Create a project

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

Create a new project

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Create a project via the Python SDK

To learn how to create a project via the SDK, see our visit our Python Guides.

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

Step 1: Start a new project

Under the top-level Projects page, select New project. This will open a modal where you can configure your project's initial settings.

Name your project, enter a description (optional), and select the type of data you will be labeling. Note: 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.

Step 2: Select a quality setting

After you name your project and select a quality setting:

• 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 certain labeled data rows as a gold standard — a mechanism for measuring other labelers' performance.

• 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. This is the option customers select for >90% of projects.

Step 3: Add project tags (optional)

Use Project tags to enable easier organization and retrieval of the project. Click Create to move on to more project configurations.

Step 4: Queue data for labeling

Labelbox allows you to send batches of data rows to a project for labeling. When you click Queue data for labeling 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 search for a dataset and select a number of data rows to add to your batch. Click Send n data rows to create the batch.

Step 5: Create and send batch

Next, you will be prompted to name your batch and assign data row priority. Click Submit batch 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.

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

If you choose to use the Standard Editor you will then be taken to a screen that lists all the ontologies created in Labelbox that match your project's data type

For instructions on creating an ontology, see Working with ontologies.

Once you finish these steps, the Start labeling button will be activated. However, there are two remaining optional setup steps available.

Step 7: Configure optional settings

View quality assurance tools like benchmarks, consensus, and reviews (optional): This will take you to the quality settings page where you can set up (benchmarks), (consensus) and (Workflow) for reviews.

Review and manage members of this project (optional): This will take you to the Members page within the Settings section. For information on member permissions, see Add members or groups to a project.

Choose human workforce configuration

Option 1: Workforce Boost

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 Workforce Boost for more details.

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

Option 2: Internal data labeling team

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

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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.

Modify project settings

To update project settings, go to the Settings tab.

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 tools

Delete a project

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

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Warning

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