README

labelme

Description

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.

\

<i>VOC dataset example of instance segmentation.</i>

<i>Other examples (semantic segmentation, bbox detection, and classification).</i>

<i>Various primitives (polygon, rectangle, circle, line, and point).</i>

Features

• 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)

Installation

There are 3 options to install labelme:

Option 1: Using pip

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.git

Option 2: Using standalone executable (Easiest)

If 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.

Option 3: Using a package manager in each Linux distribution

In some Linux distributions, you can install labelme via their package managers (e.g., apt, pacman). The following systems are currently available:

Usage

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

Command Line Arguments

• --output specifies 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, see default_config.yaml. If you would prefer to use a config file from another location, you can specify this file with the --config flag.
• Without the --nosortlabels flag, 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

FAQ

• 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.

Examples

• Image Classification
• Bounding Box Detection
• Semantic Segmentation
• Instance Segmentation
• Video Annotation

How to build standalone executable

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 \
  --onedir

Acknowledgement

This repo is the fork of mpitid/pylabelme.