How to Set a Confidence Threshold for Object Detection

Confidence thresholds in object detection determine the trade-off between false positives and missed detections, making them a deployment decision based on the relative cost of errors rather than a setting that should be left at a default value or inferred from mAP. The recommended process is to use a precision-recall curve to select several candidate thresholds, test them under otherwise identical conditions in a Roboflow Workflow, and validate the preferred option on a fixed, annotated golden set that reflects real production conditions such as varying lighting, distance, occlusion, and scene density. In a warehouse RF-DETR example, a threshold of 0.39 provided a better balance of 98.6% precision and 93.6% recall than lower or higher alternatives, though performance differed by object class. When classes require different trade-offs, per-class confidence filters can apply stricter or more permissive cutoffs while retaining a global default. Threshold changes cannot solve all detection issues: duplicate boxes should generally be addressed with non-maximum suppression and its IoU setting, while uncertain predictions can be routed to human review, active learning, model consensus, or secondary verification. Thresholds should be revisited whenever the model, production data, or the operational cost of errors changes.

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