    {"datasetrec":{"DasID":9128,"Acronym":null,"StandardTitle":"Yolov8 model weights to detect unknown underwater sounds","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>Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings.</p><p>Trained weights of model, in Python.</p><p>Code to reproduce publication results, (re)train the models, or use the models for inference can be found on: GitHub - lifewatch/sound-segregation-and-categorization (https://github.com/lifewatch/sound-segregation-and-categorization).</p>","EngDescr":"<p>Name</p><ol><li data-list-item-id=\"e3be87407d19ad3ca6ed8f08c9776788a\">Model Final (MF)</li><li data-list-item-id=\"e35b83aaff00ed838447be648d64c9a22\">Model Base (MB)</li><li data-list-item-id=\"e074d618b7aca25445d6b34980a8b9158\">Active Learning (AL)</li><li data-list-item-id=\"e2cb516a7e6ca4b9df25de9ddb0f8af39\">Random Selection (RS)</li></ol><p>File name</p><ol><li data-list-item-id=\"e8abab58e914ab6fa4de731d3d4eff1a3\">final_model.pt</li><li data-list-item-id=\"e22673c00a022da791f6633bcf1cdcd54\">MB_0_0.pt</li><li data-list-item-id=\"ea91ddc72e825e3a09fcba6b487acf5cd\">AL_0_4.pt</li><li data-list-item-id=\"ea77ba2b56436197a5ed24e8381947924\">RS_0_4.pt</li></ol><p>Pretraining data</p><ol><li data-list-item-id=\"e433fc15940f449abab88ec9899f46b7c\">All annotated BPNS data</li><li data-list-item-id=\"e914c86bbe6d657612c95bccfe5fd5fda\">Initial training set</li><li data-list-item-id=\"ed2ab7dc248445ac1ebe480d4c518f51b\">Intial training set + 10 actively selected wavs</li><li data-list-item-id=\"ec0846504c0e1b03e22c60e6d2af202fa\">Intial training set + 10 randomly selected wavs</li></ol><p>Hours</p><ol><li data-list-item-id=\"efe5b071705013bf587483ada6fb4b0ae\">23.6h</li><li data-list-item-id=\"e4124632927dc979ef651fb24dcafe8f7\">1.5h</li><li data-list-item-id=\"ea1d4d9d89dd26908a146c3814b137092\">2.3h</li><li data-list-item-id=\"e0e5df6d0aa3b00c9cc924d5e1637b767\">2.3h</li></ol>","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":"Parcericas, C.; Schall, E.; te Velde, K.; Botteldooren, D.; Devos, P.; Debusschere, E.; Flanders Marine Institute (VLIZ); Ghent University (UGent): Belgium; Alfred Wegener Institute for Polar and Marine Research (AWI): Germany; Leiden University: The Netherlands; (2024): Yolov8 model weights to detect unknown underwater sounds. Marine Data Archive.","AccessConstraints":null,"UDate":"2026-07-08","CDate":"2026-07-08","CurrencyDate":null,"RevisionDate":null,"DateLastModified":{"date":"2026-07-09 13:28:58.183000","timezone_type":1,"timezone":"+00:00"},"CheckedFlag":0,"PublicFlag":1,"VlizCoreFlag":1,"MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"StatusID":1,"DasType":"Software/models/scripts","DasTypeID":3,"DasOrigin":"Numerical calculations / models","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. 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Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings. <i>Front. Remote Sens. 5</i>: 1390687. <a href=\"https://dx.doi.org/10.3389/frsen.2024.1390687\" target=\"_blank\">https://dx.doi.org/10.3389/frsen.2024.1390687</a>","RR":"<b>Parcerisas, C. <i>et al.</i></b> (2024). Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings. <i>Front. Remote Sens. 5</i>: 1390687. <a href=\"https://dx.doi.org/10.3389/frsen.2024.1390687\" target=\"_blank\">https://dx.doi.org/10.3389/frsen.2024.1390687</a>","4":"Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings","StT":"Machine learning for efficient segregation and labeling of potential biological sounds in long-term underwater recordings","5":"Parcerisas, C.; Schall, E.; te Velde, K.; Botteldooren, D.; Devos, P.; Debusschere, E.","RSA":"Parcerisas, C.; Schall, E.; te Velde, K.; Botteldooren, D.; Devos, P.; Debusschere, E.","6":"Beschrijft deze dataset","DutchTerm":"Beschrijft deze dataset","7":2024,"AnaDate":2024,"8":null,"MonDate":null,"9":". <i>Front. Remote Sens. 5</i>: 1390687. <a href=\"https://dx.doi.org/10.3389/frsen.2024.1390687\" target=\"_blank\">https://dx.doi.org/10.3389/frsen.2024.1390687</a>","":". <i>Front. Remote Sens. 5</i>: 1390687. <a href=\"https://dx.doi.org/10.3389/frsen.2024.1390687\" target=\"_blank\">https://dx.doi.org/10.3389/frsen.2024.1390687</a>","10":"https://dx.doi.org/10.3389/frsen.2024.1390687","doi":"https://dx.doi.org/10.3389/frsen.2024.1390687"}],"urls":[{"URL":"https://github.com/lifewatch/sound-segregation-and-categorization","externalID":null,"URLTypeCode":null,"URLType":"Software packages","URLTypID":12,"downloadURL":null,"FileName":null},{"URL":"https://mda.vliz.be/directlink.php?fid=VLIZ_00000776_6662f3d66cec6504567484","externalID":null,"URLTypeCode":null,"URLType":"Online dataset","URLTypID":19,"downloadURL":null,"FileName":null},{"URL":"https://mda.vliz.be/directlink.php?fid=VLIZ_00000776_6672fdd5df4ee563984337","externalID":null,"URLTypeCode":null,"URLType":"Online dataset","URLTypID":19,"downloadURL":null,"FileName":null},{"URL":"https://mda.vliz.be/directlink.php?fid=VLIZ_00000776_6672fdd5df54b869504488","externalID":null,"URLTypeCode":null,"URLType":"Online dataset","URLTypID":19,"downloadURL":null,"FileName":null},{"URL":"https://mda.vliz.be/directlink.php?fid=VLIZ_00000776_6672fdd5df553297088456","externalID":null,"URLTypeCode":null,"URLType":"Online dataset","URLTypID":19,"downloadURL":null,"FileName":null}],"pictures":null,"urlmaps":null,"spatreps":null,"fileformats":null,"resmessage":"","complete":1}
