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auto_2d_class_selection [2019/12/16 10:17] twagner [Tutorial] |
auto_2d_class_selection [2019/12/16 14:07] twagner [2D class selection model] |
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- | ====== Cinderella: Deep learning based 2D class and micrograph selection | + | ====== Cinderella: Deep learning based binary classification tool ====== |
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- | + | Our binary classification | |
- | Our 2d class and micrograph selection | + | For class averages, it supports .hdf/.mrcs, for micrographs .mrc format |
- | For 2D classes, it supports .hdf/ | + | Cinderella was written to automate cryo-em data processing. |
- | Cinderella | + | We provide a pretrained general model for classifying class averages.([[auto2d_tutorial|see tutorial]]). |
< | < | ||
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</ | </ | ||
- | Here are a couple of examples for good / bad classes in Cinderella: | + | |
+ | ====== 2D class selection model ====== | ||
+ | Our model was trained on a set of 2D classes from ISAC. During the creation of the training dataset, I tried to ask myself "Which class would I select If I would not know the particle?" | ||
{{ :: | {{ :: | ||
- | ====== 2D class selection model ====== | ||
- | Our model was trained on a set of 2D classes from ISAC. During the creation of the training dataset, I tried to ask myself "Which class would I select If I would not know the particle?" | ||
<note important> | <note important> | ||
The training dataset does **not contain any Relion classes**, so it might be that Cinderella will not work with well them. | The training dataset does **not contain any Relion classes**, so it might be that Cinderella will not work with well them. | ||
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====== Download ====== | ====== Download ====== | ||
====Cinderella==== | ====Cinderella==== | ||
- | Version: 0.4.0 | + | Version: 0.5.0 |
- | Uploaded: | + | Uploaded: |
[[https:// | [[https:// | ||
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====Changelog==== | ====Changelog==== | ||
+ | |||
+ | === Version 0.5 === | ||
+ | * Add support for subtomograms | ||
+ | * Faster file reading | ||
+ | |||
=== Version 0.4 === | === Version 0.4 === | ||
* Balances unbalanced training datasets. | * Balances unbalanced training datasets. |