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pipeline:window:cryolo [2019/03/18 15:00]
twagner [Picking] 		pipeline:window:cryolo [2019/04/03 14:53]
twagner [Data preparation]
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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''. 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 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.
  
 ==== Configuration ==== ==== Configuration ====
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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'' (if you come to this section from another point of the tutorial, this filename might be different like ''gmodel_phosnet_X_Y.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'' (//if you come to this section from another point of the tutorial, this filename might be different like ''gmodel_phosnet_X_Y.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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 ==== Configuration ==== ==== Configuration ====
-You can configure it the same way as for a "normal" project. We recommend to use [[:cryolo_nets#network_2_yolo_with_patches|YOLO in "patch mode" with 3x3 patches]]:+You can configure it the same way as for a "normal" project. 
 <code json config.json> <code json config.json>
 { {
     "model" : {     "model" : {
-        "architecture":         "YOLO", +        "architecture":         "PhosaurusNet", 
-        "input_size":           768, +        "input_size":           1024, 
-        "anchors":              [200,200],+        "anchors":              [160,160],
         "max_box_per_image":    600,         "max_box_per_image":    600,
-        "num_patches":          3, +        "num_patches":          1, 
-        "filter":               [0.1,"tmp_filtered"]+        "filter":               [0.1,"filtered"]
     },     },
  
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 } }
 </code> </code>
 +
 //[[:cryolo_config|Click here to get more information about the configuration file]]// //[[:cryolo_config|Click here to get more information about the configuration file]]//
  
pipeline/window/cryolo.txt · Last modified: 2021/02/19 10:00 by twagner

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