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A modern, enhanced image annotation tool for machine learning

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labelImg++ is a graphical image annotation tool for bounding boxes, polygons, and keypoints, designed for machine learning and computer vision projects. It is forked from the original LabelImg with significant enhancements.

Version 3.4.0 (stable). Install with pip install labelimgplusplus.

labelImg++ demo - gallery, bounding boxes, dark theme, polygons, keypoints and save

Features

Core Annotation Features

  • Five annotation formats: PASCAL VOC, YOLO bbox, CreateML, COCO, and YOLO-seg

  • Bounding box and polygon annotation with interactive editing

  • Keypoint annotation with COCO 17-point human pose and 5-point face templates; COCO preserves keypoints on export

  • Auto-save mode for uninterrupted workflow

  • Predefined class labels with customizable list

  • Verification system to mark completed annotations

Workflow and Interface

Undo/Redo Support

Full undo/redo for annotation actions. Press Ctrl+Z to undo and Ctrl+Shift+Z to redo.

Balanced Modern Workspace

A fixed annotation-tool rail, compact command bar, unified Objects/Files inspector, integrated video timeline, and visible document status keep the active canvas uncluttered. Gallery Mode is embedded in the same workspace, so the annotation tools, inspector, and status remain available while browsing thumbnail previews across all five annotation formats.

  • Colored borders indicate status: Gray (no labels), Blue (has labels), Green (verified)

  • Bounding boxes use compact corner markers; polygons use outline previews

  • Quick size presets (S/M/L/XL) plus slider for fine control

  • Smart selection: click on nested boxes selects the inner box

  • Press Ctrl+G to toggle gallery mode

Modern UI with Feather Icons

Official Feather icons provide a consistent, accessible tool vocabulary.

Responsive DPI Scaling

Icons and UI elements scale properly on high-DPI displays (4K, Retina).

Unified Object Inspector

Search and edit rectangles, polygons, keypoints, and video tracks in one Objects view. The Files view keeps the compact file list, thumbnails, and annotation-status filter close at hand, and the inspector can be collapsed when maximum canvas space is needed.

Faster Class Confirmation

New boxes, polygons, Smart Select results, and video geometry remain provisional while a non-modal class picker opens beside the annotation. Filter or enter a class, press Enter to commit one undoable annotation, or press Escape to discard it without changing the document. Default labels and established single-class sessions bypass the picker.

Smart Select Output Modes

While Smart Select is active, choose Box or Polygon from the compact canvas control. The choice persists between sessions, and either result follows the same provisional class-confirmation and undo workflow.

Portable Whole-Video Propagation

From the integrated timeline, propagate every accepted manual anchor on the current frame or only the selected object. The portable OpenCV backend processes rectangles, polygons, and associated keypoints together, shows preview-only progress, protects later manual anchors, and commits accepted observations and explicit gap records atomically in one undo step. Editing generated geometry creates a manual correction first, then regenerates only the bounded neighboring segments as a separate undoable change.

Direct Ultralytics Dataset Export

Choose Tools → Export Ultralytics Dataset… to create a ready-to-train YOLO detection dataset with images/{train,val,test}, matching labels/{train,val,test}, and data.yaml. Configure deterministic split ratios and either copy images or create absolute local symlinks. The destination must be new or empty, and it is published only after the full export succeeds. See the Ultralytics Export Guide.

Installed Python Plugins

Add trusted command plugins from separately installed Python distributions without editing labelImg++ source. Review and enable them under Tools → Plugins…; changes take effect after restart. Plugins use a versioned public API with host-owned actions, namespaced shortcuts and settings, bounded background work, read-only document state, diagnostics, and LABELIMGPP_DISABLE_PLUGINS=1 recovery. See the Plugin Authoring Guide.

Brightness Adjustment

Adjust image brightness on-the-fly to better see annotations on dark or light images.

Dark Mode Theme

Choose between light and dark themes for comfortable annotation in any lighting condition.

  • Press Ctrl+Shift+T to toggle between themes

  • Theme preference automatically saved

  • All UI components (canvas, gallery, dialogs) respect the active theme

  • See Dark Mode Documentation for detailed information

SAM-Assisted Segmentation (optional)

Click once on an object to auto-generate a polygon, traced from a Segment-Anything mask. Install the optional extra and toggle SAM Segment:

pip install labelimgplusplus[sam]

Runs the lightweight MobileSAM model on ONNX Runtime as a CPU-friendly extra with no PyTorch dependency. The default model pair is downloaded and checksum-verified on first use. Point Tools → SAM Settings… at your own exported encoder/decoder pair to use a different SAM variant. Without the extra installed, the action stays disabled (with an install hint) and nothing else changes.

