{"datasetrec":{"DasID":8882,"Acronym":null,"StandardTitle":"Pre-trained Phytoplankton species classifier Model","OrigTitle":null,"OrigTitleLangID":null,"OrigTitleLangCode":null,"OrigTitleLang":null,"OrigTitleLangNL":null,"VersionName":null,"ContactEmail":null,"VersionDate":null,"VersionDay":null,"VersionMonth":null,"VersionYear":null,"SizeReference":null,"EngAbstract":"<p>This repository provides a pre-trained deep learning model for species-level classification of phytoplankton, developed by the Flanders Marine Institute (VLIZ). The model is designed to work with high-resolution images captured using FlowCam technology and can identify 95 phytoplankton species using a convolutional neural network (CNN) architecture.</p>","EngDescr":"<p>Pre-trained Phytoplankton Species Classifier (VLIZ) Deep Learning Model for the Identification of 95 Phytoplankton Species This repository provides a pre-trained deep learning model for species-level classification of phytoplankton, developed by the Flanders Marine Institute (VLIZ). The model is designed to work with high-resolution images captured using FlowCam technology and can identify 95 phytoplankton species using a convolutional neural network (CNN) architecture. Three different model variants are included in this repository: Exception Model (TensorFlow 1) – An earlier implementation using TensorFlow 1. Exception Model (TensorFlow 2) – A re-implementation of the same architecture using TensorFlow 2. Phytoplankton_EfficientNetV2B0.tar – The best-performing model, based on the EfficientNetV2B0 architecture, also implemented in TensorFlow 2. The classes used in training are added as txt file. This model can be used to predict phytoplankton species or to re-train a new CNN within the repo. For my information, take a look at our GitHub GitHub - ai4os-hub/phyto-plankton-classification:&nbsp;<a href=\" https://github.com/ai4os-hub/phyto-plankton-classification\"> https://github.com/ai4os-hub/phyto-plankton-classification</a></p>","OrigAbstract":null,"OrigDescr":null,"Comments":null,"ReleaseDate":null,"ReleaseDate0":null,"OrigDescrLang":null,"EmbargoDate":null,"OrigDescrLangNL":null,"OrigLangCode":null,"OrigLangCodeExtended":null,"OrigLangID":null,"DescrCompFlag":null,"DescrTransFlag":null,"Citation":"Decrop, W., &amp; Lagaisse, R. (2025). Pre-trained Phytoplankton species classifier Model. Zenodo.","AccessConstraints":null,"UDate":"2026-04-02","CDate":"2025-07-07","CurrencyDate":null,"RevisionDate":null,"DateLastModified":{"date":"2026-04-02 11:54:42.995822","timezone_type":1,"timezone":"+02:00"},"CheckedFlag":1,"PublicFlag":1,"VlizCoreFlag":1,"MarineFlag":null,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"StatusID":1,"DasType":"Data products","DasTypeID":23,"DasOrigin":"Data collection","Progress":"Completed","AccessConstraint":"Attribution (CC BY)","AccConstrEN":"Attribution (CC BY)","AccConstrDisplay":"<a rel=\"license\" href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"><img alt=\"Creative Commons License\" style=\"border:0px;height:15px;width:80px;vertical-align:middle;\" src=\"https://www.marinespecies.org/aphia/images/cc/by.png\" /></a> This dataset is licensed under a <a rel=\"license\" href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\">Creative Commons Attribution 4.0 International License</a>.","License":"https://creativecommons.org/licenses/by/4.0/","AccConstrDescription":"This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.","Lineage":null,"AccConID":21,"DOI":"10.5281/zenodo.15269453"},"dois":null,"spcols":null,"keywords":[{"ThesaurusTerm":"CNN weights","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"CNN weights","DutchTerm":"","URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"Phytoplankon classifier","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"Phytoplankon 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