🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 174 (from laksa169)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa174.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://github.com/wkentaro/labelme\')), getAhrefsUnparsedNoserviceFromURL(\'https://github.com/wkentaro/labelme\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/github.com\/wkentaro\/labelme","crawl_time":1774768172,"first_indexed_time":1469004835,"http_code":200,"src_unparsed":"com,github!\/wkentaro\/labelme s443","src_root_hash":"6325672905007345774","history_drop_reason":null,"meta_title":"GitHub - wkentaro\/labelme: Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation. · GitHub","meta_descriptions":["Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation. - wkentaro\/labelme"],"attrs_boilerpipe_text":"Image annotation with Python.\nDescription\nLabelme is a graphical image annotation tool inspired by\nhttp:\/\/labelme.csail.mit.edu\n.\nIt is written in Python and uses Qt for its graphical interface.\nLooking for a simple install without Python or Qt? Get the standalone app at\nlabelme.io\n.\nVOC dataset example of instance segmentation.\nOther examples (semantic segmentation, bbox detection, and classification).\nVarious primitives (polygon, rectangle, circle, line, and point).\nMulti-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more).\nFeatures\nImage annotation for polygon, rectangle, circle, line and point (\ntutorial\n)\nImage flag annotation for classification and cleaning (\n#166\n)\nVideo annotation (\nvideo annotation\n)\nGUI customization (predefined labels \/ flags, auto-saving, label validation, etc) (\n#144\n)\nExporting VOC-format dataset for\nsemantic segmentation\n,\ninstance segmentation\nExporting COCO-format dataset for\ninstance segmentation\nAI-assisted point-to-polygon\/mask annotation by SAM, EfficientSAM models\nAI text-to-annotation by YOLO-world, SAM3 models\n🌏 Available in 16 languages\n- 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 (\nLANG=ja_JP.UTF-8 labelme\n)\nInstallation\nThere are 3 options to install labelme:\nOption 1: Using pip\nFor more detail, check\n\"Install Labelme using Terminal\"\npip install labelme\n#\nTo install the latest version from GitHub:\n#\npip install git+https:\/\/github.com\/wkentaro\/labelme.git\nOption 2: Using standalone executable (Easiest)\nIf you're willing to invest in the convenience of simple installation without any dependencies (Python, Qt),\nyou can download the standalone executable from\n\"Install Labelme as App\"\n.\nIt's a one-time payment for lifetime access, and it helps us to maintain this project.\nOption 3: Using a package manager in each Linux distribution\nIn some Linux distributions, you can install labelme via their package managers (e.g., apt, pacman). The following systems are currently available:\nUsage\nRun\nlabelme --help\nfor detail.\nThe annotations are saved as a\nJSON\nfile.\nlabelme\n#\njust open gui\n#\ntutorial (single image example)\ncd\nexamples\/tutorial\nlabelme apc2016_obj3.jpg\n#\nspecify image file\nlabelme apc2016_obj3.jpg --output annotations\/\n#\nsave annotation JSON files to a directory\nlabelme apc2016_obj3.jpg --with-image-data\n#\ninclude 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\n#\nspecify label list\n#\nsemantic segmentation example\ncd\nexamples\/semantic_segmentation\nlabelme data_annotated\/\n#\nOpen directory to annotate all images in it\nlabelme data_annotated\/ --labels labels.txt\n#\nspecify label list with a file\nCommand Line Arguments\n--output\nspecifies 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.\nThe first time you run labelme, it will create a config file at\n~\/.labelmerc\n. Add only the settings you want to override. For all available options and their defaults, see\ndefault_config.yaml\n. If you would prefer to use a config file from another location, you can specify this file with the\n--config\nflag.\nWithout the\n--nosortlabels\nflag, 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.\nFlags are assigned to an entire image.\nExample\nLabels are assigned to a single polygon.\nExample\nFAQ\nHow to convert JSON file to numpy array?\nSee\nexamples\/tutorial\n.\nHow to load label PNG file?\nSee\nexamples\/tutorial\n.\nHow to get annotations for semantic segmentation?\nSee\nexamples\/semantic_segmentation\n.\nHow to get annotations for instance segmentation?\nSee\nexamples\/instance_segmentation\n.\nExamples\nImage Classification\nBounding Box Detection\nSemantic Segmentation\nInstance Segmentation\nVideo Annotation\nHow to build standalone executable\nLABELME_PATH=.\/labelme\nOSAM_PATH=\n$(\npython -c\n'\nimport os, osam; print(os.path.dirname(osam.__file__))\n'\n)\npip install\n'\nnumpy<2.0\n'\n#\nnumpy>=2.0 causes build errors (see #1532)\npyinstaller labelme\/labelme\/__main__.py \\\n  --name=Labelme \\\n  --windowed \\\n  --noconfirm \\\n  --specpath=build \\\n  --add-data=\n$(\nOSAM_PATH\n)\n\/_models\/yoloworld\/clip\/bpe_simple_vocab_16e6.txt.gz:osam\/_models\/yoloworld\/clip \\\n  --add-data=\n$(\nLABELME_PATH\n)\n\/config\/default_config.yaml:labelme\/config \\\n  --add-data=\n$(\nLABELME_PATH\n)\n\/icons\/\n*\n:labelme\/icons \\\n  --add-data=\n$(\nLABELME_PATH\n)\n\/translate\/\n*\n:translate \\\n  --icon=\n$(\nLABELME_PATH\n)\n\/icons\/icon-256.png \\\n  --onedir\nAcknowledgement\nThis repo is the fork of\nmpitid\/pylabelme\n.","attrs_markdown":"[Skip to content](https:\/\/github.com\/wkentaro\/labelme#start-of-content)\n\n## Navigation Menu\nToggle navigation\n\n[Sign in](https:\/\/github.com\/login?return_to=https%3A%2F%2Fgithub.com%2Fwkentaro%2Flabelme)\n\nAppearance settings\n\n- Platform\n  \n  - 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[Reload](https:\/\/github.com\/wkentaro\/labelme) to refresh your session.