Update README.md

README.md

@@ -48,6 +48,18 @@ The output folder should be an empty folder where the output model will be writt
 * input_width: This indicates the width of model's input.
 * weight_decay: Weight decay of l2 regularization of model layers.
 * augmentation: If you want to apply any kind of augmentation this parameter should first set to ``true``.
-* flip_aug: If ``true``, different types of filp will applied on image. Type of flips is given by "flip_index" in train.py file.
+* flip_aug: If ``true``, different types of filp will be applied on image. Type of flips is given with "flip_index" in train.py file.
+* blur_aug: If ``true``, different types of blurring will be applied on image. Type of blurrings is given with "blur_k" in train.py file.
+* scaling: If ``true``, scaling will be applied on image. Scale of scaling is given with "scales" in train.py file.
+* rotation_not_90: If ``true``, rotation (not 90 degree) will be applied on image. Rothation angles are given with "thetha" in train.py file.
+* rotation: If ``true``, 90 degree rotation will be applied on image.
+* binarization: If ``true``,Otsu thresholding will be applied to augment the input data with binarized images.
+* scaling_bluring: If ``true``, combination of scaling and blurring will be applied on image.
+* scaling_binarization: If ``true``, combination of scaling and binarization will be applied on image.
+* scaling_flip: If ``true``, combination of scaling and flip will be applied on image.
+* continue_training: If ``true``, it means that you have already trained a model and you would like to continue the training. So it is needed to provide the dir of trained model with "dir_of_start_model" and index for naming the models. For example if you have already trained for 3 epochs then your last index is 2 and if you want to continue from model_1.h5, you can set "index_start" to 3 to start naming model with index 3. 
+* weighted_loss: If ``true``, this means that you want to apply weighted categorical_crossentropy as loss fucntion. Be carefull if you set to ``true``the parameter "is_loss_soft_dice" should be ``false``
+* data_is_provided: If you have already provided the input data you can set this to ``true``. Be sure that the train and eval data should be in "dir_output". Since when once we provide training data we resize and augment them and then we write them in sub-directories train and eval in "dir_output". 
+* dir_train: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resize and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories.