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pipeline:window:cryolo [2019/07/18 08:31]
twagner [Overview] 		pipeline:window:cryolo [2019/08/29 16:27]
twagner [Training]
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   * 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.]]   * 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 how to use crYOLO, about supported networks and about the config file in the following articles: 
 +  * [[https://www.youtube.com/embed/JTgldM4wAAk|crYOLO talk at SBGrid]]
   * [[:cryolo_nets|crYOLO networks]]   * [[:cryolo_nets|crYOLO networks]]
   * [[:cryolo_config|crYOLO configuration file]]   * [[:cryolo_config|crYOLO configuration file]]
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 </html> </html>
 </note> </note>
 +
 ===== Installation ===== ===== Installation =====
  
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 <hidden **Alternative: Using neural-network denoising with JANNI**> <hidden **Alternative: Using neural-network denoising with JANNI**>
 <html><br></html> <html><br></html>
-Since crYOLO 1.4 you can also use neural network denoising with [[:janni|JANNI]]. The easiest way is to use the JANNI's general model ([[:janni#janni_general_model|Download here]]) but you can also [[:janni_tutorial#training_a_model_for_your_data|train JANNI for your data]]. crYOLO directly uses an interface to JANNI to filter your data, you just have to specify the path to your JANNI model, overlap of the batches (default 24), the batch size (default 3) and a path where the denoised images should be written. +Since crYOLO 1.4 you can also use neural network denoising with [[:janni|JANNI]]. The easiest way is to use the JANNI's general model ([[:janni#janni_general_model|Download here]]) but you can also [[:janni_tutorial#training_a_model_for_your_data|train JANNI for your data]]. crYOLO directly uses an interface to JANNI to filter your data, you just have to specify the path to your JANNI model, overlap of the patches (default 24), the batch size (default 3) and a path where the denoised images should be written. 
  
 To use JANNI's denoising you have to use following entry in your config.json: To use JANNI's denoising you have to use following entry in your config.json:
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 Navigate to the folder with ''config.json'' file, ''train_image'' folder, etc. Navigate to the folder with ''config.json'' file, ''train_image'' folder, etc.
  
-**1. Warm up your network**+**Train your network with 3 warmup epochs:**
  
 <code> <code>
 cryolo_train.py -c config.json -w 3 -g 0 cryolo_train.py -c config.json -w 3 -g 0
-</code> 
- 
-**2. Train your network** 
- 
-<code> 
-cryolo_train.py -c config.json -w 0 -g 0 
 </code> </code>
  
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 The training stops when the "loss" metric on the validation data does not improve 10 times in a row. This is typically enough. In case want to give the training more time to find the best model. You might increase the "not changed in a row" parameter to, for example, 15 by adding the flag //-e 15//: The training stops when the "loss" metric on the validation data does not improve 10 times in a row. This is typically enough. In case want to give the training more time to find the best model. You might increase the "not changed in a row" parameter to, for example, 15 by adding the flag //-e 15//:
 +
 <code> <code>
-cryolo_train.py -c config.json -w 0 -g 0 -e 15+cryolo_train.py -c config.json -w 3 -g 0 -e 15
 </code> </code>
 +
 to the training command. to the training command.
 ==== Picking ==== ==== Picking ====
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 === Negative stain images === === Negative stain images ===
 For the general model for **negative stain data** please use: For the general model for **negative stain data** please use:
 +<hidden **config.json for negative stain images**>
 <code json config.json> <code json config.json>
     {     {
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     }     }
 </code> </code>
 +</hidden>
  
 Please set the value in the //"anchors"// field to your desired box size. It should be size of the minimum particle enclosing square in pixel.  Please set the value in the //"anchors"// field to your desired box size. It should be size of the minimum particle enclosing square in pixel. 
pipeline/window/cryolo.txt · Last modified: 2021/02/19 10:00 by twagner

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