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pipeline:window:cryolo [2018/12/24 11:20]
twagner [Configuration] 		pipeline:window:cryolo [2018/12/27 20:35]
twagner [Visualize the results]
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 } }
 </code> </code>
 +//[[:cryolo_config|Click here to get more information about the configuration file]]//
  
 Please set the value in the //"anchors"// field to your desired box size. It should be size of the minimum enclosing square in pixels. Furthermore check if the fields //"train_image_folder"// and //"train_annot_folder"// have the correct values. Typically, 20% of the training data are randomly chosen as validation data. If you want to use specific images as validation data, you can move the images and the corresponding box files to the folders specified in //"valid_image_folder"// and //"valid_annot_folder"//. Make sure that they are removed from the original training folder! With the line below, crYOLO automatically filters your images to an absolute frequence 0.1 and write them into a folder "filtered". Please set the value in the //"anchors"// field to your desired box size. It should be size of the minimum enclosing square in pixels. Furthermore check if the fields //"train_image_folder"// and //"train_annot_folder"// have the correct values. Typically, 20% of the training data are randomly chosen as validation data. If you want to use specific images as validation data, you can move the images and the corresponding box files to the folders specified in //"valid_image_folder"// and //"valid_annot_folder"//. Make sure that they are removed from the original training folder! With the line below, crYOLO automatically filters your images to an absolute frequence 0.1 and write them into a folder "filtered".
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 For this tutorial, we assume that you have either a single GPU or want to use GPU 0. Therefore we add '-g 0' after each command below. However, if you have multiple (e.g GPU 0 and GPU 1) you could also use both by adding '-g 0 1' after each command. For this tutorial, we assume that you have either a single GPU or want to use GPU 0. Therefore we add '-g 0' after each command below. However, if you have multiple (e.g GPU 0 and GPU 1) you could also use both by adding '-g 0 1' after each command.
  
-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** **1. Warm up your network**
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 to the training command. to the training command.
 ==== Picking ==== ==== Picking ====
-You can now use the model weights saved in ''model.h5'' to pick all your images in the directory full_data. To do this, run: +You can now use the model weights saved in ''model.h5'' to pick all your images in the directory ''full_data''. To do this, run: 
 <code> <code>
 cryolo_predict.py -c config.json -w model.h5 -i full_data/ -g 0 -o boxfiles/ cryolo_predict.py -c config.json -w model.h5 -i full_data/ -g 0 -o boxfiles/
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 cryolo_boxmanager.py cryolo_boxmanager.py
 </code> </code>
-Now press //File -> Open image// folder and the select the ''full_data'' directory. The first image should pop up. Then you import the box files with //File -> Import box files// and select the ''boxfiles'' directory.+Now press //File -> Open image// folder and the select the ''full_data'' directory. The first image should pop up. Then you import the box files with //File -> Import box files// and select in the ''boxfiles'' folder the ''EMAN'' directory.
  
  
pipeline/window/cryolo.txt ยท Last modified: 2021/02/19 10:00 by twagner

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