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.

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.

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.

EcoGuardian is a mobile app that uses AI-driven image recognition to classify waste into recyclables, compost, and landfill categories.

Waste Classification into biodegradable, non-recyclable, recyclable and reusable.

♻️ waste classification and educational resources codebase

Synthetic Municipal Solid Waste Generator for AI-powered Waste Recognition System

A deep learning project that classifies garbage into six categories (cardboard, glass, metal, paper, plastic, and trash) using a Convolutional Neural Network (CNN). Includes a complete frontend-backend system built with Flask for real-time image classification.

Waste image classification using CNN (MobileNetV2 & DenseNet121) on the TrashNet dataset with augmentation and class weighting.

An AI/ML system designed to optimize smart factory operations by streamlining production lines, reducing waste, and automating material recycling.

Exploring the use of Vision Transformers (ViT) for waste classification

HR-ViT: A hybrid ResNet50–Vision Transformer model for six-class municipal waste classification (plastic, paper, metal, glass, organic, batteries), achieving 98.27% accuracy and designed for real-world recycling systems.

real time waste classification using artificial neural network

AI-powered educational platform for waste sorting developed at Magdeburg-Stendal University of Applied Sciences as part of the SMART TRASH Pillar 2 research project.

Binary waste classification using transfer learning with VGG16. Implements feature extraction, fine-tuning, and visualization.

Edge AI Prototype enhances sustainability via AI. Task 1 classifies recyclables (Organic and Inorganic) using MobileNetV2. Task 2 predicts crop yields with Random Forest. Task 3 analyzes AI ethics in medicine. Uses TensorFlow, Scikit-learn, Pandas, and TFLite.