\n\nDismiss alert\n\n{{ message }}\n\n[wkentaro](https:\/\/github.com\/wkentaro)   \/  **[labelme](https:\/\/github.com\/wkentaro\/labelme)**  Public\n\n- [Notifications](https:\/\/github.com\/login?return_to=%2Fwkentaro%2Flabelme)\n  You must be signed in to change notification settings\n- [Fork 3.7k](https:\/\/github.com\/login?return_to=%2Fwkentaro%2Flabelme)\n- [Star 15.7k](https:\/\/github.com\/login?return_to=%2Fwkentaro%2Flabelme)\n\n- [Code](https:\/\/github.com\/wkentaro\/labelme)\n- [Issues 109](https:\/\/github.com\/wkentaro\/labelme\/issues)\n- [Pull requests 73](https:\/\/github.com\/wkentaro\/labelme\/pulls)\n- [Discussions](https:\/\/github.com\/wkentaro\/labelme\/discussions)\n- [Actions](https:\/\/github.com\/wkentaro\/labelme\/actions)\n- [Security 0](https:\/\/github.com\/wkentaro\/labelme\/security)\n- [Insights](https:\/\/github.com\/wkentaro\/labelme\/pulse)\n\nAdditional navigation options\n\n- [Code](https:\/\/github.com\/wkentaro\/labelme)\n- [Issues](https:\/\/github.com\/wkentaro\/labelme\/issues)\n- [Pull requests](https:\/\/github.com\/wkentaro\/labelme\/pulls)\n- [Discussions](https:\/\/github.com\/wkentaro\/labelme\/discussions)\n- [Actions](https:\/\/github.com\/wkentaro\/labelme\/actions)\n- [Security](https:\/\/github.com\/wkentaro\/labelme\/security)\n- [Insights](https:\/\/github.com\/wkentaro\/labelme\/pulse)\n\n# wkentaro\/labelme\nmain\n\n[**27** Branches](https:\/\/github.com\/wkentaro\/labelme\/branches)\n\n[**220** Tags](https:\/\/github.com\/wkentaro\/labelme\/tags)\n\nGo to file\n\nCode\n\nOpen more actions menu\n\n## Folders and files\n| Name | Name | Last commit message | Last commit date |\n|---|---|---|---|\n| Latest commit [![wkentaro](https:\/\/avatars.githubusercontent.com\/u\/4310419?v=4&size=40)](https:\/\/github.com\/wkentaro)[wkentaro](https:\/\/github.com\/wkentaro\/labelme\/commits?author=wkentaro) [Merge pull request](https:\/\/github.com\/wkentaro\/labelme\/commit\/2cd7d5a049785cd4b8bc2fb397bbc24da07eaccd) [\\#1905](https:\/\/github.com\/wkentaro\/labelme\/pull\/1905) [from wkentaro\/dependabot\/uv\/cryptography-46.0.6](https:\/\/github.com\/wkentaro\/labelme\/commit\/2cd7d5a049785cd4b8bc2fb397bbc24da07eaccd) Open commit details success Mar 28, 2026 [2cd7d5a](https:\/\/github.com\/wkentaro\/labelme\/commit\/2cd7d5a049785cd4b8bc2fb397bbc24da07eaccd) · Mar 28, 2026 History [2,225 Commits](https:\/\/github.com\/wkentaro\/labelme\/commits\/main\/) Open commit details 2,225 Commits |   |   |   |\n\n## Repository files navigation\n- [README](https:\/\/github.com\/wkentaro\/labelme)\n- [GPL-3.0 license](https:\/\/github.com\/wkentaro\/labelme)\n\n# [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/labelme\/icons\/icon-256.png)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/labelme\/icons\/icon-256.png) labelme\n#### Image annotation with Python.\n[![](https:\/\/camo.githubusercontent.com\/8fc773cd9299e35f036a1ad51cf2849a112b342d7be3e07b8746c09c08eef545\/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6162656c6d652e737667)](https:\/\/pypi.python.org\/pypi\/labelme) [![](https:\/\/github.com\/wkentaro\/labelme\/actions\/workflows\/ci.yml\/badge.svg?branch=main&event=push)](https:\/\/github.com\/wkentaro\/labelme\/actions) [![](https:\/\/camo.githubusercontent.com\/816f80b7e3461ca33d13c074b292aa2ea7384a774c8e4092d0e4508f1a7c038a\/68747470733a2f2f646362616467652e6c696d65732e70696e6b2f6170692f7365727665722f75416a7847634a6d38333f7374796c653d666c6174)](https:\/\/discord.com\/invite\/uAjxGcJm83)\n\n[**Installation**](https:\/\/github.com\/wkentaro\/labelme#installation) \\| [**Usage**](https:\/\/github.com\/wkentaro\/labelme#usage) \\| [**Examples**](https:\/\/github.com\/wkentaro\/labelme#examples) \\| [**labelme.io ↗**](https:\/\/labelme.io\/)\n\n[![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/.readme\/annotation.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/.readme\/annotation.jpg)\n\n## Description\nLabelme is a graphical image annotation tool inspired by <http:\/\/labelme.csail.mit.edu>.  \n It 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:\/\/labelme.io\/)**.\n\n[![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/JPEGImages\/2011_000006.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/JPEGImages\/2011_000006.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClass\/2011_000006.png)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClass\/2011_000006.png) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClassVisualization\/2011_000006.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClassVisualization\/2011_000006.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObject\/2011_000006.png)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObject\/2011_000006.png) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObjectVisualization\/2011_000006.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObjectVisualization\/2011_000006.jpg)  \n *VOC dataset example of instance segmentation.*\n\n[![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/semantic_segmentation\/.readme\/annotation.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation\/.readme\/annotation.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/bbox_detection\/.readme\/annotation.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/bbox_detection\/.readme\/annotation.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/classification\/.readme\/annotation_cat.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/classification\/.readme\/annotation_cat.jpg)  \n *Other examples (semantic segmentation, bbox detection, and classification).*\n\n[![](https:\/\/user-images.githubusercontent.com\/4310419\/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif)](https:\/\/user-images.githubusercontent.com\/4310419\/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif) [![](https:\/\/user-images.githubusercontent.com\/4310419\/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif)](https:\/\/user-images.githubusercontent.com\/4310419\/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif) [![](https:\/\/user-images.githubusercontent.com\/4310419\/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif)](https:\/\/user-images.githubusercontent.com\/4310419\/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif) [![](https:\/\/user-images.githubusercontent.com\/4310419\/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif)](https:\/\/user-images.githubusercontent.com\/4310419\/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif) [![](https:\/\/user-images.githubusercontent.com\/14256482\/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png)](https:\/\/user-images.githubusercontent.com\/14256482\/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png)  \n *Various primitives (polygon, rectangle, circle, line, and point).