Tourist Plus Workflow
In this workflow, you will explore copying the Coffee Cup dataset from the Sample Project and using the dataset to explore the annotation process. Once the dataset has been annotated, you can use the dataset to train and 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 |
|---|---|
 |
 |
Annotate Dataset
Now that you have a dataset in your project, you can start annotating the dataset. This will briefly show the steps for annotating the dataset, but for an in depth tutorial on the annotation process, please see Dataset Annotations.
To annotate a dataset, first create an annotation set on the dataset card.
A new annotation set was created called "new-annotations".
Next, open the dataset gallery, by clicking on the gallery button 
on the top left of the dataset card. The dataset will contain sequences (video) 
and images. Click on any sequence card to start annotating sequences.
On the top navbar, switch to the right annotation set.
Start the AGTG server by clicking on the "AI Segment Tool" and follow the prompts as indicated.
Once the AGTG server has started, go ahead and annotate the starting frame.
Once the starting frame has been annotate, go ahead and propagate the annotations throughout the rest of the frames.
Once the propagation completes, click "Save Annotations" to save the propagated annotations.
Repeat the steps for all the sequences in the dataset. For the case of individual images, the same steps apply except there is no propagation step. You can still use the AGTG feature to quickly annotate images as shown in Add 2D annotations.
Audit Dataset
After the annotation process, review each individual frame or image in the dataset that was auto-annotated. This step is also known as the audit process which is crucial in verifying that the dataset has been properly annotated and ready for training.
To view the dataset and the annotations, click on the dataset gallery.
The auditing step may require adding new annotations for objects that were missed during the AGTG process. Or it may require removing annotations for objects that were improperly annotated. Lastly, for annotations that require minor adjustments, EdgeFirst Studio has the features for adjusting annotations. Please click on the links as provided for further instructions on each of these features.
Once you have audited the dataset and verified that it's properly annotated, split the dataset into training and validation groups.
Split Dataset
Partitioning the dataset is crucial in reserving dataset portions used for training and portions used for validation to assess the performance of the model. In EdgeFirst Studio, the partitions are 80% towards training and 20% towards validation. This operation randomly shuffles the data prior to assigning them to the specified groups.
Warning
The dataset needs to be re-split whenever new sample images or frames are added to the dataset. Newly added samples are not automatically added to any group that already exists.
Consider the following dataset without any groups reserved.
To create the dataset groups, click on the "+" button in the "Groups" field.
This will open a new dialog to specify the percentages of the partition belonging to the "Training" group or "Validation" group. By default 80% of the samples will be dedicated to training and 20% remaining will be dedicated towards the validation samples.
Once the groups are specified, click "Split" to create the groups. This will automatically divide the samples in the dataset based on the percentages of each group specified.
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 Web Workflow.