FAQ from Label Studio
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.