vision
U-Net (2D)
The original 2D U-Net, from the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation".
Levelvariant
Classvision
Parametersnot in the notes
Contextnot in the notes
Released18 May 2015
Opennessunknown
Runs onnot in the notes
Statusactive
R&D Topics
Identity
- Developer
- University of Freiburg
- Family
- U-Net
- Released
- 18 May 2015 sourcesannouncement page
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- CNN
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- image
- Output
- image
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
ModelU-Net (2D)University of Freiburg, vision
U-Net (2D)University of Freiburg, visionR&D TopicsSemantic, Instance and Panoptic Segmentation ArchitecturesMedical Image Segmentation and Screening AI (Radiology)
Use CasesMedical Image SegmentationHealthcareChest X-Ray and TB ScreeningHealthcareMammography Cancer DetectionHealthcareRobotic Waste and Recycling SortingIndustrial+3 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
Kementerian Kesehatan (Kemenkes), national TB programmeIn production, 13 Aug 2026Kemenkes TB programme with UGM, University of Melbourne, SAPDA, YPKMPResearch, 3 Aug 2026Kemenkes; RS Fatmawati; RSPONPilot, 27 Feb 2025Kemenkes; RSUP Dr. M. Djamil PadangPilot, 9 Jul 2024+6 moreDeployments of these use cases; none names this model.
Kementerian Kesehatan (Kemenkes), national TB programmeIn production, 13 Aug 2026Kemenkes TB programme with UGM, University of Melbourne, SAPDA, YPKMPResearch, 3 Aug 2026Kemenkes; RS Fatmawati; RSPONPilot, 27 Feb 2025Kemenkes; RSUP Dr. M. Djamil PadangPilot, 9 Jul 2024+6 moreDeployments of these use cases; none names this model.Organisations 1
R&D topics 2
Semantic, Instance and Panoptic Segmentation ArchitecturesNetworks that label every pixel of an image by class, by object instance, or both.produced byMedical Image Segmentation and Screening AI (Radiology)Models that outline organs and lesions or flag disease on X-ray, CT, MRI and other scans.produced by
Sources
- arxiv.org/abs/1505.04597 https://arxiv.org/abs/1505.04597
- github.com/milesial/Pytorch-UNet https://github.com/milesial/Pytorch-UNet
- arxiv.org/html/1505.04597 https://arxiv.org/html/1505.04597
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "U-Net (2D)", https://aurion.semai.id/models/u-net-original/, records of October 2026.