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auto_2d_class_selection [2019/05/28 22:12] twagner [Tutorial] |
auto_2d_class_selection [2019/07/29 14:48] twagner [Changelog] |
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====== Cinderella: Automatic 2D class selection ====== | ====== Cinderella: Automatic 2D class selection ====== | ||
- | Our automatic 2d class selection tool (**Cinderella**) is based on a deep learning network to select 2D classes. It uses the same deep neural network as crYOLO and was pretrained on a set good / bad classes. Cinderella | + | ---- |
+ | < | ||
+ | < | ||
+ | <p align=" | ||
+ | </ | ||
+ | ---- | ||
+ | |||
+ | Our automatic 2d class selection tool (**Cinderella**) is based on a deep learning network to seperate 2D classes from .hdf / .mrcs files into good and bad classes. It uses the same deep neural network as crYOLO and was pretrained on a set good / bad classes. Cinderella 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. | ||
+ | < | ||
* **License**: | * **License**: | ||
* **GitHub repository**: | * **GitHub repository**: | ||
+ | </ | ||
+ | Here are a couple of examples for good / bad classes in Cinderella: | ||
+ | |||
+ | {{ :: | ||
====== The Model ====== | ====== The Model ====== | ||
- | Our model was trained on a set of 2D classes from ISAC. The training dataset does **not contain any Relion classes**, so it might be that Cinderella will not work with them. However, you can easily [[auto_2d_class_selection# | + | 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> | ||
+ | The training dataset does **not contain any Relion classes**, so it might be that Cinderella will not work with well them. | ||
+ | </ | ||
+ | You can easily [[auto_2d_class_selection# | ||
- | Right now our model is trained on **17 datasets**. But we will increase the number often! | + | Right now our model is trained on **19 datasets**. But we will increase the number often! |
====== Download ====== | ====== Download ====== | ||
====Cinderella==== | ====Cinderella==== | ||
- | Version: 0.2.0 | + | Version: 0.3.1 |
- | Uploaded: | + | Uploaded: |
- | [[ftp://ftp.gwdg.de/pub/misc/sphire/ | + | [[https://pypi.org/project/cinderella/#files|DOWNLOAD]] |
====Pretrained model==== | ====Pretrained model==== | ||
- | Uploaded: | + | Uploaded: |
- | [[ftp:// | + | [[ftp:// |
[[auto2d_tutorial# | [[auto2d_tutorial# | ||
+ | ====Archive==== | ||
+ | Old version of cinderella and the pretrained model can be found in the [[cinderella_archive|archive]] | ||
+ | |||
+ | ====Changelog==== | ||
+ | |||
+ | === 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 | ||
====== Contribute ====== | ====== Contribute ====== | ||
Here is the repository of our training data: | Here is the repository of our training data: | ||
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</ | </ | ||
- | Install | + | Install |
< | < | ||
- | conda install numpy==1.14.5 | + | conda install numpy==1.15.4 |
- | pip install cinderella-X.Y.Z.tar.gz | + | </ |
+ | Install Cinderella for **GPU**: | ||
+ | < | ||
+ | pip install cinderella[gpu] | ||
+ | </ | ||
+ | |||
+ | **... or CPU**: | ||
+ | < | ||
+ | pip install cinderella[cpu] | ||
</ | </ | ||
====== Tutorial ====== | ====== Tutorial ====== | ||
- | [[auto2d_tutorial|We created a tutorial how to use and train Cinderella!]] | + | [[auto2d_tutorial|We created a tutorial how to use Cinderella!]] |