Project setup

Setup labeling projects in UI or using Python SDK

To get started, create and configure a data labeling project.

Create a new project

To create a project via the Labelbox UI, follow these steps:

  1. Under the Projects tab, select New project.

  2. Add a project name and an optional description for your project.

  3. Attach a dataset to your project and click Next to proceed to the next step.

  4. Choose a label editor. Editor is the native labeling interface and has the most robust labeling and QA tools. If you are using a custom editor, select the Custom Editor.

  5. Create or select an ontology (Working with Ontologies) and add Labeling instructions.

  6. Set up quality assurance tools for your project. In this step, you can turn on Benchmarks or Consensus as well as the Review step.

  7. Click Finish to complete the setup process.

Using Python SDK

# Create a new project
project = client.create_project(name="my-test-project",
                                description="a description")

# Get the Labelbox editor 
editor = next(client.get_labeling_frontends(where = LabelingFrontend.name == 'editor'))

# Get exsiting ontology (you can share an ontology across projects)
ontology = client.get_ontology("ONTOLOGY_ID")

# Get dataset
dataset = client.get_dataset("DATASET_ID")

# Setup project with editor and normalized ontology
project.setup(editor, ontology.normalized)

# Upload labeling instructions
project.upsert_instructions("LOCAL_FILE_PATH (PDF or HTML)")

#Attach the dataset to the project
project.datasets.connect(dataset)

#Detach the dataset from the project
project.datasets.disconnect(dataset)

Choose human workforce configuration

Workforce Boost
For a lot of use cases, chances are that you may want to use a dedicated external data labeling team. Labelbox offers a premium data labeling service integrated tightly into the Labelbox Annotate platform.

Currently, Workforce Boost is only available to customers who have purchased the software platform. You can get a free trial anytime by contacting Support or [email protected]

Internal data labeling team
Inviting, onboard, and manage your own data labeling team using this guide: Manage members

📘

Collaboration & rapid iteration is critical for achieving outstanding outcome with data quality.

Our very best customers run their data labeling operations through real-time collaboration with the data labeling workforce, data engineers, and ML engineers. In Labelbox, you can easily augment your internal domain experts with external data labeling service providers.

Project settings

Any time during the life of a data labeling project, you can easily update key project settings. Below are the most common things you will likely need to update:

  1. Add or remove data
  2. Update ontology
  3. Configure Model-assisted labeling (MAL)
  4. Configure QA process

Delete a project

A project can be deleted by Admin. To delete a project, go to Danger zone under project settings.

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Warning

Deleting a project will also delete all the labels that have been submitted for a project.

Complete Python SDK tutorial

Python Tutorial

Github

Google Colab

Labeling projects

Open in Github

Open in Google Colab


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