Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu.
It is written in Python and uses Qt for its graphical interface.
Looking for a simple install without Python or Qt? Get the standalone app at labelme.io.
VOC dataset example of instance segmentation.
Other examples (semantic segmentation, bbox detection, and classification).
Various primitives (polygon, rectangle, circle, line, and point).
Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more).
- Image annotation for polygon, rectangle, circle, line and point (tutorial)
- Image flag annotation for classification and cleaning (#166)
- Video annotation (video annotation)
- GUI customization (predefined labels / flags, auto-saving, label validation, etc) (#144)
- Exporting VOC-format dataset for semantic segmentation, instance segmentation
- Exporting COCO-format dataset for instance segmentation
- AI-assisted point-to-polygon/mask annotation by SAM, EfficientSAM models
- AI text-to-annotation by YOLO-world, SAM3 models
🌏 Available in 20 languages - English · 日本語 · 한국어 · 简体中文 · 繁體中文 · Deutsch · Ελληνικά · Français · Español · Italiano · Português · Nederlands · Magyar · Русский · ไทย · Tiếng Việt · Türkçe · Українська · Polski · فارسی (LANG=ja_JP.UTF-8 labelme)
There are 3 options to install labelme:
For more detail, check "Install Labelme using Terminal"
pip install labelme
# To install the latest version from GitHub:
# pip install git+https://github.com/wkentaro/labelme.gitIf you're willing to invest in the convenience of simple installation without any dependencies (Python, Qt), you can download the standalone executable from "Install Labelme as App".
It's a one-time payment for lifetime access, and it helps us to maintain this project.
On some Linux distributions, labelme is also packaged in the system's native repository and can be installed with the distribution's standard package tooling. The badge below tracks which distributions currently ship labelme and which version each one provides:
| Supported (v7.x) | Maintenance (v6.3.x) | |
|---|---|---|
| Python | 3.11 - 3.14 | 3.10 - 3.11 |
| Qt | Qt6 (PySide6) | Qt5 |
| OS | 64-bit macOS / Windows / Linux | older OSes |
labelme follows SPEC 0 (the successor to NEP 29) for dropping Python versions, in step with its core scientific dependencies (numpy, scipy, scikit-image). v6.3.x is the maintenance line for Qt5 and Python 3.10 stragglers and receives critical fixes only.
v7.0.0 raises the platform floor:
- Qt binding: the GUI moved from PyQt5 (Qt5) to PySide6 (Qt6).
pip install labelmenow pulls PySide6 instead of PyQt5. If you import labelme as a library, note that internal Qt imports are PySide6. - Python: the minimum is now Python 3.11 (3.10 is dropped).
- OS: Qt6 requires a 64-bit macOS, Windows, or Linux; older OSes that only Qt5 supported are no longer covered.
If you need to stay on PyQt5/Qt5, Python 3.10, or an older OS, pin to the v6.3.x maintenance line:
pip install 'labelme<7'All previous releases remain installable from PyPI, so existing pins keep working.
v7.0.0 also changes config parsing:
- Config booleans:
~/.labelmercis now parsed with ruamel.yaml (YAML 1.2), so the boolean spellingsyes/no/on/off(in any capitalization) are read as strings rather than booleans. If you set any boolean option this way, switch it totrue/false.
Run labelme --help for detail.
The annotations are saved as a JSON file.
labelme # just open gui
# tutorial (single image example)
cd examples/tutorial
labelme apc2016_obj3.jpg # specify image file
labelme apc2016_obj3.jpg --output annotations/ # save annotation JSON files to a directory
labelme apc2016_obj3.jpg --with-image-data # include image data in JSON file
labelme apc2016_obj3.jpg \
--labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list
# semantic segmentation example
cd examples/semantic_segmentation
labelme data_annotated/ # Open directory to annotate all images in it
labelme data_annotated/ --labels labels.txt # specify label list with a file--outputspecifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on.- The first time you run labelme, it will create a config file at
~/.labelmerc. Add only the settings you want to override. For all available options and their defaults, seedefault_config.yaml. If you would prefer to use a config file from another location, you can specify this file with the--configflag. - Without the
--nosortlabelsflag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided. - Flags are assigned to an entire image. Example
- Labels are assigned to a single polygon. Example
- How to convert JSON file to numpy array? See examples/tutorial.
- How to load label PNG file? See examples/tutorial.
- How to get annotations for semantic segmentation? See examples/semantic_segmentation.
- How to get annotations for instance segmentation? See examples/instance_segmentation.
- Image Classification
- Bounding Box Detection
- Semantic Segmentation
- Instance Segmentation
- Video Annotation
LABELME_PATH=./labelme
OSAM_PATH=$(python -c 'import os, osam; print(os.path.dirname(osam.__file__))')
pip install 'numpy<2.0' # numpy>=2.0 causes build errors (see #1532)
pyinstaller labelme/labelme/__main__.py \
--name=Labelme \
--windowed \
--noconfirm \
--specpath=build \
--add-data=$(OSAM_PATH)/_models/yoloworld/clip/bpe_simple_vocab_16e6.txt.gz:osam/_models/yoloworld/clip \
--add-data=$(LABELME_PATH)/_config/default_config.yaml:labelme/_config \
--add-data=$(LABELME_PATH)/icons/*:labelme/icons \
--add-data=$(LABELME_PATH)/translate/*:translate \
--icon=$(LABELME_PATH)/icons/icon-256.png \
--onedirThis repo is the fork of mpitid/pylabelme.