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 | 409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung | de_DE |
| 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 |
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