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--- auto_2d_class_selection [2020/08/27 12:25]
twagner [Changelog]
+++ auto_2d_class_selection [2020/08/27 15:11]
twagner [Changelog]
@@ Line 8: / Line 8: @@
 ----
-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 was written to automate cryo-em data processing.  It's open source and easy to use.
+//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/subtomograms.
+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**: 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>
 ====== 2D class selection model ======
-Our model was trained on a set of 2D classes from ISAC and Relion. 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. Here are a couple of examples for good / bad classes in Cinderella:
+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 |}}
@@ Line 37: / Line 37: @@
 ====Pretrained model (2D classes)====
-Uploaded: 27. August 2020, Dataset: 4773 good classes and 5390 bad 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]]
+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.
+  * 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 Grigory Shariv).
+  * The general models now includes **300 new good Relion classes and 2000 new bad Relion classes** (//Thanks to Takanori Nakane and Grigory Sharov//).
-  * Fix numerical instability that occur when you have classes filled with a constant value (Thanks to Grigory Sharov).
+  * Fixed numerical instability that occurs when you have classes filled with a constant value (//Thanks to Grigory Sharov//).
-  * Fix a problem with classes that contain NaN values. NaN values are now replaced with 0. (Thanks to Grigory Sharov).
+  * Fixed a problem with classes that contain NaN values. NaN values are now replaced with 0. (//Thanks to Grigory Sharov//).
-  * Fix an issue when filenames contain more than one point.
+  * Fixed an issue when filenames contain more than one point.
 === Version 0.6 ===

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