Dataset of trash objects for waste classification and detection

Attentional Learning of Trash Classification

Underwater Trash Detection

automatic trash separator using yolo, mtcnn

Classification of trash images using Convolutional Neural nets

CNN Image classification of trash of 6 categories in tensorflow and pytorch

Sorts recycling and trash using computer vision

Self-collected trash dataset used in AlphaTrash project. Contains 5600+ images of trash gathered in Thailand, sorted into general, metal, organic, paper, and plastic wastes.

On prototype, Modeling trash with CNN method

Part of my Final Year Project (FYP), this web application allows platform administrators to manage users, shops, the point system, and feedback within an AI-powered waste management & recycling rewards system. It ensures smooth operation, security, and compliance for the Trash to Treasure platform.

Web backend of machine learning trash classification system

Part of my Final Year Project (FYP), this web application enables partnered shops and cafes to verify and redeem user rewards earned through recycling. Integrated with an AI-powered trash classification system, it promotes sustainable waste management by making recycling a rewarding experience.

A computer vision application that helps classify waste items into appropriate disposal bins using a deep learning model.

Klasifikasi gambar sampah dengan menggunakan Convolutional Neural Network

Code for Automatic Trash Classification using Convolutional Neural Network Machine Learning paper, published in 2019 IEEE Conference on Cybernetics and Intelligent Systems.

A group research project about AI Image Classification for Trash Sorting for the Research Methodology in Computer Science subject conducted by students at Bina Nusantara University

A CNN model that automatically classifies waste (plastic, paper, metal, glass, cardboard, trash) to boost recycling efficiency. Trained on the TrashNet dataset, it accurately identifies different materials, reducing sorting errors and simplifying waste management.

This project compares MobileNetV2 and a custom CNN model for trash classification, using feature extraction techniques like edge detection and Local Binary Patterns (LBP). Results show that MobileNetV2 without explicit feature extraction achieves the highest accuracy, providing insights for improving waste management applications.

🗑️ Classifying images of trash (glass, metal, paper, plastic) using neural networks.

Aquatic plastic litter detection, classification and quantification