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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Introduction

NextGetting started

Last updated 8 months ago

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Segments.ai is the training data platform for computer vision engineers and labeling teams. Our powerful labeling interfaces, easy-to-use management features, and extensive API integrations help you iterate quickly between data labeling, model training and failure case discovery.

The documentation is divided into 4 sections:

  1. : end-to-end tutorials where you are guided through a complete project.

  2. How-to guides: recipes to solve specific problems relating to

  3. : provides clarifying background knowledge on certain topics.

  4. : description of the full Python SDK functionality and input and output formats.

Tutorials
Annotating data
Managing data and collaborators
Integrating Segments.ai in your workflows and ML pipelines
Background
Reference