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auto2d_tutorial [2019/12/10 08:41]
twagner [How to use SPHIRE's Cinderella] 		auto2d_tutorial [2019/12/10 08:59]
twagner
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 ====== How to use SPHIRE's Cinderella for 2D class selection ====== ====== How to use SPHIRE's Cinderella for 2D class selection ======
 +This tutorial describes how to use Cinderella to classify 2D class averages. You can either use a pretrained model (see section //Classify//) or train your own model (see section //Training//).
 +
 ==== Download & Install ==== ==== Download & Install ====
 You can find the download and installation instructions here: [[auto_2d_class_selection|Download and Installation]] You can find the download and installation instructions here: [[auto_2d_class_selection|Download and Installation]]
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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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