Labelbox

Overview & Data Types

GraphQL Introduction

A GraphQL API is very similar to a REST API however it provides a few great advantages for API consumers...

  • Everything is Typed: Both the request query and the response are strongly typed. That means you'll know your sending a valid query because the schema allows it.
  • Flexibility to pull needed data: Relationships between types are represented through the GraphQL graph allowing for complex data retrieval in a single request.
  • Easy exploration and strong tooling: Since everything is typed and built for powerful queries tools like our GraphQL explorer (https://app.labelbox.com/explorer) can be built to discover api requests and understand the API schema.

Labelbox Core Data Types

  • Project: A Project is a container that includes a labeling frontend, an ontology, datasets and labels.
  • Dataset: A dataset is a collection of DataRows. For example, if you have a CSV with 100 rows, you will have 1 Dataset and 100 DataRows, learn more
  • DataRow: A DataRow represents a single piece of data. For example, if you have a CSV with 100 rows, you will have 1 Dataset and 100 DataRows, learn more.
  • Label: Label represents an assessment on a DataRow. For example one label could contain 100 bounding boxes (annotations), learn more.
  • PredictionModel: A prediction model represents a specific version of a model, learn more.
  • Prediction: A prediction is a label made by a prediction model. Predictions can be used as a base labels to decrease labeling time, learn more.
  • AssetMetadata: AssetMetadata is a datatype to provide extra context about an asset while labeling, learn more.
  • Labeling Frontend: Is a type representing an html / javascript UI that is used to generate labels. "Image Labeling" is the default Labeling Frontend that comes in every organization. You can create new labeling frontends for an organization, learn more.

Overview & Data Types


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