Deep Learning Approach for Garbage Classification

This research introduces a pioneering framework designed to address the critical challenges of real-time garbage detection and classification, anchored by a comprehensive garbage dataset meticulously categorizing waste items into Biodegradable, Non-Biodegradable, Clothes Waste and Hazardous Waste, this study contrasts the performance of YOLOv5, YOLOv7, and YOLOv8 models. The project unfolds with a rigorous sequence encompassing data preprocessing, model training, meticulous evaluation and exhaustive testing. Significantly, the study demonstrates the unparalleled effectiveness of the model in real-time garbage classification, offering a scalable and efficient solution to bolster waste management, recycling endeavors and broader environmental conservation initiatives. By showcasing the model’s efficacy in discerning diverse waste items, this research paves the was for the integration of technology into waste management practices, potentially revolutionizing resource allocation, and fostering sustainable environmental practices.