labelme
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).
## Features - [x] Image annotation for polygon, rectangle, circle, line and point ([tutorial](examples/tutorial)) - [x] Image flag annotation for classification and cleaning ([#166](https://github.com/wkentaro/labelme/pull/166)) - [x] Video annotation ([video annotation](examples/video_annotation)) - [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc) ([#144](https://github.com/wkentaro/labelme/pull/144)) - [x] Exporting VOC-format dataset for [semantic segmentation](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation) - [x] Exporting COCO-format dataset for [instance segmentation](examples/instance_segmentation) - [x] AI-assisted point-to-polygon/mask annotation by SAM, EfficientSAM models - [x] AI text-to-annotation by YOLO-world, SAM3 models **🌏 Available in 16 languages** - English · 日本語 · 한국어 · 简体中文 · 繁體中文 · Deutsch · Français · Español · Italiano · Português · Nederlands · Magyar · Tiếng Việt · Türkçe · 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"](https://www.labelme.io/docs/install-labelme-terminal) ```bash 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"](https://www.labelme.io/docs/install-labelme-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: [](https://repology.org/project/labelme/versions) ## Usage Run `labelme --help` for detail. The annotations are saved as a [JSON](http://www.json.org/) file. ```bash 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`](labelme/config/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](examples/classification) - Labels are assigned to a single polygon. [Example](examples/bbox_detection) ### FAQ - **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial#convert-to-dataset). - **How to load label PNG file?** See [examples/tutorial](examples/tutorial#how-to-load-label-png-file). - **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](examples/semantic_segmentation). - **How to get annotations for instance segmentation?** See [examples/instance_segmentation](examples/instance_segmentation). ## Examples * [Image Classification](examples/classification) * [Bounding Box Detection](examples/bbox_detection) * [Semantic Segmentation](examples/semantic_segmentation) * [Instance Segmentation](examples/instance_segmentation) * [Video Annotation](examples/video_annotation) ## How to build standalone executable ```bash 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](https://github.com/mpitid/pylabelme).