A Review on Categorization of the Waste Using Transfer Learning
- Authors: Krantee M. Jamdaade1, Mrutunjay Biswal2, Yash Niranjan Pitre3
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View Affiliations Hide AffiliationsAffiliations: 1 Department of Data Science and Technology, K. J. Somaiya Institute of Management, Mumbai, India 2 Department of Data Science and Technology, K. J. Somaiya Institute of Management, Mumbai, India 3 Department of Data Science and Technology, K. J. Somaiya Institute of Management, Mumbai, India
- Source: Artificial Intelligence, Machine Learning and User Interface Design , pp 76-91
- Publication Date: May 2024
- Language: English
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In this paper, we have aimed to develop a system that will help waste collectors segregate different types of waste without needing much human intervention. We have experimented with various deep learning and transfer learning techniques to determine which model is more suited for this purpose. The dataset we used contained 8369 images that are classified into 9 classes: batteries, clothes, e-waste, glass, light bulbs, metal, organic, paper, and plastic. We used models like VGG16, Inceptionv3, ResNet50, MobileNET, NASNetMobile and Xception. We have also conducted a survey to know about the waste management habits of the respondents. Our experiments showed that models like MobileNET gave us the best accuracy of 93.17% and identified all the waste categories correctly and the Xception model predicted images correctly with the use of both Adam and Adadelta.
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