Results from deep learning tests using balanced databases for the classification of paper and cardboard materials.
datasetposted on 14.10.2019 by Carlos Vrancken, Stuart Wagland, Philip Longhurst
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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 dataset
Sheet 2 - classification results when using a balanced dataset with 5,000 training samples per class to classify 10 classes of fibre waste material