A regression-based composite of existing image-quality and trainability metrics, called SDQM, correlates with YOLOv11 mAP (r=0.87 on one split; 0.78±0.09 k-fold) on three synthetic-to-real detection benchmarks.
Dataset of Industrial Metal Objects
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abstract
We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our dataset contains both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data is obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions and lighting conditions. This results in over 30,000 images, accurately labelled using a new public tool. Synthetic data is obtained by carefully simulating real-world conditions and varying them in a controlled and realistic way. This leads to over 500,000 synthetic images. The close correspondence between synthetic and real-world data, and controlled variations, will facilitate sim-to-real research. Our dataset's size and challenging nature will facilitate research on various computer vision tasks involving reflective materials. The dataset and accompanying resources are made available on the project website at https://pderoovere.github.io/dimo.
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cs.CV 1years
2025 1verdicts
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SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
A regression-based composite of existing image-quality and trainability metrics, called SDQM, correlates with YOLOv11 mAP (r=0.87 on one split; 0.78±0.09 k-fold) on three synthetic-to-real detection benchmarks.