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/physical-ai-video-data-augmentation

@0482ebc
by NVIDIA Corporationnvidia/skills3.5k stars
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Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.

Use this Skill: https://skilld.dev/gh/nvidia/skills/physical-ai-video-data-augmentation

This session only. Nothing lands on disk.

assetscookbookswarehouseREADME.md

≈1.4k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Warehouse Dataset

Scene description

A fixed ground-level surveillance camera looks down the length of an active indoor warehouse construction site. The scene features an exposed red-painted steel beam ceiling with hanging cables and electrical conduits, a bare concrete floor, red steel columns at regular intervals, multiple orange A-frame ladders, a green electric scissor lift, construction materials and debris scattered on the floor, and workers wearing hard hats and high-visibility safety vests. Natural daylight enters through large open walls on the far side of the building, creating a mixed-lighting environment with bright areas near the openings and dimmer zones between columns.

Augmentation variables

Variable Options Default weights Rationale
lighting bright, moderate, dim 0.35 / 0.35 / 0.30 Indoor scene with mixed light sources (artificial overhead + natural from open walls). Cosmos can vary the overall illumination intensity, producing visually distinct bright, moderate, and dim appearances.
surface_condition dry, wet 0.55 / 0.45 Bare concrete floor state — dry (matte, dusty) vs. wet (reflective, puddles). Construction sites can have wet floors from cleaning, spills, or rain through open sides. Only 2 options to keep them clearly distinguishable.

Tuning guide

See the shared parameter reference in ../TUNING_GUIDE.md.

Scene-specific notes:

  • Prefer conservative detection_and_tracking.threshold tuning to balance worker recall in mixed bright/dim lighting.
  • Keep detection_and_tracking.classes minimal because most safety hazards are equipment- and environment-driven rather than class-heavy.
  • Adjust vlm_json.frame_fps only as needed; many warehouse events evolve over longer windows than road traffic.

Key decisions & warnings

Decision Choice Rationale Risk if wrong
Augmentation variables lighting, surface_condition Indoor scene — weather/time_of_day don't apply directly. Lighting intensity and floor wetness are the main appearance axes Cosmos can vary. Wrong variables → Cosmos generates unrealistic augmentations (e.g., outdoor weather in an indoor scene); MCQ verification questions won't match
Variable options & weights lighting: bright 0.35 / moderate 0.35 / dim 0.30; surface_condition: dry 0.55 / wet 0.45 3 lighting levels for clear visual separation. 2 surface states (dry/wet) — more options (dusty, oily) would be too subtle to distinguish. Dry slightly favored since most construction footage is dry floor. Too many fine-grained options → model can't reliably distinguish; skewed weights → underrepresented conditions
Detection classes [person] Only COCO-80 class reliably matching the scene subjects. Workers are the primary safety concern. No tracking for equipment (scissor lifts, ladders) — events involving equipment can only be detected via VLM event analysis, not bounding-box tracking
max_age 45 Workers frequently walk behind columns and large equipment, causing temporary occlusion. Higher than a fully open floor (30) but lower than an intersection (60). Too low → tracks fragment when workers go behind columns/equipment; too high → ghost tracks persist
frame_fps / sampling_fps 3 Workers and equipment move slowly. Falls and contact events develop over multiple seconds. 3 fps captures sufficient temporal detail. Too low → a quick trip-and-fall might occur between sampled frames; too high → unnecessary token cost
Event types collision: worker_equipment_contact, worker_fall; near_miss: near_miss_equipment, near_miss_falling_object; anomaly: unsafe_ladder_use, cable_trip_hazard; normal_traffic: normal_construction, equipment_operation, worker_transit 9 sub-categories covering construction safety interactions. Ladder safety and cable hazards are prominent given the scene. Missing event type → safety incidents go unlabeled; wrong category mapping → MCQ and event JSON disagree

Scene-specific warnings:

  • COCO-80 has no construction equipment classes: Scissor lifts, ladders, scaffolding, and hoists are invisible to the object detector. All equipment-related safety events rely entirely on VLM event analysis, not tracked bounding boxes. If VLM misses an event, there is no fallback.
  • Column occlusion: Red steel columns at regular intervals create blind spots. Workers may be obscured for 1–3 seconds while passing behind a column. max_age: 45 should bridge most gaps, but closely-spaced workers may get their tracks swapped.
  • Cables on floor: Yellow extension cords are everywhere in the scene. The VLM must distinguish between cables in active walkways (hazard) vs. cables in non-traffic areas (normal). This is a nuanced judgment that may produce false positives.
  • Mixed lighting complicates detection: Workers in dim areas between columns may be harder to detect. Consider raising detection_and_tracking.threshold to 0.15 if too many false positives appear in bright areas, or lowering to 0.1 if workers in dim areas are missed.

File inventory

Standard cookbook layout: see ../FILE_INVENTORY.md.

Warehouse specifics: augmentation variables lighting + surface_condition; event_analysis.md defines 9 event types across 4 categories; question_bank.json holds 11 questions covering safety, lighting, and site activity.

Source: SKILL.md on GitHub

2 warnings3mo3 checks · Risk SAFE
  • Gen Agent Trust Hub3mo

    The skill is a workflow orchestrator for video data augmentation and auto-labeling on the NVIDIA OSMO platform. It manages the end-to-end pipeline, including credential verification, configuration generation, worker execution, and result retrieval. No security issues or malicious patterns were detected; all external dependencies and network operations are associated with trusted vendors and the skill's primary functionality.

  • Socket3mo

    3 alerts: gptAnomaly

  • Snyk3mo

    Risk: MEDIUM · 1 issue

Signed by skilld at 0482ebc. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 4 months ago
Other metadata
metadata
{
  "owner": "NVIDIA",
  "service": "data",
  "version": "1.0.0",
  "reviewed": "2026-05-26",
  "author": "NVIDIA",
  "tags": [
    "physical-ai",
    "video-data-augmentation",
    "auto-labeling",
    "cosmos"
  ]
}

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