Garbage classification using convolutional neural networks (CNNs)

Abstract

Proper garbage classification is essential for effective waste management and environmental sustainability. This research paper presents a comprehensive study of garbage classification using Convolutional Neural Networks (CNNs). The objective is to develop an accurate and automated garbage classification system leveraging the power of deep learning. The proposed CNN model achieves an impressive accuracy of 98.45%, demonstrating its efficacy in classifying different waste categories. The research encompasses data collection, preprocessing, model architecture, training methodology, and evaluation. The results indicate the potential of CNNs in revolutionizing waste management practices and paving the way for a more sustainable future.