AI Agent Guide
Knowledge Guides /What acceptance checks are needed for datasets of synthetic simulation images used to train recognition models?
Last updated: · New Horizon Digital Outsourcing Team
Acceptance of synthetic datasets should cover annotation correctness, scenario coverage, and performance on real data. Visual realism is only one factor. If training and validation sets share nearly identical scenes, evaluation scores may be inflated and fail to reflect actual recognition capability.
Plan the scenario distribution first
- List target classes, sizes, viewing angles, distances, occlusions, and lighting variations, along with easily confused negative examples. Variation ranges should reflect the application rather than randomizing colors alone. For conditions lacking real samples, clarify that synthetic data supplements assumptions only.
Check alignment between annotations and images
- Verify class IDs, bounding-box or mask boundaries, handling of invisible objects, and coordinate formats. Spot-check rendering failures, excessive occlusion, and duplicate frames. Automatic annotation export does not guarantee that annotation rules match the training tool; load the data to check.
Split data to prevent leakage
- Split by scene, asset, or acquisition batch, rather than merely shuffling adjacent frames. Retain independent real samples for comparison and examine false positives and false negatives under different conditions. Compare training benefits against a baseline with identical settings rather than presenting a high score in isolation.
Deliver the data and its usage boundaries
- Provide a data dictionary, generation parameters, split manifests, and quality-inspection records. Record permitted uses of assets, models, and textures. If real validation data is unavailable, state that conclusions demonstrate performance only within synthetic scenarios and do not guarantee real-world accuracy.