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pipeline:window:cryolo:configuration [2019/09/17 17:35] shaikh |
pipeline:window:cryolo:configuration [2020/03/16 15:20] (current) 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 calculate how well the model performs and whether it still improves.)). 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 separate folders. __Make sure that they are removed from the original training folder!__ You can then specify the new validation folders in //" | * 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 how well the model performs and whether it still improves.)). 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 separate folders. __Make sure that they are removed from the original training folder!__ You can then specify the new validation folders in //" | ||
- | * 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 [[: | ||
</ | </ |