*\n\n[![](https:\/\/private-user-images.githubusercontent.com\/4310419\/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzQ3Njg0NzQsIm5iZiI6MTc3NDc2ODE3NCwicGF0aCI6Ii80MzEwNDE5LzU1OTM3Mzg5Ni01M2JmMDlkYi1iMDk3LTQ4YjctOWYzMi1hYjQ5MGRhNWFjNTMuZ2lmP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI2MDMyOSUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNjAzMjlUMDcwOTM0WiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9MzgwYzUzM2QzNzZhZGQzMTRiZmI0NzQ3M2U1MDQwZDkxMDg4M2Y0OGIzYmM2OTRmMDNkYmFkODA1MjkwMzc3ZCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)](https:\/\/private-user-images.githubusercontent.com\/4310419\/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)\n\n[![](https:\/\/private-user-images.githubusercontent.com\/4310419\/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)](https:\/\/private-user-images.githubusercontent.com\/4310419\/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)\n\n*Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more).*\n\n## Features\n- Image annotation for polygon, rectangle, circle, line and point ([tutorial](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/tutorial))\n- Image flag annotation for classification and cleaning ([\\#166](https:\/\/github.com\/wkentaro\/labelme\/pull\/166))\n- Video annotation ([video annotation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/video_annotation))\n- GUI customization (predefined labels \/ flags, auto-saving, label validation, etc) ([\\#144](https:\/\/github.com\/wkentaro\/labelme\/pull\/144))\n- Exporting VOC-format dataset for [semantic segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation), [instance segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation)\n- Exporting COCO-format dataset for [instance segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation)\n- AI-assisted point-to-polygon\/mask annotation by SAM, EfficientSAM models\n- AI text-to-annotation by YOLO-world, SAM3 models\n\n**🌏 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`)\n\n## Installation\nThere are 3 options to install labelme:\n\n### Option 1: Using pip\nFor more detail, check [\"Install Labelme using Terminal\"](https:\/\/www.labelme.io\/docs\/install-labelme-terminal)\n```\npip install labelme\n\n# To install the latest version from GitHub:\n# pip install git+https:\/\/github.com\/wkentaro\/labelme.git\n```\n### Option 2: Using standalone executable (Easiest)\nIf 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).\n\nIt's a one-time payment for lifetime access, and it helps us to maintain this project.\n\n### Option 3: Using a package manager in each Linux distribution\nIn some Linux distributions, you can install labelme via their package managers (e.g., apt, pacman). The following systems are currently available:\n\n[![Packaging status](https:\/\/camo.githubusercontent.com\/a071432e6036317337e84e6e609c2e0f69de37bdb18a55daada24dc359f8454a\/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f766572746963616c2d616c6c7265706f732f6c6162656c6d652e737667)](https:\/\/repology.org\/project\/labelme\/versions)\n\n## Usage\nRun `labelme --help` for detail.  \n The annotations are saved as a [JSON](http:\/\/www.json.org\/) file.\n```\nlabelme  # just open gui\n\n# tutorial (single image example)\ncd examples\/tutorial\nlabelme apc2016_obj3.jpg  # specify image file\nlabelme apc2016_obj3.jpg --output annotations\/  # 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\/semantic_segmentation\nlabelme data_annotated\/  # Open directory to annotate all images in it\nlabelme data_annotated\/ --labels labels.txt  # specify label list with a file\n```\n### Command Line Arguments\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 `~\/.labelmerc`. Add only the settings you want to override. For all available options and their defaults, see [`default_config.yaml`](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/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.\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](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/classification)\n- Labels are assigned to a single polygon. [Example](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/bbox_detection)\n### FAQ\n- **How to convert JSON file to numpy array?** See [examples\/tutorial](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/tutorial#convert-to-dataset).\n- **How to load label PNG file?** See [examples\/tutorial](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/tutorial#how-to-load-label-png-file).\n- **How to get annotations for semantic segmentation?** See [examples\/semantic\\_segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation).\n- **How to get annotations for instance segmentation?** See [examples\/instance\\_segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation).\n## Examples\n- [Image Classification](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/classification)\n- [Bounding Box Detection](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/bbox_detection)\n- [Semantic Segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation)\n- [Instance Segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation)\n- [Video Annotation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/video_annotation)\n## How to build standalone executable\n```\nLABELME_PATH=.\/labelme\nOSAM_PATH=$(python -c 'import os, osam; print(os.path.dirname(osam.__file__))')\npip install 'numpy<2.0'  # numpy>=2.0 causes build errors (see #1532)\npyinstaller labelme\/labelme\/__main__.py \\\n  --name=Labelme \\\n  --windowed \\\n  --noconfirm \\\n  --specpath=build \\\n  --add-data=$(OSAM_PATH)\/_models\/yoloworld\/clip\/bpe_simple_vocab_16e6.txt.gz:osam\/_models\/yoloworld\/clip \\\n  --add-data=$(LABELME_PATH)\/config\/default_config.yaml:labelme\/config \\\n  --add-data=$(LABELME_PATH)\/icons\/*:labelme\/icons \\\n  --add-data=$(LABELME_PATH)\/translate\/*:translate \\\n  --icon=$(LABELME_PATH)\/icons\/icon-256.png \\\n  --onedir\n```\n## Acknowledgement\nThis repo is the fork of [mpitid\/pylabelme](https:\/\/github.com\/mpitid\/pylabelme).\n\n## About\nImage annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.