Smart Video Annotation (optional)

Open local MP4, MOV, MKV, and AVI video, annotate on exact presentation timestamps, interpolate rectangle keyframes, and propagate rectangles with reviewable optical-flow suggestions. Video work is stored in a sibling <video>.labelimgpp.sqlite project; existing image annotations and formats are unchanged.

pip install labelimgplusplus[video]

The timeline supports frame stepping, exact timecode seeks, variable-rate media, playback without audio, track markers, and verified-frame markers. Accepted frames export to VOC, YOLO, YOLO-seg, COCO, or CreateML. See the Smart Video Annotation Guide for the project, review, and export workflow.

Installation

labelImg++ requires Python 3.8 or newer.

Build from Source

Ubuntu/Linux:

sudo apt-get install pyqt5-dev-tools
pip3 install -r requirements/requirements-linux-python3.txt
make qt5py3
python3 labelImgPlusPlus.py

macOS:

pip3 install pyqt5 lxml
make qt5py3
python3 labelImgPlusPlus.py

Windows:

pip install pyqt5 lxml
pyrcc5 -o libs/resources.py resources.qrc
python labelImgPlusPlus.py

Quick Start

  1. Open images: Click the file dropdown button or press Ctrl+U to load a directory

  2. Create annotations: Press W or click Create RectBox, then drag to draw

  3. Label objects: Select a class from the popup dialog

  4. Save: Press Ctrl+S to save annotations

  5. Navigate: Use D (next) and A (previous) to move between images

  6. Review: Press Ctrl+G for gallery mode to review all annotations

For video, press Ctrl+Alt+V, draw a rectangle or polygon on the paused frame, then use the Tracks tab and Tools menu to add keyframes, track, review suggestions, and export accepted frames.

Supported Annotation Formats

Format

Extension

Annotation support

PASCAL VOC

.xml

Bounding boxes and the labelImg++ polygon extension

YOLO (bbox)

.txt

Normalized bounding boxes, with classes.txt for class names

CreateML

.json

Bounding boxes in Apple’s CreateML annotation format

COCO

.json

Bounding boxes, polygon segmentation, and keypoints

YOLO-seg

.txt

Normalized polygons; rectangles are written as four-point polygons

Polygon drawing and editing are available in the application. Saving polygons as YOLO bounding boxes or CreateML warns before converting each polygon to its enclosing box. Keypoints are preserved by COCO; the other formats do not encode them.

Keyboard Shortcuts

These are the defaults. They can be changed from Help → Keyboard Shortcuts.

File Operations

Ctrl + O

Open file

Ctrl + Alt + V

Open video or video project

Ctrl + U

Open directory

Ctrl + R

Change save directory

Ctrl + S

Save current annotation

Ctrl + Y

Cycle annotation format

Ctrl + Shift + S

Save as

Navigation

D

Next image

A

Previous image

Ctrl + G

Toggle Gallery Mode

In video mode, A/D step exact frames, Ctrl+Space toggles playback, Shift+K adds a keyframe to the selected track, T/Shift+T track forward or backward, and Shift+Enter/Backspace accept or reject the current suggestion. Space continues to verify the current frame.

Annotation

W

Create bounding box

P

Create polygon

K

Enter keypoint mode

Ctrl + Z

Undo

Ctrl + Shift + Z

Redo

Ctrl + D

Duplicate selected box

Del

Delete selected box

Space

Mark image as verified

Arrow keys

Move selected box

View

Ctrl + +

Zoom in

Ctrl + -

Zoom out

Ctrl + F

Fit window

Ctrl + Shift + F

Fit width

Ctrl + Shift + T

Toggle dark mode theme

Configuration

Predefined Classes

Edit data/predefined_classes.txt to customize the label options:

dog
cat
person
car
bicycle

Reset Settings

If you encounter issues, reset the settings:

rm ~/.labelImgSettings.json

Or use Menu > File > Reset All

Release History

This source tree identifies itself as 3.4.0, the stable 3.4 release. See the release history for version-by-version changes and GitHub Releases for published artifacts.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

License

MIT License

Based on LabelImg by Tzutalin.

Author

Maintained by Abhik Sarkar

Acknowledgments

  • Original LabelImg by Tzutalin

  • Feather Icons for modern iconography

  • All contributors and users of labelImg++

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