[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9676":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},9676,"labelme","wkentaro\u002Flabelme","wkentaro","Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.","https:\u002F\u002Flabelme.io",null,"Python",15954,3675,147,113,0,22,91,7,45,"GNU General Public License v3.0",false,"main",[25,26,27,28,29,30,31,32,33],"annotations","classification","computer-vision","deep-learning","image-annotation","instance-segmentation","python","semantic-segmentation","video-annotation","2026-06-12 02:02:11","\u003Ch1 align=\"center\">\n  \u003Cimg src=\"labelme\u002Ficons\u002Ficon-256.png\" width=\"200\" height=\"200\">\u003Cbr\u002F>labelme\n\u003C\u002Fh1>\n\n\u003Ch4 align=\"center\">\n  Image annotation with Python.\n\u003C\u002Fh4>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Flabelme\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Flabelme.svg\">\u003C\u002Fa>\n  \u003C!-- \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Flabelme\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Flabelme.svg\">\u003C\u002Fa> -->\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme\u002Factions\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme\u002Factions\u002Fworkflows\u002Ftest.yml\u002Fbadge.svg?branch=main&event=push\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002FuAjxGcJm83\">\u003Cimg src=\"https:\u002F\u002Fdcbadge.limes.pink\u002Fapi\u002Fserver\u002FuAjxGcJm83?style=flat\">\u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"#installation\">\u003Cb>Installation\u003C\u002Fb>\u003C\u002Fa>\n  | \u003Ca href=\"#usage\">\u003Cb>Usage\u003C\u002Fb>\u003C\u002Fa>\n  | \u003Ca href=\"#examples\">\u003Cb>Examples\u003C\u002Fb>\u003C\u002Fa>\n  | \u003Ca href=\"https:\u002F\u002Flabelme.io\">\u003Cb>labelme.io ↗\u003C\u002Fb>\u003C\u002Fa>\n  \u003C!-- | \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme\u002Fdiscussions\">\u003Cb>Community\u003C\u002Fb>\u003C\u002Fa> -->\n  \u003C!-- | \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLI6LvFw0iflh3o33YYnVIfOpaO0hc5Dzw\">\u003Cb>Youtube FAQ\u003C\u002Fb>\u003C\u002Fa> -->\n\u003C\u002Fdiv>\n\n\u003Cbr\u002F>\n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\"examples\u002Finstance_segmentation\u002F.readme\u002Fannotation.jpg\" width=\"70%\">\n\u003C\u002Fdiv>\n\n## Description\n\nLabelme is a graphical image annotation tool inspired by \u003Chttp:\u002F\u002Flabelme.csail.mit.edu>.\\\nIt is written in Python and uses Qt for its graphical interface.\n\n> Looking for a simple install without Python or Qt? Get the standalone app at **[labelme.io](https:\u002F\u002Flabelme.io)**.\n\n\u003Cimg src=\"examples\u002Finstance_segmentation\u002Fdata_dataset_voc\u002FJPEGImages\u002F2011_000006.jpg\" width=\"19%\" \u002F> \u003Cimg src=\"examples\u002Finstance_segmentation\u002Fdata_dataset_voc\u002FSegmentationClass\u002F2011_000006.png\" width=\"19%\" \u002F> \u003Cimg src=\"examples\u002Finstance_segmentation\u002Fdata_dataset_voc\u002FSegmentationClassVisualization\u002F2011_000006.jpg\" width=\"19%\" \u002F> \u003Cimg src=\"examples\u002Finstance_segmentation\u002Fdata_dataset_voc\u002FSegmentationObject\u002F2011_000006.png\" width=\"19%\" \u002F> \u003Cimg src=\"examples\u002Finstance_segmentation\u002Fdata_dataset_voc\u002FSegmentationObjectVisualization\u002F2011_000006.jpg\" width=\"19%\" \u002F>\\\n\u003Ci>VOC dataset example of instance segmentation.\u003C\u002Fi>\n\n\u003Cimg src=\"examples\u002Fsemantic_segmentation\u002F.readme\u002Fannotation.jpg\" width=\"30%\" \u002F> \u003Cimg src=\"examples\u002Fbbox_detection\u002F.readme\u002Fannotation.jpg\" width=\"30%\" \u002F> \u003Cimg src=\"examples\u002Fclassification\u002F.readme\u002Fannotation_cat.jpg\" width=\"35%\" \u002F>\\\n\u003Ci>Other examples (semantic segmentation, bbox detection, and classification).\u003C\u002Fi>\n\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F4310419\u002F47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif\" width=\"30%\" \u002F> \u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F4310419\u002F47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif\" width=\"30%\" \u002F> \u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F14256482\u002F46932075-92145f00-d080-11e8-8d09-2162070ae57c.png\" width=\"32%\" \u002F>\\\n\u003Ci>Various primitives (polygon, rectangle, circle, line, and point).\u003C\u002Fi>\n\n\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F53bf09db-b097-48b7-9f32-ab490da5ac53\" width=\"32%\" \u002F>\n\u003Cp>\u003Ci>Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more).