Announced at GTC, technical artists, software developers, and ML engineers can now build custom, physically accurate, synthetic data generation pipelines in the…
Announced at GTC, technical artists, software developers, and ML engineers can now build custom, physically accurate, synthetic data generation pipelines in the cloud with NVIDIA Omniverse Replicator.
Omniverse Replicator is a highly extensible framework built on the NVIDIA Omniverse platform that enables physically accurate 3D synthetic data generation to accelerate the training and accuracy of perception networks.
Omniverse Replicator is now deployable in the cloud through containers hosted on NVIDIA NGC and SaaS available for early access by application. The Replicator suite of tools and content also now features a new Replicator Insight app for enhanced viewing and inspecting of generated data, plus new SimReady content and guides for plug-and-play synthetic data workflows.
Numerous partners are integrating Omniverse Replicator in their existing tools to extend their synthetic data workflows. Siemens with their SynthAI software, SmartCow, Mirage, and Lightning AI are among the first to use Omniverse Replicator to accelerate high-quality synthetic data generation.
For developers and enterprises who want the flexibility and scalability of cloud deployment, Omniverse Replicator is now available as container deployments on AWS. You can become a member of NGC to access containers and self-service deploy on Amazon EC2 G5 instances featuring A10G Tensor Core GPUs.
Generating synthetic data and improving AI models is an iterative process requiring the ability to view and analyze generated datasets along the way. This process can be quite cumbersome as data is not easily navigable and annotations are not easily inspected.
At GTC, we released Omniverse Replicator Insight early access, an app that enables you to view, inspect, and analyze generated datasets with a range of annotations efficiently and intuitively. Replicator Insight lets you browse generated datasets from different sensors on a frame-by-frame basis, select points of interest to view, and inspect specific annotations of specific objects.
Viewing, inspection, and analysis of generated datasets is efficient and intuitive on Replicator Insight with a range of annotations. It enables you to browse through the generated datasets from different sensors on a frame-by-frame basis. You can select objects of interest to view and inspect annotations for specific objects.
Replicator Insight lets developers and researchers take a leap towards data-centric AI training, integrating synthetic data more seamlessly into the model improvement process.
Omniverse Replicator SimReady Universal Scene Description (USD) assets help you get started generating synthetic data to narrow the gap between simulation and reality:
- High-fidelity 3D assets that jump-start synthetic data generation at the pixel level.
- Contextual content assets that help train data for diversity, context, and behaviors in a scene.
Conveyor belts, ramps, and cardboard boxes are just a few examples of SimReady assets available in the Omniverse Replicator library.
You can access the first collection of free SimReady assets by downloading Omniverse today. For more information about new assets and other resources, see New Cloud Applications, SimReady Assets, and Tools for Omniverse Developers Announced at GTC.
Several partners are using Omniverse Replicator to accelerate the training and performance of AI perception networks. Their applications span every phase of end-to-end synthetic data generation workflows. With Omniverse Replicator as a foundational platform for their applications, these partners are helping customers strengthen datasets and improve the accuracy of AI models for a variety of industry use cases.
Mirage is helping ML engineers understand where their dataset is weak and integrate synthetic data that fixes these weaknesses. Replicator is the backbone from which Mirage’s customers generate high-fidelity data to improve their ML models.
Lightning AI lets you build models and use or create Lightning Apps: powerful, end-to-end machine learning systems that are fully customizable. The Omniverse Replicator Lightning App lets you quickly generate synthetic data to reduce the cost and effort associated with gathering and labeling real-world data.
With Lightning AI, researchers and developers can run parallel AutoML jobs, find the best-performing object detection model, and verify performance on real-world data for synthetic data generation.
SmartCow leverages Omniverse Replicator to generate synthetic data with variations simply and effectively. Adding those variations through Replicator enables SmartCow to continuously improve model accuracy with ease. SmartCow uses Replicator in its iterative process to generate additional variations from data drift detections and create improved models.
Siemens is collaborating with NVIDIA to bring the Omniverse Replicator high-fidelity rendering capabilities and SDK to SynthAI’s cloud. This will ensure a simple, streamlined workflow from product design and collaboration to synthetic data generation and model training and ending with successful deployment.
For more information about how Siemens’ SynthAI, SmartCow, and Mirage are building on Replicator, add the How to Build a Custom Synthetic Data Pipeline to Train AI Perception Models GTC session to your calendar.
To get hands-on training with Replicator, join the Generate Synthetic Data Using Omniverse Replicator for Perception Models DLI training lab at GTC with NVIDIA Omniverse Replicator product manager, Nyla Worker.
For more information and the latest news, see the following resources:
- In the Omniverse Resource Center, you can learn how to build custom USD-based applications and extensions for the platform.
- Follow Omniverse on Instagram, Twitter, YouTube, and Medium for additional resources and inspiration.
- Check out the Omniverse forums and join our Discord Server and Twitch to chat with the community.
- Visit the NVIDIA-Omniverse GitHub repo to explore code samples and extensions built by the community.