Tourist Workflow
In this workflow, you will explore copying the Coffee Cup dataset from the Sample Project and using the dataset to train a vision model that detects coffee cups. Once the model is trained, you will see how to validate the model and then finally deploy the model on your PC.
Copy Dataset
To copy a dataset, navigate to the dataset you would like to copy. On the dataset card, select the "Copy Dataset" from the dataset options as shown below.
This will open a new dialog for the user to specify the "Destination". The "Destination" will be the location of the copied dataset. The "Source" will be set by default to the current dataset card you've selected. However, you can also modify the location here. In the example below, the original dataset is the "Source" which is the "Coffee Cup" dataset from the "Sample Project". The copied dataset will be placed as specified in the "Destination" fields. By default a new dataset container will be created in the specified project. However, you can create a dataset container before copying and specify this dataset container in the "Destination" fields.
Once the options are specified, go ahead and click "Apply" at the bottom right to start the copy process. The progress for the dataset copy will be shown on the new dataset card that was created in the project destination that was specified.
Once the copying process completes, the frames and the annotations have been copied.
| Original Dataset | Copied Dataset |
|---|---|
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Train a Vision Model
Now that you have a fully annotated dataset that is split into training and validation samples, you can start training a Vision model. This will briefly show the steps for training a model, but for an in depth tutorial, please see Training Vision Models.
Navigate back to the "Projects" page. You can go back to the "Projects" page by clicking the Apps Menu waffle button on the top right of the Navigation bar. Click the first selection to take you to the "Projects page".
From the "Projects" page, click on "Model Experiments" of your project.
Create a new experiment by clicking "New Experiment" on the top right corner. Enter the name the description of this experiment. Click "Create New Experiment".
Navigate to the "Training Sessions".
Create a new training session by clicking on the "New Session" button on the top right corner.
Follow the settings indicated and keep the rest of the settings by their default. Click "Start Session" to start the training session.
Session Name
Do not include any forward slash "/" in the session names as this can result in missing model artifacts.
No Datasets Available
In case there are no datasets visible on the dropdown (3). Please refresh your browser.
The session progress will be shown like the following below.
Once completed the session card will appear like the following below.
On the train session card, expand the session details.
The trained models will be listed under "Artifacts".
| Session Details | Artifacts |
|---|---|
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Validate Vision Model
Now that you have trained a Vision model, you can now start validating your Vision model. This will briefly show the steps for validating a model, but for an in depth tutorial, please see Validating Vision Models.
On the train session card, expand the session details.
Click the "Validate" button.
Specify the name of the validation session and the model and the dataset for validation. The rest of the settings were kept as defaults. Click "Start Session" at the bottom to start the validation session.
No Datasets Available
In case there are no datasets visible on the dropdown. Please refresh your browser.
The validation session progress will appear in the "Validation" page as shown below.
Once completed the session card will appear like the following below.
The validation metrics are displayed as charts which can be found by clicking the validation charts.
Deploy the Model
Once you have validated your trained model, take a look at examples of deploying this model across different platforms. You can find a checklist of supported devices. We support validation on specific targets and applications for live video inference. Certain platforms are still under development.
| Platform | On Target Validation | Live Video | In Development |
|---|---|---|---|
| PC / Linux | ✓ | ||
| Mac/MacOS | ✓ | ||
| i.MX 8M Plus EVK | ✓ | ✓ | |
| NVIDIA Orin | ✓ | ||
| Kinara ARA-2 | ✓ | ||
| Raivin Radar Fusion | ✓ | ✓ | ✓ |
| i.MX 95 EVK | ✓ | ✓ |
If you wish to run validation on device, please follow instructions below.
Additional Platforms
Support for additional platforms beyond these listed will be available soon. Let us know which platform you'd like to see supported next!
Nest Steps
Explore more features by following the Tourist Plus Workflow.