Testing a Model Using the Data Studio
![../_images/dcl-test-model-mode.png](../_images/dcl-test-model-mode.png)
The Data Studio has two ways to test a model on your dataset:
Running a Model During Data Collection: Connect to a model during data collection and get the model results in real-time
Running a Model in the Project Explorer: Run a model on any previously collected CSV or WAV files in your project
This lets you see how your model will perform on real data before flashing it to a device. After getting the results from the model you can then save the results to your project and re-train your model using the new results. This is a powerful feature that we will go over in detail below.
Running a Model During Data Collection
The Data Studio has an option to connect to your Knowledge Pack during data collection and see the model results in real-time. You can then save the results to your project and use them for re-training your model. Note - This feature is only supported on devices that implement the Simple Streaming capture protocol
Click on the Test Model button in the left navigation bar
![../_images/dcl-navigation-bar-left-test-model-button.png](../_images/dcl-navigation-bar-left-test-model-button.png)
Connect to your device
![../_images/dcl-sensor-connect1.png](../_images/dcl-sensor-connect1.png)
If you are not connected to a Knowledge Pack, click Connect in the Test Model tab
![../_images/dcl-test-model-knowledge-pack-connect.png](../_images/dcl-test-model-knowledge-pack-connect.png)
Select a Knowledge Pack
![../_images/dcl-knowledge-pack-select-screen.png](../_images/dcl-knowledge-pack-select-screen.png)
Connect to the Knowledge Pack
![../_images/dcl-test-model-knowledge-pack-connected.png](../_images/dcl-test-model-knowledge-pack-connected.png)
You will now see your model results in real-time in the graph
![../_images/dcl-test-model-mode.png](../_images/dcl-test-model-mode.png)
(Optional) You can click Start Recording and the Data Studio will save the Knowledge Pack results to your project. This lets you quickly add additional training data to your project
![../_images/dcl-test-model-start-recording.png](../_images/dcl-test-model-start-recording.png)
(Optional) In the Save Confirmation screen you can edit or delete the Knowledge Pack results before saving the results to your project
![../_images/dcl-test-model-results-save-confirmation.png](../_images/dcl-test-model-results-save-confirmation.png)
Running a Model in the Project Explorer
The Project Explorer has an option to connect to a Knowledge Pack and run a model on any previously collected files you have added to your project. You can then save the results to your project and use them for re-training your model.
Open the Project Explorer
![../_images/dcl-open-project-explorer.png](../_images/dcl-open-project-explorer.png)
Select the files you want to use by holding (Shift + Click) or (Ctrl + Click)
Right-Click and select Segments → Run Model
![../_images/dcl-project-explorer-run-model.png](../_images/dcl-project-explorer-run-model.png)
Select a Knowledge Pack
![../_images/dcl-knowledge-pack-select-screen.png](../_images/dcl-knowledge-pack-select-screen.png)
Select a Session. This is where the Knowledge Pack results will be saved
![../_images/dcl-session-management.png](../_images/dcl-session-management.png)
Save the results. (Optional: You can edit or delete the Knowledge Pack results before saving)
![../_images/dcl-project-explorer-review-results.png](../_images/dcl-project-explorer-review-results.png)
Automated Labeling Using a Model
By using the model results from either of the two methods above you can re-train your model with new data. By repeating this process you can quickly improve/augment your model training data with more robust and accurate results.