SYNTHETIC DATA FOR REAL DATA SCIENCE

The Platform for Synthetic Data Generation for Training and Validating Vision-based ML & AI Systems

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Rendered.ai synthetic data images showing annotation

Our Partners

Key Features of the Rendered.ai Platform

Customized Synthetic Data Generation

Generate data that addresses gaps, corrects bias, and enhances AI/ML system accuracy.

Characterizing and Cataloging Datasets

Access datasets quickly and compare real and synthetic data

Collaborative Environment

Share 3D assets, sensor models, and datasets to collaborate effectively with your team.

Privacy and Security

Use and share your data without worrying about privacy or security breaches

Cloud Resources

Rapidly define data channels and create datasets in high-performance environments.

Cost-effective Solution

Create data as needed, all within a set subscription price

Configure and Simulate Scenarios

Rendered.ai provides easy-to-use tools for creating and configuring dataset generation to simulate the wide diversity of sensors, scenarios, and data capture required for training models. Dataset generation can be reconfigured and run multiple times to explore the impact of designed variation on algorithm performance.

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Physically Accurate Rendering

Any game engine can be used to generate images, but for algorithm training, datasets need to emulate the physics-derived properties of real sensor imagery and video. Our platform is agnostic of simulation technology so that you can use your own simulator, a renderer we’ve create, or even bring in best-of-breed industry simulators for complex imagery such as x-ray, hyperspectral, and multispectral content.

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AI & ML Pipeline Integration

The platform offers an open source framework for packaging simulations and a well-documented SDK for integrating and controlling synthetic data generation from AI pipelines.

Rendered.ai also offers Jupyter Notebook samples for learning our SDKs and we can provide custom training on demand.

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Supporting Computer Vision Innovation

Synthetic data supports users who can’t acquire real data because of cost, security, or because the actual sensor doesn’t yet exist! Once a synthetic data channel is created, it can be updated and used repeatedly to create data for training, validation, and testing.

Simulated data can even be used to create good ‘bad’ data for proving that algorithms should fail specific edge cases or in implausible scenarios. The platform even contains tools for comparing datasets to explore changes to configuration might alter dataset performance.

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100% Accurately Labeled Data in the Cloud on Demand

Data labeling is expensive and may even be impossible either because of the volume of data or because the sensor type is difficult for humans to interpret. Rendered.ai synthetic data channels can be designed to label everything and anything that is important for the data scientist. The platform also provides tools to generate the specific format of labels needed for a customer’s training toolset.

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Empower Your Projects with Synthetic Data

Enhance your AI projects with the power of synthetic data. Boost performance, improve accuracy, and unlock new possibilities.

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