# HumanSignal/label-studio

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26,469 stars · 3,396 forks · TypeScript · apache-2.0

## Links

- GitHub: https://github.com/HumanSignal/label-studio
- Homepage: https://labelstud.io
- awesome-repositories: https://awesome-repositories.com/repository/humansignal-label-studio.md

## Topics

`annotation` `annotation-tool` `annotations` `boundingbox` `computer-vision` `data-labeling` `dataset` `datasets` `deep-learning` `image-annotation` `image-classification` `image-labeling` `image-labelling-tool` `label-studio` `labeling` `labeling-tool` `mlops` `semantic-segmentation` `text-annotation` `yolo`

## Description

Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows.

The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated pre-labeling, and real-time model-assisted annotation. It features a declarative interface configuration system that uses markup to define custom labeling tools, alongside plugin-based extensibility that allows for the injection of custom logic. To support enterprise-scale operations, it includes granular role-based access control, collaborative feedback tools, and automated task distribution management.

The system covers a broad capability surface, including automated data ingestion from cloud storage, programmatic pipeline management via REST APIs, and comprehensive data export options. It also provides built-in observability tools to monitor annotator performance, inter-annotator agreement, and model quality.

The application is packaged as a portable, container-ready microservice designed for deployment in scalable, cloud-native environments.

## Tags

### Artificial Intelligence & ML

- [Annotation Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/annotation-tools.md) — Label Studio provides tools for object detection, tracking, and semantic segmentation using boxes, polygons, and keypoints across image and video frames. ([source](https://labelstud.io/))
- [Data Annotation Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/data-annotation-platforms.md) — A web-based environment for labeling diverse media types to create and manage high-quality training datasets for machine learning models.
- [Annotation Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-datasets/annotation-platforms.md) — | Creating and managing high-quality training datasets for machine learning by labeling diverse media types like images, text, and audio.
- [Automated Visual Data Annotation](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-visual-data-annotation.md) — Provides automated pipelines for generating and refining labels to accelerate the creation of visual training datasets. ([source](https://labelstud.io/guide/ml))
- [Data Labeling Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/data-labeling-tools.md) — A platform that integrates with model backends to support active learning, automated pre-labeling, and human-in-the-loop annotation workflows.
- [Dataset Management](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management.md) — | Organizing team workflows with role-based access control, task distribution, and threaded communication to ensure consistent and high-quality dataset production.
- [Human Feedback Collection](https://awesome-repositories.com/f/artificial-intelligence-ml/human-feedback-collection.md) — Label Studio gathers human preferences, corrections, and rankings to support reinforcement learning from human feedback and model fine-tuning. ([source](https://labelstud.io/))
- [Annotation Integration Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/training-and-evaluation-pipelines/annotation-integration-pipelines.md) — | Connecting annotation workflows to cloud storage and external systems via APIs and webhooks to automate data ingestion and model training cycles. ([source](https://labelstud.io/))
- [Object Mask Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/image-segmentation/object-mask-generators.md) — Uses specialized models to automatically generate precise masks and bounding boxes for visual data annotation. ([source](https://labelstud.io/guide/ml_tutorials))
- [Pre-annotation Importers](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-data-annotation/pre-annotation-importers.md) — Label Studio uploads model-generated predictions alongside datasets to allow human reviewers to verify, edit, or correct automated annotations. ([source](https://labelstud.io/guide/))
- [Programmatic Data Ingestion](https://awesome-repositories.com/f/artificial-intelligence-ml/data-annotation-platforms/programmatic-data-ingestion.md) — Supports programmatic ingestion of raw data and media references from cloud storage and databases for labeling projects. ([source](https://labelstud.io/guide/tasks))
- [Data Annotation Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/data-annotation-workflows.md) — Orchestrates the lifecycle of data labeling tasks, including project-specific instructions and task presentation sequences. ([source](https://labelstud.io/guide/setup_project))
- [Model Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/model-inference.md) — Provides a bridge to connect external machine learning services for real-time predictions and active learning feedback.
- [Document Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/document-analysis.md) — Label Studio extracts information through named entity recognition and optical character recognition for complex, large-scale document analysis. ([source](https://labelstud.io/))
- [Machine Learning Model APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/inference-servers-and-runtimes/machine-learning-model-apis.md) — Connects external machine learning services to the backend for automated predictions and active learning feedback loops. ([source](https://labelstud.io/guide/label_studio_compare))
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Label Studio identifies and labels specific people, places, or organizations within raw text using natural language processing to structure data for downstream analysis. ([source](https://labelstud.io/guide/ml_tutorials))
- [Speech Transcription](https://awesome-repositories.com/f/artificial-intelligence-ml/speech-transcription.md) — Converts speech to text and extracts text from images using integrated automatic speech recognition and optical character recognition models. ([source](https://labelstud.io/guide/ml_tutorials))
- [Text Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/text-classification.md) — Label Studio sorts unstructured text into predefined categories using machine learning models to organize large datasets and improve information retrieval. ([source](https://labelstud.io/guide/ml_tutorials))
- [Event Webhooks](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-data-annotation/event-webhooks.md) — Triggers automated HTTP notifications to external systems whenever data is created or modified within the platform. ([source](https://labelstud.io/guide/webhook_create))
- [Text Generation Services](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/text-generation-services.md) — Label Studio integrates large language models to assist in text generation, summarization, or retrieval-augmented generation tasks directly within the annotation workflow. ([source](https://labelstud.io/guide/ml_tutorials))
- [Model Performance Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-analysis.md) — Label Studio creates custom benchmarks and rubrics for side-by-side model comparisons and retrieval relevance grading. ([source](https://labelstud.io/))
- [Region Relationships](https://awesome-repositories.com/f/artificial-intelligence-ml/region-based-detection/object-region-classifiers/region-relationships.md) — Label Studio connects distinct labeling entities using unique identifiers to establish relationships between objects, such as drawing directional arrows between detected items. ([source](https://labelstud.io/guide/task_format))
- [Prediction Visibility Controls](https://awesome-repositories.com/f/artificial-intelligence-ml/prediction-visualization/prediction-visibility-controls.md) — Controls the visibility and selection of model-generated prediction sets within the labeling interface for annotators. ([source](https://labelstud.io/guide/ml))

