![]() ![]() ![]() The most common templates and use cases for labeling include the following cases: Label Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. Included templates for labeling data in Label Studio Embed it in your data pipeline REST API makes it easy to make it a part of your pipeline.Integration with machine learning models so that you can visualize and compare predictions from different models and perform pre-labeling.Import from files or from cloud storage in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives.Support for multiple data types including images, audio, text, HTML, time-series, and video.Configurable label formats let you customize the visual interface to meet your specific labeling needs.Streamlined design helps you focus on your task, not how to use the software.Multiple projects to work on all your datasets in one instance.Multi-user labeling sign up and login, when you create an annotation it's tied to your account.# postgres (assumes default postgres user,db,pass)ĭJANGO_DB=default DJANGO_SETTINGS_MODULE=_studio python -m pytest -vv -n autoĭJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=_studio python -m pytest -vv -n auto What you get from Label Studio Run Label Studio in a Docker container and access it at pip install -r deploy/requirements-test.txt Official Label Studio docker image is here and it can be downloaded with docker pull. Run with Docker Compose (Label Studio + Nginx + PostgreSQL).Or, sign up for a free trial of our Enterprise edition. Install Label Studio locally, or deploy it in a cloud instance. Read an introductory blog post to learn more. ![]() Have a custom dataset? You can customize Label Studio to fit your needs. Integrate Label Studio with your existing tools.Set up machine learning models with Label Studio. ![]()
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