\n\n[labelme.io](https:\/\/labelme.io\/ \"https:\/\/labelme.io\")\n\n### Topics\n[python](https:\/\/github.com\/topics\/python \"Topic: python\")   [computer-vision](https:\/\/github.com\/topics\/computer-vision \"Topic: computer-vision\")   [deep-learning](https:\/\/github.com\/topics\/deep-learning \"Topic: deep-learning\")   [image-annotation](https:\/\/github.com\/topics\/image-annotation \"Topic: image-annotation\")   [video-annotation](https:\/\/github.com\/topics\/video-annotation \"Topic: video-annotation\")   [annotations](https:\/\/github.com\/topics\/annotations \"Topic: annotations\")   [classification](https:\/\/github.com\/topics\/classification \"Topic: classification\")   [semantic-segmentation](https:\/\/github.com\/topics\/semantic-segmentation \"Topic: semantic-segmentation\")   [instance-segmentation](https:\/\/github.com\/topics\/instance-segmentation \"Topic: instance-segmentation\")\n\n### Resources\n[Readme](https:\/\/github.com\/wkentaro\/labelme#readme-ov-file)\n\n### License\n[GPL-3.0 license](https:\/\/github.com\/wkentaro\/labelme#GPL-3.0-1-ov-file)\n\n### Citation\nCite this repository\n\nLoading\n\nSomething went wrong.\n\n### Uh oh\\!\nThere was an error while loading. [Please reload this page](https:\/\/github.com\/wkentaro\/labelme).\n\n[Activity](https:\/\/github.com\/wkentaro\/labelme\/activity)\n\n### Stars\n[**15\\.7k** stars](https:\/\/github.com\/wkentaro\/labelme\/stargazers)\n\n### Watchers\n[**148** watching](https:\/\/github.com\/wkentaro\/labelme\/watchers)\n\n### Forks\n[**3\\.7k** forks](https:\/\/github.com\/wkentaro\/labelme\/forks)\n\n[Report repository](https:\/\/github.com\/contact\/report-content?content_url=https%3A%2F%2Fgithub.com%2Fwkentaro%2Flabelme&report=wkentaro+%28user%29)\n\n## [Releases 86](https:\/\/github.com\/wkentaro\/labelme\/releases)\n[v6.0.0 Latest Mar 28, 2026](https:\/\/github.com\/wkentaro\/labelme\/releases\/tag\/v6.0.0)\n\n[\\+ 85 releases](https:\/\/github.com\/wkentaro\/labelme\/releases)\n\n## [Packages 0](https:\/\/github.com\/users\/wkentaro\/packages?repo_name=labelme)\nNo packages published\n\n## [Used by 1\\.3k](https:\/\/github.com\/wkentaro\/labelme\/network\/dependents)\n[![@UTokyo-FieldPhenomics-Lab](https:\/\/avatars.githubusercontent.com\/u\/43122558?s=64&v=4) ![@accountbelongstox](https:\/\/avatars.githubusercontent.com\/u\/35165283?s=64&v=4) ![@hu-qi](https:\/\/avatars.githubusercontent.com\/u\/17986122?s=64&v=4) ![@mikodo20001](https:\/\/avatars.githubusercontent.com\/u\/132320086?s=64&v=4) ![@anoban](https:\/\/avatars.githubusercontent.com\/u\/99625466?s=64&v=4) ![@Prazeen7](https:\/\/avatars.githubusercontent.com\/u\/119245711?s=64&v=4) ![@miaojuncn](https:\/\/avatars.githubusercontent.com\/u\/20398034?s=64&v=4) ![@gangqi88](https:\/\/avatars.githubusercontent.com\/u\/46935155?s=64&v=4) + 1,334](https:\/\/github.com\/wkentaro\/labelme\/network\/dependents)\n\n## [Contributors](https:\/\/github.com\/wkentaro\/labelme\/graphs\/contributors)\n### Uh oh\\!\nThere was an error while loading. [Please reload this page](https:\/\/github.com\/wkentaro\/labelme).\n\n## Languages\n- [Python 99.5%](https:\/\/github.com\/wkentaro\/labelme\/search?l=python)\n- [Makefile 0.5%](https:\/\/github.com\/wkentaro\/labelme\/search?l=makefile)\n\n## Footer\n© 2026 GitHub, Inc.\n\n### Footer navigation\n- [Terms](https:\/\/docs.github.com\/site-policy\/github-terms\/github-terms-of-service)\n- [Privacy](https:\/\/docs.github.com\/site-policy\/privacy-policies\/github-privacy-statement)\n- [Security](https:\/\/github.com\/security)\n- [Status](https:\/\/www.githubstatus.com\/)\n- [Community](https:\/\/github.community\/)\n- [Docs](https:\/\/docs.github.com\/)\n- [Contact](https:\/\/support.github.com\/?tags=dotcom-footer)\n- Manage cookies\n- Do not share my personal information\n\nYou can’t perform that action at this time.","attrs_readable_markdown":"Image annotation with Python.\n\n[![](https:\/\/camo.githubusercontent.com\/8fc773cd9299e35f036a1ad51cf2849a112b342d7be3e07b8746c09c08eef545\/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6162656c6d652e737667)](https:\/\/pypi.python.org\/pypi\/labelme) [![](https:\/\/github.com\/wkentaro\/labelme\/actions\/workflows\/ci.yml\/badge.svg?branch=main&event=push)](https:\/\/github.com\/wkentaro\/labelme\/actions) [![](https:\/\/camo.githubusercontent.com\/816f80b7e3461ca33d13c074b292aa2ea7384a774c8e4092d0e4508f1a7c038a\/68747470733a2f2f646362616467652e6c696d65732e70696e6b2f6170692f7365727665722f75416a7847634a6d38333f7374796c653d666c6174)](https:\/\/discord.com\/invite\/uAjxGcJm83)\n\n[![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/.readme\/annotation.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/.readme\/annotation.jpg)\n\nDescription\n\nLabelme is a graphical image annotation tool inspired by [http:\/\/labelme.csail.mit.edu](http:\/\/labelme.csail.mit.edu\/).  \n It 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:\/\/labelme.io\/)**.\n\n[![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/JPEGImages\/2011_000006.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/JPEGImages\/2011_000006.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClass\/2011_000006.png)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClass\/2011_000006.png) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClassVisualization\/2011_000006.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationClassVisualization\/2011_000006.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObject\/2011_000006.png)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObject\/2011_000006.png) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObjectVisualization\/2011_000006.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation\/data_dataset_voc\/SegmentationObjectVisualization\/2011_000006.jpg)  \n *VOC dataset example of instance segmentation.*\n\n[![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/semantic_segmentation\/.readme\/annotation.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation\/.readme\/annotation.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/bbox_detection\/.readme\/annotation.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/bbox_detection\/.readme\/annotation.jpg) [![](https:\/\/github.com\/wkentaro\/labelme\/raw\/main\/examples\/classification\/.readme\/annotation_cat.jpg)](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/classification\/.readme\/annotation_cat.jpg)  \n *Other examples (semantic segmentation, bbox detection, and classification).*\n\n[![](https:\/\/user-images.githubusercontent.com\/4310419\/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif)](https:\/\/user-images.githubusercontent.com\/4310419\/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif) [![](https:\/\/user-images.githubusercontent.com\/4310419\/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif)](https:\/\/user-images.githubusercontent.com\/4310419\/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif) [![](https:\/\/user-images.githubusercontent.com\/14256482\/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png)](https:\/\/user-images.githubusercontent.com\/14256482\/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png)  \n *Various primitives (polygon, rectangle, circle, line, and point).