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LabelImg is a graphical image annotation tool and label object bounding boxes in images

Project description


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. Besides, it also supports YOLO and CreateML formats.

Demo Image Demo Image

Watch a demo video


Get from PyPI but only python3.0 or above

This is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora.

pip3 install labelImg

Build from source

Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended.

Ubuntu Linux

Python 3 + Qt5

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

Python 3 + Qt5

brew install qt  # Install qt-5.x.x by Homebrew
brew install libxml2

or using pip

pip3 install pyqt5 lxml # Install qt and lxml by pip

make qt5py3

Python 3 Virtualenv (Recommended)

Virtualenv can avoid a lot of the QT / Python version issues

brew install python3
pip3 install pipenv
pipenv run pip install pyqt5==5.15.2 lxml
pipenv run make qt5py3
pipenv run python3
[Optional] rm -rf build dist; python py2app -A;mv "dist/" /Applications

Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/


Install Python, PyQt5 and install lxml.

Open cmd and go to the labelImg directory

pyrcc4 -o libs/ resources.qrc
For pyqt5, pyrcc5 -o libs/ resources.qrc

Windows + Anaconda

Download and install Anaconda (Python 3+)

Open the Anaconda Prompt and go to the labelImg directory

conda install pyqt=5
conda install -c anaconda lxml
pyrcc5 -o libs/ resources.qrc

Use Docker

docker run -it \
--user $(id -u) \
--workdir=$(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 \

make qt4py2;./

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


Steps (PascalVOC)

  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.

Steps (YOLO)

  1. In data/predefined_classes.txt define the list of classes that will be used for your training.

  2. Build and launch using the instructions above.

  3. Right below “Save” button in the toolbar, click “PascalVOC” button to switch to YOLO format.

  4. You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.

A txt file of YOLO format will be saved in the same folder as your image with same name. A file named “classes.txt” is saved to that folder too. “classes.txt” defines the list of class names that your YOLO label refers to.


  • Your label list shall not change in the middle of processing a list of images. When you save an image, classes.txt will also get updated, while previous annotations will not be updated.

  • You shouldn’t use “default class” function when saving to YOLO format, it will not be referred.

  • When saving as YOLO format, “difficult” flag is discarded.

Create pre-defined classes

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


Ctrl + u

Load all of the images from a directory

Ctrl + r

Change the default annotation target dir

Ctrl + s


Ctrl + d

Copy the current label and rect box

Ctrl + Shift + d

Delete the current image


Flag the current image as verified


Create a rect box


Next image


Previous image


Delete the selected rect box


Zoom in


Zoom out


Keyboard arrows to move selected rect box

Verify Image:

When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.


The difficult field is set to 1 indicates that the object has been annotated as “difficult”, for example, an object which is clearly visible but difficult to recognize without substantial use of context. According to your deep neural network implementation, you can include or exclude difficult objects during training.

How to reset the settings

In case there are issues with loading the classes, you can either:

  1. From the top menu of the labelimg click on Menu/File/Reset All

  2. Remove the .labelImgSettings.pkl from your home directory. In Linux and Mac you can do:

    rm ~/.labelImgSettings.pkl

How to contribute

Send a pull request


Free software: MIT license

Citation: Tzutalin. LabelImg. Git code (2015).

Stargazers over time


1.8.6 (2021-10-10)

  • Display box width and height

1.8.5 (2021-04-11)

  • Merged a couple of PRs

  • Fixed issues

  • Support CreateML format

1.8.4 (2020-11-04)

  • Merged a couple of PRs

  • Fixed issues

1.8.2 (2018-12-02)

  • Fix pip depolyment issue

1.8.1 (2018-12-02)

  • Fix issues

  • Support zh-Tw strings

1.8.0 (2018-10-21)

  • Support drawing sqaure rect

  • Add item single click slot

  • Fix issues

1.7.0 (2018-05-18)

  • Support YOLO

  • Fix minor issues

1.6.1 (2018-04-17)

  • Fix issue

1.6.0 (2018-01-29)

  • Add more pre-defined labels

  • Show cursor pose in status bar

  • Fix minor issues

1.5.2 (2017-10-24)

  • Assign different colors to different lablels

1.5.1 (2017-9-27)

  • Show a autosaving dialog

1.5.0 (2017-9-14)

  • Fix the issues

  • Add feature: Draw a box easier

1.4.3 (2017-08-09)

  • Refactor setting

  • Fix the issues

1.4.0 (2017-07-07)

  • Add feature: auto saving

  • Add feature: single class mode

  • Fix the issues

1.3.4 (2017-07-07)

  • Fix issues and improve zoom-in

1.3.3 (2017-05-31)

  • Fix issues

1.3.2 (2017-05-18)

  • Fix issues

1.3.1 (2017-05-11)

  • Fix issues

1.3.0 (2017-04-22)

  • Fix issues

  • Add difficult tag

  • Create new files for pypi

1.2.3 (2017-04-22)

  • Fix issues

1.2.2 (2017-01-09)

  • Fix issues

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