  Planktonic organisms are of fundamental importance to marine ecosystems: they
form the basis of the food web, provide the link between the atmosphere and the
deep ocean, and influence global-scale biogeochemical cycles. Scientists are
increasingly using imaging-based technologies to study these creatures in their
natural habit. Images from such systems provide an unique opportunity to model
and understand plankton ecosystems, but the collected datasets can be enormous.
The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for
example, is an \emph{in situ} system that has been continuously imaging
plankton since 2006. To date, it has generated more than 700 million samples.
Manual classification of such a vast image collection is impractical due to the
size of the data set. In addition, the annotation task is challenging due to
the large space of relevant classes, intra-class variability, and inter-class
similarity. Methods for automated classification exist, but the accuracy is
often below that of human experts. Here we introduce WHOI-Plankton: a large
scale, fine-grained visual recognition dataset for plankton classification,
which comprises over 3.4 million expert-labeled images across 70 classes. The
labeled image set is complied from over 8 years of near continuous data
collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO).
We discuss relevant metrics for evaluation of classification performance and
provide results for a traditional method based on hand-engineered features and
two methods based on convolutional neural networks.
