Model Overview Panel

Model List

All deep learning models available in the Deep Trainer folder, both trained and untrained, are listed in the top section of the Model Overview panel. You can also create new models and import models from this section of the panel.

Model list

Model list options and categories
	Description
Filter	Lets you filter the model list by Model Name key words. You can also sort the list in ascending or descending order.
Model Name	Indicates the name assigned to the model. You can edit the name of a model by double-clicking. Note Models names with the symbol * appended to their name indicate that the model is not saved. Unsaved changes include modifications to the model's architecture and updates to the training weights.
Model Type	Indicates the model type, as follows: Semantic segmentation… Trained for binary and multi-class semantic segmentation tasks. The number of classes is indicated for this type of model, for example n = 4. Regression… Trained for super-resolution and denoising tasks.
Model Status	Indicates the model status, as follows: Not loaded… The model is not loaded in the Deep Learning Tool. Click the Load button to load a selected model. Ready… The model is loaded and can be edited or trained. Edited… The model's architecture was edited. You should note that you must save an edited model to continue to training.
Parameters Count	Indicates the parameters count.
Date	Indicates the last date and time that the model was saved.

A number of additional options — New, Import from Keras, Import Folders, Duplicate, Delete, Load, and Reset — are also available in the Model box.

Additional options
	Description
New	Lets you create a new model for super-resolution, denoising, or segmentation purposes (see Model Generator).
Import from Keras	Lets you import HDF5 files (.h5 and .hdf5 extensions) that were created with Keras. Keras models that your import into Dragonfly's Deep Learning Tool must meet the following requirements: The model has 3-dimensional input and output shapes, or equivalently, the input batch shape is 4-dimensional. The input shape is explicitly specified in the Z direction (last dimension). The input shape is either unspecified or equal in the X and Y directions. The output either matches an input shape in the X and Y directions, or the input shape is explicitly specified. The model has only one output.
Import Folders	Lets you import a model or models contained within a selected folder.
Duplicate	Creates a copy of the selected model. Note The name of a duplicated model can be edited by double-clicking it in the Model Name column.
Delete	Deletes the selected model.
Load	Loads the selected model.
Unload	Unloads the selected model.

Details

Details about the selected model — such as a general description, its architecture, author name and affiliation, copyright, and version number — are available in the Model information section of the Model Overview panel, as shown below. You should note that you do not need to load a model to view the associated metadata, which is taken from the accompanying JSON file.

Model details

Author and contact information that is added to each generated model is taken directly from your User Profile settings (see Adding Your User Profile).

Model information details
	Description
General documentation	Provides a description of the selected model, if it was previously entered in the Model Generator dialog, as well as the model's architecture, parameters, and input dimension.
Name	Is the name of the model's author.
Contact	Is the entered contact name.
Email	Is the supplied email.
Organization	Is the author's or contact's indicated affiliation.
Address	Is the address of the affiliated organization.
Copyright	Is the copyright date entered by the author.
Creation date	Is the creation date and time of the model.
Version	Is the version number of the model.

Classes

The Classes box, shown below, is available for models trained for semantic segmentation and provides a list of the model classes.

Classes

You can set a class a visible or not visible for previews, change the highlight color of a class, and rename classes.

Apply

You can apply trained models to selected datasets with the options in the Apply box on the Model Training panel and on the Model Overview panel of the Deep Learning tool. You can also generate previews to evaluate a model's performance before processing you data.

Apply options

Apply

See Previewing Training Results and Applying Deep Models for information about generating previews and applying deep models to process your data.

What's Next

Model Generator