# Dataset Annotation

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

- **Type:** Skill
- **Install:** `agentstack add skill-sharpai-deepcamera-dataset-annotation`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [SharpAI](https://agentstack.voostack.com/s/sharpai)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [SharpAI](https://github.com/SharpAI)
- **Source:** https://github.com/SharpAI/DeepCamera/tree/master/skills/annotation/dataset-annotation
- **Website:** http://www.sharpai.org

## Install

```sh
agentstack add skill-sharpai-deepcamera-dataset-annotation
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## 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-detection
```

## Protocol

### Aegis → Skill (stdin)
```jsonl
{"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)
```jsonl
{"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

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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [SharpAI](https://github.com/SharpAI)
- **Source:** [SharpAI/DeepCamera](https://github.com/SharpAI/DeepCamera)
- **License:** MIT
- **Homepage:** http://www.sharpai.org

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-sharpai-deepcamera-dataset-annotation
- Seller: https://agentstack.voostack.com/s/sharpai
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.