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 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.

This repo contains all the source code and obtained data for the waste classification

Waste classification system using MobileNetV2 transfer learning. Flask web app with upload, camera capture, and batch processing for 7 waste categories

an object detection model to find waste on the fly

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

MARJ: AI-powered waste classification system. Deep learning for smarter recycling and automated sorting.

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

♻️ Classify waste images into categories using transfer learning and deploy with Flask for real-time predictions, enhancing waste management automation.

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.

Web app basata su intelligenza artificiale per identificare, catalogare e classificare correttamente i rifiuti.

A lightweight deep‑learning project that classifies images of household garbage into 12 waste categories using an EfficientNet backbone. Includes training scripts, evaluation tools, dataset download automation, and visualization utilities for inspecting predictions.

This project implements a deep learning–based garbage classification system using a custom Convolutional Neural Network (CNN). It automatically classifies waste images into recyclable categories, supporting efficient and smart waste segregation through AI.

This repository contains the source code and documentation for the Waste Classification project. The project includes a Laravel-based web backend, Python prediction scripts, and a Jupyter notebook with experiments and analysis.

Garbage_classification_model using CNN model