Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging

This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this woodnet-showcase GitHub repository. The artifacts amount to the following: acer-artifact.hdf5 : Exemplary (256, 256, 256) subvolume from a Acer pseudoplatanus sub-μ-CT scan pinus-artifact.hdf5 : Exemplary (256, 256, 256) subvolume from a Pinus sylvestris sub-μ-CT scan weights-artifact.pth : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples