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

Vrancken, CarlosWagland, StuartLonghurst, Philip2024-05-272024-05-272019-10-14Vrancken, 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.9968051https://dspace.lib.cranfield.ac.uk/handle/1826/21679For 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 materialCC BY 4.0https://creativecommons.org/licenses/by/4.0/'waste material recognition''deep learning''artificial intelligence''balanced dataset''Artificial Intelligence and Image Processing'Results from deep learning tests using balanced databases for the classification of paper and cardboard materials.Dataset10.17862/cranfield.rd.9968051