Label Studio

Open Label Studio

Label Studio Q&A

What is Label Studio?

Label Studio is an open-source data labeling tool designed to prepare training data for computer vision, natural language processing, speech, voice, and video models. It offers flexibility for labeling all types of data.

How to use Label Studio?

To use Label Studio, you can follow these steps:\n1. Install the Label Studio package through pip, brew, or clone the repository from GitHub.\n2. Launch Label Studio using the installed package or Docker.\n3. Import your data into Label Studio.\n4. Choose the data type (images, audio, text, time series, multi-domain, or video) and select the specific labeling task (e.g., image classification, object detection, audio transcription).\n5. Start labeling your data using customizable tags and templates.\n6. Connect to your ML/AI pipeline and use webhooks, Python SDK, or API for authentication, project management, and model predictions.\n7. Explore and manage your dataset in the Data Manager with advanced filters.\n8. Support multiple projects, use cases, and users within the Label Studio platform.

Can Label Studio handle different types of data?

Yes, Label Studio is designed to handle various data types such as images, audio, text, time series, and videos.

Can I integrate Label Studio with my ML/AI pipeline?

Absolutely! Label Studio provides webhooks, Python SDK, and API for seamless integration with your ML/AI pipeline, allowing you to authenticate, create projects, import tasks, manage model predictions, and more.

Does Label Studio support ML-assisted labeling?

Yes, Label Studio offers ML-assisted labeling by utilizing predictions to assist in the labeling process. It has backend integration with ML models, saving time and improving efficiency.

Can I connect Label Studio to cloud object storage?

Yes, Label Studio allows connectivity to cloud object storage through integrations with S3 and GCP, enabling direct labeling of data stored in the cloud.

Is Label Studio suitable for multi-project and multi-user environments?

Definitely! Label Studio supports multiple projects, use cases, and users within a single platform, making it versatile for various labeling requirements.

Label Studio's Core Features

Flexible data labeling for all data types

Support for computer vision, natural language processing, speech, voice, and video models

Customizable tags and labeling templates

Integration with ML/AI pipelines via webhooks, Python SDK, and API

ML-assisted labeling with backend integration

Connectivity to cloud object storage (S3 and GCP)

Advanced data management with the Data Manager

Support for multiple projects and users

Trusted by a large community of Data Scientists

Label Studio's Use Cases

Preparing training data for computer vision models

Preparing training data for natural language processing models

Preparing training data for speech and voice models

Preparing training data for video models

Classification of images, audio, text, and time series data

Object detection and tracking in images and videos

Semantic segmentation of images

Speaker diarization and emotion recognition in 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 applications requiring various types of data labeling

Label Studio Traffic

Monthly Visits: 126.7K
Avg.Visit Duration: 00:04:09
Page per Visit: 2.97
Bounce Rate: 41.68%
Feb 2023 - Mar 2024 All Traffic
Geography
Top 5 Regions United States: 17.48%
China: 14.90%
Russia: 9.90%
Taiwan: 7.79%
India: 5.96%
Feb 2023 - Mar 2024 Desktop Only
Traffic Sources
Search: 52.47%
Direct: 37.58%
Referrals: 7.82%
Social: 1.48%
Mail: 0.64%
Display Ads: 0.00%

Label Studio Categories: AI Developer Tools