TUdatalib : OSS-Net trained models

dc.contributor.author	Reich, Christoph
dc.contributor.author	Prangemeier, Tim
dc.contributor.author	Cetin, Özdemir
dc.contributor.author	Koeppl, Heinz
dc.date.accessioned	2021-10-22T08:54:03Z
dc.date.available	2021-10-22T08:54:03Z
dc.date.issued	2021-10-22
dc.identifier.uri	https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2991
dc.identifier.uri	https://doi.org/10.48328/tudatalib-659
dc.description	Trained OSS-Net models of the paper "OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data".	de_DE
dc.relation	IsPartOf;arXiv;2110.10640
dc.rights	MIT License
dc.rights.uri	https://opensource.org/licenses/MIT
dc.subject	deep learning	de_DE
dc.subject	3d semantic segmentation	de_DE
dc.subject	oss-net	de_DE
dc.subject	bmvc2021	de_DE
dc.subject.classification	4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahren	de_DE
dc.subject.classification	4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing
dc.subject.ddc	004
dc.title	OSS-Net trained models	de_DE
dc.type	Model	de_DE
dc.description.version	Trained OSS-Net models as PyTorch state dictionaries.	de_DE
tud.unit	TUDa
tud.history.classification	Version=2016-2020;409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung