cinderella_micrographs []

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====== How to use SPHIRE's Cinderella for micrograph selection ======

This tutorial describes how to use Cinderella to sort micrographs. Unfortunately, we cannot provide a pretrained model yet. Therefore the first step is to train a model (see section //Training//) and to apply a model (see section //Classify//).



==== Download & Install ====
You can find the download and installation instructions here: [[auto_2d_class_selection|Download and Installation]]


==== Training ====

The first step is to train Cinderella with manually selected good and bad micrographs. Create two folders, one containing manually selected good micrographs (e.g ''GOOD_MICS/'') and one contain bad micrographs (e.g ''BAD_MICS/''). Both folders can contain subfolders.
<note question>
**How many micrographs do I need?**

We typically start with 30 good and 30 bad micrographs.
</note>

Then specify the paths into a config file like this:

<code json config.json>
{
	"model": {
		"input_size": [512,512]
	},

	"train": {
		"batch_size": 6,
		"good_path": "GOOD_MICS/",
		"bad_path": "BAD_MICS/",
		"pretrained_weights": "",
		"saved_weights_name": "my_model.h5",
		"learning_rate": 1e-4,
		"nb_epoch": 100,
		"nb_early_stop": 15
	}
}
</code>
The fields in the section **model** have the following meaning:
  * **input_size**: This is the image size to which each micrograph is resized to.
  * **mask_radius**: (Optional) Circular mask radius which is applied after resizing to the input size. If not given, it uses 0.4 * **input_size** as default.

The fields in the section **train** have the following meaning:
  * **batch_size**: How many micrographs are in one mini-batch. If you have memory problems, you can try to reduce this value.
  * **good_path**: Path to folder with good micrographs.
  * **bad_path**: Path to folder with bad micrographs.
  * **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 example_config.json --gpu 1
</code>

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


==== Classify ====
Suppose you want to separate good and bad micrographs in the folder ''micrographs'' and you want to save a list with the filenames of the good / bad micrographgs into the folder ''output_folder''. Furthermore you want to use the model ''my_model.h5'' and the GPU with ID=1. Micrographs with a confidence bigger than 0.5 should be classified as good micrograph.

This is the command to run:

<code>
sp_cinderella_predict.py -i micrographs/ -w model.h5 -o output_folder/ -t 0.5 --gpu 1
</code>

You will find the files ''bad.txt'' and ''good.txt'' in  your ''output_folder''.
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