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v1.0 — mDeepSTORM3D training and demo data

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@engillett engillett released this 30 May 07:05
· 2 commits to main since this release
v1.0
89b3427

What's in this release

This v1.0 release attaches the four large MATLAB data files referenced by the 4_mDeepSTORM3D/ subfolder of the repository. They are too large for regular git push (GitHub's 100 MB per-file hard limit) and are hosted here as release assets instead.

File Size Purpose
Din.mat 168 MB DeepSTORM3D training input — simulated PSF image stack used to train the localization network. Produced by running 4_mDeepSTORM3D/DeepSTORM3D/GeneratingTrainingExamples.py (or demo1.py).
Dtar.mat 123 MB DeepSTORM3D training target — ground-truth localization labels paired with Din.mat. Produced by the same script.
target_bol.mat 176 MB Demo evaluation ground-truth volume. Produced by demo3.py against a pretrained model.
pred_bol.mat 176 MB Demo predicted output volume from the trained network. Produced by demo3.py.

Total: 641 MB.

How to use

  1. Download the four .mat files from the Assets section below.
  2. Place them inside 4_mDeepSTORM3D/ in your local clone of this repository, next to demo1.py...demo5.py.
  3. The demos and the training scripts expect them at that path.

Alternatively, you can regenerate all four files from scratch by running the demos — this is how the original DeepSTORM3D (Nehme et al. 2020) intends users to obtain the data. Training takes ~30 hours on a Titan Xp; demo evaluation takes seconds.

Citation

If you use this code or data, please cite:

Gillett, E., Chatterjee, S., Chatterjee, J., Kovalenko, N., Xu, C., et al. Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy. Methods and Applications in Fluorescence (2026), MAF-101478.

License

See LICENSE (and LICENSE-Landes in 4_mDeepSTORM3D/) in the repository. Non-commercial educational and research use only.

Assets 6
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