Model Generator

The Model Generator dialog provides options — architectures, model types, input dimensions, and others — for generating new deep learning models for denoising, semantic segmentation, and super-resolution.

Click the New button on the Model Overview panel to open the Model Generator dialog, shown below.

Model Generator dialog

Model Generator options
	Description
Show architectures for	Lets you filter the available architectures to those recommended for segmentation, super-resolution, and denoising. Semantic Segmentation… Filters the Architecture list to models best suited for semantic segmentation, which is the process of associating each pixel of an image with a class label, such as a material phase or anatomical feature. Super-resolution… Filters the Architecture list to models best suited for super-resolution. Denoising… Filters the Architecture list to models best suited for denoising.
Architecture	Lists the default models supplied with the Deep Learning Tool. Architectures can be filtered by type.
Architecture description	Provides a short description of the selected architecture and a link for further information (see also Deep Learning Architectures).
Model type	Lets you choose the type of deep learning model — Regression or Semantic segmentation — that you need to generate. Regression… Regression model types are suitable for super-resolution and denoising. Semantic segmentation… Semantic segmentation model types are suitable for binary and multi-class segmentation tasks.
Class Count	Available only for semantic segmentation model types, this parameter lets you enter the number of classes required. The minimum number of classes is '2', which would be for a binary segmentation task, while the maximum is '20' for multi-class segmentations.
Input count	Lets you include multiple inputs for training. For example, when you are working with data from simultaneous image acquisition systems you might want to select each modality as an input.
Input Dimension	Lets you choose to train slice-by-slice (2D) or to allow training over multiple slices (3D). Choose '2D' if you want limit training to 2D, i.e. slice-by-slice. Choose '3D' and then a number equal to or greater than '3' to train in 3D, in which case multiple slices of the input dataset will be considered for the output target (see Configuring Multi-Slice Inputs). Note Not all data is suitable for training in 3D. For example, in cases in which features are not consistent over multiple slices.
Name	Lets you enter a name for the generated model. Note Names are automatically formatted as: `SelectedArchitecture_EnteredName`.
Description	Lets you enter a description of your model.
Parameters	Lists the hyperparameters associated with the selected architecture and the default values for each. Note Refer to the documents referenced in the Architecture Description box for information about the implemented architectures and their associated parameters.

Editable Parameters for Deep Learning Architectures

A limited number of the basic parameters for Deep Learning architectures are available for editing in the Model Generator. These are listed below. You can also edit the basic and advanced training parameters of a Deep Learning model after it is generated.

Refer to the documents referenced in Deep Learning Architectures for more information about the editable parameters available for the implemented architectures.

Editable parameters for Deep Learning model architectures
	Description
Attention U-Net	Depth level… Depth of the network, as determined by the number of pooling layers. Initial filter count… Filter count at the first convolution layer.
Autoencoder	Initial filter count… Filter count at the first convolutional layer. Kernel size… Convolutional filters kernel size. Pooling size… Pooling window size.
BiSeNet	Patch size… Size of the input patches.
DeepLabV3+	Backbone… Backbone to use — 'Xception' or 'MobileNetV2'. Patch size… Fixed size of the input patches. Output stride… Ratio of the image size to the encoder output size.
EDSR	Scale… Ratio of the input size to the output size. Patch size… Fixed size of the input patches. Filter count… Filter count at each convolution layer. ResNet block count… The number of times to repeat ResNet blocks. Use Tanh activation… Determines if Tanh activation will be applied — True or False.
FC-DenseNet	Model type… Model variation to be generated — FC-DenseNet56, FC-DenseNet67, or FC-DenseNet103.
LinkNet	Patch size… Fixed size of the input patches. Initial filter count… Filter count at the first convolution layer.
Noise2Noise	Initial filter count… Filter count at the first convolution layer.
Noise2Noise_SRResNet	Filter count… Filter count at each convolution layer. ResNet block count… The number of times to repeat ResNet blocks.
PSPNet	Backbone… Backbone to use — ResNet50 or ResNet101. Patch size… Fixed size of the input patches. Filter count… Filter count at each convolution layer.
Sensor3D	Depth level… Depth of the network, as determined by the number of pooling layers, and patch size. Initial filter count… Filter count at the first convolution layer.
U-Net	Depth level… Depth of the network, as determined by the number of pooling layers. Initial filter count… Filter count at the first convolution layer.
U-Net 3D	Topology… The topology of the model. Initial filter count… Filter count at the first convolution layer. Use batch normalization… Determines if batch normalization will be applied — True or False.
U-Net++	Depth level… Depth of the network, as determined by the number of pooling layers. Initial filter count… Filter count at the first convolution layer.
WDSR	Model type… Model variation to be generated — WDSR-A or WDSR-B. Scale… Ratio of the input size to the output size. Patch size…Fixed size of the input patches. Filter count… Filter count at each convolution layer. ResNet block count… The number of times to repeat ResNet blocks. ResNet block expansion… The ratio to multiple the number of filters in the ResNet block expansion layer.