Dataset Annotation

SkillDatabases & data

Lets your agent label images for training object detection models, with AI-suggested boxes and export to COCO format.

Then ask your AI: use the Dataset Annotation skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.

No other account needed.

Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.

About this skill

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

What this skill tells your AI

The instructions your AI receives, as published by sharpai/deepcamera in skills/annotation/dataset-annotation/SKILL.md and read by Ahel’s review.

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

Signals

GitHub stars: 3k
Forks: 488
Last commit: Sep 2026

Ahel review

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Advanced

Item type: skill
Key: dataset-annotation
Source: github.com/sharpai/deepcamera

github.com/sharpai/deepcamera