Below are the key definitions that you will see in the product, API, and the docs.
A project is a labeling environment in Labelbox. Think of it as a factory assembly line for producing labels. The initial state of the project can start with raw data, pre-existing ground truth, or pre-labeled data.
A model is an environment to evaluate a specific AI model performance
The Catalog is a warehouse of all data within an organization
An Asset is a single cloud-hosted file such as an image, a video, or a text file.
A Data Row is the container that houses all of the following information for a single Asset:
A Dataset is a set of Data Rows
Nearly everything in Labelbox is strongly typed. Schema is the master blueprint of your training data. It contains Ontologies, Features, and Metadata.
An ontology is a collection of Features and their relationships. It is essential for data labeling, model training, and evaluation.
A feature is the master definition of the entity you want the model to predict or humans to label. There are two kinds of features: objects and classifications. A feature can have multiple deeply nested sub-features.
Metadata is information about data. There are two types of metadata: reserved keys (user cannot change) and custom (user-defined).
An annotation is an instance of a Feature.
A label is a collection of all annotations on a data row
An editor is a labeling tool designed to create, review and edit annotations
Updated 4 days ago