What Are Micro-Models? Encord's Semi-Supervised Annotation Approach for Physical AI Data

Micro-models are small, deliberately narrow machine-learning models trained on a limited labeled seed set to automate specific annotation tasks, using semi-supervised learning to generate pseudo-labels across larger unlabeled datasets and route uncertain cases to human reviewers. Unlike broad foundation models, they prioritize task-specific precision, quick training, and modular combination into larger annotation workflows. This approach is presented as particularly useful for Physical AI, where robot, vehicle, and drone datasets combine synchronized RGB, depth, LiDAR, radar, IMU, and proprioceptive streams whose labels must remain spatially and temporally calibrated. Inconsistent cross-sensor annotations can cause model failures, costly retraining, and safety risks in deployment. Encord positions its platform as supporting this workflow through synchronized 3D Scenes, sensor-calibration support, cross-sensor label propagation, model-assisted pre-labeling, dataset curation, and evaluation tools, allowing teams to focus manual effort on edge cases and low-confidence predictions rather than labeling every sensor stream from scratch.

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