A game-changer for computer vision training
Unity Computer Vision solutions help you overcome the barriers of real-world data generation by creating labeled synthetic data at scale. This data can be used to train computer vision models for object detection, image segmentation, and classification across retail, manufacturing, security, agriculture and healthcare.
- Auto-labeled: No human annotation required
- Private: Compliant with privacy standards
- Safe: Recreate edge-case scenarios
- Iterative: Generate variations in datasets with simple code changes
- Variable and scalable: Produce training data that captures real-world complexity
- Affordable and accessible: Small ML teams can generate massive datasets within budget
Object-model training pipeline with synthetic data
There are three distinct stages in the synthetic data training pipeline:
- Content creation
- Synthetic data generation and analytics
- Model training
There are several ways to create content. You can check out the Asset Store, scan your assets into Unity, or contact our team to help you with asset creation in Unity.
Synthetic data generation
Unity offers tools to generate synthetic datasets for use in perception-based computer vision tasks such as object detection, semantic segmentation, and more. You do not need prior experience with Unity or C# to get started.
You can either use your existing ML pipeline or implement using our suggested solution, Google AI Platform.