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:
|
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 |
Updated 4 days ago