Donated on 10/17/2023

An image classification dataset of waste items across 9 major material types, collected within an authentic landfill environment.

Dataset Characteristics


Subject Area

Computer Science

Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

For what purpose was the dataset created?

RealWaste was created as apart of an honors thesis researching how convolution neural networks could perform on authentic waste material when trained on objects in pure and unadulterated forms, when compared to training via real waste items.

What do the instances in this dataset represent?

Color images of waste items captured at the point of reception in a landfill environment. Images are released in 524x524 resolution in line with accompanying research paper. For full size resolution images, please contact the corresponding author.

Additional Information

The labels applied to the images represent the material type present, however further refinement of labelling may be performed given the moderate dataset size (i.e., splitting the plastic class in transparent and opaque components). Under the proposed labels, image counts are as follows: - Cardboard: 461 - Food Organics: 411 - Glass: 420 - Metal: 790 - Miscellaneous Trash: 495 - Paper: 500 - Plastic: 921 - Textile Trash: 318 - Vegetation: 436

Has Missing Values?


Introductory Paper

RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning

By Sam Single, Saeid Iranmanesh, Raad Raad. 2023

Published in Information

Variable Information

Class Labels

Cardboard, Food Organics, Glass, Metal, Miscellaneous Trash, Paper, Plastic, Textile Trash, and Vegetation


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1 citations


wasteMulti-class classification


Sam Single

Saeid Iranmanesh

Raad Raad


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