Waste Classification Dataset
Nnamoko, Nonso and Barrowclough, Joseph and Procter, Jack

@article{procter2022waste,
title= {Waste Classification Dataset},
doi= {10.3390/infrastructures7040047},
journal= {Mendeley Data},
author= {Nnamoko, Nonso and Barrowclough, Joseph and Procter, Jack},
year= {2022},
url= {https://data.mendeley.com/datasets/n3gtgm9jxj/2},
abstract= {24,705 images of household waste in two classes, organic and recyclable. A restructured version of a Kaggle dataset, with a notebook for data augmentation and a notebook for replicating the experiments.

## Contents

* 13,880 organic images and 10,825 recyclable images
* RGB .jpg files
* Restructured from Sashaank Sekar's Kaggle waste classification dataset (25,077 images), with 372 palette mode images removed
* Includes Jupyter notebooks for augmentation and for replicating the experiments

## Related paper

* Nnamoko, N., Barrowclough, J., Procter, J. *Solid Waste Image Classification Using Deep Convolutional Neural Network.* Infrastructures 7(4), 47, 2022. [https://doi.org/10.3390/infrastructures7040047](https://doi.org/10.3390/infrastructures7040047)

## Source

Mendeley Data, DOI 10.17632/n3gtgm9jxj.2: [https://data.mendeley.com/datasets/n3gtgm9jxj/2](https://data.mendeley.com/datasets/n3gtgm9jxj/2). License: CC BY 4.0.

**Reference:** Nnamoko, Barrowclough and Procter, Solid Waste Image Classification Using Deep Convolutional Neural Network. 2022. https://doi.org/10.3390/infrastructures7040047},
keywords= {Image classification, waste classification, recycling, environment},
terms= {},
license= {https://creativecommons.org/licenses/by/4.0/},
superseded= {}
}