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---
title: Project settings
short: Project settings
tier: opensource
type: guide
order: 119
order_enterprise: 0
meta_title: Project settings
meta_description: Brief descriptions of all the options available when configuring the project settings
section: "Create & Manage Projects"
parent: "manage_projects_lso"

date: 2024-02-06 22:28:27

!!! error Enterprise
There are many more possible controls and configurations available for Label Studio Enterprise users. For more information on those options, see Project settings in Label Studio Enterprise.

General

Use these settings to specify some basic information about the project.

Field	Description
Project Name	Enter a name for the project.
Description	Enter a description for the project.
Color	You can select a color for the project. The project is highlighted with this color when viewing the Projects page.
Task Sampling	Sequential sampling–Tasks are shown to annotators in the same order that they appear on the Data Manager Random sampling–Tasks are shown in random order.

Labeling interface

The labeling interface is the central configuration point for projects. This determines how tasks are presented to annotators.

For information on setting up the labeling interface, see Labeling configuration.

Annotation

Labeling Instructions: Specify instructions to show the users as they annotate task. This field accepts HTML formatting. Enable **Show before labeling** to display a pop-up message to users when they enter the label stream. If disabled, users will need to click the **Show instructions** action at the bottom of the labeling interface.
Live Predictions: If you have an ML backend or model connected, you can use this setting to determine whether tasks should be pre-labeled using predictions from the model. For more information, see [Integrate Label Studio into your machine learning pipeline](ml). Use the drop-down menu to select the predictions source. For example, you can select a [connected model](#Model) or a set of [predictions](#Predictions).

Model

Click Connect Model to connect a machine learning (ML) backend to your project. For more information on connecting a model, see Machine learning integration.

You have the following configuration options:

Field Description

Start model training on annotation submission Triggers the connected ML backend to start the training process each time an annotation is created or updated.

This is part of an active learning loop where the model can be continuously improved as new annotations are added to the dataset. When this setting is enabled, the ML backend's fit() method is called, allowing the model to learn from the most recent annotations and potentially improve its predictions for subsequent tasks.

Interactive preannotations (Available when creating or editing a model connection)

Enable this option to allow the model to assist with the labeling process by providing real-time predictions or suggestions as annotators work on tasks.

In other words, as you interact with data (for example, by drawing a region on an image, highlighting text, or asking an LLM a question), the ML backend receives this input and returns predictions based on it.

Field	Description
Start model training on annotation submission	Triggers the connected ML backend to start the training process each time an annotation is created or updated. This is part of an active learning loop where the model can be continuously improved as new annotations are added to the dataset. When this setting is enabled, the ML backend's `fit()` method is called, allowing the model to learn from the most recent annotations and potentially improve its predictions for subsequent tasks.
Interactive preannotations	(Available when creating or editing a model connection) Enable this option to allow the model to assist with the labeling process by providing real-time predictions or suggestions as annotators work on tasks. In other words, as you interact with data (for example, by drawing a region on an image, highlighting text, or asking an LLM a question), the ML backend receives this input and returns predictions based on it.

And the following actions are available from the overflow menu next to a connected model:

Action	Description
Start Training	Manually initiate training. Use this action if you want to control when the model training occurs, such as after a specific number of annotations have been collected or at certain intervals.
Send Test Request	(Available from the overflow menu next to the connected model) Use this for troubleshooting and sending a test resquest to the connected model.
Edit	Edit the model name, URL, and parameters. For more information, see Connect a model to Label Studio.
Delete	Remove the connection to the model.

Predictions

From here you can view predictions that have been imported or generated when executing the Retrieve Predictions action from the Data Manager. For more information, see Import pre-annotated data into Label Studio.

Cloud storage

This is where you connect Label Studio to a cloud storage provider:

Source Cloud Storage--This is where the source data for your project is saved. When you sync your source storage, Label Studio retrieves data to be annotated.
Target Cloud Storage--This is where your annotations are saved. When you sync your target storage, annotations are sent from Label Studio to the target storage location.

For more information, see Sync data from external storage.

Webhooks

You can use webhooks to integration third-party applications. For more information, see Set up webhooks in Label Studio and our integrations directory.

Danger Zone

From here, you can access actions that result in data loss, and should be used with caution.

Drop All Tabs
If the Data Manager is not loading, dropping all Data Manager tabs can help.
Delete Project
Deleting a project permanently removes all tasks, annotations, and project data from Label Studio.