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Overcome costs and challenges in acquiring and using real-world data
for training machine learning and artificial intelligence systems.
Synthetic images from Rendered.ai helped validate the potential to test human oocyte viability through deep learning
Generate tens of thousands of images in a managed HPC environment
Build scenarios with procedural world generation or bring your own
reality capture models
The Rendered.ai Platform uses industry-standard models and techniques to create an unlimited of variety of synthetic imagery for AI training optimization.
The Rendered.ai Platform enables collaborative workflows between data science team members.
User friendly graphical interface for collaborative configuration of synthetic sensor data generation
Use the same scene and content to create synthetic datasets for multiple sensor modes
Rendered.ai's cloud-native platform enables rapid innovation by teams needing to generate and exploit synthetic datasets.
"Working with Rendered, we were able to use synthetic data to significantly improve
computer vision algorithm performance for detecting economically important objects in satellite
imagery. AP scores improved across the board for identifying rare and unusual objects when
combining synthetic images with actual satellite imagery compared with using real images alone.
The objects in our study have been historically difficult to identify using automated algorithms
but represent high value information for monitoring and reporting."
"We saw that synthetic data is
a critical capability for computer vision algorithm development and for optimizing detection
performance to extract vital information from real sensor-based data."
James Crawford, Orbital Insight
Founder, Chairman, CTO
Orbital Insight Technical Report
Using Synthetic Imagery to Train Detection Models of Rare Objects in Satellite Imagery
What "Synthetic Data" is in the context of AI, and why it's important for machine learning
Why we take a physics-based approach, and more about tools designed for Machine Learning Realism
What makes synthetic data “good”? We discuss repeatable, human understandable, closed-loop active learning workflows
A single easy-to-use tool for Synthetic data. Build a deeper understanding of outcomes with testable hypotheses on your data
Geekwire, October 8, 2021
BELLEVUE, Wash., October 5, 2021
TechCrunch, October 5, 2021
BELLEVUE, Wash., February 11, 2021
BELLEVUE, Wash., October 6, 2020
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