Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty

arXiv:2609.21822 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/ultralytics/ultralytics

Terminology

• Benchmark — 48× in this paper · explained in 2 episodes
• Grounding — 44× in this paper · explained in 3 episodes
• Calibration — 37× in this paper · explained in 7 episodes
• Alignment — 16× in this paper · explained in 1 episode
• Accuracy — 9× in this paper · explained in 1 episode
• Uncertainty Quantification — 6× in this paper · explained in 8 episodes
• Isotonic Regression — 5× in this paper
• Average Similarity — 4× in this paper
• European Conference on Computer Vision — 4× in this paper
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Redundancy — 3× in this paper · explained in 2 episodes
• Winter Conference on Applications of Computer Vision — 3× in this paper
• Acceptance Bias — 2× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Instance Segmentation — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Platt Scaling — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Annotation Ambiguity — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Pascal Visual Object Classes — 1× in this paper
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance

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