### Data & Databases

- [Model-Assisted Labelers](https://awesome-repositories.com/f/data-databases/label-based-data-selection/metadata-labelers/model-assisted-labelers.md) — | Integrating machine learning models to provide automated predictions and active learning loops that accelerate the manual data annotation process. ([source](https://labelstud.io/guide/ml))
- [Data Import Utilities](https://awesome-repositories.com/f/data-databases/data-import-utilities.md) — Label Studio uploads raw data files from local storage or connects to external cloud buckets and databases to prepare datasets for labeling. ([source](https://labelstud.io/guide/quick_start))
- [Storage Abstraction Layers](https://awesome-repositories.com/f/data-databases/storage-abstraction-layers.md) — Maps remote cloud storage buckets to the local workspace to manage large datasets without moving raw files.
- [Annotation Conversion Tools](https://awesome-repositories.com/f/data-databases/annotation-conversion-tools.md) — Transforms annotation files into various formats to ensure compatibility with diverse machine learning frameworks. ([source](https://labelstud.io/guide/export))
- [Data Preprocessing Pipelines](https://awesome-repositories.com/f/data-databases/data-preprocessing-pipelines.md) — Applies automated preprocessing routines to raw data inputs to prepare them for manual annotation or model training. ([source](https://github.com/HumanSignal/label-studio/blob/develop/web/README.md))
- [External Datastore Configurations](https://awesome-repositories.com/f/data-databases/external-datastore-configurations.md) — Label Studio connects to a PostgreSQL instance to store labeling tasks and annotations, providing improved performance and scalability for large-scale projects. ([source](https://labelstud.io/guide/storedata))
- [Time Series Segmenters](https://awesome-repositories.com/f/data-databases/time-series-data-modeling/time-series-segmenters.md) — Label Studio identifies events and segments within time series plots, optionally using synchronized audio or video streams for context. ([source](https://labelstud.io/))
- [Bulk Data Operations](https://awesome-repositories.com/f/data-databases/bulk-data-operations.md) — Enables bulk updates to multiple tasks to accelerate workflows for repetitive data or filtered subsets. ([source](https://labelstud.io/guide/enterprise_features))
- [Cloud Data Access](https://awesome-repositories.com/f/data-databases/cloud-data-access.md) — Label Studio downloads original media files such as images, audio, or text from the annotation environment for use in external machine learning backend processing. ([source](https://labelstud.io/guide/export))
- [Annotation Snapshots](https://awesome-repositories.com/f/data-databases/data-snapshotting/annotation-snapshots.md) — Enables asynchronous creation and retrieval of annotation data snapshots to handle large-scale projects without performance degradation. ([source](https://labelstud.io/guide/export))
- [Persistent Storage Volumes](https://awesome-repositories.com/f/data-databases/persistent-storage-volumes.md) — Label Studio mounts network-backed persistent volume claims with multi-pod read and write access to ensure shared data consistency across containerized instances. ([source](https://labelstud.io/guide/persistent_storage))
- [Annotation Filtering](https://awesome-repositories.com/f/data-databases/search-result-filtering/annotation-filtering.md) — Label Studio organizes tasks by specific criteria or values to control the order and selection of items presented to annotators. ([source](https://labelstud.io/guide/manage_data))

