Documentation
  • Introduction
  • Tutorials
    • Getting started
    • Python SDK quickstart
    • Model-assisted labeling
  • How to annotate
    • Label images
      • View and navigate in the image interfaces
      • Image interface settings
      • Image segmentation interface
      • Image vector interface
    • Label 3D point clouds
      • View and navigate in the 3D interface
      • Upload, view, and overlay images
      • 3D interface settings
      • 3D point cloud cuboid interface
      • 3D point cloud vector interface
      • 3D point cloud segmentation interface
      • Merged point cloud view (for static objects)
      • Batch mode (for dynamic objects)
      • Smart cuboid propagation
      • 3D to 2D Projection
      • Tips for labeling cuboid sequences
    • Label sequences of data
      • Use track IDs in sequences
      • Use keyframe interpolation
    • Annotate object links (beta)
    • Customize hotkeys
  • How to manage
    • Add collaborators to a dataset
    • Create an organization
    • Configure the label editor
    • Customize label queue
    • Search within a dataset
    • Clone a dataset
    • Work with issues
    • Bulk change label status
    • Manage QA processes
  • How to integrate
    • Import data
      • Cloud integrations
    • Export data
      • Structure of the release file
      • Exporting image annotations to different formats
    • Integrations
      • Hugging Face
      • W&B
      • Databricks
      • SceneBox
    • Create an API key
    • Upload model predictions
    • Set up webhooks
  • Background
    • Main concepts
    • Sequences
    • Label queue mechanics
    • Labeling metrics
    • 3D Tiles
    • Security
  • Reference
    • Python SDK
    • Task types
    • Sample formats
      • Supported file formats
    • Label formats
    • Categories and attributes
    • API
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On this page
  • Working with attributes
  • Object attributes
  • Scene attributes
  • In the labeling interface
  • Editing the configuration file directly

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  1. How to manage

Configure the label editor

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Last updated 2 months ago

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Once you have created a dataset, you can further configure the labeling interface under Settings -> Labeling -> Categories:

Here you can set the color, name, and description for each category.

Working with attributes

Object attributes

You can add one or more object-level attributes or object attributes to a category by expanding its row.

Attributes come in 4 types:

  • Select box

  • Multi-select box

  • Checkbox

  • Text

  • Number

You can optionally configure following settings for each object attribute:

  • Default value: default value of the attribute.

  • Mandatory: whether the attribute is mandatory. Mandatory attributes raise a warning when not filled in.

  • Synced across frames ("Sequence-level"): Only in sequence datasets. Whether an attribute should remain constant across all frames for an object with a certain track ID. If false, the attribute can change on each frame.

  • Synced across sensors ("Across sensors"): Only in multi-sensor datasets. Whether an attribute should remain constant across all sensors for an object with a certain track ID. If false, the attribute can change on each sensor.

  • Sensors: Only in multi-sensor datasets. Whether an attribute applies to 2D sensors, 3D sensors, or all sensors.

Scene attributes

You can add scene attributes by clicking the "Edit image attributes" button. Scene attributes are not linked to a specific object, but rather to a frame or the scene as a whole.

In the labeling interface

When labeling, the object and scene attributes will be shown in the sidebar on the right. Scene attributes are always visible, while object attributes are only shown when an object is selected which has a category with object attributes.

Editing the configuration file directly

If you click on the "Raw" tab, you can see the configuration in JSON format. You can copy-paste this configuration from one dataset to another, or update it programmatically using the . The configuration should adhere to the format defined .

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