Training-Free Metrics for Synthetic Object Detection Data: A Proxy for Detector Performance
Synthetic images are increasingly used to augment scarce real data for object detection. However, not all synthetic sets help equally, and the only way to know a set's value is to train a detector on it, which is slow and demands dense annotation. We ask whether a training-free metric can instead rank candidate synthetic training sets by their downstream utility. Existing image-set metrics such as FID, KID, and MMD compare two feature distributions with a single global statistic, which we show is mis-specified for detection-data selection in two ways: it is blind to per-image composition (object count, box scale, class mix), and even at fixed composition its global averaging washes out th...
| Authors | Myeongseok Nam, Donghun Yeo, Seungwook Kim |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 16 July 2026 |
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