Labelbox•April 30, 2021
Labelbox customers across industries count their ontologies as a critical part of the IP for their AI application. A detailed ontology is integral to a successful labeling process. An ontology ensures that all the labels in a project follow the same set of rules, reducing the possibility of confusion for the AI/ML model. Being able to quickly create, edit, and reuse ontologies is key to faster iteration cycles in machine learning.
The ontology management system in Labelbox can be imagined as a tree. The schema nodes, which contain information about the parents, children, and structure of the ontology, are the trunk and branches. The feature schema are the reusable "leaves" of the tree. Each feature schema contains information for rendering a specific feature of its kind, such as a class name, color, or type of annotation (like a bounding box). Each annotation uses the corresponding feature schema as a blueprint.
This design enables Labelbox customers to:
Watch the Labelbox Academy video below to learn more about the ontology management system design.
Watch the Labelbox Academy video below for a visual walkthrough of ontology creation in Labelbox. Here are a few things to keep in mind as you create yours:
Learn more about how you can use Labelbox at Labelbox Academy: Learning the essentials on May 6th — a virtual event where we’ll share customer stories, new features, and demos of key functionalities of Labelbox.
Labelbox•November 17, 2022
Measure and improve pre-labeling workflows with the automation efficiency score
Most ML teams operate blindly or struggle to quantify the efficiency of their pre-labeling process. Today, Labelbox is launching the automation efficiency score –a new way to track, measure, and improve your pre-labeling efficiency and time savings in single dashboard.