Key definitions

Below are the key definitions that you will see in the product, API, and the docs.

Term

Definition

Project

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.

Model

A model is an environment to evaluate a specific AI model performance

Catalog

The Catalog is a warehouse of all data within an organization

Asset

An Asset is a single cloud-hosted file such as an image, a video, or a text file.

Data Row

A Data Row is the container that houses all of the following information for a single Asset:

  • URL to your cloud-hosted file
  • Metadata
  • Media attributes (e.g., data type, size, etc.)
  • Annotation information
  • Attachments (files that provide context for your labelers)

Dataset

A Dataset is a set of Data Rows

Schema

Nearly everything in Labelbox is strongly typed. Schema is the master blueprint of your training data. It contains Ontologies, Features, and Metadata.

Ontology

An ontology is a collection of Features and their relationships. It is essential for data labeling, model training, and evaluation.

Feature

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

Metadata is information about data. There are two types of metadata: reserved keys (user cannot change) and custom (user-defined).

Annotation

An annotation is an instance of a Feature.

Label

A label is a collection of all annotations on a data row

Editor

An editor is a labeling tool designed to create, review and edit annotations


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