Unity Computer Vision

Diverse, affordable and unbiased synthetic data, perfectly labeled to train smarter computer vision models.

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Benefits of using synthetic training data

Perfectly labeled

Synthetic images come pre-labeled and annotated, reducing the potential for human error.

Unlimited data diversity

Generate training data capturing edge-case scenarios, what-if situations, environmental variations and more.

Reduced risk

Restrictions around real-world data collection do not apply to machine-generated synthetic images.

Up to 80% cost savings

Generate massive datasets without breaking your budget, at a fraction of the cost of real-world data collection.

Up to 30x faster model development

Shorten training iteration cycles and accelerate deployment of computer vision models.

Up to 30% more accurate detections

Training with purely synthetic images or augmenting with a small sample of real images greatly improves your model performance.

Customizable annotations

Customize the method of labeling that your application requires, from simple bounding boxes to complex semantic annotations impossible to obtain through manual labeling.

Randomizable parameters

Vary every aspect of your scene including lighting, background objects, camera specifications, occlusions, and more to build a robust training dataset that is performant under real conditions.

Case studies

Boeing: Synthetic data at scale

In this Q&A interview, learn how Boeing worked with Unity to generate over 100,000 synthetic images to better train the machine learning algorithms of its augmented reality (AR)-powered aircraft inspection application.

Audere: State-of-the-art medical testing

Gain insight into how Audere, a digital health nonprofit, is using Unity for synthetic data generation to confront COVID-19 testing challenges.

Collage of Audere testing devices

Neural Pocket: Boosting computer vision model performance

Learn how AI startup Neural Pocket used Unity Computer Vision to significantly reduce computer vision model development costs and time to deployment (from 24 weeks to 1 week).

Smarter, safer manufacturing with synthetic data

Learn how SecureAmerica Institute and Amentum are bringing together digital twins, simulation, synthetic data and machine learning in a sensor fusion project to enhance manufacturing.

Romain Angénieux, Head of Simulation, Neural Pocket

“Unity’s computer vision tools enable us to work more quickly and cost-effectively. As a result, we can train and deploy our computer vision models at a fraction of the typical time and cost.”

Romain Angénieux, Head of Simulation, Neural Pocket

Generate large-scale synthetic datasets to accelerate computer vision training

Frequently asked questions

We don’t have Unity developers. Can we use Unity Computer Vision?

We have Computer Vision and Unity experts who can generate synthetic datasets for your projects. Please contact us for pricing.

How much does it cost?

We offer tiered pricing; the more synthetic images you need, the lower price you pay per image. Please contact us for pricing.

When should I use synthetic training data?

You can benefit from synthetic data when:  

  • You have only a small sample set of real-world data. In this case you can use Unity Computer Vision to generate a large amount of synthetic data to augment your real-world data and boost your model performance.
  • You are not able to collect the right real-world data for your project. In this case you can use Unity Computer Vision to generate high-quality labeled synthetic images and bootstrap your models with purely synthetic data.
I’m a Unity developer. How can I get started?

If you have Unity expertise, you can build your own datasets with our tools for free.

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