  Domain shift significantly influences the performance of deep learning
algorithms, particularly for object detection within volumetric 3D images.
Annotated training data is essential for deep learning-based object detection.
However, annotating densely packed objects is time-consuming and costly.
Instead, we suggest training models on individually scanned objects, causing a
domain shift between training and detection data. To address this challenge, we
introduce the BugNIST dataset, comprising 9154 micro-CT volumes of 12 bug types
and 388 volumes of tightly packed bug mixtures. This dataset is characterized
by having objects with the same appearance in the source and target domains,
which is uncommon for other benchmark datasets for domain shift. During
training, individual bug volumes labeled by class are utilized, while testing
employs mixtures with center point annotations and bug type labels. Together
with the dataset, we provide a baseline detection analysis, with the aim of
advancing the field of 3D object detection methods.
