yolo-detector

name	yolo-detector
description	Use when designing or verifying Ultralytics YOLO detection, segmentation, tracking, or pose inference with licensed models and stable JSON outputs.

YOLO Detector

Purpose

Define a reproducible YOLO inference boundary for detection, segmentation, tracking, or pose. Keep model configuration, preprocessing, postprocessing, result serialization, licensing, and verification explicit.

Use When

• The task explicitly selects Ultralytics YOLO or requires its supported modes.
• An image, video, camera, or API workflow needs structured predictions.
• A demo must emit both machine-readable results and optional annotations.

Do Not Use When

• A browser-side MediaPipe landmark task satisfies the requirement.
• The model family is undecided; use cv-project-router first.
• The task is training orchestration, identity recognition, or a medical claim.

Inputs

• Mode: detect, segment, track, or pose.
• Model identifier, exact library/model version, source, license, checksum, and approved cache location.
• Image/frame contract, class allowlist, confidence and IoU thresholds, and device policy.
• Required JSON fields, coordinate space, annotation policy, and latency target.

Default Stack

• Python, Ultralytics, OpenCV, and NumPy in a demo-owned environment.
• CPU baseline and a small pinned model only after explicit download approval.
• Absolute pixel xyxy boxes in JSON, with source dimensions included.
• OpenCV capture delegated to opencv-realtime-camera and web boundaries delegated to cv-webapp-starter.

Workflow

1. Confirm the selected YOLO task and reject modes not supported by the pinned model. Record Ultralytics AGPL-3.0 or Enterprise licensing implications.
2. Require explicit approval before adding the dependency or downloading weights. Never execute model-repository scripts or arbitrary remote code.
3. Pin library and model revisions, record source/checksum/cache location, and keep weights outside Git.
4. Define image size, color order, confidence, IoU/NMS, class filters, maximum detections, device selection, warm-up, and deterministic seed where relevant.
5. Convert library-specific results at one adapter boundary. Validate finite confidence values, class identifiers, source dimensions, and clipped boxes.
6. For tracking, state tracker configuration and lifecycle; track IDs are session-local and must not be treated as identity.
7. Make annotated media optional and derived from normalized JSON, not a second source of truth.
8. End with vision-verifier using a licensed static fixture and expected structural assertions rather than brittle exact detections alone.

Output Contract

Emit versioned JSON with this minimum shape:

{
  "schema_version": "1.0",
  "task": "object-detection",
  "source": {"width": 1280, "height": 720},
  "model": {"id": "approved-model", "version": "pinned-version"},
  "detections": [
    {
      "id": "det-1",
      "class_id": 0,
      "label": "person",
      "confidence": 0.93,
      "bbox": {
        "x_min": 120.4
         
         
         
         
      
    
  
        

Segmentation, pose, and tracking extensions must be versioned and documented. Artifact references must be opaque relative paths or authorized URLs, never local filesystem paths.

Verification

• Validate JSON against the application schema, including empty detections, finite numbers, coordinate bounds, and stable class mappings.
• Run CPU inference on a licensed fixture after model availability is approved.
• Test corrupt input, unsupported mode, missing weights, offline cache miss, timeout, and unavailable accelerator fallback.
• Separate model-load, warm-up, preprocess, inference, and postprocess timing.
• Review annotated output manually only as additional evidence; do not replace structural tests with a screenshot.

Failure Modes

• Weight download or cache miss without approval: stop and report the exact artifact required; do not trigger a hidden network download.
• Accelerator unavailable: fall back to the declared CPU baseline or fail clearly when the acceptance criterion requires acceleration.
• Empty detections: return a valid empty list, not a transport error.
• Invalid coordinates or NaN confidence: reject the result at the adapter boundary and report the affected frame/request.

Safety Notes

• Review Ultralytics licensing before commercial, closed-source, or networked deployment. Library licensing does not grant rights to every model or dataset.
• Never accept arbitrary model paths, URLs, pickles, or remote code from a request. Use a server-side allowlist.
• Do not label persistent tracks as people or identities. Do not infer sensitive attributes from detections.
• Keep weights, uploads, annotations, private media, and output videos out of Git.