[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-3618":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":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":45,"readmeContent":46,"aiSummary":47,"trendingCount":16,"starSnapshotCount":16,"syncStatus":48,"lastSyncTime":49,"discoverSource":50},3618,"label-studio","HumanSignal\u002Flabel-studio","HumanSignal","Label Studio is a multi-type data labeling and annotation tool with standardized output format","https:\u002F\u002Flabelstud.io",null,"TypeScript",27567,3572,181,867,0,8,56,291,39,45,"Apache License 2.0",false,"develop",[26,27,28,29,30,31,32,33,34,35,36,37,38,5,39,40,41,42,43,44],"annotation","annotation-tool","annotations","boundingbox","computer-vision","data-labeling","dataset","datasets","deep-learning","image-annotation","image-classification","image-labeling","image-labelling-tool","labeling","labeling-tool","mlops","semantic-segmentation","text-annotation","yolo","2026-06-12 02:00:51","\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F12534576\u002F192582340-4c9e4401-1fe6-4dbb-95bb-fdbba5493f61.png\"\u002F>\n\n![GitHub](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fheartexlabs\u002Flabel-studio?logo=heartex) ![label-studio:build](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio\u002Fworkflows\u002Flabel-studio:build\u002Fbadge.svg) ![GitHub release](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fv\u002Frelease\u002Fheartexlabs\u002Flabel-studio?include_prereleases)\n\n[Website](https:\u002F\u002Flabelstud.io\u002F) • [Docs](https:\u002F\u002Flabelstud.io\u002Fguide\u002F) • [Join Slack Community \u003Cimg src=\"https:\u002F\u002Fapp.heartex.ai\u002Fdocs\u002Fimages\u002Fslack-mini.png\" width=\"18px\"\u002F>](https:\u002F\u002Fslack.labelstud.io\u002F?source=github-1)\n\n\n## What is Label Studio?\n\n\u003C!-- \u003Ca href=\"https:\u002F\u002Flabelstud.io\u002Fblog\u002Frelease-130.html\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio\u002Fraw\u002Fmaster\u002Fdocs\u002Fthemes\u002Fhtx\u002Fsource\u002Fimages\u002Frelease-130\u002FLS-Hits-v1.3.png\" align=\"right\" \u002F>\u003C\u002Fa> -->\n\nLabel Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.\n\n- [Try out Label Studio](#try-out-label-studio)\n- [What you get from Label Studio](#what-you-get-from-label-studio)\n- [Included templates for labeling data in Label Studio](#included-templates-for-labeling-data-in-label-studio)\n- [Set up machine learning models with Label Studio](#set-up-machine-learning-models-with-Label-Studio)\n- [Integrate Label Studio with your existing tools](#integrate-label-studio-with-your-existing-tools)\n\n![Gif of Label Studio annotating different types of data](\u002Fimages\u002Fannotation_examples.gif)\n\nHave a custom dataset? You can customize Label Studio to fit your needs. Read an [introductory blog post](https:\u002F\u002Ftowardsdatascience.com\u002Fintroducing-label-studio-a-swiss-army-knife-of-data-labeling-140c1be92881) to learn more. \n\n## Try out Label Studio\n\nInstall Label Studio locally or deploy it in a cloud instance. [Or sign up for a free trial of our Starter Cloud edition!](https:\u002F\u002Fhumansignal.com\u002Fplatform\u002Fstarter-cloud\u002F) You can learn more about what each edition offers [here](https:\u002F\u002Flabelstud.io\u002Fguide\u002Flabel_studio_compare). \n\n- [Install locally with Docker](#install-locally-with-docker)\n- [Run with Docker Compose (Label Studio + Nginx + PostgreSQL)](#run-with-docker-compose)\n- [Install locally with pip](#install-locally-with-pip)\n- [Install locally with poetry](#install-locally-with-poetry)\n- [Install locally with Anaconda](#install-locally-with-anaconda)\n- [Install for local development](#install-for-local-development)\n- [Deploy in a cloud instance](#deploy-in-a-cloud-instance)\n\n### Install locally with Docker\nOfficial Label Studio docker image is [here](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fheartexlabs\u002Flabel-studio) and it can be downloaded with `docker pull`. \nRun Label Studio in a Docker container and access it at `http:\u002F\u002Flocalhost:8080`.\n\n\n```bash\ndocker pull heartexlabs\u002Flabel-studio:latest\ndocker run -it -p 8080:8080 -v $(pwd)\u002Fmydata:\u002Flabel-studio\u002Fdata heartexlabs\u002Flabel-studio:latest\n```\nYou can find all the generated assets, including SQLite3 database storage `label_studio.sqlite3` and uploaded files, in the `.\u002Fmydata` directory.