What is Synthesis AI?
Synthesis AI offers revolutionary synthetic data solutions designed specifically for computer vision and perception applications. With the demand for high-quality labeled data surging, traditional data collection methods are no longer sufficient. Synthesis AI provides a streamlined way to generate privacy-compliant, unbiased datasets, allowing businesses to train their models effectively while ensuring compliance with data protection regulations.
What are the features of Synthesis AI?
- Privacy-Compliant Human Data: Synthesis AI generates synthetic human data that adheres to privacy regulations, eliminating risks associated with sensitive personal information.
- Unbiased Datasets: The platform ensures the creation of diverse datasets, addressing the biases often found in real-world data, thereby promoting fair outcomes in machine learning applications.
- Faster to Production: Accelerate development and deployment cycles with synthetic data, allowing companies to get their products to market quickly without the delays associated with traditional data sourcing.
- Perfectly Labeled 3D Data: Users receive comprehensive multi-modal data, enabling the creation of pixel-perfect annotations for depth, surface normals, and more.
- Unlimited Data Generation: Synthesis AI allows for unlimited synthetic data to build sophisticated spatial applications, essential for AR/VR/XR environments.
- Simulation for Edge Cases: Capture rare events and edge cases through simulation, ensuring robust performance and safety in applications like autonomous driving and security systems.
What are the characteristics of Synthesis AI?
- Versatile Applications: The platform caters to various sectors including automotive, biometrics, security, and consumer devices.
- High-quality 3D Models: Synthesis AI offers rich datasets making it easier for developers to model different scenarios, enhancing the learning experience of AI systems.
- Comprehensive Scenario Simulation: The ability to simulate complex environments helps in understanding and improving system responsiveness to real-world scenarios.
What are the use cases of Synthesis AI?
- Biometrics & Security: Improve ID verification processes by using millions of synthetically generated images. This helps in training facial recognition systems without compromising individual privacy.
- Driver Monitoring: Simulate varied driver and occupant behaviors captured through multi-modal cameras enhancing the development of driver monitoring systems.
- Activity Recognition: Create multi-person scenarios across diverse environments for effective activity recognition and threat detection in security applications.
- Consumer Devices & Applications: Utilize AR/VR/XR capabilities for developing human-centric models, essential for headsets and other consumer electronics.
- Virtual Try-On: Millions of synthetic identities and clothing options allow fashion brands to create robust models for virtual try-on experiences, providing customers an interactive shopping platform.
- Pedestrian Detection: By simulating multi-person scenarios in complex outdoor environments, companies can enhance their pedestrian detection models essential for autonomous vehicles.
How to use Synthesis AI?
- Identify Your Needs: Understand your specific application and data requirements, whether it is for biometrics, automotive applications, or consumer devices.
- Select the Appropriate Data Set: Choose from available synthetic data sets tailored to your project needs.
- Implement in Your Development Environment: Integrate the synthetic datasets into your machine learning model training pipeline.
- Test and Iterate: Use the synthetic data to train your models, testing the robustness through various simulated scenarios.
