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--- pipeline:window:cryolo [2019/05/17 08:25]
twagner [Advanced parameters]
+++ pipeline:window:cryolo [2019/07/09 21:11]
twagner [Data preparation]
@@ Line 30: / Line 30: @@
 ==== Data preparation ====
 CrYOLO supports MRC, TIF and JPG files. It can work with 32 bit data, 8 bit data and 16 bit data.
-It will work on original MRC files, but it will probably improve when the data are filtered. Therefore you should low-pass filter them to a reasonable level. Since Version 1.2 crYOLO can automatically do that for you. You just have to add
+It will work on original MRC files, but it will probably improve when the data are denoised. Therefore you should low-pass filter them to a reasonable level. Since Version 1.2 crYOLO can automatically do that for you. You just have to add
 <code>
 "filter":               [0.1,"filtered"]
 </code>
 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 ''filtered''.
+<hidden **Alternativ: Using neural-network denoising with JANNI**>
+Since crYOLO 1.4 you can also use neural network denoising with [[:janni|JANNI]]. The easiest way is to use the JANNI's general model but you can also train one for your data. crYOLO directly an interface to JANNI to filter your data, you just have to specify the path to you JANNI model, the batch size (default 32), overlap of the batches (default 24) and a path where the denoised images should be written. To use JANNI's denoising you have to use following entry in your config.json:
+<code>
+"filter":               ["path/to/janni_model.h5",32,24,"filtered"]
+</code>
+</hidden>
+<html><br></html>
 If you followed the installation instructions, you now have to activate the cryolo virtual environment with

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