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  4. Research data - Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data
 
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Research data - Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data

Citation Link: https://doi.org/10.15480/882.14344
Type
Dataset
Version
1.0.0
Date Issued
2025-01-22
Author(s)
Sardhara, Trushal 
Betriebseinheit Elektronenmikroskopie BEEM  
Researcher
Cyron, Christian J.  
Ritter, Martin  orcid-logo
Aydin, Roland  
DOI
https://doi.org/10.15480/882.14344
TORE-URI
https://tore.tuhh.de/handle/11420/53280
Abstract
This dataset contains simulated and domain-adapted multi-voltage FIB tomography data on hierarchical nanoporous gold, trained machine learning model weights, and segmentation results of synthetic and real hierarchical nanoporous gold data. For more information, please refer to the published research article: Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data
Subjects
Domain adaptation
Fast simulation
Synthetic data
FIB-SEM tomography
3D reconstruction
DDC Class
620.1: Engineering Mechanics and Materials Science
Funding(s)
SFB 986: Teilprojekt B09 - Mikrostrukturbasierte Klassifizierung und elektronenmikroskopische Analyse nanoporöser Metalle durch maschinelles Lernen  
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)  
License
https://creativecommons.org/licenses/by/4.0/
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Data.zip

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Description.pdf

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68.59 KB

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Simulation_models.zip

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2.35 GB

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Domain adaptation_models.zip

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2.33 GB

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Segmentation_models.zip

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557.41 MB

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readme.pdf

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41.18 KB

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