CrYOLO is a fast and accurate particle picking procedure. It's based on convolutional neural networks and utilizes the popular You Only Look Once (YOLO) object detection system.

• crYOLO makes picking fast – On a modern GPU it will pick your particles at up to 6 micrographs per second.
• crYOLO makes picking smart – The network learns the context of particles (e.g. not to pick particles on carbon or within ice contamination )
• crYOLO makes training easy – You might use a general network model and skip training completely. However, if the general model doesn't give you satisfactory results or if you would like to improve them, you might want to train a specialized model specific for your data set by selecting particles (no selection of negative examples necessary) on a small number of micrographs.
• crYOLO makes training tolerant – Don't worry if you miss quite a lot particles during creation of your training set. crYOLO will still do the job.

In this tutorial we explain our recommended configurations for single particle and filament projects. You can find more information how to use crYOLO, about supported networks and about the config file in the following articles:

• crYOLO talk at SBGrid
• crYOLO networks
• crYOLO configuration file

You can find the download and installation instructions here: Download and Installation

Depending what you want to do, you can follow one of these self-contained Tutorials:

1. I would like to train a model from scratch for picking my particles
2. I would like to pick particles without training using a general model
3. I would like to train a model from scratch for picking filaments
4. I would like to refine a general model for my particle

The first, second and third tutorial are the most common use cases and well tested. The fourth tutorial is still experimental but might give you better results in less time and less training data.

Any questions? Problems? Suggestions?

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