Validate Vision Model
Now that you have trained a model, you can now validate the performance of your model. This will briefly show the steps for validating a model, but for an in depth tutorial, please see Validating Vision Models.
If you haven't already, click on the training session card for more information.
On the top right corner of the page, click on the "validate" button as indicated.
Specify the name of the validation session and the model and the dataset for validation. Under the model selection, you can select various trained model artifacts from the choices of ONNX, TFLite, TensorRT, Kinara, Hailo, etc. Choose the model you plan to deploy on target. The purpose of validation is to assess the model of whether or not it meets the performance requirements needed to be deployed on target. The rest of the settings were kept as defaults. Click "Start Session" at the bottom to start the validation session.
InsufficientInstanceCapacity
If you see this error after starting your validation session, retry creating the session. This can happen when AWS reports that no EC2 instances are currently available to launch; the current workaround is to retry.
Go to the created validation session by first going back to the "Model Experiments" page.
Next, click the validation sessions of the model experiments.
The validation session progress will appear in the "Validation" page as shown below.
Once completed the session card will appear like the following below. To view the validation metrics, click on the validation charts button as indicated.
The validation metrics should appear like the following. For more information, please see the Validation Metrics section.
You can navigate back to the training session by clicking on the "Training Session" link under the "Session" tab.
