How Often Should You Retrain a Computer Vision Model?

Computer vision models should be retrained in response to production evidence rather than a fixed schedule, particularly when validated performance declines, image conditions or task definitions change, repeated failure patterns emerge, or annotation errors are discovered. Before retraining, teams should rule out issues with cameras, image preprocessing, confidence thresholds, tracking, and workflow logic, since these may resolve errors without modifying the model. Effective retraining relies on collecting and correcting targeted, diverse production examples while preserving dataset versions and existing successful cases, then fine-tuning or training a candidate model. The candidate should be evaluated against the deployed model on identical test data and current production images using both technical and operational metrics, and deployed gradually only if it improves the measures most important to the application. Tools such as Roboflow Vision Events, active learning, dataset versioning, and model evaluation can support this cycle by capturing failures, incorporating operator feedback, preparing new training data, and comparing model versions.

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