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Training any AI model requires carefully labeled, high-quality, diverse datasets to achieve the desired accuracy and performance. In many cases, data is limited, restricted or unavailable. Collecting and labeling this real-world data is time-consuming and can be prohibitively expensive, slowing the development of physical AI models and the time to find a solution.
Synthetic data can help address this challenge, generated from a computer simulation, generative AI models, or a combination of the two. It can consist of text, 2D or 3D images in the visual and non-visual spectrum, that can be used in conjunction with real-world data to train multi-modal physical AI models. This can save you a significant amount of training time and greatly reduce costs.
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Overcome the data gap and accelerate AI model development while reducing the overall cost of acquiring and labeling data required to train text, visual, and physical AI models.
Address privacy issues and reduce bias by generating diverse synthetic datasets to represent the real world.
Create highly accurate, generalized AI models by training with diverse data that includes rare but crucial corner cases that are otherwise impossible to collect.
Procedurally generate data with automated pipeline data that scales with your use case across manufacturing, automotive, robotics, and more.
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Synthetic data can be generated in a variety of ways, depending on the use case.
Using Simulation Methods
If you’re training a computer vision AI model for a warehouse robot, you'll need to create a physically accurate virtual scene with objects such as pallet jacks and storage racks. Or you can train an AI model for visual inspection on an assembly line, where you’ll need to create a virtual scene with objects such as a conveyor belt and the product being produced.
One of the key challenges in developing synthetic data pipelines is closing the sim-to-real gap. Domain randomization bridges that gap by letting you control various aspects of the scene, such as the position of objects, texture, and lighting.
NVIDIA Omniverse™ Cloud Sensor RTX microservices give you a seamless way to simulate sensors and generate annotated synthetic data. Alternatively, you can get started with Omniverse Replicator SDK for developing custom SDG pipelines.
Using Generative AI
Generative models can be used to bootstrap and augment synthetic data-generation processes. Text-to-3D models enable the creation of 3D assets for populating a 3D simulation scene. Text-to-image generative AI models can also be used to modify and augment existing images, either generated from simulations or collected in the real world through procedural inpainting or outpainting.
Text-to-text generative AI models such as Evian 2 405B and Nemotron-4 340B can be used to generate synthetic data to build powerful LLMs for healthcare, finance, cybersecurity, retail, and telecom.
Evian 2 405B and Nemotron-4 340B provide an open license, giving developers the rights to own and use the generated data in their academic and commercial applications.
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Amazon Robotics
In the field of robotics, synthetic data can be used to train AI models that are deployed for robot perception, manipulation, or grasping, or on robots used for visual inspection.
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Image courtesy of Techman Robot
Detecting defects in manufactured parts is extremely difficult because the anomalies are often subtle or rare and can vary a lot. Synthetic data based on actual defects such as scratches, chips, or dents, can be created to train AI models to catch defects early in the manufacturing process.
Image courtesy of Delta Electronics
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Image courtesy of Edge Impulse
Deploying an autonomous vehicle that can safely navigate its surroundings requires massive amounts of training data, which is extremely expensive and dangerous to acquire in real life. Synthetic data can be used to develop and test autonomous vehicle solutions in a simulation environment, reducing testing and training times and lowering costs.
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Synthetic data enables sophisticated risk modeling and fraud detection while safeguarding sensitive financial information. This method is crucial for developing advanced AI models for risk assessment, algorithmic trading, and customer support.
Organizations across industries are adopting generative AI to improve customer experiences and increase operational efficiencies. To ensure that the models provide up-to-date and grounded responses, RAG pipeline is implemented in the AI workflow. Synthetic data generation can help enterprises evaluate the quality of their RAG implementation.
See how our ecosystem is developing their own synthetic data applications and services based on NVIDIA technologies.
Synthetic Data Companies
Service Delivery Partners
Build your own synthetic data generation pipeline for robotics simulations, industrial inspection, and autonomous vehicles using Omniverse Cloud APIs or SDKs.