### Business & Productivity Software

- [Annotation Project Management](https://awesome-repositories.com/f/business-productivity-software/annotation-project-management.md) — Provides a centralized environment for organizing data and coordinating collaborative annotation workflows among multiple users. ([source](https://labelstud.io/guide/get_started))

### Development Tools & Productivity

- [Annotation Export APIs](https://awesome-repositories.com/f/development-tools-productivity/project-export-environments/annotation-export-apis.md) — Label Studio converts completed annotations and source data into standard machine learning formats for use in training or evaluating predictive models. ([source](https://labelstud.io/guide/))
- [Annotation Pipelines](https://awesome-repositories.com/f/development-tools-productivity/workflow-automations/annotation-pipelines.md) — Exposes REST APIs to programmatically manage data, annotations, and project configurations within machine learning pipelines. ([source](https://cdn.jsdelivr.net/gh/HumanSignal/label-studio@develop/README.md))
- [External Service Connectors](https://awesome-repositories.com/f/development-tools-productivity/external-service-connectors.md) — Automates downstream machine learning workflows by triggering external services via HTTP requests upon project events. ([source](https://labelstud.io/guide/webhooks))
- [Task Prioritization](https://awesome-repositories.com/f/development-tools-productivity/task-prioritization.md) — Prioritizes annotation tasks using sequential, uniform, or prediction-score-based logic to optimize workflow efficiency. ([source](https://labelstud.io/guide/start))

### DevOps & Infrastructure

- [Self-Hosted AI Infrastructure](https://awesome-repositories.com/f/devops-infrastructure/self-hosted-ai-infrastructure.md) — | Deploying secure, containerized annotation environments within private networks or Kubernetes clusters to maintain full control over sensitive data and infrastructure.
- [Annotation Task Distribution](https://awesome-repositories.com/f/devops-infrastructure/distributed-task-queues/annotation-task-distribution.md) — Routes annotation tasks to team members automatically based on configurable overlap, locking, and queue management rules. ([source](https://labelstud.io/guide/enterprise_features))
- [Cloud Storage Integrations](https://awesome-repositories.com/f/devops-infrastructure/cloud-storage-integrations.md) — Label Studio links cloud containers to the application to import data or export annotations using account keys or service principal authentication. ([source](https://labelstud.io/guide/storage_azure))
- [Kubernetes Deployments](https://awesome-repositories.com/f/devops-infrastructure/kubernetes-deployments.md) — Label Studio orchestrates the application lifecycle within a cluster environment using package management to handle installation, upgrades, and configuration updates. ([source](https://labelstud.io/guide/install_k8s))