\u003C\u002Fi>\u003C\u002Fp>\n\n## Features\n\n- [x] Image annotation for polygon, rectangle, circle, line and point ([tutorial](examples\u002Ftutorial))\n- [x] Image flag annotation for classification and cleaning ([#166](https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme\u002Fpull\u002F166))\n- [x] Video annotation ([video annotation](examples\u002Fvideo_annotation))\n- [x] GUI customization (predefined labels \u002F flags, auto-saving, label validation, etc) ([#144](https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme\u002Fpull\u002F144))\n- [x] Exporting VOC-format dataset for [semantic segmentation](examples\u002Fsemantic_segmentation), [instance segmentation](examples\u002Finstance_segmentation)\n- [x] Exporting COCO-format dataset for [instance segmentation](examples\u002Finstance_segmentation)\n- [x] AI-assisted point-to-polygon\u002Fmask annotation by SAM, EfficientSAM models\n- [x] AI text-to-annotation by YOLO-world, SAM3 models\n\n**🌏 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`)\n\n## Installation\n\nThere are 3 options to install labelme:\n\n### Option 1: Using pip\n\nFor more detail, check [\"Install Labelme using Terminal\"](https:\u002F\u002Fwww.labelme.io\u002Fdocs\u002Finstall-labelme-terminal)\n\n```bash\npip install labelme\n\n# To install the latest version from GitHub:\n# pip install git+https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme.git\n```\n\n### Option 2: Using standalone executable (Easiest)\n\nIf you're willing to invest in the convenience of simple installation without any dependencies (Python, Qt),\nyou can download the standalone executable from [\"Install Labelme as App\"](https:\u002F\u002Fwww.labelme.io\u002Fdocs\u002Finstall-labelme-app).\n\nIt's a one-time payment for lifetime access, and it helps us to maintain this project.\n\n### Option 3: Linux distribution packages\n\nOn 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:\n\n[![Packaging status](https:\u002F\u002Frepology.org\u002Fbadge\u002Fvertical-allrepos\u002Flabelme.svg)](https:\u002F\u002Frepology.org\u002Fproject\u002Flabelme\u002Fversions)\n\n## Usage\n\nRun `labelme --help` for detail.\\\nThe annotations are saved as a [JSON](http:\u002F\u002Fwww.json.org\u002F) file.\n\n```bash\nlabelme  # just open gui\n\n# tutorial (single image example)\ncd examples\u002Ftutorial\nlabelme apc2016_obj3.jpg  # specify image file\nlabelme apc2016_obj3.jpg --output annotations\u002F  # save annotation JSON files to a directory\nlabelme apc2016_obj3.jpg --with-image-data  # include image data in JSON file\nlabelme apc2016_obj3.jpg \\\n  --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball  # specify label list\n\n# semantic segmentation example\ncd examples\u002Fsemantic_segmentation\nlabelme data_annotated\u002F  # Open directory to annotate all images in it\nlabelme data_annotated\u002F --labels labels.txt  # specify label list with a file\n```\n\n### Command Line Arguments\n\n- `--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.\n- The first time you run labelme, it will create a config file at `~\u002F.labelmerc`. Add only the settings you want to override. For all available options and their defaults, see [`default_config.yaml`](labelme\u002Fconfig\u002Fdefault_config.yaml). If you would prefer to use a config file from another location, you can specify this file with the `--config` flag.\n- 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.\n- Flags are assigned to an entire image. [Example](examples\u002Fclassification)\n- Labels are assigned to a single polygon. [Example](examples\u002Fbbox_detection)\n\n### FAQ\n\n- **How to convert JSON file to numpy array?** See [examples\u002Ftutorial](examples\u002Ftutorial#convert-to-dataset).\n- **How to load label PNG file?** See [examples\u002Ftutorial](examples\u002Ftutorial#how-to-load-label-png-file).\n- **How to get annotations for semantic segmentation?** See [examples\u002Fsemantic_segmentation](examples\u002Fsemantic_segmentation).\n- **How to get annotations for instance segmentation?** See [examples\u002Finstance_segmentation](examples\u002Finstance_segmentation).\n\n## Examples\n\n- [Image Classification](examples\u002Fclassification)\n- [Bounding Box Detection](examples\u002Fbbox_detection)\n- [Semantic Segmentation](examples\u002Fsemantic_segmentation)\n- [Instance Segmentation](examples\u002Finstance_segmentation)\n- [Video Annotation](examples\u002Fvideo_annotation)\n\n## How to build standalone executable\n\n```bash\nLABELME_PATH=.\u002Flabelme\nOSAM_PATH=$(python -c 'import os, osam; print(os.path.dirname(osam.__file__))')\npip install 'numpy\u003C2.0'  # numpy>=2.0 causes build errors (see #1532)\npyinstaller labelme\u002Flabelme\u002F__main__.py \\\n  --name=Labelme \\\n  --windowed \\\n  --noconfirm \\\n  --specpath=build \\\n  --add-data=$(OSAM_PATH)\u002F_models\u002Fyoloworld\u002Fclip\u002Fbpe_simple_vocab_16e6.txt.gz:osam\u002F_models\u002Fyoloworld\u002Fclip \\\n  --add-data=$(LABELME_PATH)\u002Fconfig\u002Fdefault_config.yaml:labelme\u002Fconfig \\\n  --add-data=$(LABELME_PATH)\u002Ficons\u002F*:labelme\u002Ficons \\\n  --add-data=$(LABELME_PATH)\u002Ftranslate\u002F*:translate \\\n  --icon=$(LABELME_PATH)\u002Ficons\u002Ficon-256.png \\\n  --onedir\n```\n\n## Acknowledgement\n\nThis repo is the fork of [mpitid\u002Fpylabelme](https:\u002F\u002Fgithub.com\u002Fmpitid\u002Fpylabelme).\n","Labelme 是一个基于 Python 的图像标注工具，支持多边形、矩形、圆形、线条、点以及 AI 辅助标注等多种标注方式。其核心功能包括丰富的标注类型和直观的图形界面（使用 Qt 开发），并能够生成符合多种计算机视觉任务需求的数据集，如实例分割、语义分割、目标检测等。该工具特别适合于需要进行图像数据预处理的研究人员或开发者，在深度学习模型训练前准备高质量的标注数据时使用。此外，Labelme 还提供了无需安装 Python 或 Qt 的独立应用程序版本，进一步降低了使用的门槛。",2,"2026-06-11 03:24:07","top_topic"]