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deep_loco
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sample implementation of deeploco
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Download README
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README.md
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deep_loco
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# deep_loco A sample implementation of [deeploco](https://www.biorxiv.org/content/early/2018/02/16/267096). train_script.py trains a neural net to do localization using simulated data generated from a z-stack from the 2016 SMLM challenge. You'll need a machine with a (reasonably) powerful GPU to train quickly (set use_cuda=True). To try this on a new dataset you'll need a z-stack (see empirical_sim.py) and to make sure that the simulated data looks as similar as possible to the real data. This could be quite difficult: you'll need to adjust many hardcoded values in empirical_sim.py, as well as the generative model settings (in train_script.py). You might also need to modify the learning rate schedule of the network. localize.py gives an example of how to use a pretrained network to do localization.
