Dataset Annotation
AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods
Install
$ agentstack add skill-sharpai-deepcamera-dataset-annotation ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-sharpai-deepcamera-dataset-annotation) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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-detectionProtocol
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.txtSource & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: SharpAI
- Source: SharpAI/DeepCamera
- License: MIT
- Homepage: http://www.sharpai.org
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.