Labelbox documentation

Getting started

Hi and welcome to Labelbox!

This section contains beginner tutorials for new Labelbox users. These end-to-end exercises will show you the basic functionality of Labelbox by taking you through some simple, yet common, workflows in Labelbox.

You can find our more advanced instructional guides in our How-to guides section.

Basics

Tutorial

Github

Google Colab

Fundamentals

Open in Github

open-in-colab.svg

Data Rows

Open in Github

open-in-colab.svg

Datasets

Open in Github

open-in-colab.svg

Labels

Open in Github

open-in-colab.svg

Ontologies

Open in Github

open-in-colab.svg

Projects

Open in Github

open-in-colab.svg

User management

Open in Github

open-in-colab.svg

Project configuration

Tutorial

Github

Google Colab

Project setup

Open in Github

open-in-colab.svg

Queue management

Open in Github

open-in-colab.svg

Webhooks

Open in Github

open-in-colab.svg

Model-assisted labeling (MAL)

Tutorial

Github

Google Colab

MAL basics

Open in Github

open-in-colab.svg

MAL for images

Open in Github

open-in-colab.svg

MAL for named entity recognition

Open in Github

open-in-colab.svg

MAL for tiled imagery

Open in Github

open-in-colab.svg

Debugging MAL

Open in Github

open-in-colab.svg

MAL with subclasses

Open in Github

open-in-colab.svg

Label exports

Tutorial

Github

Google Colab

Image annotation export

Open in Github

open-in-colab.svg

Text annotation export

Open in Github

open-in-colab.svg

Video annotation export

Open in Github

open-in-colab.svg

This beginner tutorial helps you set up a sample project in Labelbox.

  1. Go to Labelbox and sign in.

  2. Once you are signed in, click Create project.

  3. Under Project info, enter the following information:

    1. Name: “My First Project”

    2. Description: “Learning how to set up a project in Labelbox”

    3. Then, click Next.

  4. Under Choose data, do the following:

    1. Click Add data.

    2. Click Choose file to upload and select 1-5 sample images from your computer (PNG or JPG format).

    3. Name the dataset "My first dataset" and click Start upload.

    4. The dataset should automatically be attached to the project. Click Next.

  5. In the Configure Editor step, do the following:

    1. On Editor, click the Setup button.

    2. Click Add object.

    3. Enter “Sample object 1” as the class name and select Bounding box from the dropdown menu.

    4. Click on the color dot next to Sample object 1 and enter #FFB31C as the color.

    5. Click Add object again.

    6. Enter “Sample object 2” as the class name and select Segmentation from the dropdown menu.

    7. Click Add classification and for the instructions enter “Is it daytime?”

    8. Toggle on the Required option and leave Searchable on.

    9. Under options, enter “yes”.

    10. Click Add option, and enter “no”.

    11. Click Done.

    12. Click Confirm to go back to the project setup. Then, click Next.

  6. In the Select settings step, do the following:

    1. Toggle on the Review step option. Set the Coverage to 50% and click Confirm changes.

    2. Click Finish.

  7. To label your data in the Editor, do the following:

    1. From the Overview tab, click Start labeling.

    2. From the Tools menu, select Sample object 1 and draw a bounding box anywhere on the image.

    3. Select Sample object 2 and draw a Segmentation mask anywhere on the image. To complete the shape, click on the first point.

    4. Under Is it daytime? select yes.

    5. Click Submit.

    6. Complete steps 24-28 for the remaining assets in the dataset. Then click Go to project overview.

  8. To export your labels, do the following:

    1. Go to the Export tab and click Generate export.

    2. From the Tasks menu, download My First Project: Labels export.

    3. Open the exported JSON file in any code editor.

  1. Copy and paste this JSON into a text editor on your computer. Save the JSON file as sample-tiled-imagery-dataset.json.

  2. Go to the Create dataset page in Labelbox.

  3. Select Choose files to upload and choose sample-tiled-imagery-dataset.json.

  4. Under Select integration, choose No integration selected and click Start upload.

  5. From the Datasets tab, you should be able to preview the assets in your dataset as thumbnails (may take a minute) for Labelbox to process the dataset and render the thumbnails).

    Screen_Shot_2021-05-17_at_2_19_30_PM.png
  6. At the bottom, set Show to 50 to change the view to 50 results per page.

  7. Click on the first asset. You will be able to see a preview of the asset and its associated metadata.

    asset-preview.png
  8. Exit the asset preview window to go back to the dataset view.

  9. Select 3 assets by clicking on the checkbox in the top left corner of the thumbnails. Then, go to the 3 selected dropdown under the Add more data button and click Delete. You can either follow through with the delete or cancel to return to the dataset view.