Infinite possibilities
Infinite use cases
World-class ML teams use synthetic data across many application areas.
Synthetic data to train various exercise models from pose estimation to rep counting, form correction to activity classification. Datasets include poor lighting, challenging occlusions, low-contrast clothing, and uncommon camera angles.
Synthetic data to train AR and VR models ranging from hand pose estimation to people segmentation, gesture recognition to natural scene understanding. Datasets include messy backgrounds, grasping objects, complex nails, and diverse wrist-worn accessories from an egocentric or third-person point of view.
Synthetic data to train models related to warehouse safety including PPE detection (hard hats, safety vests, etc), ergonomics feedback, and aisle congestion monitoring. Import real-world objects (e.g. specific PPE or assembly line products) into your simulated dataset.
Synthetic data to train accurate pose estimation and object detection models for automated stores (like AmazonGo). Import specific store layouts, procedurally add inventory to shelves, include diverse shoppers, and generate multi-camera views of a scene.
Synthetic data to train models for residential or enterprise security applications, including package stealing, gun detection, shoplifting, and pet/animal identification.
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Our datasets
Our open source datasets are available for both academic and commercial use.
We are leading the charge for synthetic data in computer vision
Published at the NeurIPS
Data-Centric AI Workshop
Introducing InfiniteForm, our synthetic, minimal bias dataset for fitness applications.
Read paper
