u_net_plusplus: Create a U-Net++ architecture

In jniedballa/imageseg: Deep Learning Models for Image Segmentation

Create a U-Net++ architecture

Description

Create a U-Net++ architecture.

Usage

u_net_plusplus(
  net_h,
  net_w,
  grayscale = FALSE,
  blocks = 4,
  n_class = 1,
  filters = 16,
  kernel_initializer = "he_normal"
)

Arguments

net_h	Input layer height.
net_w	Input layer width.
grayscale	Defines input layer color channels - 1 if 'TRUE', 3 if 'FALSE'.
blocks	Number of blocks in the model.
n_class	Number of classes.
filters	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_initializer	Initializer for the kernel weights matrix.

Details

The function was ported to R from Python code in https://github.com/albertsokol/pneumothorax-detection-unet/blob/master/models.py. For more details, see https://github.com/MrGiovanni/UNetPlusPlus.

Value

U-Net++ model.

Examples

## Not run: 
# U-Net++ model for 256x256 pixel RGB input images with a single output class

model <- u_net_plusplus(net_h = 256, 
net_w = 256, 
filters = 32,
blocks = 3 
)
 
model


## End(Not run)

jniedballa/imageseg documentation built on Nov. 21, 2023, 4:42 p.m.