SDQM: Synthetic Data Quality Metric for Object Detection Dataset
  Evaluation
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. This paper introduces 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 YOLOv11, a leading object detection model, while previous metrics only
exhibited moderate or weak correlations. Additionally, it provides actionable
insights for 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
68e7073f7ae125f9582e6873
Ayush Zenith
claimed_verified
2025-10-09T06:50:48.310Z
641a78a24577db917f6fe90a
https://cdn-avatars.huggingface.co/v1/production/uploads/641a78a24577db917f6fe90a/SDFCNsBD7UP3b_Ce0jSEW.jpeg
Ayush Zenith
user
ayushzenith
68e7073f7ae125f9582e6874
Arnold Zumbrun
68e7073f7ae125f9582e6875
Neel Raut
68e7073f7ae125f9582e6876
Jing Lin
SDQM: Synthetic Data Quality Metric for Object Detection Dataset
  Evaluation
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. This paper introduces 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 YOLOv11, a leading object detection model, while previous metrics only
exhibited moderate or weak correlations. Additionally, it provides actionable
insights for 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
A new metric, SDQM, evaluates synthetic data quality for object detection tasks without requiring model convergence, improving dataset selection and quality.
synthetic data generative models object detection Synthetic Dataset Quality Metric mean Average Precision YOLOv11