Deep learning model review

Available with Image Analyst license.

Understanding deep learning models is essential before using them in inferencing. Reviewing a model gives you an indication of how it was trained and how it might perform. In many cases, you may have multiple models to compare. The Review Deep Learning Model pane allows you to review the deep learning models that were trained and created. To open the Review Deep Learning Model pane, click the Deep Learning Tools drop-down menu Deep Learning Tools, and choose Review Deep Learning Model Review Deep Learning Model.

The Review Deep Learning Model pane displays information from the Esri Model Definition file (*.emd) and the contents of the ModelCharacteristic folder. If these files or folders don’t exist, the pane will display the Insufficient information available at: <folder name> error message. The table below describes the contents of the Review Deep Learning Model pane.

ItemDescription

Model

Use the Browse button Browse to find the model that you want to review. All the models associated with it will be added to the Model drop-down list. You can switch between models and remove models from the drop-down list.

Compare

Use the Compare button Compare to compile the metrics of all the currently loaded models into a report for comparison and evaluation.

Model Type

The name of the model architecture.

Backbone

The name of the preconfigured neural network that was used as the architecture for the training model.

Learning Rate

The learning rate used in the training of the neural networks. If you did not specify the value, it will be calculated by the training tool.

Training and Validation loss

This section displays a graph that shows training loss and validation loss over the course of training the model.

Training and validation loss

Analysis of the model

A metric or number, depending on the model architecture. For example, pixel classification models will display the following metrics for each class: precision, recall, and the f1 score. Object detection models will display the average precision score.

Sample Results

Shows examples of ground reference and predictions pairs.

Examples of ground reference and predictions pairs

Epochs Details

A table containing information for each epoch, such as training loss, validation loss, time, and other metrics.

Epochs Details table

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