Lilly Tech Systems
Learn Label Studio
Master the leading open-source data annotation platform. Learn to label images, text, and audio data — build high-quality training datasets for your machine learning models.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Label Studio? Data annotation fundamentals, supported data types, and the annotation workflow.
2. Installation & Setup
Install via pip or Docker, configure projects, import data, and explore the annotation interface.
3. Image Labeling
Bounding boxes, polygons, segmentation masks, keypoints, and image classification templates.
4. Text Labeling
Named entity recognition, text classification, sentiment analysis, and relation extraction.
5. ML Backend
ML-assisted labeling, pre-annotations, active learning, and connecting custom ML models.
6. Best Practices
Team workflows, quality control, export formats, inter-annotator agreement, and scaling strategies.
What You'll Learn
By the end of this course, you'll be able to:
Label Images
Create bounding boxes, polygons, and segmentation masks for object detection and image segmentation tasks.
Annotate Text
Perform named entity recognition, text classification, and sentiment analysis with custom labeling interfaces.
Use ML-Assisted Labeling
Connect ML backends for pre-annotations and active learning to speed up the labeling process dramatically.
Manage Teams
Set up team workflows, measure inter-annotator agreement, and ensure consistent, high-quality annotations.