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--- pipeline:window:cryolo:configuration [2019/09/18 12:47]
twagner
+++ pipeline:window:cryolo:configuration [2020/01/10 14:45]
twagner
@@ Line 8: / Line 8: @@
   * 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 //"Validation configuration"// tab.
-  * 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"// tab.
+  * 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 //"Denoising options"// tab.
+  * When training from scratch, crYOLO is initialized with weights learned on the ImageNet training data (transfer learning). It might be helpful if you set the pretrained_weights options in the //"Training options"// tab to the current general model.
 <note tip>
 **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 change the filter argument in the //Model/Denoising tab// from LOWPASS to JANNI and specify the path to your JANNI model:
+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 change the filter argument in the //Denoising tab// from LOWPASS to JANNI and specify the path to your JANNI model:
 {{ :pipeline:window:cryolo:cryolo_configuration_02.png?300 |}}
 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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