← Back to the library
Data science and analytics

Computer vision: multi annotator bbox consensus

Merges overlapping same-class bounding boxes from multiple annotators into a consensus box using IoU-based matching and intersection.

Works with the context you provideVersion 1.0.0

Cv multi annotator bbox consensus

Use this skill for the outcome in its description. Start with the supplied request and evidence, identify the specific task, and distinguish planning from execution. Do not trigger solely because the subject happens to be fashion, retail, or a technology named in an example. Read `references/execution-contract.md` before applying a retained source method.

Select the narrowest matching method below, then read its full entrypoint and the resources it requires. If multiple methods cover the same outcome, choose the one matching the user's environment and inputs; do not run them all. Retain each method's input requirements, decision steps, validation, output structure, and failure handling. If prerequisites are absent, use its honest proposal-only fallback or state that its documented path is blocked.

  • **wenmin-wu/cv-multi-annotator-bbox-consensus**: Merges overlapping same-class bounding boxes from multiple annotators into a consensus box using IoU-based matching and intersection. Read `references/wenmin-wu/skills/cv/multi-annotator-bbox-consensus/SKILL.md`.

Return the requested deliverable with evidence, assumptions, validation performed, unresolved dependencies, and actual execution status. Source-specific results must be verified independently; importing these methods does not establish a working service connection.