*\n\n[![](https:\/\/private-user-images.githubusercontent.com\/4310419\/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)](https:\/\/private-user-images.githubusercontent.com\/4310419\/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)\n\n*Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more).*\n\nFeatures\n\n- Image annotation for polygon, rectangle, circle, line and point ([tutorial](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/tutorial))\n- Image flag annotation for classification and cleaning ([\\#166](https:\/\/github.com\/wkentaro\/labelme\/pull\/166))\n- Video annotation ([video annotation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/video_annotation))\n- GUI customization (predefined labels \/ flags, auto-saving, label validation, etc) ([\\#144](https:\/\/github.com\/wkentaro\/labelme\/pull\/144))\n- Exporting VOC-format dataset for [semantic segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation), [instance segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation)\n- Exporting COCO-format dataset for [instance segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation)\n- AI-assisted point-to-polygon\/mask annotation by SAM, EfficientSAM models\n- AI text-to-annotation by YOLO-world, SAM3 models\n\n**🌏 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`)\n\nInstallation\n\nThere are 3 options to install labelme:\n\nOption 1: Using pip\n\nFor more detail, check [\"Install Labelme using Terminal\"](https:\/\/www.labelme.io\/docs\/install-labelme-terminal)\n```\npip install labelme\n\n# To install the latest version from GitHub:\n# pip install git+https:\/\/github.com\/wkentaro\/labelme.git\n```\nOption 2: Using standalone executable (Easiest)\n\nIf 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).\n\nIt's a one-time payment for lifetime access, and it helps us to maintain this project.\n\nOption 3: Using a package manager in each Linux distribution\n\nIn some Linux distributions, you can install labelme via their package managers (e.g., apt, pacman). The following systems are currently available:\n\n[![Packaging status](https:\/\/camo.githubusercontent.com\/a071432e6036317337e84e6e609c2e0f69de37bdb18a55daada24dc359f8454a\/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f766572746963616c2d616c6c7265706f732f6c6162656c6d652e737667)](https:\/\/repology.org\/project\/labelme\/versions)\n\nUsage\n\nRun `labelme --help` for detail.  \n The annotations are saved as a [JSON](http:\/\/www.json.org\/) file.\n```\nlabelme  # just open gui\n\n# tutorial (single image example)\ncd examples\/tutorial\nlabelme apc2016_obj3.jpg  # specify image file\nlabelme apc2016_obj3.jpg --output annotations\/  # 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\/semantic_segmentation\nlabelme data_annotated\/  # Open directory to annotate all images in it\nlabelme data_annotated\/ --labels labels.txt  # specify label list with a file\n```\nCommand 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 `~\/.labelmerc`. Add only the settings you want to override. For all available options and their defaults, see [`default_config.yaml`](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/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.\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](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/classification)\n- Labels are assigned to a single polygon. [Example](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/bbox_detection)\n\nFAQ\n\n- **How to convert JSON file to numpy array?** See [examples\/tutorial](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/tutorial#convert-to-dataset).\n- **How to load label PNG file?** See [examples\/tutorial](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/tutorial#how-to-load-label-png-file).\n- **How to get annotations for semantic segmentation?** See [examples\/semantic\\_segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation).\n- **How to get annotations for instance segmentation?** See [examples\/instance\\_segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation).\n\nExamples\n\n- [Image Classification](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/classification)\n- [Bounding Box Detection](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/bbox_detection)\n- [Semantic Segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/semantic_segmentation)\n- [Instance Segmentation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/instance_segmentation)\n- [Video Annotation](https:\/\/github.com\/wkentaro\/labelme\/blob\/main\/examples\/video_annotation)\n\nHow to build standalone executable\n```\nLABELME_PATH=.\/labelme\nOSAM_PATH=$(python -c 'import os, osam; print(os.path.dirname(osam.__file__))')\npip install 'numpy<2.0'  # numpy>=2.0 causes build errors (see #1532)\npyinstaller labelme\/labelme\/__main__.py \\\n  --name=Labelme \\\n  --windowed \\\n  --noconfirm \\\n  --specpath=build \\\n  --add-data=$(OSAM_PATH)\/_models\/yoloworld\/clip\/bpe_simple_vocab_16e6.txt.gz:osam\/_models\/yoloworld\/clip \\\n  --add-data=$(LABELME_PATH)\/config\/default_config.yaml:labelme\/config \\\n  --add-data=$(LABELME_PATH)\/icons\/*:labelme\/icons \\\n  --add-data=$(LABELME_PATH)\/translate\/*:translate \\\n  --icon=$(LABELME_PATH)\/icons\/icon-256.png \\\n  --onedir\n```\nAcknowledgement\n\nThis repo is the fork of [mpitid\/pylabelme](https:\/\/github.com\/mpitid\/pylabelme).","meta_canonical":null}

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URL	https://github.com/wkentaro/labelme
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Meta Title	GitHub - wkentaro/labelme: Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation. · GitHub
Meta Description	Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation. - wkentaro/labelme
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Boilerpipe Text	Image annotation with Python. 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 . 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 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 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" 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 .
