cryolo_train_general_model []

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====== Train your own general model ======

Training a model for a specific dataset is very easy with crYOLO. However, you might have multiple data collections of the same particle with different settings, a different camera or another microscope. A model trained on the data of one data collection, might not perform very good on a dataset from another data collection.

However, you can easily train a crYOLO model that **generalize** well on data recorded under multiple conditions. To achieve this, all you have to do is to **merge training data** of multiple datasets. The result will be a model that can be applied to a new dataset from a new data collection without additional training. 

Here is our recommendation how to organize the training data. Instead of copying your images and box files directly into ''train_images'' / ''train_annot'', you can copy them into subfolders. One for each data collection:

{{ ::gen_models.png?300 |}}

The //train_image_folder// and //train_annot_folder// parameters in the [[cryolo_config|crYOLO configuration file (e.g. config.json)]] still point to the root directories ''train_images'' and ''train_annot'' respectively. The parameter //anchors// should be set roughly to the average of all particle box sizes. Other than that, the training of a general model does not differ from [[http://sphire.mpg.de/wiki/doku.php?id=pipeline:window:cryolo#picking_particles_-_using_a_model_trained_for_your_data|training a model from scratch]].

One minor hint: When running the training of a general model, we always use [[http://sphire.mpg.de/wiki/doku.php?id=pipeline:window:cryolo#advanced_parameters|--warm_restarts]].
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