Label Studio Setup
Comprehensive guide for Label Studio setup and usage on local server for data labeling and annotation.
npx skills add majiayu000/claude-skill-registry --skill label-studio-setup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Implements a comprehensive setup and usage guide for Label Studio on a local server, covering installation (Docker, Docker Compose, and local pip), configuration (DB, Redis, storage, ML backend), project templates for Image Classification, Object Detection, Segmentation, NER, Video and Audio labeling, data import/export (JSON, CSV, COCO, YOLO), labeling interface customization (CSS, hotkeys, conditional logic), user management, quality control (review workflow, consensus), ML backend integration (pre-annotation, active learning), and API usage for project management.
How it works
- Provides step-by-step installation commands for Docker and local environments, including docker pull, docker run, and docker-compose.yml with PostgreSQL and Redis services.
- Shows how to start/stop services and view logs via docker-compose commands.
- Details Python configuration blocks for database, Redis, storage, security, and ML backend endpoints.
- Demonstrates project setup through code and XML configurations for various annotation tasks (Image Classification, Object Detection, Segmentation, NER, Video, Audio) and corresponding Python client calls to create projects with label_config templates.
- Includes data import/export examples, including importing images, JSON, CSV, pre-annotations, and exporting as JSON, COCO, YOLO, CSV, or filtered by finished tasks.
- Covers labeling interface customization via CSS, hotkeys, and conditional logic blocks for dynamic labeling.
- Outlines User Management API usage for creating, listing, updating, and deleting users, plus assigning/removing project members.
- Describes Quality Control features: enabling review workflow and consensus settings with corresponding code samples.
- Details ML Backend integration: a Flask example for a prediction endpoint, connecting ML backends to a Label Studio project, and an active learning example with uncertainty scoring.
- Includes partial API usage examples for managing projects via the label-studio-sdk client.
When to use it
Use when you need to provision a self-hosted Label Studio instance with a full workflow: multi-type annotation projects, data import/export, UI customization, user/permission management, QC, and optional ML backend integration on a local server.
What it can touch
- Docker (Docker and Docker Compose) to run Label Studio and dependent services.
- Local filesystem paths for data and Postgres/Redis volumes as shown in the docker-compose.yml and data-dir examples.
- Python environments for installing Label Studio and dependencies (pip install label-studio, etc.).
- Label Studio server endpoints (via docker image or local start commands).
- ML backend service endpoints (e.g., http://localhost:9090) as configured in ML_BACKEND_HOST.
Caveats
- Requires knowledge of Docker, Python, PostgreSQL, Redis, and web server basics.
- Uses example credentials and keys that should be replaced in real deployments (e.g., LABEL_STUDIO_USERNAME, LABEL_STUDIO_PASSWORD, SECRET_KEY).
- Some sections assume network accessibility to external image URLs and model endpoints; local environments may need adjustments for security and networking.
# Label Studio Setup ## Overview Label Studio is an open-source data labeling platform that provides tools for image, text, audio, and video annotation. This skill covers Label Studio installation, project setup, data import/export, labeling interface customization, user management, quality control, ML backend integration, API usage, backup and migration, and production deployment. ## Prerequisites - Understanding of Docker and containerization - Knowledge of Python programming - Familiarity with data annotation concepts - Basic understanding of PostgreSQL and Redis - Knowledge of web server configuration (Nginx) ## Key Concepts ### Label Studio Components - **Web Application**: Django-based UI for labeling - **Database**: PostgreSQL for data storage - **Cache**: Redis for session management - **ML Backend**: Optional ML model integration for pre-annotation - **Storage**: File storage for media assets ### Annotation Types - **Image Classification**: Single label per image - **Object Detection**: Bounding box annotations - **Semantic Segmentation**: Pixel-level annotations - **Named Entity Recognition (NER)**: Text entity extraction - **Video Annotation**: Frame-by-frame labeling -
- Overview
- Prerequisites
- Key Concepts
- Label Studio Components
- Annotation Types
- Quality Control
- Implementation Guide
- Installation
- Project Setup
- Data Import/Export
- Labeling Interface Customization
- User Management
- ML Backend Integration
- API Usage
Pull Label Studio image docker pull heartexlabs/label-studio:latest Create data directory mkdir -p label-studio/data Run Label Studio docker run -it \ Start with Docker Compose docker-compose up -d Stop docker-compose down
What does the Label Studio Setup skill do?
Comprehensive guide for Label Studio setup and usage on local server for data labeling and annotation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill label-studio-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.