AI Labelling tool for computer vision
Project description
Ultimate AI Labeller
Desktop image annotation tool for object detection datasets (Tkinter + Ultralytics).
Features
- Bounding-box annotation with drag, move, and resize handles
- Undo/redo history (
Ctrl+Z,Ctrl+Y) - Image navigation (
Fnext/save,Dprevious) - Auto red-region proposal (
A) - YOLO detection from UI (
Run Detection) - Startup source selection:
- Open Images Folder
- Open YOLO Dataset
- Open RF-DETR Dataset
- Detection model management:
- Official model mode (
yolo26m.ptpath by default) - Import custom models (
.pt,.onnx) viaBrowse Model - Select model from dropdown library
- Official model mode (
- Auto-detect and propagate options
- Scrollable right settings panel
- Remove/restore bad frames from split
- Image dropdown jump
- Session resume (last project/split/image/model settings)
- English/Chinese UI switch and light/dark theme
- Export all annotations by format:
YOLO (.txt)full dataset exportJSONfull dataset export (per-image annotation json)
Dataset Structure
your_project/
images/
train/
val/
test/
labels/
train/
val/
test/
- Image extensions:
.png,.jpg,.jpeg - Label format: YOLO txt (
class cx cy w h, normalized) - Full guide (ZH):
docs/dataset-structure-guide.md
Removed frames are moved to:
your_project/
removed/
train|val|test/
images/
labels/
Install
From PyPI:
pip install ultimate_ai_labeller
From local wheel:
pip install dist/ultimate_ai_labeller-0.1.5-py3-none-any.whl
From source:
pip install .
For development:
pip install -e .
Run
ai-labeller
Or:
python src/ai_labeller/main.py
Shortcuts
F: save and next imageD: previous imageA: auto red detectionCtrl+Z: undoCtrl+Y: redoDelete: delete selected box
Notes
- Default detection model mode is
Official YOLO26m.pt (Bundled). - If the official model file is unavailable locally, import a custom
.pt/.onnxmodel from the UI. - To use your own Tk app icon, put
app_icon.pnginsrc/ai_labeller/assets/. - Session file:
~/.ai_labeller_session.json.
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