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
Powered by GitBook
On this page
  • Create a new dataset
  • Upload images
  • Label and review
  • Export data

Was this helpful?

  1. Tutorials

Getting started

This tutorial shows how you can create a dataset, upload some images in it, label and review them, and finally create a dataset release to export your data.

PreviousIntroductionNextPython SDK quickstart

Last updated 3 years ago

Was this helpful?

If you want to know how to interact programmatically with Segments.ai, follow the instead.

First, make sure you've and are logged in. Clicking on the Segments.ai logo in the upper left corner, takes you to your home screen:

Create a new dataset

From the home screen, click the "New dataset" button. Choose a dataset name and description, set the visibility, and choose a category. Then click "Next".

Choose "Segmentation (bitmap)" for the task type in this tutorial. We will be labeling a dataset of pets in this tutorial, so we add three object categories: cat, dog and other.

Finally, click "Create dataset".

Upload images

Now that you've created a new dataset, let's add some images to it. In your dataset, go to the Samples tab and click the "Add Samples" button.

A sample can be an image, video or 3D pointcloud. In this tutorial we're working with images.

Select a few images and upload them. The images appear as thumbnails in the Samples tab.

Label and review

Press the "Start labeling" button. This brings you into the labeling workflow, where you will be presented with unlabeled images until the labeling queue is empty.

When done labeling, press the little cross icon in the top right corner to exit the labeling interface.

Optionally, press the "Start reviewing" button. This brings you into the reviewing workflow, where you can accept or reject labeled images. Rejected images go back to the labeling queue for correction.

Instead of using the "Start labeling" and "Start reviewing" buttons, you can also open and edit any image directly by clicking its thumbnail. Note that in this case, the image is not prevented from being edited by someone else at the same time.

Export data

Go to the Releases tab. Here you can create a snapshot of your dataset with a download link to the finished labels.

Press the "Create a new release button" and choose a name and description for the release. Creating the release may take a few seconds.

Label all objects in the image and press "Submit", until all images are labeled. More details on effectively using the segmentation interface can be found .

For more details on exporting data to different formats, see the section.

here
Export data
Python SDK quickstart
created an account
On the home screen you see a list of all your own datasets, and the datasets of others in which you're a collaborator.