Waste image classification into organic or recyclable ones with CNN algorithm.
A NodeMCU-ML based project which performs extensive waste classification by leveraging ResNet50's precision and ESP8266's extensibility.
Automated waste classification using ML/DL. Combines web scraping (41K images), a Telegram bot, and an Arduino-based bin with sensors. Models include GBM (96% acc), ResNet, MLP, and ensemble stacking (95% acc). Applications: a Telegram bot for photo-based predictions and a physical bin that sorts waste automatically.
AI-powered waste classification system using deep learning, Combines a custom CNN and EfficientNet (transfer learning). Achieves 99% training and 95% validation accuracy. Classifies images into cardboard, glass, metal, paper, plastic, and trash. Includes prediction, evaluation, and visualization tools.
Image classification system for classifying waste products using Transfer Learning and Fine-Tuning with VGG16, TensorFlow, and Keras.
This repo contains all the source code and obtained data for the waste classification
an object detection model to find waste on the fly
This project automates trash sorting using a Raspberry Pi-controlled robotic arm, leveraging TensorFlow Lite and OpenCV for real-time classification of paper, plastic, and metal waste.
An Application built to help user dispose waste properly and help the environment in the process by motivating them to do the same
AI-powered plastic detection and classification system using computer vision. Identifies and categorizes plastic waste types for recycling and environmental monitoring. Real-time inference with high accuracy.
Waste classification system using MobileNetV2 transfer learning. Flask web app with upload, camera capture, and batch processing for 7 waste categories
Sebuah rest api waste management menggunakan nextjs dan prisma sebagai ORM.
Waste image classification using CNN (MobileNetV2 & DenseNet121) on the TrashNet dataset with augmentation and class weighting.
AI-powered waste classification system using Deep Learning (MobileNetV2). Features real-time camera detection, batch processing, and detailed analytics. Built with Flask and TensorFlow.
Real-time web application using YOLOv8-seg for waste detection, segmentation, and actionable segregation guidance. Handles overlapping waste with multi-dataset training (TACO, TrashNet, Roboflow).
EcoWaste AI uses MobileNetV2 to classify waste as organic or recyclable and a RandomForest model to estimate CO₂ savings based on item weight. It helps users make better disposal choices by providing predictions, confidence scores, carbon-impact estimates, and simple eco-tips through an easy interactive interface.
BinThere is a premium, real-time waste management ecosystem. It uses ESP32-bound sensors to track fill levels in dual-compartment bins, providing insights via a dark glassmorphic React dashboard and native desktop client. The system features advanced fleet analytics, automated IoT routing, and LLM-driven image classification
Synthetic Municipal Solid Waste Generator for AI-powered Waste Recognition System
Real-time waste classification using Amazon Nova on AWS Bedrock — point your camera at any item and instantly get a bin recommendation: Waste, Recycling, or Compost.
To associate your repository with the waste-classification topic, visit your repo's landing page and select "manage topics."