DAPlankton: Benchmark Dataset For Multi-Instrument Plankton Recognition Via Fine-Grained Domain Adaptation

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. $\mathrm{DAPlankton}_{\text {LAB }}$ contains images of cultured phytoplankton providing a balanced dataset with minimal label uncertainty. $\mathrm{DAPlankton}_{\text {SEA }}$ 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.