Managing Household Waste Through Transfer Learning
by Suman KunwarCreative Commons Attribution 4.0 International License
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.
Keywords: Garbage Classification; Transfer Learning; Deep Learning; Waste Management; Carbon Emission
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References
Kaza, S.; Yao, L.C.; Bhada-Tata, P.; Van Woerden, F. (2018). What a waste 2. 0: a global snapshot of solid waste management to 2050. World Bank: Washington DC, USA. https://doi.org/10.1596/978-1-4648-1329-0.
Mridha, K.; Shaw, R.N.; Ghosh, A. (2021). Intelligent based waste management awareness developed by transfer learning. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pages 1–5, Kuala Lumpur, Malaysia. IEEE. https://doi.org/10.1109/GUCON50781.2021.9573586.
Ferronato, N.; Torretta, V. (2019). Waste mismanagement in developing countries: a review of global issues. International Journal of Environmental Research and Public Health, 16(6), 1060. https://doi.org/10.3390/ijerph16061060.
Fadhullah, W.; Imran, N.I.N.; Ismail, S.N.S.; Jaafar, M.H.; Abdullah, H. (2022). Household solid waste management practices and perceptions among residents in the East Coast of Malaysia. BMC Public Health, 22(1), 1. https://doi.org/10.1186/s12889-021-12274-7.
Liu, W.; Ouyang, H.; Liu, Q.; Cai, S.; Wang, C.; Xie, J.; Hu, W. (2022). Image recognition for garbage classification based on transfer learning and model fusion. Mathematical Problems in Engineering, 2022, 1–12. https://doi.org/10.1155/2022/4793555.
Shreya, M.; Nimal Yughan, V.; Katyal, J.; Ramesh, R. (2023). Technical solutions for waste classification and management: A mini-review. Waste Management & Research: The Journal for a Sustainable Circular Economy, 41(4), 801–815. https://doi.org/10.1177/0734242X221135262.
Kunwar, S. (2023). Mwaste: An app that uses deep learning to manage household waste. Clean Technologies and Recycling, 3(3), 119–133. https://doi.org/10.3934/ctr.2023008.
Narayan, Y. (2021). Deepwaste: applying deep learning to waste classification for a sustainable planet. https://doi.org/10.48550/ARXIV.2101.05960.
Kaack, L.H.; Donti, P.L.; Strubell, E.; Kamiya, G.; Creutzig, F.; Rolnick, D. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12(6), 518–527. https://doi.org/10.1038/s41558-022-01377-7.
Pan, S.J.; Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
Poudel, S.; Poudyal, P. (2022). Classification of waste materials using cnn based on transfer learning. Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation, pp. 29–33, Kolkata India. ACM. https://doi.org/10.1145/3574318.3574345.
Vo, A.H.; Hoang Son, L.; Vo, M.T.; Le, T. (2019). A novel framework for trash classification using deep transfer learning. IEEE Access, 7, 178631–178639. https://doi.org/10.1109/ACCESS.2019.2959033.
Tan, M.; Le, Q.V. (2021). Efficientnetv2: Smaller models and faster training. https://doi.org/10.48550/arXiv.2104.00298.
Kunwar, S. (2023). Garbage dataset. https://doi.org/10.34740/KAGGLE/DS/2814684.
Lilhore, U.K.; Simaiya, S.; Dalal, S.; Damaševicius, R. (2023). A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment. Multimedia Tools and Applications, 83, 29505–29529. https://doi.org/10.1007/s11042-023-16677-z.
Wulansari, A.; Setyanto, A.; Luthfi, E. T. (2022). A convolutional neural network based on transfer learning for medical waste classification during pandemic covid-19. 2022 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 86–91, Surabaya, Indonesia. IEEE. https://doi.org/10.1109/ISITIA56226.2022.9855374.
Mehedi, M.H.K.; Arafin, I.; Hasan, M.; Rahman, F.; Tasin, R.; Rasel, A.A. (2023). A transfer learning approach for efficient classification of waste materials. 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0636–0640, Las Vegas, NV, USA. IEEE. https://doi.org/10.1109/CCWC57344.2023.10099127.
Huang, G.; He, J.; Xu, Z.; Huang, G. (2020). A combination model based on transfer learning for waste classification. Concurrency and Computation: Practice and Experience, 32(19), e5751. https://doi.org/10.1002/cpe.5751.
Srivatsan, K.; Dhiman, S.; Jain, A. (2021). Waste classification using transfer learning with convolutional neural networks. IOP Conference Series: Earth and Environmental Science, 775(1), 012010. https://doi.org/10.1088/1755-1315/775/1/012010.
Das, D.; Sen, A.; Hossain, S.M.M.; Deb, K. (2023). Trash image classification using transfer learning based deep neural network. In Vasant, P., Weber, G.-W., Marmolejo-Saucedo, J. A., Munapo, E., and Thomas, J. J., editors, Intelligent Computing & Optimization, volume 569, pp. 561–571. Springer International Publishing: Cham, Switzerland. https://doi.org/10.1007/978-3-031-19958-5_53.
Devi, D.; Biswas, S.K.; Purkayastha, B. (2020). A review on solution to class imbalance problem: Undersampling approaches. 2020 International Conference on Computational Performance Evaluation (ComPE), pp. 626–631. https://doi.org/10.1109/ComPE49325.2020. 9200087.
Hancock, J.; Khoshgoftaar, T.M.; Johnson, J.M. (2022). Informative evaluation metrics for highly imbalanced big data classification. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1419–1426. https://doi.org/10.1109/ ICMLA55696.2022.00224.
Shijie, J.; Ping, W.; Peiyi, J.; Siping, H. (2017). Research on data augmentation for image classification based on convolution neural networks. 2017 Chinese Automation Congress (CAC), pp. 4165–4170. https://doi.org/10.1109/CAC.2017.8243510.
Zhang, J.; He, T.; Sra, S.; Jadbabaie, A. (2020). Why gradient clipping accelerates training: A theoretical justification for adaptivity. arXiv:1905.11881 [cs, math]. https://doi.org/10.48550/ arXiv.1905.11881.
Maji, K.; Gupta, S. (2023). Evaluation of various loss functions and optimization techniques for mri brain tumor detection. 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 1–6. https://doi.org/10.1109/ ICDCECE57866.2023.10151232.
Mikołajczyk, A.; Grochowski, M. (2019). Style transfer-based image synthesis as an efficient regularization technique in deep learning. 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 42–47. https://doi.org/ 10.1109/MMAR.2019.8864616.
Rezatofighi, H.; Tsoi, N.; Gwak, J.; Sadeghian, A.; Reid, I.; Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 658–666. https://doi.org/10.1109/CVPR.2019.00075.
Date, P.; Carothers, C.D.; Mitchell, J.E.; Hendler, J.A.; Magdon-Ismail, M. (2020). Training deep neural networks with constrained learning parameters. 2020 International Conference on Rebooting Computing (ICRC), 107–115. https://doi.org/10.1109/ ICRC2020.2020.00018.
Akiba, T.; Sano, S.; Yanase, T.; Ohta, T.; Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623–2631. https://doi.org/ 10.1145/3292500.3330701.