LabelImg is a graphical image annotation tool.

It is written in Python and uses Qt for its graphical interface.

Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet.

Watch a demo video

• Windows & Linux
• OS X. Binaries for OS X are not yet available. Help would be appreciated. At present, it must be built from source.

Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8.

Python 2 + Qt4

sudo apt-get install pyqt4-dev-tools
sudo pip install lxml
make qt4py2
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 + Qt5

sudo apt-get install pyqt5-dev-tools
sudo pip3 install lxml
make qt5py3
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 2 + Qt4

brew install qt qt4
brew install libxml2
make qt4py2
python labelImg.py
python  labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Download and setup Python 2.6 or later, PyQt4 and install lxml.

Open cmd and go to labelImg directory

pyrcc4 -o resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

pip install labelImg
labelImg
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

I tested pip on Ubuntu14.04 and 16.04. However, I didn't test pip on MacOS and Windows

docker run -it \
--user $(id -u) \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
-v /tmp/.X11-unix:/tmp/.X11-unix \
tzutalin/py2qt4

make qt4py2;./labelImg.py

You can pull the image which has all of the installed and required dependencies. Watch a demo video

1. Build and launch using the instructions above.
2. Click 'Change default saved annotation folder' in Menu/File
3. Click 'Open Dir'
4. Click 'Create RectBox'
5. Click and release left mouse to select a region to annotate the rect box
6. You can use right mouse to drag the rect box to copy or move it

The annotation will be saved to the folder you specify.

You can refer to the below hotkeys to speed up your workflow.

You can edit the data/predefined_classes.txt to load pre-defined classes

Send a pull request

Free software: MIT license

1. ImageNet Utils to download image, create a label text for machine learning, etc
2. Docker hub to run it