Product Information
What is Label?
Label Studio is an open-source data annotation tool designed to prepare training data for computer vision, natural language processing, speech, audio, and video models. It offers flexibility suitable for all types of data labeling tasks.
How to use Label?
To use Label Studio, follow these steps:
1. Install the Label Studio package via pip, brew, or by cloning the repository from GitHub.
2. Launch Label Studio using the installed package or Docker.
3. Import your data into Label Studio.
4. Select the data type (images, audio, text, time series, multi-domain, or video) and choose a specific annotation task (e.g., image classification, object detection, audio transcription).
5. Annotate the data using customizable labels and templates.
6. Connect to your ML/AI pipeline via webhooks, Python SDK, or API for authentication, project management, and model predictions.
7. Browse and manage your dataset with advanced filters in the Data Manager.
8. Support multiple projects, use cases, and users within the Label Studio platform.
Core Functions of Label
Flexible Data Annotation for All Data Types
Support Computer Vision, NLP, Speech, Audio & Video Models
Customizable labels and annotation templates
Integration with ML/AI pipelines via Webhooks, Python SDK, and API
ML-assisted annotation for backend integration
Connect to cloud object storage (S3 and GCP)
Advanced data management with Data Manager
Support for multiple projects and users
Trusted widely by the data science community
Usage Scenarios of Label
- Prepare training data for computer vision models
- Prepare training data for natural language processing models
- Prepare training data for speech and audio models
- Prepare training data for video models
- Classify images, audio, text, and time-series data
- Object detection and tracking for images and videos
- Semantic segmentation of images
- Speaker analysis and emotion recognition for audio
- Audio Transcription
- Document classification and named entity extraction
- Question answering and sentiment analysis
- Time-series analysis and event recognition
- Dialogue processing and optical character recognition
- Multi-domain application scenarios requiring various data annotation types
Common Questions about Label
Can Label Studio handle different types of data?
Can I integrate Label Studio with my ML/AI pipeline?
Does Label Studio support ML-assisted labeling?
Can I connect Label Studio to cloud object storage?
Is Label Studio suitable for multi-project and multi-user environments?
