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 
, and choose 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.
| Item | Description |
|---|---|
Model | Use the Browse button |
Compare | Use the Compare button |
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.
|
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.
|
Epochs Details | A table containing information for each epoch, such as training loss, validation loss, time, and other metrics.
|
to find the model that you want to review. All the models associated with it will be added to the 
to compile the metrics of all the currently loaded models into a report for comparison and evaluation.
