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auto2d_tutorial [2019/12/10 08:48]
twagner 		auto2d_tutorial [2019/12/10 09:00]
twagner
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 ==== Training ==== ==== Training ====
 If you would like to train Cinderella with your own classes, you can easily do it. If you would like to train Cinderella with your own classes, you can easily do it.
-First you have to separate your good and bad classes into separate files. Create two folders, on containing good classes (e.g ''GOOD_CLASSES/'') and one contain bad classes (e.g ''BAD_CLASSES/''). Both folders can contain multiple .hdf / .mrcs files.+First you have to separate your good and bad classes into separate files. Create two folders, one containing good classes (e.g ''GOOD_CLASSES/'') and one contain bad classes (e.g ''BAD_CLASSES/''). Both folders can contain multiple .hdf / .mrcs files.
  
 Then specify the paths into a config file like this: Then specify the paths into a config file like this:
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   * **input_size**: This is the image size to which each class is resized to.   * **input_size**: This is the image size to which each class is resized to.
   * **batch_size**: How many classes are in one mini-batch. If you have memory problems, you can try to reduce this value.   * **batch_size**: How many classes are in one mini-batch. If you have memory problems, you can try to reduce this value.
-  * **good_classes**: Path to folder with good classes. +  * **good_path**: Path to folder with good classes. 
-  * **bad_classes**: Path to folder with bad classes. +  * **bad_path**: Path to folder with bad classes. 
-  * **pretrained_weights**: Path to weights that are used to initialize the network. It can be empty. As Cinderella is using the same network architecture as crYOLO, we are typically using the general network of crYOLO as pretrained weights.+  * **pretrained_weights**: Path to weights that are used to initialize the network. It can be empty. As Cinderella is using the same network architecture as crYOLO, we are typically using the [[downloads:cryolo_1#general_phosaurusnet_models|general network of crYOLO]] as pretrained weights.
   * **saved_weights_name**: Final model filename   * **saved_weights_name**: Final model filename
   * **learning_rate**: Learning rate, should not be changed.   * **learning_rate**: Learning rate, should not be changed.
   * **nb_epoch**: Maximum number of epochs to train. However, it will stop earlier (see nb_early_stop).   * **nb_epoch**: Maximum number of epochs to train. However, it will stop earlier (see nb_early_stop).
-  * **nb_early_stop**: If the validation loss did not improve "nb_early_stop"x in a row, the training will stop automatically.+  * **nb_early_stop**: If the validation loss did not improve "nb_early_stop" times in a row, the training will stop automatically.
  
 The next step is to run the training: The next step is to run the training:
auto2d_tutorial.txt · Last modified: 2020/08/28 07:36 by twagner

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