Cumulative Consensus Score: Label-Free and Model-Agnostic Evaluation of Object Detectors in Deployment

We introduce the Cumulative Consensus Score (CCS), a method for evaluating object detection models without ground-truth annotations. CCS applies test-time data augmentation to each image and measures the spatial consistency of predicted bounding boxes across augmented views using Intersection over Union. The approach achieves over 90% congruence with F1-score, Probabilistic Detection Quality, and Optimal Correction Cost in controlled experiments. Operating in a model-agnostic manner across single-stage and two-stage detectors, CCS works at the case level to identify underperforming scenarios and provides a robust foundation for DevOps-style continuous monitoring of object detectors in real-world deployment.