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Label Studio - 开源多类型数据标注与注释工具
Label Studio is an open-source multi-type data labeling and annotation tool with a standardized output format. It enables teams to customize interfaces for images, text, audio, video, and time series data, supporting various annotation tasks for machine learning and data science projects.
JavaScript
Added on 2025年7月6日23,052
Stars2,864
ForksJavaScript
LanguageProject Introduction
Summary
Label Studio is a leading open-source tool designed to streamline the process of preparing structured data for machine learning models. It supports a wide range of data types and annotation tasks, providing a flexible and scalable solution for individuals and teams.
Problem Solved
The preparation of high-quality labeled data is often a bottleneck in machine learning development. Existing tools are often specialized for one data type, lack flexibility, or are not suitable for large-scale team collaboration. Label Studio addresses these issues by offering a unified, customizable, and collaborative platform for diverse data types.
Core Features
Multi-type Data Support
Supports image classification, object detection, segmentation, text classification, NER, audio transcription, time series annotation, and more within a single platform.
Customizable Interface
Easily configure annotation interfaces using simple front-end code (HTML/CSS/JavaScript) tailored to specific project needs.
Flexible Integrations
Provides REST API and Python client for seamless integration into existing data pipelines and MLOps workflows.
Collaborative Annotation
Allows multiple annotators to work on projects simultaneously, with features for managing tasks, reviewing annotations, and calculating inter-annotator agreement.
Tech Stack
Python
Django
React
PostgreSQL
Docker
Kubernetes
使用场景
Label Studio is versatile and can be applied across various industries and research areas requiring structured data annotation.
场景一：自动驾驶计算机视觉数据标注
Details
Labeling images and videos for object detection, segmentation, and tracking to train models for autonomous navigation systems.
User Value
Provides the necessary tools for precise pixel-level and bounding box annotations on large visual datasets.
场景二：自然语言处理 (NLP) 数据准备
Details
Annotating text data for sentiment analysis, named entity recognition (NER), text classification, and relationship extraction.
User Value
Offers flexible interfaces for complex text annotation tasks required for training robust NLP models.
场景三：语音与音频数据处理
Details
Transcribing and classifying audio data for speech recognition models, speaker identification, and audio event detection.
User Value
Supports audio playback and waveform visualization for accurate timestamped transcription and labeling.
场景四：时序数据分析与标注
Details
Annotating time series data from sensors, financial markets, or IoT devices for anomaly detection, pattern recognition, or forecasting models.
User Value
Enables visual annotation and segmentation of time series data for training specific analytical models.
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