# SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation

Source: [SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation](<https://www.sota2.com/research/paper/68fa5e6d1e209131ce9e2311>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-12T05:00:05.418Z

Authors: Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin

Published: 2025-10-08T03:01:26.000Z

## Abstract

The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses significant challenges in creating robust models. To address this, synthetic data generated through simulations and generative models has emerged as a promising solution, enhancing dataset diversity and improving the performance, reliability, and resilience of models. However, evaluating the quality of this generated data requires an effective metric. We introduce the Synthetic Dataset Quality Metric (SDQM) to assess data quality for object detection tasks without requiring model training to converge. This metric enables more efficient generation and selection of synthetic datasets, addressing a key challenge in resource-constrained object detection tasks. In our experiments, SDQM demonstrated a strong correlation with the mean average precision (mAP) scores of YOLO11, a leading object detection model, whereas previous metrics only exhibited moderate or weak correlations. In addition, it provides actionable insights into improving dataset quality, minimizing the need for costly iterative training. This scalable and efficient metric sets a new standard for evaluating synthetic data. The code for SDQM is available at https://github.com/ayushzenith/SDQM

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dataset Quality Metric Correlation on Synthetic Data Quality YOLO11n mAP50](<https://www.sota2.com/research/sota/dataset-quality-metric-correlation-on-synthetic-data-quality-yolo11n-map50.md>): Dataset Quality Metric Correlation; Synthetic Data Quality YOLO11n mAP50 (evaluation); PCC: 0.8719.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.06596>)

[PDF](<https://arxiv.org/pdf/2510.06596.pdf>)