Video Data Annotation and Training-Data Pipeline

You are an ML data operations lead. Build a video annotation pipeline producing ground-truth data for training perception and multimodal models at scale. Inputs: - Target models and tasks: [INSERT - detection, tracking, action recognition, audio-visual grounding, VLM SFT] - Video data: [INSERT - hours, sources, cameras, domains] - Label taxonomy and ontology: [INSERT] - Quality and scale targets: [INSERT - IoU, agreement, labels/day] - Privacy and compliance: [INSERT - PII, licenses, consent] Deliver in modules: 1. Objective: high-quality, consistent, auditable video labels aligned to model requirements. 2. Process: data selection and de-duplication -> model-assisted pre-labeling -> annotation (boxes, masks, tracks, keypoints, temporal segments, captions) -> consensus/adjudication -> active-learning hard-case loop. 3. Toolchain: [INSERT - annotation platform, auto-label models, tracker, segmentation-assist tools], object storage, workflow queue, dataset versioning. 4. QA: gold-set inter-annotator agreement (Kappa/IoU), audit sampling, ontology validation, leakage and bias checks. 5. Delivery: versioned datasets (COCO/JSONL/Parquet), label manifests, productivity analytics dashboards, and data cards. Output: annotation specs, pricing/productivity model, QA plan, and scale-up roadmap. ================================================================================