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name: detection-and-zones description: "Use supervision Detections, model adapters, compact masks, filtering, zones, slicing, smoothing, and CSV/JSON sinks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Detection And Zones

Use this sub-skill when the task centers on detection data, model-result normalization, segmentation mask containers, filtering, overlap suppression/merging, zones, tiled inference, smoothing, or saving detections.

Natural triggers

Route here for prompts mentioning:

• sv.Detections, Detections.empty, indexing/filtering, data, metadata, tracker_id, class_name.
• from_ultralytics, from_inference, from_transformers, from_vlm, from_lmm, from_sam, from_sam3, from_tensorflow, from_mmdetection, from_detectron2, from_yolov5, from_yolo_nas, from_easyocr, from_ncnn, or other Detections.from_* adapters.
• CompactMask, compact_masks=True, dense vs compact masks, mask/box/OBB geometry, ORIENTED_BOX_COORDINATES.
• with_nms, with_soft_nms, with_nmm, OverlapFilter, OverlapMetric, IoU/IoS, duplicate detections.
• PolygonZone, LineZone, count in zone, line crossing, zone trigger masks, Position anchors.
• InferenceSlicer, small object detection, tiled inference, overlap windows, GeoTIFF/windowed raster slicing.
• DetectionsSmoother, CSVSink, JSONSink, save detections, custom detection data serialization.

First response routing

1. Identify the raw input type: already a Detections, a framework result object, a VLM response, manual NumPy arrays, or persisted rows.
2. Open API reference for exact container fields, adapter signatures, constants, and tool parameters.
3. Open workflows for common recipes: normalization, filtering, zones, slicing, compact masks, smoothing, and CSV/JSON export.
4. Open troubleshooting before changing code when shapes, optional dependencies, missing metadata, tracker IDs, masks, zones, or slicer behavior are unclear.

Boundaries

Handle:

• Detection containers and aligned per-detection arrays.
• Adapter normalization from model/framework/VLM outputs into sv.Detections.
• Detection utility functions that transform boxes, masks, polygons, overlaps, or VLM label matching.
• Zone trigger logic and counts, including tracker-dependent line crossing.
• Slicer, smoother, CSV sink, and JSON sink behaviors.

Route elsewhere:

• Pure drawing, color styling, label layout, and annotator composition belong to the annotators sub-skill.
• Tracking identity persistence, tracker configuration, keypoint containers, and keypoint deprecations belong to the tracking-keypoints sub-skill; return here once tracker_id is present for LineZone or smoothing.
• Image/video I/O, OpenCV backend selection, media helpers, assets, and low-level geometry primitives belong to the media-utils sub-skill unless they directly affect detection boxes/masks/zones.
• Dataset format conversion and dataset splitting/merging belong to the datasets sub-skill.
• mAP/MAR/precision/recall/F1/confusion-matrix evaluation belongs to the metrics sub-skill.

Operating defaults

• Target package facts: supervision 0.31.0.dev0, Python >=3.10, base install via pip install supervision.
• Optional model frameworks are caller-managed; Supervision adapters normalize results but do not install or download model packages.
• Optional GeoTIFF/windowed raster support needs the geotiff extra or rasterio.
• OpenCV native wheels are optional for the package; do not assume native OpenCV is installed when writing detection-only code.
• Prefer constants from supervision.config (CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES) over string literals in code that writes detections.data.
• Treat all detection-level fields as aligned length-N data; filtering must preserve alignment by indexing the Detections object, not by slicing only one array.