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For training we used a p3.2xlarge instance from Amazon Web Services with a single V100 GPU while for inference we used a Lenovo P1 Gen 2 laptop. Further we used the Scientific Data Storage (SDS) service from Heidelberg University. Training and inference were performed using a singularity container image based on the TensorFlow Docker container image. For random augmentation we used the respective function in the image python module. The code is available at Link: https://heidata.uni-heidelberg.de/privateurl.xhtml?token=366931ac-50a2-43f9-880f-88d63e07d493.
Copyright and License information: ©2022 Kriegsmann, Lobers, Zgorzelski, Kriegsmann, Janßen, Meliß, Muley, Sack, Steinbuss and Kriegsmann
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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