auto_2d_class_selection []

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====== Cinderella: Deep learning based binary classification tool ======

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<html>
<center><i>"The good ones go into the pot, the bad ones go into your crop."</i></center>
<p align="center">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;From the fairy tale Cinderella</p>
</html>
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Our binary classification  tool (//Cinderella//) is based on a deep learning network to classify class averages, micrographs or subtomograms into good and bad categories.
Cinderella supports ''.hdf/.mrcs'' ** files for class averages**, ''.mrc'' **files for micrographs**, and ''.hdf'' **files for subtomograms**.
//Cinderella// was written to automate cryo-em data processing.  It's open source and easy to use.
We provide a pretrained general model for classifying class averages ([[auto2d_tutorial|see tutorial]]). But you can easily train it with your own set of classes, micrographs, and/or subtomograms.

<note>
  * **License**: [[https://github.com/MPI-Dortmund/sphire_classes_autoselect/blob/master/LICENSE|MIT]]
  * **Repository**: [[https://github.com/MPI-Dortmund/sphire_classes_autoselect|GitHub]]
</note>


====== 2D class selection model ======
Our model was trained on a set of 2D classes from both [[https://sphire.mpg.de/wiki/doku.php?id=pipeline:isac:sxisac2|ISAC]] and Relion. During the creation of the training data set, we tried to answer the question, "Which class would I select If I would not know the particle?" when deciding what is a "good" class. Here are a couple of examples for good/bad classes in //Cinderella//: 

{{ ::cinderellea.png?450 |}}


You can easily [[auto_2d_class_selection#contribute|contribute]] your own classes!  

Right now our model is trained on **4773 good classes and 5390 bad classes**.
====== Download ======
====Cinderella====
Version: 0.7.0

Uploaded: 27. August 2020

[[https://pypi.org/project/cinderella/#files|DOWNLOAD]]

====Pretrained model (2D classes)====
Uploaded: 27. August 2020, Dataset: 4773 good classes and 5390 bad classes.

[[ftp://ftp.gwdg.de/pub/misc/sphire/auto2d_models/gmodel_cinderella07_202008_N10163.h5|DOWNLOAD]]
====Archive====
Old versions of cinderella and the pretrained model can be found in the [[cinderella_archive|archive]].

====Changelog====

=== Version 0.7 ===
  * Now uses a **circular masks by default**. This allows to use full rotation during data augmentation. Can be deactivated by setting the field ''mask_radius'' in the configuration file to -1. In case you want to use an model trained with Cinderella < 0.7 please set the radius to -1. Otherwise you case specify any radius you want. **By default** (no ''mask_radius'' provided) it will use 0.4*''input_size''.
  * The general models now includes **300 new good Relion classes and 2000 new bad Relion classes** (//Thanks to Takanori Nakane and Grigory Sharov//).
  * Fixed numerical instability that occurs when you have classes filled with a constant value (//Thanks to Grigory Sharov//).
  * Fixed a problem with classes that contain NaN values. NaN values are now replaced with 0. (//Thanks to Grigory Sharov//).
  * Fixed an issue when filenames contain more than one point.

=== Version 0.6 ===
  * Fix an issue for classes in mrcs format
  * Minor changes

=== Version 0.5 ===
  * Add support for subtomograms
  * Faster file reading

=== Version 0.4 ===
  * Balances unbalanced training datasets.
  * It is now possible to train Cinderella to select micrographs
  * Updated the general model for 2D class selection.

=== Version 0.3.1 ===
  * Downgrade to tensorflow 1.10.1 again, as user report long initialization times
  * Only report the number of good / bad classes + their fraction.

=== Version 0.3.0 ===
  * More data augmentation (add rotation)
  * Better sampling of validation data. It is now ensured that each file contributes some validation data.
  * Updated tensorflow to 1.12.3  to make it compatible to the crYOLO environment
====== Contribute ======
Here is the repository of our training data:

[[https://owncloud.gwdg.de/index.php/s/4gMddIT0mdCRcR5|Download the public training data]]

Unfortunately, we cannot upload the complete training dataset, as some classes are from projects that are not yet published.

If you want to contribute with your own classes, please upload them here:

[[https://owncloud.gwdg.de/index.php/s/8Qm0ZUgaBUBX4sr|Contribute good / bad classes]]

Ideally, please upload separate HDF/mrcs files for good and bad classes. You can do this separation with EMAN2's e2display. However, you can also upload the classes without separation and we will try to do it.
====== Installation ======
The following instructions assume that pip and [[https://conda.io/projects/conda/en/latest/user-guide/install/index.html|anaconda]] or [[https://docs.conda.io/en/latest/miniconda.html|miniconda]] are available.
In case you have a old cinderella environment installed, first remove the old one with:
<code>
conda env remove --name cinderella
</code>
After that, create a new virtual environment:
<code>
conda create -n cinderella -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy==1.14.5
</code>

Activate the environment:
<code>
source activate cinderella
</code>

Install Cinderella for **GPU**:
<code>
pip install cinderella[gpu]
</code>

**... or CPU**:
<code>
pip install cinderella[cpu]
</code>

====== Tutorial ======
We created three tutorials:

  * [[auto2d_tutorial|How to use Cinderella for 2D class selection]]
  * [[cinderella_micrographs|How to use Cinderella for micrograph selection]]
  * [[cinderella_tomograms|How to use Cinderella for subtomogram selection]]
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