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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
Researcher
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
No Thumbnail Available
Name
Data.zip
Size
792.94 MB
Format
ZIP
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Name
Description.pdf
Size
68.59 KB
Format
Adobe PDF
No Thumbnail Available
Name
Simulation_models.zip
Size
2.35 GB
Format
ZIP
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Name
Domain adaptation_models.zip
Size
2.33 GB
Format
ZIP
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Name
Segmentation_models.zip
Size
557.41 MB
Format
ZIP
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Name
readme.pdf
Size
41.18 KB
Format
Adobe PDF