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pipeline:window:cryolo [2019/07/09 21:00] twagner [Data preparation] |
pipeline:window:cryolo [2019/07/18 08:30] twagner [Overview] |
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* [[: | * [[: | ||
+ | < | ||
You can find more technical details in our paper: | You can find more technical details in our paper: | ||
- | [[https://www.biorxiv.org/content/10.1101/356584v2|SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EM | + | [[https://doi.org/10.1038/s42003-019-0437-z|Wagner, T. et al. SPHIRE-crYOLO |
- | ]] | + | |
+ | |||
+ | |||
+ | |||
+ | </ | ||
+ | |||
+ | < | ||
+ | We are also proud that crYOLO was recommended by F1000: | ||
+ | |||
+ | //" | ||
+ | < | ||
< | < | ||
- | <a href=" | + | <a href=" |
</ | </ | ||
+ | </ | ||
===== Installation ===== | ===== Installation ===== | ||
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to the model section in your config file to filter your images down to an absolute frequency of 0.1. The filtered images are saved in folder '' | to the model section in your config file to filter your images down to an absolute frequency of 0.1. The filtered images are saved in folder '' | ||
- | <hidden Alternative: | + | crYOLO will automatically check if an image in full_data is available in the '' |
+ | |||
+ | < | ||
+ | < | ||
+ | Since crYOLO 1.4 you can also use neural network denoising with [[: | ||
+ | |||
+ | To use JANNI' | ||
< | < | ||
- | " | + | " |
</ | </ | ||
+ | |||
+ | 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) | ||
+ | |||
+ | < | ||
</ | </ | ||
< | < | ||
+ | |||
If you followed the installation instructions, | If you followed the installation instructions, | ||
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</ | </ | ||
crYOLO will automatically check if an image in full_data is available in the '' | crYOLO will automatically check if an image in full_data is available in the '' | ||
+ | |||
+ | <note tip> | ||
+ | **Alternative: | ||
+ | |||
+ | Since crYOLO 1.4 you can also use neural network denoising with [[: | ||
+ | |||
+ | To use JANNI' | ||
+ | |||
+ | < | ||
+ | " | ||
+ | </ | ||
+ | |||
+ | 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) | ||
+ | |||
+ | </ | ||
Please note the wiki entry about the [[: | Please note the wiki entry about the [[: | ||
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[{{ : | [{{ : | ||
+ | <note warning> | ||
Right now, **this filtering does not yet work for filaments**. | Right now, **this filtering does not yet work for filaments**. | ||
+ | </ | ||
+ | |||
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There are two general **[[: | There are two general **[[: | ||
=== CryoEM images === | === CryoEM images === | ||
- | For the general **[[: | + | For the general **[[: |
+ | <hidden **config.json for low-pass filtered cryo-images**> | ||
<code json config.json> | <code json config.json> | ||
{ | { | ||
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} | } | ||
</ | </ | ||
- | Please | + | </ |
+ | < | ||
+ | For the general model trained with **neural-network denoised cryo images** (with JANNI' | ||
+ | <hidden **config.json for neural-network denoised cryo-images**> | ||
+ | <code json config.json> | ||
+ | { | ||
+ | " | ||
+ | " | ||
+ | " | ||
+ | " | ||
+ | " | ||
+ | " | ||
+ | " | ||
+ | } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | You can download the file '' | ||
+ | </ | ||
+ | < | ||
+ | In all cases please | ||
=== Negative stain images === | === Negative stain images === | ||
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===== Evaluate your results ===== | ===== Evaluate your results ===== | ||
- | + | <note warning> | |
- | The evaluation tool allows you, based on your validation data, to get statistics about your training. Unfortunately, | + | Unfortunately, |
+ | </ | ||
+ | The evaluation tool allows you, based on your validation data, to get statistics about your training. | ||
If you followed the tutorial, the validation data are selected randomly. With crYOLO 1.1.0 a run file for each training is created and saved into the folder runfiles/ in your project directory. This run file contains which files were selected for validation, and you can run your evaluation as follows: | If you followed the tutorial, the validation data are selected randomly. With crYOLO 1.1.0 a run file for each training is created and saved into the folder runfiles/ in your project directory. This run file contains which files were selected for validation, and you can run your evaluation as follows: | ||
< | < |