AI Smart Waste Classifier

Revolutionize waste management with AI! In this video, I demonstrate how to build a Smart Waste Classifier using the latest YOLOv11n-seg architecture. Sorting trash manually is a major global challenge, leading to pollution and inefficient recycling. This project solves that problem by using Instance Segmentation to automatically detect and mask four common waste types: Plastic Bottles, Containers, Plastic Bags, and Crumpled Paper. By training a custom model on a high-quality dataset annotated via Labellerr, we achieve pixel-perfect accuracy that allows for precise robotic sorting and volume estimation. Whether you are interested in Environmental Tech, Computer Vision, or Smart City solutions, this step-by-step guide covers the full inference pipeline on Kaggle using a T4 GPU. Key Highlights of this Project: Instance Segmentation vs. Detection: Why pixel masks are better for waste. Custom Dataset: How Labellerr-verified data improves accuracy. Real-World Impact: From Automated Material Recovery to Smart Bins. Optimized Performance: Running YOLOv11 on the edge for real-time results. Github: https://github.com/Labellerr Cookbook: https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision/blob/main/fine-tune%20YOLO%20for%20various%20use%20cases/AI_Waste_Classifier.ipynb #YOLOv11 #WasteClassifier #ComputerVision #MachineLearning #SmartRecycling #InstanceSegmentation #AIforGood #Sustainability #Labellerr #Kaggle #ObjectDetection #DeepLearning #EnvironmentalTech #PythonProgramming #SmartCity #AutomatedSorting #Robotics #ArtificialIntelligence #WasteManagement #Innovation