  Plankton recognition provides novel possibilities to study various
environmental aspects and an interesting real-world context to develop domain
adaptation (DA) methods. Different imaging instruments cause domain shift
between datasets hampering the development of general plankton recognition
methods. A promising remedy for this is DA allowing to adapt a model trained on
one instrument to other instruments. In this paper, we present a new DA dataset
called DAPlankton which consists of phytoplankton images obtained with
different instruments. Phytoplankton provides a challenging DA problem due to
the fine-grained nature of the task and high class imbalance in real-world
datasets. DAPlankton consists of two subsets. DAPlankton_LAB contains images of
cultured phytoplankton providing a balanced dataset with minimal label
uncertainty. DAPlankton_SEA consists of images collected from the Baltic Sea
providing challenging real-world data with large intra-class variance and class
imbalance. We further present a benchmark comparison of three widely used DA
methods.
