OpenParable
Skip to content
← Back to skills

Dataset Annotation

Documents
dataset-annotation

Dataset Annotation is an Agent Skill for documents published in the SharpAI/DeepCamera repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods

View on GitHub ↗
QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/SharpAI/DeepCamera/blob/HEAD/skills/annotation/dataset-annotation/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/dataset-annotation, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Dataset Annotation

AI-assisted dataset creation for training custom detection models. Supports three annotation methods with COCO format export.

What You Get

  • BBox annotation — draw bounding boxes, AI auto-suggests
  • SAM2 annotation — click to segment, get pixel-perfect masks
  • DINOv3 annotation — click a patch, find similar objects across frames via visual grounding
  • Object tracking — annotate keyframes, DINOv3 interpolates across the video
  • COCO export — standard images[], annotations[], categories[] format
  • Kaggle/HuggingFace upload — push datasets directly to platforms

Annotation Loop

1. Feed frames from clips → auto-detect objects
2. Human reviews → corrects bboxes, adds labels
3. Save as COCO dataset
4. Train improved model
5. Repeat with better auto-detection

Protocol

Aegis → Skill (stdin)

{"event": "frame", "camera_id": "...", "frame_path": "/tmp/frame.jpg", "frame_number": 0, "width": 1920, "height": 1080}
{"event": "detections", "frame_number": 0, "detections": [{"class": "person", "bbox": [100, 50, 200, 350], "confidence": 0.9, "track_id": "t1"}]}
{"event": "save_dataset", "name": "front_door_people", "format": "coco"}

Skill → Aegis (stdout)

{"event": "ready", "methods": ["bbox", "sam2", "dinov3"], "export_formats": ["coco", "yolo", "voc"]}
{"event": "annotation", "frame_number": 0, "annotations": [{"category": "person", "bbox": [100, 50, 200, 350], "track_id": "t1", "is_keyframe": true}]}
{"event": "dataset_saved", "format": "coco", "path": "~/datasets/front_door_people/", "stats": {"images": 150, "annotations": 423, "categories": 5}}

Setup

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt