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Unity Computer Vision
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Build high-quality synthetic data faster with our expanded suite of open-source tools.
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Free tools and content for generating synthetic data

Check out our newly open sourced and academically released tools, datasets, and dataset generators for the creation of synthetic data for computer vision model training.

Unity Perspective 1.0 featuring grocery products
Unity Perception 1.0

The Perception package provides a toolkit for generating large-scale datasets for computer vision training and validation. Unity Perception 1.0 is a new, more complete release that includes new labels, randomizers, samples, and rendering capabilities.

Data analysis and visualization in Unity engine
Data analysis and visualization in Python

PySOLO tools is a new open-source python package that provides utilities to analyze and visualize data in the new SOLO format.

SynthHomes demo
Synthetic Homes

Now available as an open-source release, Unity SynthHomes is a 100,000-image dataset of synthetic home interiors and an associated dataset generator binary.

Synthetic Humans
Synthetic Humans

Now available for academic use only, Unity Synthetic Humans is a 3D person generator built from the ground up for human-centric computer vision.

“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 / BOEING VANCOUVERSenior Manager
“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 / OUVACEO
Case studies
Ouva
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 Case Study
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
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
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
AI recognizing dogs
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.

computer vision for the home
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.

3D content for synthetic data
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
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
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
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
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
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
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?
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
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.

Standard Cognition, Unity
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.

Accelerate computer vision training
Accelerate computer vision training

Looking for help in synthetic data generation and computer vision? Get expert consulting and professional services from our team of experts.

Frequently asked questions

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

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

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When can I use synthetic training data?

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