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auto_2d_class_selection [2019/12/16 10:44]
twagner [Cinderella: Deep learning based binary classification tool] 		auto_2d_class_selection [2020/08/27 15:11]
twagner [Changelog]
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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. +Our binary classification  tool (//Cinderella//) is based on a deep learning network to classify class averages, micrographs or subtomograms into good and bad categories. 
-For class averages, it supports .hdf/.mrcs, for micrographs .mrc format and for subtomograms it expect that they are saved in a .hdf file. +Cinderella supports ''.hdf/.mrcs'' ** files for class averages**, ''.mrc'' **files for micrographs**, and ''.hdf'' **files for subtomograms**. 
-Cinderella provides a pretrained general model for classifying 2D classes and was written to automate cryo-em data processing. It's open source and easy to use ([[auto2d_tutorial|see tutorial]]). You can easily train it with your own set of classes/micrographs.+//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> <note>
-  * **License**: MIT +  * **License**: [[https://github.com/MPI-Dortmund/sphire_classes_autoselect/blob/master/LICENSE|MIT]] 
-  * **GitHub repository**: https://github.com/MPI-Dortmund/sphire_classes_autoselect+  * **Repository**: [[https://github.com/MPI-Dortmund/sphire_classes_autoselect|GitHub]]
 </note> </note>
  
-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 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 |}} {{ ::cinderellea.png?450 |}}
  
-====== 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?" to decide which is a good class. +
-<note important> +
-The training dataset does **not contain any Relion classes**, so it might be that Cinderella will not work with well them. +
-</note>+
 You can easily [[auto_2d_class_selection#contribute|contribute]] your own classes!   You can easily [[auto_2d_class_selection#contribute|contribute]] your own classes!  
  
-Right now our model is trained on **19 datasets**. But we will increase the number often!+Right now our model is trained on **4773 good classes and 5390 bad classes**.
 ====== Download ====== ====== Download ======
 ====Cinderella==== ====Cinderella====
-Version: 0.5.0+Version: 0.7.0
  
-Uploaded: 16. December 2019+Uploaded: 27. August 2020
  
 [[https://pypi.org/project/cinderella/#files|DOWNLOAD]] [[https://pypi.org/project/cinderella/#files|DOWNLOAD]]
  
 ====Pretrained model (2D classes)==== ====Pretrained model (2D classes)====
-Uploaded: 10. December 2019, Datasets: 20 +Uploaded: 27. August 2020, Dataset: 4773 good classes and 5390 bad classes.
- +
-[[ftp://ftp.gwdg.de/pub/misc/sphire/auto2d_models/gmodel_cinderella_201912_N20.h5|DOWNLOAD]] +
- +
-[[auto2d_tutorial#classify|Valid configuration file]]+
  
 +[[ftp://ftp.gwdg.de/pub/misc/sphire/auto2d_models/gmodel_cinderella07_202008_N10163.h5|DOWNLOAD]]
 ====Archive==== ====Archive====
-Old versions of cinderella and the pretrained model can be found in the [[cinderella_archive|archive]]+Old versions of cinderella and the pretrained model can be found in the [[cinderella_archive|archive]].
  
 ====Changelog==== ====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 === === Version 0.5 ===
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 ====== Tutorial ====== ====== Tutorial ======
-We created two tutorials:+We created three tutorials:
  
   * [[auto2d_tutorial|How to use Cinderella for 2D class selection]]   * [[auto2d_tutorial|How to use Cinderella for 2D class selection]]
auto_2d_class_selection.txt · Last modified: 2020/08/27 15:11 by twagner

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