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name: object-detection-metrics description: "Guides legacy Object-Detection-Metrics PASCAL VOC AP/mAP evaluation from detection text folders and direct Python API classes." disable-model-invocation: true metadata: disco-role: operating license: MIT

Object-Detection-Metrics

Use this repo skill when the user needs the legacy Object-Detection-Metrics toolkit behavior for PASCAL VOC-style object-detection metrics: AP, mAP, precision/recall curves, text-folder evaluation, or direct use of the BoundingBox / BoundingBoxes / Evaluator classes.

This skill is self-contained for operating guidance. Prefer its bundled helpers and references instead of reopening or running original repository examples or scripts.

Quick route

User task	Read next
Evaluate groundtruths/ and detections/ text folders for VOC AP/mAP	sub-skills/file-evaluation/SKILL.md
Handle xywh versus xyrb, absolute versus relative coordinates, or YOLO-like normalized boxes in text files	sub-skills/file-evaluation/references/file-format.md
Run a noninteractive, safe replacement for the legacy folder-evaluation script	sub-skills/file-evaluation/scripts/voc_metrics_eval.py
Build BoundingBox objects and call Evaluator.GetPascalVOCMetrics from Python data	sub-skills/python-api/SKILL.md
Choose every-point versus 11-point AP, understand duplicate detections, or inspect inclusive IoU behavior	sub-skills/python-api/references/metric-behavior.md
Check whether a user-provided checkout/copy can import the source-style API	sub-skills/python-api/scripts/api_metric_smoke.py or scripts/check_env.py
Decide whether this skill is stale for a checkout	references/repo-provenance.md
Understand selected capabilities and non-goals	references/workflow-map.md
Diagnose install/import/dependency/version problems	references/troubleshooting.md

Scope and non-goals

This skill covers:

• PASCAL VOC AP/mAP from simple text folders.
• Ground-truth and detection schemas with xywh and xyrb/XYX2Y2 coordinates.
• Relative YOLO-like center/size coordinates when a shared image size is available.
• Direct Python API use of the legacy source modules.
• Safe noninteractive metric helpers and smoke checks.

This skill does not cover:

• COCO metrics, video/STT-AP metrics, UI workflows, or file formats added by the newer successor toolkit.
• Training or running an object detector.
• General computer-vision model evaluation outside this repository's PASCAL VOC metric implementation.
• Paper distillation or benchmark-survey reproduction.

Environment model

For folder evaluation, the bundled file-evaluation helper is standard-library-only and does not require the original checkout.

For direct source API use, the legacy repository is source-style rather than a pip-installable package. A user working with a checkout or copied source must make the lib directory importable and install the source API dependencies used by imports:

python -m pip install numpy matplotlib opencv-python-headless
python sub-skills/python-api/scripts/api_metric_smoke.py --repo-root /path/to/checkout

Use opencv-python-headless on servers unless GUI drawing is explicitly needed. Set MPLBACKEND=Agg before importing Evaluator when plotting in a headless process.

Minimal validation commands

Check only the generated helpers:

python scripts/check_env.py
python sub-skills/file-evaluation/scripts/voc_metrics_eval.py --help
python sub-skills/python-api/scripts/api_metric_smoke.py --help

Check a user-provided checkout or copied lib directory plus API dependencies:

python scripts/check_env.py --repo-root /path/to/checkout --require-api-deps
python sub-skills/python-api/scripts/api_metric_smoke.py --repo-root /path/to/checkout --case duplicate

Run folder AP/mAP with the self-contained helper:

python sub-skills/file-evaluation/scripts/voc_metrics_eval.py \
  --gt-folder /path/to/groundtruths \
  --det-folder /path/to/detections \
  --threshold 0.5 \
  --output-text voc-results.txt \
  --output-json voc-results.json \
  --pretty

Metric reminders

• Default AP is PASCAL VOC every-point interpolation.
• IoU uses inclusive pixel-area arithmetic: (right - left + 1) * (bottom - top + 1).
• Detections are sorted by confidence within each class.
• Only one detection can match a ground-truth object; duplicate detections become false positives.
• mAP is the mean AP across classes with at least one ground-truth positive.

Provenance and refresh

Read references/repo-provenance.md before using this skill as evidence for a different checkout. Refresh the repo skill if the commit, dirty state, package layout, core source files, CLI flags, or public examples differ from the recorded snapshot.