RealWaste
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
Image
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
-
# Instances
4752
# 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?
No
Introductory Paper
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
Dataset Files
| File | Size |
|---|---|
| realwaste-main/RealWaste/Vegetation/Vegetation_109.jpg | 242.8 KB |
| realwaste-main/RealWaste/Vegetation/Vegetation_110.jpg | 242.7 KB |
| realwaste-main/RealWaste/Vegetation/Vegetation_111.jpg | 241 KB |
| realwaste-main/RealWaste/Vegetation/Vegetation_107.jpg | 239.6 KB |
| realwaste-main/RealWaste/Vegetation/Vegetation_108.jpg | 239.5 KB |
0 to 5 of 4753
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset realwaste = fetch_ucirepo(id=908) # data (as pandas dataframes) X = realwaste.data.features y = realwaste.data.targets # metadata print(realwaste.metadata) # variable information print(realwaste.variables)
Single, S., Iranmanesh, S., & Raad, R. (2023). RealWaste [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5SS4G.
Keywords
Creators
Sam Single
sam.single62@gmail.com
Saeid Iranmanesh
Raad Raad
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.