Training-Free Metrics for Synthetic Object Detection Data: A Proxy for Detector Performance

2026-06-18

AI Summary

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.

Abstract

With the recent advent of image generative models, synthetic data are increasingly being used to supplement limited real datasets for training computer vision models. However, not all synthetic datasets improve performance equally, and their effectiveness can only be assessed by training a downstream model, which is computationally expensive and time-consuming. This problem is pronounced in the task of object detection, where the required annotations are much more dense due to bounding boxes. In this paper, we propose a pre-computable metric family, dubbed Conditional-Composition Domain Match (CCDM), which serves as a proxy for the relative utility of candidate synthetic training sets for downstream detection. Experiments on the VisDrone-DET dataset show that the CCDM metric families achieve a Spearman correlation of 1.0 with the downstream performance of YOLOv8, clearly outperforming existing metrics for synthetic image evaluation.

Problem

• synthetic data
• object detection
• training-free metrics
• detector performance
• data evaluation

Innovation

• synthetic data
• object detection
• training-free metric
• domain match
• YOLOv8