### Security & Cryptography

- [Role-Based Access Control](https://awesome-repositories.com/f/security-cryptography/role-based-access-control.md) — Enforces granular security policies by mapping user identities to specific permissions across projects and administrative functions.
- [Self-Hosted Enterprise Environments](https://awesome-repositories.com/f/security-cryptography/self-hosted-enterprise-environments.md) — A containerized application for secure, private data labeling that supports air-gapped deployments and enterprise-grade role-based access control.
- [API Request Authentication](https://awesome-repositories.com/f/security-cryptography/identity-access-management/authentication-strategies/machine-and-protocol-identity/api-machine-authentication/api-request-authentication.md) — Label Studio requires unique access tokens for REST API interactions to secure programmatic access to user accounts and data resources. ([source](https://labelstud.io/guide/security))
- [Access Authentication](https://awesome-repositories.com/f/security-cryptography/user-access-management/access-authentication.md) — Secures the annotation environment by requiring individual user accounts with email and password credentials for data access. ([source](https://labelstud.io/guide/quick_start))
- [Authentication Providers](https://awesome-repositories.com/f/security-cryptography/authentication-providers.md) — Integrates with external identity providers like Google or Apple to simplify team member login and authentication. ([source](https://slack.labelstud.io/))
- [SCIM Provisioning](https://awesome-repositories.com/f/security-cryptography/identity-access-management/identity-management/digital-identity-provisioning/scim-provisioning.md) — Automates user provisioning and group membership synchronization using standard protocols like SCIM or SAML. ([source](https://labelstud.io/guide/enterprise_features))
- [Workspace Hierarchies](https://awesome-repositories.com/f/security-cryptography/identity-and-access-management/workspace-hierarchies.md) — Structures users and projects into isolated, hierarchical organizations and workspaces to manage data access by team. ([source](https://labelstud.io/guide/enterprise_features))
- [Media Proxying Services](https://awesome-repositories.com/f/security-cryptography/media-access-controls/media-proxying-services.md) — Label Studio fetches media from private cloud storage using either temporary pre-signed URLs or a secure proxy to ensure data remains protected. ([source](https://labelstud.io/guide/storage))
- [Request Forgery Protections](https://awesome-repositories.com/f/security-cryptography/network-infrastructure-security/web-network-security/network-security/network-routing-access-control/network-access-controls/request-forgery-protections.md) — Blocks unauthorized internal network requests by restricting the application from accessing sensitive local infrastructure. ([source](https://labelstud.io/guide/security))

### User Interface & Experience

- [Annotation Layout Configurators](https://awesome-repositories.com/f/user-interface-experience/interface-labeling/annotation-layout-configurators.md) — Defines the layout and input fields of the annotation workspace using a structured configuration format. ([source](https://labelstud.io/guide/frontend_reference))
- [Run-Length Encoding Converters](https://awesome-repositories.com/f/user-interface-experience/animation-and-motion-systems/image-masking/segmentation-mask-handlers/run-length-encoding-converters.md) — Transforms image segmentation masks into run-length encoded formats for efficient data import and model training. ([source](https://labelstud.io/guide/predictions))
- [Project Configuration APIs](https://awesome-repositories.com/f/user-interface-experience/interface-configuration-management/programmatic-configuration-interfaces/project-configuration-apis.md) — Enables programmatic initialization and updates of labeling project configurations via server-side requests. ([source](https://labelstud.io/guide/setup))
- [Workspace Visibility Controls](https://awesome-repositories.com/f/user-interface-experience/interface-element-management/workspace-visibility-controls.md) — Allows customization of the workspace by toggling the visibility of navigation panels, buttons, and information bars. ([source](https://labelstud.io/guide/frontend_reference))

### Graphics & Multimedia

- [Transcription Tools](https://awesome-repositories.com/f/graphics-multimedia/media-processing-analysis/media-manipulation/media-processing-workflows/audio-analysis-synthesis/transcription-tools.md) — Label Studio transcribes speech, identifies speakers, and tags emotional content using waveform or spectrogram visualizations. ([source](https://labelstud.io/))

### Software Engineering & Architecture

- [Annotation Collaboration](https://awesome-repositories.com/f/software-engineering-architecture/team-collaboration-tools/annotation-collaboration.md) — Prevents conflicts during collaborative labeling by locking tasks during active editing sessions. ([source](https://labelstud.io/guide/labeling))
- [Interface Plugins](https://awesome-repositories.com/f/software-engineering-architecture/extensible-plugin-architectures/interface-plugins.md) — Supports injecting custom JavaScript logic into the annotation interface to extend functionality and integrate specialized tools.
- [Production-Ready Microservices](https://awesome-repositories.com/f/software-engineering-architecture/software-architecture/distributed-computing-paradigms/production-ready-microservices.md) — Packages the platform as a portable, containerized microservice for scalable deployment in cloud-native environments.
- [Interface Event Subscriptions](https://awesome-repositories.com/f/software-engineering-architecture/event-subscribers/interface-event-subscriptions.md) — Hooks into interface lifecycle and interaction events to trigger custom logic or external actions. ([source](https://labelstud.io/guide/frontend_reference))

### System Administration & Monitoring

- [Agent Observability](https://awesome-repositories.com/f/system-administration-monitoring/agent-observability.md) — Label Studio integrates observability tools to facilitate human-in-the-loop review of agentic traces and decision-making processes. ([source](https://labelstud.io/))
- [Application Quality Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/application-quality-monitoring.md) — Label Studio tracks inter-annotator agreement and performance metrics through dashboards to identify quality issues and manage annotator reliability. ([source](https://labelstud.io/guide/enterprise_features))