  In scientific imaging, deep learning has become a pivotal tool for image
analytics. However, handling large volumetric datasets, which often exceed the
memory capacity of standard GPUs, require special attention when subjected to
deep learning efforts. This paper introduces qlty, a toolkit designed to
address these challenges through tensor management techniques. qlty offers
robust methods for subsampling, cleaning, and stitching of large-scale spatial
data, enabling effective training and inference even in resource-limited
environments.
