Vrancken, Carlos Wagland, Stuart Longhurst, Philip Results from deep learning tests using balanced databases for the classification of paper and cardboard materials. <div>For methodology used to obtain these results please refer to the publication: "Deep learning in material recovery: Development of method to create training database".</div><div><br></div><div>These results were obtained using grayscale version of the images.</div><div><br></div><div>The "Balanced dataset - classification results" spreadsheet includes:<br></div><div><br></div><div>Sheet 1 - classification results when classifying 3 classes of fibre materials using increasing number of samples per class in a balanced training dataset</div><div><br></div><div>Sheet 2 - classification results when using a balanced dataset with 5,000 training samples per class to classify 10 classes of fibre waste material</div> waste material recognition;deep learning;artificial intelligence;balanced dataset;Artificial Intelligence and Image Processing 2019-10-14
    https://cord.cranfield.ac.uk/articles/dataset/Results_from_deep_learning_tests_using_balanced_databases_for_the_classification_of_paper_and_cardboard_materials_/9968051
10.17862/cranfield.rd.9968051.v1