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From the fairy tale Cinderella
Our binary classification tool (Cinderella) is based on a deep learning network to classify class averages, micrographs or subtomograms into good and bad categories.
.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 (see tutorial). But you can easily train it with your own set of classes, micrographs, and/or subtomograms.
Our model was trained on a set of 2D classes from both 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:
You can easily contribute your own classes!
Right now our model is trained on 4773 good classes and 5390 bad classes.
Uploaded: 27. August 2020
Uploaded: 27. August 2020, Dataset: 4773 good classes and 5390 bad classes.
Old versions of cinderella and the pretrained model can be found in the archive.
mask_radiusin 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_radiusprovided) it will use 0.4*
Here is the repository of our 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:
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.
conda env remove --name cinderella
After that, create a new virtual environment:
conda create -n cinderella -c anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy==1.14.5
Activate the environment:
source activate cinderella
Install Cinderella for GPU:
pip install cinderella[gpu]
… or CPU:
pip install cinderella[cpu]