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On this page
  • Getting started with NVIDIA Omniverse
  • Preliminary steps
  • Installing the Edge Impulse Omniverse extension
  • Generating a synthetic dataset
  • Next steps: building a machine learning model

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  1. Integrations

NVIDIA Omniverse

PreviousEmbedded IDEs - Open-CMSISNextScailable

Last updated 2 months ago

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is a scalable, multi-GPU real-time reference development platform for building and operating metaverse applications and based on Pixar's Universal Scene Description and NVIDIA RTXā„¢ technology.

NVIDIA Omniverse stack

This tutorial describes how you can use the Edge Impulse extension within NVIDIA Omniverse to upload your synthetic datasets to your Edge Impulse project for computer vision tasks, validate your trained model locally, and view inferencing results directly in your Omniverse synthetic environment.

Getting started with NVIDIA Omniverse

Preliminary steps

Installing the Edge Impulse Omniverse extension

Generating a synthetic dataset

Note: Adding bounding boxes

To preview your bounding boxes, click on the icon to the right of the camera perspective button, select either tight or loose 2D bounding boxes and then "Show Window".

You should see a popup like this:

Once you have confirmed your bounding boxes, you can check "bounding_box_2d_loose" and/or "bounding_box_2d_tight" under the "Parameters" tab in the Synthetic Data Recorder and the bounding boxes for your recorded data will appear in the output directory:

Then, using the Edge Impulse extension you installed in the previous step, upload your dataset and download your trained model by following the steps below:

  1. Create a free Edge Impulse account: https://studio.edgeimpulse.com/

  2. Connect to your Edge Impulse project by setting your API key (this key is obtained from your Edge Impulse project Dashboard > Keys > API Keys), then click Connect

  3. Once your project is connected to your Edge Impulse Omniverse extension, select either the Data Upload or Classification drop-downs:

For synthetic data collection, select Data Upload, then specify your dataset's local path on your computer, select the dataset category to upload to (training, testing, or anomaly), then click Upload to Edge Impulse:

To add bounding box data, click the checkbox next to "Add Bounding Boxes" and then specify and RGB path with your RGB images and Bounding Box path with the raw bounding box data from Replicator, then click Upload to Edge Impulse:

For classification tasks and to see the results of inferencing from your trained model in Omniverse, select Classification, then click Classify current scene frame to start inferencing locally on the edge within your Omniverse environment:

Next steps: building a machine learning model

With everything set up you can now build your machine learning model with these tutorials:

Check out for information on getting started as a first-time user with the Omniverse platform.

Now continue with .

Once you have installed NVIDIA Omniverse, you can now install the Edge Impulse extension into your Omniverse environment by .

by Edge Impulse expert George Igwegbe to create a synthetic dataset using NVIDIA Omniverse Replicator.

In order to collect bounding box data from your scene, semantic information for the objects of interest must be specified. A comprehensive guide on how to do this can be found .

Select Window
Preview Bounding Boxes
Data Recorder
NVIDIA Omniverse Edge Impulse extension
Data Upload
Data Upload with Bounding Boxes
Classification

Looking to connect different sensors? The lets you easily send data from any sensor into Edge Impulse.

NVIDIA's documentation
Create an NVIDIA Omniverse account
Create an Edge Impulse account
NVIDIA's Omniverse installation guide
following the README in the extension's GitHub repository
Follow this tutorial
here
Image classification
Detect objects with bounding boxes
Detect objects with centroids (FOMO)
Data forwarder
NVIDIA Omniverseā„¢