Label Studio FAQ
What is Label Studio?
Label Studio is an open-source data annotation tool designed for preparing training datasets for various AI models, including those for computer vision, NLP, speech, and video.
How do I use Label Studio?
To use Label Studio, install it via pip, brew, or GitHub. Launch it using the installed package or Docker, import your data, select the data type and task, and start annotating with customizable tags and templates. Integrate it with your ML/AI pipeline and manage your dataset with advanced filters.
Can Label Studio handle different types of data?
Yes, Label Studio supports multiple data types, including images, audio, text, time series, and videos.
Is Label Studio compatible with my ML/AI pipeline?
Absolutely. Label Studio integrates seamlessly with ML/AI workflows using webhooks, Python SDK, and API for authentication, project management, and model predictions.
Does Label Studio offer ML-assisted annotation?
Yes, Label Studio provides ML-assisted annotation by utilizing backend integrations with ML models to streamline the annotation process.
Can I connect Label Studio to cloud storage?
Yes, Label Studio supports connectivity to cloud storage services like S3 and GCP.
Is Label Studio suitable for multi-project and multi-user environments?
Yes, Label Studio is designed to handle multiple projects and users within a single platform, making it ideal for diverse annotation needs.