PRODUCTS

Unity Computer Vision

Build accurate, production-ready models faster.

Jack Hsu, Senior Manager, Boeing Vancouver

“Wherever there is a requirement for data to drive machine learning, there is a role for synthetic data. Creating synthetic datasets in a virtual world means you can create millions of images very quickly compared to going out to the field and taking pictures.”

Jack Hsu, Senior Manager, Boeing Vancouver
Dogan Demir, CEO, Ouva

“At Ouva, our patient monitoring platform used Unity Computer Vision to generate synthetic data and reduced our month-long live data capture cycles to a week, while our dataset grew by 10X and model accuracies improved by 5 to 10%.”

Dogan Demir, CEO, Ouva

Case studies

Ouva

Ouva’s simulated healthcare data platform harnesses the power of synthetic data to improve model performance by over 10%, reduce labeling costs by up to $40,000, create balanced datasets in hours instead of weeks, and reduce iteration cycles from weeks to days.

Boeing

In this 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.

Passio

Gain insight into how Passio combines Unity’s synthetic data with real-world data to expand its datasets and speed up AI training for AI and augmented reality (AR) applications.

Neural Pocket

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).

Benefits of using synthetic training data

Prelabeled

Synthetic images come prelabeled and annotated, reducing the potential for human error.

Reduced risk

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

Unlimited data diversity

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

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.

Accelerate computer vision training

Overcome data challenges for training computer vision models by generating prelabeled, diverse training data on-demand.

Frequently asked questions

Synthetic data doesn’t look like real data. Does it really work?

Check out our papers to see how models trained with synthetic and real data outperform models trained using only real data:

What types of computer vision applications can be trained with synthetic data?

Our customers use Unity to generate synthetic data for a variety of computer vision applications, including human detection, object detection, manufacturing defect detection, consumer electronics applications in the home, and more.

When can I use synthetic training data?

You can use synthetic training data when:  

  1. You have only a small sample set of real-world data. In this case you can augment your real-world data with a large amount of synthetic data generated by Unity Computer Vision and boost your model performance.
     
  2. 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.

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