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The following list can be accessed by the terminal
FLAG
Supported script
Use
Defaults
Note
-c
ALL
Specify configuration file to use
Config.yaml
The Config file used in initializing some parameters in the pipeline
-a
ALL
override configuration arguments in the config file
None
Using -a has higher priority than the configuration file selected with -c. E.g: -a Global.model_name= Sauvola2
INTRODUCTION TO PARAMETERS OF CONFIGURATION FILE
Global
Parameter
Use
Defaults
Note
use_gpu
Set using GPU or not
True
Used to indicate whether to use GPU or not
model_name
Specify the name of the run to be using in naming the model
Sauvalo_Finetune
This will be used in naming the created saved model name besides to Wandb Initializations if Used
pretrained_model
Set the path to pretrained model
pretrained_models/Sauvola_demo.h5
If path is None or doesn't exist, the model will start from scratch. If the path exists, parameters related to Architecture will be ommited and will be initialized from the saved model
Datset Folder should contain all images with names=TRAIN_*, and for each image there should be ground truth and source having same name but one ending with _source.png and groundtruth with _target.png e.g. for one image: Bickely2010_H01_source.png, Bickely2010_H01_target.png
Add WandbCallback to the list to enable wandb API Visualizations
patience
Set patience to be used in ['EarlyStopping','ReduceLROnPlateau']
15
Note in ReduceLROnPlateau the patiance is divied by 2
Architecture
In Sauvolanet, the network is divided into four stages: SauvolaMultiWindow, Pixelwise Window Attention (PWA), and Adaptive Sauolva Threshold (AST)
Parameter
Use
Defaults
Note
SauvolaMultiWindow
SauvolaMultiWindow Class
window_size_list
Sets the windows list sizes
[3,5,7,11,15,19]
[int], the used window sizes to compute Sauvola based thresholds
norm_type
SauvolaMultiWindow Class
'bnorm'
str, one of {'inorm', 'bnorm'}, the normalization layer used in the conv_blocks {inorm: InstanceNormalization, bnorm: BatchNormalization}
activation
Set the activation class name
'relu'
str, the used activation function inside the SauvolaMultiWindow Convolutions
base_filters
Sets the number of base filters
4
the number of base filters used in conv_blocks, i.e. the 1st conv uses base_filter of filters the 2nd conv uses 2*base_filter of filters and Kth conv uses K*base_filter of filters
init_k
Set param k in Sauvola binarization
0.2
Initialize param k in Sauvola binarization
init_R
Set param R in Sauvola binarization
0.5
Initialize param R in Sauvola binarization
train_k
Set param k training flag
True
whether or not train the param k in Sauvola binarization
train_R
Set param R training flag
True
whether or not train the param R in Sauvola binarization
DifferenceThresh
DifferenceThresh Class
init_alpha
Set param alpha in Sauvola binarization
16
Initialize param alpha in Sauvola binarization
train_alpha
Set param alpha training flag
True
whether or not train the param alpha in Sauvola binarization