4(1), 14–22. https://doi.org/10.53623/idwm.v4i1.408
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