4Data supplies unlimited, photorealistic synthetic datasets generated by mathematical models for training AI systems. The data is fully annotated, diverse, and scalable, offering a cost‑effective alternative to manual collection or AI‑based generation. Customers subscribe to access the synthetic data platform, paying for usage or volume to accelerate model development and improve performance.
Funding
Funding not disclosed
Founders
Product
Problem
Training AI models requires large, diverse, and accurately annotated datasets, but the manual collection and annotation of real-world data is often time-consuming, expensive, error-prone, and difficult to scale. This bottleneck hinders the development and deployment of robust AI applications.
Solution
4Data provides a synthetic data generation platform that leverages mathematical techniques to create unlimited, diverse, and complex datasets with precise annotations. This technology eliminates the need for extensive real-world data collection, enabling faster and more efficient AI model development. The platform allows users to generate tailor-made data for novel AI applications, including corner cases to improve model robustness. 4Data's synthetic data can be used to train a wide variety of AI models, including object detectors, segmentation models, pose estimators, optical flow estimators, and stereo vision models. The generated data includes a variety of annotations such as 2D/3D bounding boxes, segmentation masks, depth maps, surface normals, and optical flow vectors.
Target Audience
4Data's primary customers are AI developers and researchers who need large, high-quality datasets for training computer vision models, as well as organizations seeking AI consultancy services for designing and deploying AI solutions.
Features
- Parametric object design using mathematical equations to control shape and texture.
- Random scene generation combining designed objects with other random elements to create diverse and challenging scenes.
- Automated annotation generation, providing accurate labels for training AI models.
- Custom training pipeline to maximize AI performance with synthetic data.
- Model conversion to common formats for deployment.
- Data generation for various computer vision tasks, including object detection, segmentation, and pose estimation.