#Introduction

#Label Studio

Label Studio is an open-source multi-type data labeling and annotation tool that provides standardized output formats. It supports data labeling for multiple data types, including images, audio, text, time series, and video.

It contains the following main components:

• Backend Service: Django-based Python web service providing REST API, Python SDK, and machine learning integration
• Frontend Interface: React-based web UI providing complete annotation interface, including project management, data management, annotation tools, and result export
• Database: Supports PostgreSQL 13+ database for storing project data and annotation results
• Cache System: Redis for caching and task queue management (optional)

Label Studio helps teams build and maintain high-quality data labeling workflows: from simple image classification to complex multi-modal data annotation tasks.

#Core Concepts

#Project

Projects are the basic organizational unit for data labeling in Label Studio, including:

• Project Settings: Annotation configuration, data import settings, user permissions, etc.
• Data Management: Data import, storage, and version control
• Annotation Interface: Configurable annotation tools and interface
• Annotation Results: Storage and management of annotation data

Each project has independent configuration and data space, supporting multi-user collaborative annotation.

#Labeling Interface

The labeling interface is the core tool for users to perform data annotation, supporting:

• Multiple Annotation Types: Image classification, object detection, text classification, named entity recognition, etc.
• Configurable Interface: Customize annotation interface through configuration language
• Template Support: Provides various predefined annotation templates
• Shortcut Support: Shortcut functions to improve annotation efficiency

The labeling interface uses a specially designed configuration language that can flexibly adapt to various annotation needs.

#Data Manager

The data manager is the core management tool for project data, providing:

• Data Import: Support importing data from files, cloud storage (AWS S3, Google Cloud Storage)
• Data Formats: Support JSON, CSV, TSV, and other formats
• Data Preview: View and preview data to be annotated
• Data Filtering: Filter data by status, annotator, labels, and other conditions

The data manager supports batch operations and advanced search functionality.

#Annotations

Annotations are labels and comments added by users to data, including:

• Annotation Data: Labels, bounding boxes, segmentation regions added by users
• Annotation Metadata: Annotation time, annotator, confidence, and other information
• Annotation Status: Draft, completed, skipped, and other statuses
• Annotation Quality: Annotation quality scoring and validation

Annotation data is stored in standardized JSON format for easy subsequent processing and analysis.

#Machine Learning Integration

Label Studio provides powerful machine learning integration capabilities:

• Pre-annotation: Use machine learning models for pre-annotation to improve efficiency
• Online Learning: Real-time training and model updates during annotation
• Active Learning: Intelligently select complex samples that need annotation
• Model Comparison: Compare prediction results from different models

Supports multiple machine learning frameworks and model formats.

#Core Concept Relationships

• Projects are the basic containers for organizing annotation tasks and data
• Labeling Interfaces define how users interact with data for annotation
• Data Managers handle data import, storage, and organization within projects
• Annotations store the actual labeling results and metadata
• Machine Learning Integration connects external models for pre-annotation and active learning

#Documentation

Label Studio provides comprehensive official documentation and API references to help users understand and use platform features in depth:

#Official Documentation

• Main Documentation: https://labelstud.io/guide/
  • Detailed introduction to Label Studio's core concepts and workflows
  • Includes installation guides, quick start, and best practices
  • Provides common use cases, example code, tutorials, and API references