Markdown	[Skip to content](https://github.com/wkentaro/labelme#start-of-content) ## Navigation Menu Toggle navigation [Sign in](https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fwkentaro%2Flabelme) Appearance settings - Platform - AI CODE CREATION - [GitHub CopilotWrite better code with AI](https://github.com/features/copilot) - [GitHub SparkBuild and deploy intelligent apps](https://github.com/features/spark) - [GitHub ModelsManage and compare prompts](https://github.com/features/models) - [MCP RegistryNewIntegrate external tools](https://github.com/mcp) - DEVELOPER WORKFLOWS - [ActionsAutomate any workflow](https://github.com/features/actions) - [CodespacesInstant dev environments](https://github.com/features/codespaces) - [IssuesPlan and track work](https://github.com/features/issues) - [Code ReviewManage code changes](https://github.com/features/code-review) - APPLICATION SECURITY - [GitHub Advanced SecurityFind and fix vulnerabilities](https://github.com/security/advanced-security) - [Code securitySecure your code as you build](https://github.com/security/advanced-security/code-security) - [Secret protectionStop leaks before they start](https://github.com/security/advanced-security/secret-protection) - EXPLORE - [Why GitHub](https://github.com/why-github) - [Documentation](https://docs.github.com/) - [Blog](https://github.blog/) - [Changelog](https://github.blog/changelog) - [Marketplace](https://github.com/marketplace) [View all features](https://github.com/features) - Solutions - BY COMPANY SIZE - [Enterprises](https://github.com/enterprise) - [Small and medium teams](https://github.com/team) - [Startups](https://github.com/enterprise/startups) - [Nonprofits](https://github.com/solutions/industry/nonprofits) - BY USE CASE - [App Modernization](https://github.com/solutions/use-case/app-modernization) - [DevSecOps](https://github.com/solutions/use-case/devsecops) - [DevOps](https://github.com/solutions/use-case/devops) - [CI/CD](https://github.com/solutions/use-case/ci-cd) - [View all use cases](https://github.com/solutions/use-case) - BY INDUSTRY - [Healthcare](https://github.com/solutions/industry/healthcare) - [Financial services](https://github.com/solutions/industry/financial-services) - [Manufacturing](https://github.com/solutions/industry/manufacturing) - [Government](https://github.com/solutions/industry/government) - [View all industries](https://github.com/solutions/industry) [View all solutions](https://github.com/solutions) - Resources - EXPLORE BY TOPIC - [AI](https://github.com/resources/articles?topic=ai) - [Software Development](https://github.com/resources/articles?topic=software-development) - [DevOps](https://github.com/resources/articles?topic=devops) - [Security](https://github.com/resources/articles?topic=security) - [View all topics](https://github.com/resources/articles) - EXPLORE BY TYPE - [Customer stories](https://github.com/customer-stories) - [Events & webinars](https://github.com/resources/events) - [Ebooks & reports](https://github.com/resources/whitepapers) - [Business insights](https://github.com/solutions/executive-insights) - [GitHub Skills](https://skills.github.com/) - SUPPORT & SERVICES - [Documentation](https://docs.github.com/) - [Customer support](https://support.github.com/) - [Community forum](https://github.com/orgs/community/discussions) - [Trust center](https://github.com/trust-center) - [Partners](https://github.com/partners) [View all resources](https://github.com/resources) - Open Source - COMMUNITY - [GitHub SponsorsFund open source developers](https://github.com/sponsors) - PROGRAMS - [Security Lab](https://securitylab.github.com/) - [Maintainer Community](https://maintainers.github.com/) - [Accelerator](https://github.com/accelerator) - [GitHub Stars](https://stars.github.com/) - [Archive Program](https://archiveprogram.github.com/) - REPOSITORIES - [Topics](https://github.com/topics) - [Trending](https://github.com/trending) - [Collections](https://github.com/collections) - Enterprise - ENTERPRISE SOLUTIONS - [Enterprise platformAI-powered developer platform](https://github.com/enterprise) - AVAILABLE ADD-ONS - [GitHub Advanced SecurityEnterprise-grade security features](https://github.com/security/advanced-security) - [Copilot for BusinessEnterprise-grade AI features](https://github.com/features/copilot/copilot-business) - [Premium SupportEnterprise-grade 24/7 support](https://github.com/premium-support) - [Pricing](https://github.com/pricing) Search or jump to... # Search code, repositories, users, issues, pull requests... 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Dismiss alert {{ message }} [wkentaro](https://github.com/wkentaro) / [labelme](https://github.com/wkentaro/labelme) Public - [Notifications](https://github.com/login?return_to=%2Fwkentaro%2Flabelme) You must be signed in to change notification settings - [Fork 3.7k](https://github.com/login?return_to=%2Fwkentaro%2Flabelme) - [Star 15.7k](https://github.com/login?return_to=%2Fwkentaro%2Flabelme) - [Code](https://github.com/wkentaro/labelme) - [Issues 109](https://github.com/wkentaro/labelme/issues) - [Pull requests 73](https://github.com/wkentaro/labelme/pulls) - [Discussions](https://github.com/wkentaro/labelme/discussions) - [Actions](https://github.com/wkentaro/labelme/actions) - [Security 0](https://github.com/wkentaro/labelme/security) - [Insights](https://github.com/wkentaro/labelme/pulse) Additional navigation options - [Code](https://github.com/wkentaro/labelme) - [Issues](https://github.com/wkentaro/labelme/issues) - [Pull requests](https://github.com/wkentaro/labelme/pulls) - [Discussions](https://github.com/wkentaro/labelme/discussions) - [Actions](https://github.com/wkentaro/labelme/actions) - [Security](https://github.com/wkentaro/labelme/security) - [Insights](https://github.com/wkentaro/labelme/pulse) # wkentaro/labelme main [27 Branches](https://github.com/wkentaro/labelme/branches) [220 Tags](https://github.com/wkentaro/labelme/tags) Go to file Code Open more actions menu ## Folders and files \| Name \| Name \| Last commit message \| Last commit date \| \|---\|---\|---\|---\| \| Latest commit [![wkentaro](https://avatars.githubusercontent.com/u/4310419?v=4&size=40)](https://github.com/wkentaro)[wkentaro](https://github.com/wkentaro/labelme/commits?author=wkentaro) [Merge pull request](https://github.com/wkentaro/labelme/commit/2cd7d5a049785cd4b8bc2fb397bbc24da07eaccd) [\#1905](https://github.com/wkentaro/labelme/pull/1905) [from wkentaro/dependabot/uv/cryptography-46.0.6](https://github.com/wkentaro/labelme/commit/2cd7d5a049785cd4b8bc2fb397bbc24da07eaccd) Open commit details success Mar 28, 2026 [2cd7d5a](https://github.com/wkentaro/labelme/commit/2cd7d5a049785cd4b8bc2fb397bbc24da07eaccd) · Mar 28, 2026 History [2,225 Commits](https://github.com/wkentaro/labelme/commits/main/) Open commit details 2,225 Commits \| \| \| \| ## Repository files navigation - [README](https://github.com/wkentaro/labelme) - [GPL-3.0 