---

name: conversion description: "Convert common annotation and detector-output formats into mAP evaluator text files." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Conversion

Use this sub-skill when the user needs annotation or detector-output files transformed into the evaluator's required per-image text files before running mAP/AP evaluation.

What this sub-skill covers

• Ground-truth conversions:
  • PASCAL VOC XML object annotations to ground-truth/*.txt rows.
  • YOLO normalized label files to ground-truth/*.txt rows using a class list and image dimensions.
  • keras-yolo3 ground-truth annotation files to one evaluator text file per image.
• Detection-result conversions:
  • darkflow JSON detections to detection-results/*.txt rows.
  • darknet result.txt detector output to detection-results/*.txt rows.
  • keras-yolo3 detection annotation files to one evaluator text file per image.
• Safer conversion behavior than the legacy one-off scripts: explicit input paths, explicit output directories, no moving or renaming source files.

Boundaries and routing

• Do not compute AP/mAP metrics here. After conversion, route evaluator execution to the sibling evaluation sub-skill.
• Do not repair mismatched ground-truth/detection-result file sets here. Route class lookup, intersection checks, and repair workflows to the sibling data-validation sub-skill.
• Do not depend on the original repository checkout. Use the bundled helper and bundled references in this sub-skill.

Start here

1. Read references/data-formats.md to identify the source format and the exact evaluator row format you must produce.
2. Follow references/workflows.md for the matching conversion command.
3. If the conversion fails, use references/troubleshooting.md before changing data or assumptions.
4. Use the bundled converter: scripts/convert_annotations.py.

Quick route table

User request	Route
"Convert VOC XML annotations"	voc-xml-gt workflow in references/workflows.md.
"Convert YOLO labels for ground truth"	yolo-gt workflow; require class list plus image dimensions.
"Convert darkflow JSON detections"	darkflow-json-dr workflow.
"Convert darknet result.txt"	darknet-result-dr workflow.
"Convert keras-yolo3 annotations"	keras-yolo3 --gt or keras-yolo3 --dr; choose flat or recursive output layout.

Output contract

Successful conversion produces evaluator-ready .txt files in an explicit output directory. Each output file represents one image. Ground-truth and detection-result files should later have matching basenames before evaluation; checking and repairing that intersection belongs to data-validation.