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Ede, Jeffrey M. (2020) Warwick electron microscopy Datasets. Machine Learning : Science and Technology, 1 (4). 045003. doi:10.1088/2632-2153/ab9c3c
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Official URL: https://doi.org/10.1088/2632-2153/ab9c3c
Abstract
Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electron microscopy datasets available to the wider community. There are three main datasets containing 19769 scanning transmission electron micrographs, 17266 transmission electron micrographs, and 98340 simulated exit wavefunctions, and multiple variants of each dataset for different applications. To visualize image datasets, we trained variational autoencoders to encode data as 64-dimensional multivariate normal distributions, which we cluster in two dimensions by t-distributed stochastic neighbor embedding. In addition, we have improved dataset visualization with variational autoencoders by introducing encoding normalization and regularization, adding an image gradient loss, and extending t-distributed stochastic neighbor embedding to account for encoded standard deviations. Our datasets, source code, pretrained models, and interactive visualizations are openly available at https://github.com/Jeffrey-Ede/datasets.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics Z Bibliography. Library Science. Information Resources > ZA Information resources |
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| Divisions: | Faculty of Science > Physics | |||||||||
| Library of Congress Subject Headings (LCSH): | Data sets , Electron microscopy, Machine learning , Document markup languages | |||||||||
| Journal or Publication Title: | Machine Learning : Science and Technology | |||||||||
| Publisher: | IOP Publishing Ltd | |||||||||
| ISSN: | 2632-2153 | |||||||||
| Official Date: | 11 September 2020 | |||||||||
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| Volume: | 1 | |||||||||
| Number: | 4 | |||||||||
| Article Number: | 045003 | |||||||||
| DOI: | 10.1088/2632-2153/ab9c3c | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| Copyright Holders: | © 2020 The Author(s). Published by IOP Publishing Ltd | |||||||||
| RIOXX Funder/Project Grant: |
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