Create a project

Developer guide: Create a project via the Python SDK

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The project is where you orchestrate all of your labeling operations. Use this guide to learn how to create a project and configure all available project settings.

Create a new project

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

Step 1: Start a new project

Under the top-level Annotate page, select New project. This will open a modal where you can configure your project type. Note that you will only be able to attach data rows that match the data type you set here. For example, you cannot select video as the data type upon project creation and then send image data rows to this project later.

After selecting a project type, name your project, optionally enter a description and select the type of data you will be labeling. If creating an LLM data generation project, you will also input a number of data rows to be generated by labelers.

Step 2: Select a quality setting

After you select the data type and name your project, select a quality setting from the following options:

• Benchmarks: Select benchmarks if you know that each data row you send to this labeling project will only need to be labeled once. Benchmarks allow you to designate, if desired, certain labeled data rows as a gold standard and work as a mechanism for measuring the performance of other labelers.

• Consensus: Select consensus if you know that each data row will need to be labeled multiple times and, hence, should be labeled by multiple labelers.

If you are not sure where to start, select Benchmarks. Customers select this option for greater than 90% of projects.

Step 3: Add project tags (optional)

Configure project tags to enable easier organization and retrieval of the project.

You can also modify your project tags after setting up your project. From the project home page, hover over Tags and select the edit icon to configure your project tags.

Step 4: Add data

Labelbox allows you to send a batch or batches of data rows to a project for labeling. When you click Add data you will be sent to Catalog, where you can select a batch of data rows and send it to the project you are configuring.

In this step, you can filter and search your data rows to curate a batch. Select the data rows you wish to send to the project, then select Queue batch of n to add the data rows to the labeling queue.

Step 5: Submit batch

Next, you will have the option to configure the data row priority settings. Select Submit to send this batch to your labeling project.

Step 6: Configure the editor / attach an ontology

The final required step is to select the labeling interface (editor) that your project will use. To learn more about the various editors available, see our docs on labeling editors.

The Standard editor (recommended) is the native labeling interface and has the most robust labeling and quality analysis tools. However, if you need to set up a custom editor, select the Custom Editor.

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Custom editor is no longer supported

On June 30, 2024, we will sunset the custom editor for all customers. For more information on this deprecation, see Deprecations.

When you select Standard Editor, you will see a screen that lists all ontologies in your organization that match your project's data type. Select the best ontology for your project or create a new ontology. For instructions on creating an ontology, see Working with ontologies.

Once you finish these steps, the Start labeling button will be activated.

Choose human workforce configuration

Option 1: Boost Workforce

Many customers need to use a dedicated external data labeling team. Labelbox offers a premium data labeling service integrated tightly into the Labelbox Annotate platform. See Boost Workforce for more details.

Option 2: Internal data labeling team

To learn how to invite, set up, and manage your own data labeling team, see Manage members.

Modify project settings

To update project settings, go to the Settings tab from with a project.

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 rows.
2. Update the ontology.
3. Manage the team members assigned to a project.
4. Configure webhooks.

Delete a project

A project can only be deleted by an organization-wide admin. To delete a project, go to Settings > Danger zone, and proceed with the deletion.

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Warning

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