Results from deep learning tests using balanced databases for the classification of paper and cardboard materials.

Date published

2019-10-14 14:28

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Cranfield University

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Dataset

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Citation

Vrancken, Carlos; Wagland, Stuart; Longhurst, Philip (2019). Results from deep learning tests using balanced databases for the classification of paper and cardboard materials.. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.9968051

Abstract

For methodology used to obtain these results please refer to the publication: "Deep learning in material recovery: Development of method to create training database".These results were obtained using grayscale version of the images.The "Balanced dataset - classification results" spreadsheet includes:Sheet 1 - classification results when classifying 3 classes of fibre materials using increasing number of samples per class in a balanced training datasetSheet 2 - classification results when using a balanced dataset with 5,000 training samples per class to classify 10 classes of fibre waste material

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Software Description

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Github

Keywords

'waste material recognition', 'deep learning', 'artificial intelligence', 'balanced dataset', 'Artificial Intelligence and Image Processing'

DOI

10.17862/cranfield.rd.9968051

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CC BY 4.0

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