cryolo_config

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cryolo_config [2018/12/20 18:00]
twagner
cryolo_config [2019/10/11 17:51]
twagner
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   * #01 ''​architecture'':​ The network used in the backend of crYOLO. Right we support "​crYOLO",​ "​YOLO",​ "​PhosaurusNet"​   * #01 ''​architecture'':​ The network used in the backend of crYOLO. Right we support "​crYOLO",​ "​YOLO",​ "​PhosaurusNet"​
  
-  * #02 ''​input_size'':​ This is the size to which the input is rescaled before passed through the network. In the example given here it would be 768x768. The input could either be the whole micrograph or a patch (in case you use the patchmode).+  * #02 ''​input_size'':​ This is the size to which the input is rescaled before passed through the network ​(it is NOT the size of your micrograph!). In the example given here it would be 768x768. The input could either be the whole micrograph or a patch (in case you use the patchmode). The input size have to be a multiple of 32
  
   * #03 ''​anchors'':​ Anchors in YOLO are kind of a priori knowledge. You should specifiy your box size here.   * #03 ''​anchors'':​ Anchors in YOLO are kind of a priori knowledge. You should specifiy your box size here.
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   * #05 ''​overlap_patches'':​ Optional. Only needed when using patch mode. Specifies how much the patches overlap. In our lab, we always keep the default value.   * #05 ''​overlap_patches'':​ Optional. Only needed when using patch mode. Specifies how much the patches overlap. In our lab, we always keep the default value.
  
-  * #06 ''​num_patches'':​ Optional. If specified the patch mode will be used. A value of "​2"​ means, that 2x2 patches will be used.+  * #06 ''​num_patches'':​ Optional. If specified the patch mode will be used. A value of "​2"​ means, that 2x2 patches will be used. With PhosaurusNet you typically don't need it.
  
   * #07 ''​filter'':​ Optional. Specifies the absolute cut-off frequency for the low-pass filter and the corresponding output folder. CrYOLO will automatically filter the data in ''​train_image_folder''​ ''​valid_image_folder''​ and save it into the output folder. It will automatically check if a image provided in the train_image_folder is already filtered and use it in case. Otherwise it will filter it.   * #07 ''​filter'':​ Optional. Specifies the absolute cut-off frequency for the low-pass filter and the corresponding output folder. CrYOLO will automatically filter the data in ''​train_image_folder''​ ''​valid_image_folder''​ and save it into the output folder. It will automatically check if a image provided in the train_image_folder is already filtered and use it in case. Otherwise it will filter it.
  • cryolo_config.txt
  • Last modified: 2019/10/11 17:51
  • by twagner