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

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dc.contributor.authorVrancken, Carlos
dc.contributor.authorWagland, Stuart
dc.contributor.authorLonghurst, Philip
dc.date.accessioned2024-05-27T06:24:44Z
dc.date.available2024-05-27T06:24:44Z
dc.date.issued2019-10-14 14:28
dc.description.abstractFor 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
dc.identifier.citationVrancken, 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
dc.identifier.doi10.17862/cranfield.rd.9968051
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21679
dc.publisherCranfield University
dc.relation.referenceshttps://doi.org/10.1016/j.eswa.2019.01.077'
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject'waste material recognition'
dc.subject'deep learning'
dc.subject'artificial intelligence'
dc.subject'balanced dataset'
dc.subject'Artificial Intelligence and Image Processing'
dc.titleResults from deep learning tests using balanced databases for the classification of paper and cardboard materials.
dc.typeDataset

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