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name: supervision description: "Route Supervision computer-vision package workflows for detections, annotators, datasets, tracking, metrics, and media utilities." disable-model-invocation: true metadata: disco-role: operating license: MIT

Supervision

Use this repo skill when a task uses the supervision Python package as a computer-vision utility layer: normalized detections, visual annotation, dataset conversion, tracking/keypoints, evaluation metrics, image/video helpers, zones, sinks, slicers, and optional OpenCV backend behavior.

Install and minimal check

pip install supervision
python -c "import supervision as sv; print(sv.__version__)"

Add extras only for workflows that need them:

pip install "supervision[metrics]"   # pandas-backed metric tables and evaluation
pip install "supervision[geotiff]"   # rasterio/windowed GeoTIFF slicing

Native OpenCV is optional. Use Supervision's fallback backend by default; install exactly one OpenCV wheel only when the application needs native OpenCV behavior, GUI windows, webcam capture, or broader codec compatibility.

Run the bundled check_supervision_install.py helper when the environment is unknown.

Start here

• Read overview for the package map, core concepts, optional extras, and deprecation summary.
• Read troubleshooting for cross-cutting install/import/backend/deprecation failures.
• Read repo provenance before deciding whether this skill matches a different checkout or package version.

Route by task

User intent or signals	Sub-skill
Detections, model adapters, VLM parsing, compact masks, filtering, NMS/NMM, zones, tiled inference, smoothing, CSV/JSON sinks	detection-and-zones
Drawing boxes, masks, labels, traces, heatmaps, zones, comparison panels, and other high-level scene + detections/keypoints visualization	annotators
YOLO/COCO/Pascal VOC/LabelMe/CreateML dataset loading/export, dataset splitting/merging, classification folder datasets	datasets
KeyPoints, keypoint adapters/annotators, keypoint-to-detection conversion, tracker IDs, ByteTrack compatibility/migration, line crossing with identity	tracking-keypoints
mAP, mAR, precision, recall, F1, confusion matrices, MetricTarget, AveragingMethod, dataset-backed evaluation	metrics
Image/video/file helpers, primitive drawing, colors, geometry, notebook display, assets, OpenCV fallback/native backend, GUI/windows, codecs	media-utils

Operating principles

1. Treat sv.Detections as the shared object between model adapters, filters, zones, trackers, annotators, datasets, sinks, slicers, and metrics.
2. Keep aligned arrays aligned. Filter the container object, not individual xyxy, confidence, class_id, mask, tracker_id, or data arrays.
3. Import metadata keys from supervision.config when writing reusable code: CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES, and AREA_DATA_FIELD.
4. Keep heavy optional model/framework imports lazy and model-specific. Supervision adapters convert already-produced results; they do not download models or install external frameworks.
5. Use scene.copy() before annotation when the original image or frame must be preserved. Annotators draw on the provided scene and return it.
6. Prefer current APIs: supervision.key_points/sv.KeyPoints, keypoint_confidence, sv.VLM, and Detections.from_vlm. Treat supervision.keypoint, KeyPoints.confidence, sv.ByteTrack, sv.LMM, and from_lmm as compatibility or migration topics.
7. Do not rely on original repository docs, examples, tests, notebooks, scripts, or local checkout files as runtime dependencies. The necessary operating guidance is distilled into this skill tree.

Common route combinations

• Annotate model output: use detection-and-zones to normalize raw results into Detections, then annotators to draw on images or video frames.
• Evaluate a dataset: use datasets to load/convert annotations, detection-and-zones to normalize predictions, and metrics to compute and interpret scores.
• Count objects in video zones: use media-utils for video frames, detection-and-zones for PolygonZone/LineZone, tracking-keypoints when stable tracker_id is needed, and annotators for overlays.
• Debug backend/image/video issues: use media-utils first, then return to the workflow sub-skill that owns the higher-level operation.