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--- pipeline:window:cryolo [2019/08/30 14:17]
twagner [Configuration]
+++ pipeline:window:cryolo [2019/09/13 19:24]
twagner
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 You can find the download and installation instructions here: [[howto:download_latest_cryolo|Download and Installation]]
+===== Tutorials =====
+Depending what you want to do, you can follow one of these Tutorials:
+  - I would like to train a model from scratch for picking my particles
+  - I would like to train a model from scratch for picking filaments.
+  - I would like to refine a general model for my particles.
+The **first and the second tutorial** are the most common use cases and well tested. The **third tutorial** is still experimental but might give you better results in less time or less training data.
 ===== Picking particles - Using a model trained for your data =====
@@ Line 74: / Line 86: @@
 Now create a third folder with the name ''train_image''. Now for each box file, copy the corresponding image from ''full_data'' into ''train_image''((While it is nice to keep the things organized, you don't have to copy your training images in a separate folder. In the configuration file (see below) you can also simply specify the full_data directory as "//train_image_folder//". crYOLO will find the correct images using the box files.)). crYOLO will detect image / box file pairs by search taking the box file an searching for an image filename which contains the box filename.
+==== Start crYOLO ====
+You can use crYOLO either by command line or by using the GUI. The GUI should be easier for most users. You can start it with:
+<code>
+cryoloo.py
+</code>
+SCREENSHOT HERE
+The crYOLO GUI is basically a visualization of the commandline interface. On left side, you find all possible "Actions":
+  * **conifg**: With this action you create the configuration file that you need to run crYOLO.
+  * **train**: This action let you train crYOLO from scratch or refine an existing model.
+  * **predict**: If you have your model ready, you can pick the particles on your dataset using this command.
+  * **evaluation**: This action helps you need to quantify the "goodness" of your model.
+Each action has several parameters which are organized in tabs. Once you chosen your settings you can press "Start", the command will be applied and crYOLO shows you the output:
+SCREENSHOT HERE
+It will tell you when something went wrong. Pressing "edit" brings you back to your settings, where you can either edit the settings (in case something went wrong) or go to the next action.
 ==== Configuration ====
@@ Line 137: / Line 169: @@
 **Alternative: 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 ([[:janni#janni_general_model|Download here]]) but you can also [[:janni_tutorial#training_a_model_for_your_data|train JANNI for your data]]. crYOLO directly uses an interface to JANNI to filter your data, you just have to specify the path to your JANNI model, overlap of the batches (default 24), the batch size (default 3) and a path where the denoised images should be written.
+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 ([[:janni#janni_general_model|Download here]]) but you can also [[:janni_tutorial#training_a_model_for_your_data|train JANNI for your data]]. crYOLO directly uses an interface to JANNI to filter your data, you just have to specify the path to your JANNI model, overlap of the patches (default 24), the batch size (default 3) 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:

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