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pipeline:window:cryolo [2019/04/03 14:53]
twagner [Data preparation] 		pipeline:window:cryolo [2019/04/15 15:27]
twagner [Overview]
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   * 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 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 **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 **lenient** -- Don't worry if you miss quite a lot particles during creation of your training set. [[:cryolo_picking_unlabeled|crYOLO will still do the job.]]+  * crYOLO makes training **tolerant** -- Don't worry if you miss quite a lot particles during creation of your training set. [[:cryolo_picking_unlabeled|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 about supported networks and about the config file in the following articles: In this tutorial we explain our recommended configurations for single particle and filament projects. You can find more information about supported networks and about the config file in the following articles:
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 Create a new directory called ''train_annotation'' and save it there. Close boxmanager. Create a new directory called ''train_annotation'' and save it there. Close boxmanager.
  
-Now create a third folder with the name ''train_image''. Now for each box file, copy the corresponding image from ''full_data'' into ''train_image''((While it is nice to keep the things organiced, you don't have to copy your data in a seperate folder. You can also simply specify the full_data directory as ''//train_image_folder//''. crYOLO will find the correct images using the box files.)). crYOLO will detect image / box file pairs by search taking the box file an searching for an image filename which contains the box filename.+Now create a third folder with the name ''train_image''. Now for each box file, copy the corresponding image from ''full_data'' into ''train_image''((While it is nice to keep the things organized, you don't have to copy your training images in a separate folder. In the configuration file (see below) you can also simply specify the full_data directory as "//train_image_folder//". crYOLO will find the correct images using the box files.)). crYOLO will detect image / box file pairs by search taking the box file an searching for an image filename which contains the box filename.
  
 ==== Configuration ==== ==== Configuration ====
pipeline/window/cryolo.txt ยท Last modified: 2021/02/19 10:00 by twagner

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