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pipeline:window:cryolo:configuration [2019/09/17 10:11] twagner |
pipeline:window:cryolo:configuration [2020/01/10 14:53] twagner |
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- | ====SectionA===== | + | You now have to create a configuration file for your picking project. It contains all important constants and paths and helps you to reproduce your results later on. |
- | You now have to create a configuration file your picking project. It contains all important constants and paths and helps you to reproduce your results later on. | + | |
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: | ||
{{ : | {{ : | ||
- | < | ||
- | 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: |
- | * By default, your images | + | |
- | </note> | + | |
+ | * By default, your micrographs | ||
+ | * 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) | ||
Line 20: | Line 20: | ||
</ | </ | ||
<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 [[: | ||
</ | </ | ||
- | You can now press the Start button to create you configuration file. | ||
- | ====SectionB==== | ||
- | <hidden **Create the configuration file using the command line:**> | ||
- | |||
- | To create a basic configuration file that will work for most projects is very simple. I assume your box files for training are in the folder '' | ||
- | |||
- | < | ||
- | cryoloo.py config config_cryolo.json 160 --train_image_folder train_image --train_annot_folder train_annot | ||
- | </ | ||
- | |||
- | To get a full description of all available options type: | ||
- | < | ||
- | cryoloo.py config -h | ||
- | </ | ||
- | |||
- | // | ||
- | |||
- | If you want to specify seperate validation folders you can use the %%--%%valid_image_folder and %%--%%valid_annot_folder options: | ||
- | |||
- | < | ||
- | cryoloo.py config config_cryolo.json 160 --train_image_folder train_image --train_annot_folder train_annot --valid_image_folder valid_img --valid_annot_folder valid_annot | ||
- | </ | ||
- | |||
- | </ |