DeepCamera dataset-annotation

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

install

source · Clone the upstream repo

git clone https://github.com/SharpAI/DeepCamera

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/SharpAI/DeepCamera "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/annotation/dataset-annotation" ~/.claude/skills/sharpai-deepcamera-dataset-annotation && rm -rf "$T"

manifest: skills/annotation/dataset-annotation/SKILL.md

source content

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