\n\n#### Override default Docker install\nYou can override the default launch command by appending the new arguments:\n```bash\ndocker run -it -p 8080:8080 -v $(pwd)\u002Fmydata:\u002Flabel-studio\u002Fdata heartexlabs\u002Flabel-studio:latest label-studio --log-level DEBUG\n```\n\n#### Build a local image with Docker\nIf you want to build a local image, run:\n```bash\ndocker build -t heartexlabs\u002Flabel-studio:latest .\n```\n\n### Run with Docker Compose\nDocker Compose script provides production-ready stack consisting of the following components:\n\n- Label Studio\n- [Nginx](https:\u002F\u002Fwww.nginx.com\u002F) - proxy web server used to load various static data, including uploaded audio, images, etc.\n- [PostgreSQL](https:\u002F\u002Fwww.postgresql.org\u002F) - production-ready database that replaces less performant SQLite3.\n\nTo start using the app from `http:\u002F\u002Flocalhost` run this command:\n```bash\ndocker-compose up\n```\n\n### Run with Docker Compose + MinIO\nYou can also run it with an additional MinIO server for local S3 storage. This is particularly useful when you want to \ntest the behavior with S3 storage on your local system. To start Label Studio in this way, you need to run the following command:\n````bash\n# Add sudo on Linux if you are not a member of the docker group\ndocker compose -f docker-compose.yml -f docker-compose.minio.yml up -d\n````\nIf you do not have a static IP address, you must create an entry in your hosts file so that both Label Studio and your \nbrowser can access the MinIO server. For more detailed instructions, please refer to [our guide on storing data](docs\u002Fsource\u002Fguide\u002Fstoredata.md).\n\n\n### Install locally with pip\n\n```bash\n# Requires Python >=3.10\npip install label-studio\n\n# Start the server at http:\u002F\u002Flocalhost:8080\nlabel-studio\n```\n\n### Install locally with poetry\n\n```bash\n### install poetry\npip install poetry\n\n### set poetry environment\npoetry new my-label-studio\ncd my-label-studio\npoetry add label-studio\n\n### activate poetry environment\npoetry shell\n\n### Start the server at http:\u002F\u002Flocalhost:8080\nlabel-studio\n```\n\n### Install locally with Anaconda\n\n```bash\nconda create --name label-studio\nconda activate label-studio\nconda install psycopg2\npip install label-studio\n```\n\n### Install for local development\n\nYou can run the latest Label Studio version locally without installing the package from pypi. \n\n```bash\n# Install all package dependencies\npip install poetry\npoetry install\n# Run database migrations\npython label_studio\u002Fmanage.py migrate\npython label_studio\u002Fmanage.py collectstatic\n# Start the server in development mode at http:\u002F\u002Flocalhost:8080\npython label_studio\u002Fmanage.py runserver\n```\n\n### Deploy in a cloud instance\n\nYou can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform: \n\n\u003Ca href=\"https:\u002F\u002Fwww.heroku.com\u002Fdeploy?template=https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio\u002Ftree\u002Fheroku-persistent-pg\">\u003Cimg src=\"https:\u002F\u002Fwww.herokucdn.com\u002Fdeploy\u002Fbutton.svg\" alt=\"Deploy\" height=\"30px\">\u003C\u002Fa>\n[\u003Cimg src=\"https:\u002F\u002Faka.ms\u002Fdeploytoazurebutton\" height=\"30px\">](https:\u002F\u002Fportal.azure.com\u002F#create\u002FMicrosoft.Template\u002Furi\u002Fhttps%3A%2F%2Fraw.githubusercontent.com%2Fhumansignal%2Flabel-studio%2Fdevelop%2Fazuredeploy.json)\n[\u003Cimg src=\"https:\u002F\u002Fdeploy.cloud.run\u002Fbutton.svg\" height=\"30px\">](https:\u002F\u002Fdeploy.cloud.run)\n\n\n#### Apply frontend changes\n\nFor information about updating the frontend, see [label-studio\u002Fweb\u002FREADME.md](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio\u002Fblob\u002Fdevelop\u002Fweb\u002FREADME.md#installation-instructions).\n\n\n#### Install dependencies on Windows \nTo run Label Studio on Windows, download and install the following wheel packages from [Gohlke builds](https:\u002F\u002Fwww.lfd.uci.edu\u002F~gohlke\u002Fpythonlibs) to ensure you're using the correct version of Python:\n- [lxml](https:\u002F\u002Fwww.lfd.uci.edu\u002F~gohlke\u002Fpythonlibs\u002F#lxml)\n\n```bash\n# Upgrade pip \npip install -U pip\n\n# If you're running Win64 with Python 3.8, install the packages downloaded from Gohlke:\npip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl\n\n# Install label studio\npip install label-studio\n```\n\n### Run test suite\nTo add the tests' dependencies to your local install:\n\n```bash\npoetry install --with