Config Description

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

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

Parameter	Use	Defaults	Note
Optimizer	Set Optimizer class name	`adam`	Check keras Optimizers for more
Loss	Set Loss function name	`hinge`	Check keras Losses for more
Epoch	Set Epochs	`100`
batch_size	Set Batch Size	`1`
dataset	Set the Dataset path	`Dataset`	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`
Callbacks	Callbacks Class
callbacks	Set callbacks to be used	[`'ModelCheckpoint'`,`'TensorBoard'`, `'EarlyStopping'`,`'ReduceLROnPlateau'`]`	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

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 `2base_filter` of filters and Kth conv uses `Kbase_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