license](https://github.com/wkentaro/labelme) # [![](https://github.com/wkentaro/labelme/raw/main/labelme/icons/icon-256.png)](https://github.com/wkentaro/labelme/blob/main/labelme/icons/icon-256.png) labelme #### Image annotation with Python. [![](https://camo.githubusercontent.com/8fc773cd9299e35f036a1ad51cf2849a112b342d7be3e07b8746c09c08eef545/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6162656c6d652e737667)](https://pypi.python.org/pypi/labelme) [![](https://github.com/wkentaro/labelme/actions/workflows/ci.yml/badge.svg?branch=main&event=push)](https://github.com/wkentaro/labelme/actions) [![](https://camo.githubusercontent.com/816f80b7e3461ca33d13c074b292aa2ea7384a774c8e4092d0e4508f1a7c038a/68747470733a2f2f646362616467652e6c696d65732e70696e6b2f6170692f7365727665722f75416a7847634a6d38333f7374796c653d666c6174)](https://discord.com/invite/uAjxGcJm83) [Installation](https://github.com/wkentaro/labelme#installation) \\| [Usage](https://github.com/wkentaro/labelme#usage) \\| [Examples](https://github.com/wkentaro/labelme#examples) \\| [labelme.io ↗](https://labelme.io/) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/.readme/annotation.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/.readme/annotation.jpg) ## 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](https://labelme.io/). [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg) VOC dataset example of instance segmentation. [![](https://github.com/wkentaro/labelme/raw/main/examples/semantic_segmentation/.readme/annotation.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation/.readme/annotation.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/bbox_detection/.readme/annotation.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection/.readme/annotation.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/classification/.readme/annotation_cat.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/classification/.readme/annotation_cat.jpg) Other examples (semantic segmentation, bbox detection, and classification). [![](https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif)](https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif) [![](https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif)](https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif) [![](https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif)](https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif) [![](https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif)](https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif) [![](https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png)](https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png) Various primitives (polygon, rectangle, circle, line, and point). [![](https://private-user-images.githubusercontent.com/4310419/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzQ3Njg0NzQsIm5iZiI6MTc3NDc2ODE3NCwicGF0aCI6Ii80MzEwNDE5LzU1OTM3Mzg5Ni01M2JmMDlkYi1iMDk3LTQ4YjctOWYzMi1hYjQ5MGRhNWFjNTMuZ2lmP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI2MDMyOSUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNjAzMjlUMDcwOTM0WiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9MzgwYzUzM2QzNzZhZGQzMTRiZmI0NzQ3M2U1MDQwZDkxMDg4M2Y0OGIzYmM2OTRmMDNkYmFkODA1MjkwMzc3ZCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)](https://private-user-images.githubusercontent.com/4310419/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU) [![](https://private-user-images.githubusercontent.com/4310419/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)](https://private-user-images.githubusercontent.com/4310419/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzQ3Njg0NzQsIm5iZiI6MTc3NDc2ODE3NCwicGF0aCI6Ii80MzEwNDE5LzU1OTM3Mzg5Ni01M2JmMDlkYi1iMDk3LTQ4YjctOWYzMi1hYjQ5MGRhNWFjNTMuZ2lmP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI2MDMyOSUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNjAzMjlUMDcwOTM0WiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9MzgwYzUzM2QzNzZhZGQzMTRiZmI0NzQ3M2U1MDQwZDkxMDg4M2Y0OGIzYmM2OTRmMDNkYmFkODA1MjkwMzc3ZCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU) Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more). ## Features - Image annotation for polygon, rectangle, circle, line and point ([tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial)) - Image flag annotation for classification and cleaning ([\#166](https://github.com/wkentaro/labelme/pull/166)) - Video annotation ([video annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation)) - GUI customization (predefined labels / flags, auto-saving, label validation, etc) ([\#144](https://github.com/wkentaro/labelme/pull/144)) - Exporting VOC-format dataset for [semantic segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation), [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation) - Exporting COCO-format dataset for [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation) - AI-assisted point-to-polygon/mask annotation by SAM, EfficientSAM models - 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) ``` 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: [![Packaging status](https://camo.githubusercontent.com/a071432e6036317337e84e6e609c2e0f69de37bdb18a55daada24dc359f8454a/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f766572746963616c2d616c6c7265706f732f6c6162656c6d652e737667)](https://repology.org/project/labelme/versions) ## Usage Run `labelme --help` for detail. The annotations are saved as a [JSON](http://www.json.org/) 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`](https://github.com/wkentaro/labelme/blob/main/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](https://github.com/wkentaro/labelme/blob/main/examples/classification) - Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection) ### FAQ - How to convert JSON file to numpy array? See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#convert-to-dataset). - How to load label PNG file? See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#how-to-load-label-png-file). - How to get annotations for semantic segmentation? See [examples/semantic\_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation). - How to get annotations for instance segmentation? See [examples/instance\_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation). ## Examples - [Image Classification](https://github.com/wkentaro/labelme/blob/main/examples/classification) - [Bounding Box Detection](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection) - [Semantic Segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation) - [Instance Segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation) - [Video Annotation](https://github.com/wkentaro/labelme/blob/main/examples/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](https://github.com/mpitid/pylabelme). ## About Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation. [labelme.io](https://labelme.io/ "https://labelme.io") ### Topics [python](https://github.com/topics/python "Topic: python") [computer-vision](https://github.com/topics/computer-vision "Topic: computer-vision") [deep-learning](https://github.com/topics/deep-learning "Topic: deep-learning") [image-annotation](https://github.com/topics/image-annotation "Topic: image-annotation") [video-annotation](https://github.com/topics/video-annotation "Topic: video-annotation") [annotations](https://github.com/topics/annotations "Topic: annotations") [classification](https://github.com/topics/classification "Topic: classification") [semantic-segmentation](https://github.com/topics/semantic-segmentation "Topic: semantic-segmentation") [instance-segmentation](https://github.com/topics/instance-segmentation "Topic: instance-segmentation") ### Resources [Readme](https://github.com/wkentaro/labelme#readme-ov-file) ### License [GPL-3.0 license](https://github.com/wkentaro/labelme#GPL-3.0-1-ov-file) ### Citation Cite this repository Loading Something went wrong. ### Uh oh\! 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Readable Markdown	Image annotation with Python. [![](https://camo.githubusercontent.com/8fc773cd9299e35f036a1ad51cf2849a112b342d7be3e07b8746c09c08eef545/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6162656c6d652e737667)](https://pypi.python.org/pypi/labelme) [![](https://github.com/wkentaro/labelme/actions/workflows/ci.yml/badge.svg?branch=main&event=push)](https://github.com/wkentaro/labelme/actions) [![](https://camo.githubusercontent.com/816f80b7e3461ca33d13c074b292aa2ea7384a774c8e4092d0e4508f1a7c038a/68747470733a2f2f646362616467652e6c696d65732e70696e6b2f6170692f7365727665722f75416a7847634a6d38333f7374796c653d666c6174)](https://discord.com/invite/uAjxGcJm83) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/.readme/annotation.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/.readme/annotation.jpg) Description Labelme is a graphical image annotation tool inspired by [http://labelme.csail.mit.edu](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](https://labelme.io/). [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png) [![](https://github.com/wkentaro/labelme/raw/main/examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg) VOC dataset example of instance segmentation. [![](https://github.com/wkentaro/labelme/raw/main/examples/semantic_segmentation/.readme/annotation.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation/.readme/annotation.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/bbox_detection/.readme/annotation.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection/.readme/annotation.jpg) [![](https://github.com/wkentaro/labelme/raw/main/examples/classification/.readme/annotation_cat.jpg)](https://github.com/wkentaro/labelme/blob/main/examples/classification/.readme/annotation_cat.jpg) Other examples (semantic segmentation, bbox detection, and classification). [![](https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif)](https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif) [![](https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif)](https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif) [![](https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png)](https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png) Various primitives (polygon, rectangle, circle, line, and point). [![](https://private-user-images.githubusercontent.com/4310419/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzQ3Njg0NzQsIm5iZiI6MTc3NDc2ODE3NCwicGF0aCI6Ii80MzEwNDE5LzU1OTM3Mzg5Ni01M2JmMDlkYi1iMDk3LTQ4YjctOWYzMi1hYjQ5MGRhNWFjNTMuZ2lmP1gtQW16LUFsZ29yaXRobT1BV1M0LUhNQUMtU0hBMjU2JlgtQW16LUNyZWRlbnRpYWw9QUtJQVZDT0RZTFNBNTNQUUs0WkElMkYyMDI2MDMyOSUyRnVzLWVhc3QtMSUyRnMzJTJGYXdzNF9yZXF1ZXN0JlgtQW16LURhdGU9MjAyNjAzMjlUMDcwOTM0WiZYLUFtei1FeHBpcmVzPTMwMCZYLUFtei1TaWduYXR1cmU9MzgwYzUzM2QzNzZhZGQzMTRiZmI0NzQ3M2U1MDQwZDkxMDg4M2Y0OGIzYmM2OTRmMDNkYmFkODA1MjkwMzc3ZCZYLUFtei1TaWduZWRIZWFkZXJzPWhvc3QifQ.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU)](https://private-user-images.githubusercontent.com/4310419/559373896-53bf09db-b097-48b7-9f32-ab490da5ac53.gif?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.HKFgJUKdxtaADtN4j7gpj7tyrIpUGqCM2YaQqEIA8NU) Multi-language support (English, 中文, 日本語, 한국어, Deutsch, Français, and more). Features - Image annotation for polygon, rectangle, circle, line and point ([tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial)) - Image flag annotation for classification and cleaning ([\#166](https://github.com/wkentaro/labelme/pull/166)) - Video annotation ([video annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation)) - GUI customization (predefined labels / flags, auto-saving, label validation, etc) ([\#144](https://github.com/wkentaro/labelme/pull/144)) - Exporting VOC-format dataset for [semantic segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation), [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation) - Exporting COCO-format dataset for [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation) - AI-assisted point-to-polygon/mask annotation by SAM, EfficientSAM models - 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) ``` 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: [![Packaging status](https://camo.githubusercontent.com/a071432e6036317337e84e6e609c2e0f69de37bdb18a55daada24dc359f8454a/68747470733a2f2f7265706f6c6f67792e6f72672f62616467652f766572746963616c2d616c6c7265706f732f6c6162656c6d652e737667)](https://repology.org/project/labelme/versions) Usage Run `labelme --help` for detail. The annotations are saved as a [JSON](http://www.json.org/) 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`](https://github.com/wkentaro/labelme/blob/main/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](https://github.com/wkentaro/labelme/blob/main/examples/classification) - Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection) FAQ - How to convert JSON file to numpy array? See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#convert-to-dataset). - How to load label PNG file? See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#how-to-load-label-png-file). - How to get annotations for semantic segmentation? See [examples/semantic\_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation). - How to get annotations for instance segmentation? See [examples/instance\_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation). Examples - [Image Classification](https://github.com/wkentaro/labelme/blob/main/examples/classification) - [Bounding Box Detection](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection) - [Semantic Segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation) - [Instance Segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation) - [Video Annotation](https://github.com/wkentaro/labelme/blob/main/examples/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](https://github.com/mpitid/pylabelme).
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