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

WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification