cinderella_tomograms []

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== How to use SPHIRE's Cinderella for subtomogram selection ======

You can use Cinderella to classify Subtomograms into good/bad categories. This is useful if you want to sort Particles which were previously picked with e.g. template matching. 

==== Training ====
To train cinderella we have to create training data. To do that, we extract the central slices from your tomogram (step 1) and select bad and good particles using eman2 [[https://blake.bcm.edu/emanwiki/EMAN2/Programs/e2displaym|e2display]] (step 2). In step 3 we train the actual model using cinderella.

=== 1. Extract central slices ===
To extract the central slices from e.g. my_subtomograms.hdf and to save it into sub_central.mrcs run:
<code>
sp_cinderella_extract.py -i my_subtomograms.hdf -o sub_central.mrcs
</code>

=== 2. Select good / bad training examples with e2display ===
  - Start e2display from eman2 and select the central slice file (in our example ''sub_central.mrcs''). 
  - Press ►[Show Stack] to display the file. 
  - Now click with the central mouse button (mouse wheel) on any particle. In the new dialog press the button ►[Sets] and select the tab "Sets". 
  - There should already be a class "bad_particles". Create a new class and name it "good_particles". Highlight the set to which you want to add particle.
  - If you now click on particles in the overview, they will be added to the currently selected set.
  - After you finished the selection, press ►[Save] for each selected class. You should save the classes into separate folders (e.g. ''good/'' and ''bad/''). Both folders can contain multiple files (e.g. examples from another tomogram).
{{ ::eman2_set_arrow.png?300 |}}
<note question>
**How many examples do I need for training?**

We typically start with 40 good and 40 bad examples.
</note>
=== 3. Start training ===
After your created your training data, you can start the training :-) 

You need to specify all settings into one config file. To do so, create an empty file using
<code>
touch config.json
</code>

Copy the following configuration into the new file and adapt it to your needs. The only entries you might want to change are the //input_size//, //good_path//, //bad_path// and //pretrained_weights//.

<code json config.json>
{
	"model": {
		"input_size": [32,32]
	},

	"train": {
		"batch_size": 32,
		"good_path": "good/",
		"bad_path": "bad/",
		"pretrained_weights": "",
		"saved_weights_name": "my_model.h5",
		"learning_rate": 1e-4,
		"nb_epoch": 100,
		"nb_early_stop": 15
	}
}
</code>
The fields have the following meaning:
  * **input_size**: Each central slice is resized to these dimensions.
  * **batch_size**: The number of images in one mini-batch. If you have memory problems you can try to reduce this value.
  * **good_path**: Path of folder containing good central slices.
  * **bad_path**: Path of folder containing bad central slices.
  * **pretrained_weights**: Path to weights that are used to initialize the network. It can be empty. As Cinderella is using the same network architecture as crYOLO, we are typically using the [[downloads:cryolo_1#general_phosaurusnet_models|general network of crYOLO]] as pretrained weights.
  * **saved_weights_name**: Final model filename.
  * **learning_rate**: Learning rate, should not be changed.
  * **nb_epoch**: Maximum number of epochs to train. However, it will stop earlier (see nb_early_stop).
  * **nb_early_stop**: If the validation loss did not improve "nb_early_stop" times in a row, the training will stop automatically.

The next step is to run the training:

<code>
sp_cinderella_train.py -c config.json --gpu 1
</code>

This will train a classification network on the GPU with ID=1. Once the training finishes, you get a ''my_model.h5'' file. This can then be used to classify subtomograms into good / bad categories.



==== Prediction ====

To run the prediction on 'my_subtomograms.hdf' just on the GPU with ID=1 do:

<code>
sp_cinderella_predict.py -i my_subtomograms.hdf -w my_model.h5 -o output_folder/
</code>

In the output folder you will find two new mrcs files with the classified subtomograms. To check the results with e2display, you have to extract the central slices again (see [[cinderella_tomograms#extract_central_slices|Extract central slices]]).
User Tools

Site Tools

Page Tools
