{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/whoi-plankton-a-large-scale-fine-grained","title":"WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification","arxiv_id":"1510.00745","date":"2015-10-02","proceeding":null,"authors":["Eric C. Orenstein","Oscar Beijbom","Emily E. Peacock","Heidi M. Sosik"],"abstract":"Planktonic organisms are of fundamental importance to marine ecosystems: they\nform the basis of the food web, provide the link between the atmosphere and the\ndeep ocean, and influence global-scale biogeochemical cycles. Scientists are\nincreasingly using imaging-based technologies to study these creatures in their\nnatural habit. Images from such systems provide an unique opportunity to model\nand understand plankton ecosystems, but the collected datasets can be enormous.\nThe Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for\nexample, is an \\emph{in situ} system that has been continuously imaging\nplankton since 2006. To date, it has generated more than 700 million samples.\nManual classification of such a vast image collection is impractical due to the\nsize of the data set. In addition, the annotation task is challenging due to\nthe large space of relevant classes, intra-class variability, and inter-class\nsimilarity. Methods for automated classification exist, but the accuracy is\noften below that of human experts. Here we introduce WHOI-Plankton: a large\nscale, fine-grained visual recognition dataset for plankton classification,\nwhich comprises over 3.4 million expert-labeled images across 70 classes. The\nlabeled image set is complied from over 8 years of near continuous data\ncollection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO).\nWe discuss relevant metrics for evaluation of classification performance and\nprovide results for a traditional method based on hand-engineered features and\ntwo methods based on convolutional neural networks.","url_abs":"http://arxiv.org/abs/1510.00745v1","url_pdf":"http://arxiv.org/pdf/1510.00745v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"fine-grained-visual-recognition","task_name":"Fine-Grained Visual Recognition"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"whoi-plankton","name":"WHOI-Plankton","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}