Supercharge your training
Bring synthetic datasets into your computer vision training pipeline for rapid iteration and faster deployment.
Generated by Unity
Unity’s computer vision experts will build a dataset for you.
What you get:
- Upfront consulting for tailored dataset generation
- Tiered pricing that makes large datasets affordable
- Iterations with our engineering team to ensure dataset fit
Generated by you
Use your Unity skills to build your own dataset.
What you get:
- Free access to the Unity Perception SDK and our tutorials
- An extensible library of labeling and randomization tools
- The option to purchase services to help with asset creation
Applications of synthetic data
Bridge the training gap in human-centric AI models
Unity’s human-centric data generator generates highly parametric, simulation-ready 3D human assets. Build unbiased AI models for augmented and virtual reality applications, autonomous driving, human pose estimation, action recognition, tracking and more that involve people – without using real-world human data.
Fast-track smart home application development
Whether you are training a security application or an intelligent home gadget or robot, synthetic data can fast-track your development, training and deployment process. Unity Computer Vision offers an easy-to-use dataset generator that comes preloaded with home dataset templates.
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
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).
Resources
Unlock data-driven AI development
Learn more about Unity Computer Vision, how to explore our sample datasets and generate your own sample datasets with our prebuilt environments.
Unlocking intelligent solutions in the home
Find out how our tools and services enable development of more capable computer vision applications for the home while mitigating roadblocks and challenges.
Getting started with 3D content for synthetic data
Synthetic data is powered by your library of 3D assets. Learn about sources and techniques for acquiring 3D content for common computer vision problems.
The factory of the future
Download our report to learn the vital role of computer vision, robotics simulation and real-time 3D technology to the future of manufacturing.
AI and machine learning, explained
Get up to speed on key terms in machine learning, computer vision, synthetic data and more.
Teaching robots to see with Unity
Empower your robots to accurately pick up an object without explicit knowledge of the object’s location. See how to collect synthetic data and train a deep learning model to predict the pose of a given object.
Train object detection model with synthetic data
Discover how you can generate a massive synthetic dataset to train your machine learning models.
Generate and analyze synthetic data at scale
Learn how to use tools from Unity to generate and analyze synthetic datasets with an illustrative example of object detection.
Myriad use cases enabled by synthetic data
Synthetic data is helping many organizations overcome the challenge of acquiring labeled data for training machine learning models. Discover the breadth of use cases it enables.
Can you find Waldo using synthetic data?
See how Unity’s Perception package was deployed to create Waldo-like images for training a neural net, which was then trained using the fastai library.
Create synthetic images for deep learning
Follow this tutorial to learn how to set up Unity and the Unity Perception package to create synthetic images that train neural nets in deep learning, AI and computer vision.
Synthetic aided computer vision algorithm development
See how Standard Cognition used Unity to reduce the financial costs and algorithm development time for data collection and labeling in their digital checkout system.
Explore our ecosystem
Check out Unity’s AI, machine learning and simulation products and learn how they can help you solve diverse problems. You can also become an early tester for future product releases – sign up here.
Robotics Simulation
Prototype, test and train your robots in high-fidelity, realistic simulations before deploying them to the real world.
Unity Simulation Pro
This simulation-optimized runtime build makes it simpler, faster and more cost-effective to test and train powerful simulations at scale, on premises or in a private cloud.
Unity Machine Learning Agents (ML-Agents)
Create intelligent, responsive agents with a toolkit leveraging deep learning technology.
Frequently asked questions
We have Computer Vision and Unity experts who can generate synthetic datasets for your projects. Please contact us for pricing.
We offer tiered pricing; the more synthetic images you need, the lower the price you pay per image. Please contact us for pricing.
If you have Unity expertise, you can build your own datasets with our tools for free.
You can use synthetic training data when:
- 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.
- 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.