Agent Skills
› SharpAI/DeepCamera
› dataset-annotation
dataset-annotation
GitHub提供AI辅助的数据集标注功能，支持BBox、SAM2和DINOv3三种方法，具备视频对象追踪能力，并支持COCO格式导出及直接上传至Kaggle/HuggingFace平台。
Trigger Scenarios
需要为训练检测模型准备标注数据
对图像或视频帧进行目标检测和分割标注
将标注结果导出为标准数据集格式
Install
npx skills add SharpAI/DeepCamera --skill dataset-annotation -g -y
SKILL.md
Frontmatter
{
"name": "dataset-annotation",
"version": "1.0.0",
"parameters": [
{
"name": "method",
"type": "select",
"group": "Annotation",
"label": "Annotation Method",
"default": "dinov3",
"options": [
"bbox",
"sam2",
"dinov3"
]
},
{
"name": "export_format",
"type": "select",
"group": "Export",
"label": "Export Format",
"default": "coco",
"options": [
"coco",
"yolo",
"voc"
]
},
{
"name": "auto_detect",
"type": "boolean",
"group": "Annotation",
"label": "Auto-detect Before Annotation",
"default": true,
"description": "Run detection first, then human corrects"
},
{
"name": "detection_model",
"type": "select",
"group": "Annotation",
"label": "Detection Model",
"default": "yolov8n",
"options": [
"yolov8n",
"yolov11n",
"dinov3"
]
},
{
"name": "dataset_dir",
"type": "string",
"group": "Storage",
"label": "Dataset Directory",
"default": "~\/datasets"
}
],
"description": "AI-assisted dataset annotation with COCO export — bbox, SAM2, DINOv3 methods",
"capabilities": {
"annotation": {
"script": "scripts\/annotate.py",
"description": "Dataset annotation with AI assistance and COCO export"
}
}
}
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
Version History
- 2264fcb Current 2026-08-20 16:02