test\n```\n\nAlternatively, it is possible to run the unit tests from a Docker container in which the test dependencies are installed:\n\n\n```bash\nmake build-testing-image\nmake docker-testing-shell\n```\n\nIn either case, to run the unit tests:\n\n```bash\ncd label_studio\n\n# sqlite3\nDJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=core.settings.label_studio pytest -vv\n\n# postgres (assumes default postgres user,db,pass. Will not work in Docker\n# testing container without additional configuration)\nDJANGO_DB=default DJANGO_SETTINGS_MODULE=core.settings.label_studio pytest -vv\n```\n \n## What you get from Label Studio\n\nhttps:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F525ad5ff-6904-4398-b507-7e8954268d69\n\n- **Multi-user labeling** sign up and login, when you create an annotation it's tied to your account.\n- **Multiple projects** to work on all your datasets in one instance.\n- **Streamlined design** helps you focus on your task, not how to use the software.\n- **Configurable label formats** let you customize the visual interface to meet your specific labeling needs.\n- **Support for multiple data types** including images, audio, text, HTML, time-series, and video. \n- **Import from files or from cloud storage** in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives. \n- **Integration with machine learning models** so that you can visualize and compare predictions from different models and perform pre-labeling.\n- **Embed it in your data pipeline** REST API makes it easy to make it a part of your pipeline\n\n## Included templates for labeling data in Label Studio \n\nLabel Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. The most common templates and use cases for labeling include the following cases:\n\n\u003Cimg src=\"\u002Fimages\u002Ftemplate-types.png\" \u002F>\n\n## Set up machine learning models with Label Studio\n\nConnect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps:\n\n1. Start your own machine learning backend server. See [more detailed instructions](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio-ml-backend).\n2. Connect Label Studio to the server on the model page found in project settings.\n\nThis lets you:\n\n- **Pre-label** your data using model predictions. \n- Do **online learning** and retrain your model while new annotations are being created. \n- Do **active learning** by labeling only the most complex examples in your data.\n\n## Integrate Label Studio with your existing tools\n\nYou can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools.  \n\n## Ecosystem\n\n| Project | Description |\n|-|-|\n| label-studio | Server, distributed as a pip package |\n| [Frontend library](web\u002Flibs\u002Feditor\u002F) | The Label Studio frontend library. This uses React to build the UI and mobx-state-tree for state management. |  \n| [Data Manager library](web\u002Flibs\u002Fdatamanager\u002F) | A library for the Data Manager, our data exploration tool. | \n| [label-studio-converter](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio-sdk\u002Ftree\u002Fmaster\u002Fsrc\u002Flabel_studio_sdk\u002Fconverter) | Encode labels in the format of your favorite machine learning library |\n| [label-studio-transformers](https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio-transformers) | Transformers library connected and configured for use with Label Studio |\n\n## Citation\n\nInclude a citation for Label Studio in the **References** section of your articles:\n\n```tex\n@misc{Label Studio,\n  title={{Label Studio}: Data labeling software},\n  url={https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio},\n  note={Open source software available from https:\u002F\u002Fgithub.com\u002FHumanSignal\u002Flabel-studio},\n  author={\n    Maxim Tkachenko and\n    Mikhail Malyuk and\n    Andrey Holmanyuk and\n    Nikolai Liubimov},\n  year={2020-2025},\n}\n```\n\n## License\n\nThis software is licensed under the [Apache 2.0 LICENSE](\u002FLICENSE) © [Heartex](https:\u002F\u002Fwww.heartex.com\u002F). 2020-2025\n\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F12534576\u002F192582529-cf628f58-abc5-479b-a0d4-8a3542a4b35e.png\" title=\"Hey everyone!\" width=\"180\" \u002F>\n","Label Studio 是一个支持多种类型数据标注的工具，具有标准化输出格式。其核心功能包括对音频、文本、图像、视频和时间序列等多种数据类型的标注，并且提供了简洁直观的用户界面以及多种模型格式的导出选项。该工具采用TypeScript开发，具备良好的扩展性和自定义能力，允许用户根据特定需求调整配置。Label Studio 适用于需要准备或改进训练数据以提升机器学习模型准确性的场景，尤其适合计算机视觉、自然语言处理等领域的研究人员及开发者使用。此外，它还支持与现有工具集成，并提供云端部署选项，方便团队协作和项目管理。",2,"2026-06-11 02:55:02","top_language"]