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pipeline:window:cryolo:configuration [2019/09/17 14:30] twagner |
pipeline:window:cryolo:configuration [2020/03/16 15:20] twagner |
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You can either use the command line to create the configuration file or the GUI. For most users, the GUI should be easier. Select the //config// action and fill in the general fields: | You can either use the command line to create the configuration file or the GUI. For most users, the GUI should be easier. Select the //config// action and fill in the general fields: | ||
- | {{ : | + | {{ : |
- | At this point you could already press the Start button to generate the config file but you might want to take these options into account: | + | At this point you could already press the [Start] button to generate the config file but you might want to take these options into account: |
- | * During training, crYOLO also needs validation data((Micrographs that are selected as validation data, are not used to train crYOLO. These micrographs are used to calcuate | + | * During training, crYOLO also needs validation data((Micrographs that are selected as validation data are not used to train crYOLO. These micrographs are used to calculate |
- | * By default, your micrographs are low pass filtered to an absolute frequency of 0.1 and saved to disk. You can change the cutoff threshold and the directory for filtered micrographs in the //"Model/Denoising options"// | + | * By default, your micrographs are low pass filtered to an absolute frequency of 0.1 and saved to disk. You can change the cutoff threshold and the directory for filtered micrographs in the //" |
+ | * When training from scratch, crYOLO is initialized with weights learned on the ImageNet training data (transfer learning((From Wikipedia: Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.))). However, it might improve the training if you set the pretrained_weights options in the //" | ||
<note tip> | <note tip> | ||
**Alternative: | **Alternative: | ||
- | Since crYOLO 1.4 you can also use neural network denoising with [[: | + | Since crYOLO 1.4 you can also use neural network denoising with [[: |
{{ : | {{ : | ||
I recommend to use denoising with JANNI only together with a GPU as it is rather slow (~ 1-2 seconds per micrograph on the GPU and 10 seconds per micrograph on the CPU) | I recommend to use denoising with JANNI only together with a GPU as it is rather slow (~ 1-2 seconds per micrograph on the GPU and 10 seconds per micrograph on the CPU) | ||
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</ | </ | ||
<note tip> | <note tip> | ||
- | You can also modify all options and parameters directly in the config.json file. Please note the wiki entry about the [[: | + | **Editing the configuration file** |
+ | |||
+ | You can also modify all options and parameters directly in the config.json file. It can be opened by any text editor. Please note the wiki entry about the [[: | ||
</ | </ |