Training-Free Metrics for Synthetic Object Detection Data: A Proxy for Detector Performance
This work addresses the high training cost associated with evaluating synthetic object detection datasets by introducing CCDM (Conditional-Composition Domain Match), the first family of training-free proxy metrics tailored for synthetic detection data. CCDM predicts the relative utility of synthetic data for downstream detectors by precomputing image-level and instance-level conditional composition and domain-matching similarities. Evaluated on VisDrone-DET, CCDM achieves a Spearman correlation coefficient of 1.0 with YOLOv8 performance, substantially outperforming existing evaluation methods and significantly enhancing the efficiency of synthetic data selection.
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