---
title: Zero-shot object detection and image segmentation with Grounding DINO
type: guide
tier: all
order: 15
hide_menu: true
hide_frontmatter_title: true
meta_title: Image segmentation in Label Studio using a Grounding DINO backend
meta_description: Label Studio tutorial for using Grounding DINO for zero-shot object detection in images
categories:
- Computer Vision
- Image Annotation
- Object Detection
- Grounding DINO

image: "/guide/ml_tutorials/grounding-dino.png"

https://github.com/HumanSignal/label-studio-ml-backend/assets/106922533/d1d2f233-d7c0-40ac-ba6f-368c3c01fd36

Grounding DINO backend integration

This integration will allow you to:

• Use text prompts for zero-shot detection of objects in images.
• Specify the detection of any object and get state-of-the-art results without any model fine tuning.

See here for more details about the pre-trained Grounding DINO model.

Before you begin

Before you begin, you must install the Label Studio ML backend.

This tutorial uses the grounding_dino example.

Quickstart

1. Make sure Docker is installed.
2. Edit docker-compose.yml to include the following:

• LABEL_STUDIO_HOST sets the endpoint of the Label Studio host. Must begin with http://

• LABEL_STUDIO_ACCESS_TOKEN sets the API access token for the Label Studio host. This can be found by logging
  into Label Studio and going to the Account & Settings page.

  Example:

• LABEL_STUDIO_HOST=http://123.456.7.8:8080
• LABEL_STUDIO_ACCESS_TOKEN=your-api-key

1. Run docker compose up
2. Check the IP of your backend using docker ps. You will use this URL when connecting the backend to a Label Studio project. Usually this is http://localhost:9090.

3. Create a project and edit the labeling config (an example is provided below). When editing the labeling config, make sure to add all rectangle labels under the RectangleLabels tag, and all corresponding brush labels under the BrushLabels tag.

<View>
    <Style>
    .lsf-main-content.lsf-requesting .prompt::before { content: ' loading...'; color: #808080; }
    </Style>
    <View className="prompt">
        <Header value="Enter a prompt to detect objects in the image:"/>
    <TextArea name="prompt" toName="image" editable="true" rows="2" maxSubmissions="1" showSubmitButton="true"/>
    </View>
    <Image name="image" value="$image"/>

    <RectangleLabels name="label" toName="image">
        <Label value="cats" background="yellow"/>
        <Label value="house" background="blue"/>
    </RectangleLabels>
</View>

1. From the Model page in the project settings, connect the model.
2. Go to an image task in your project. Enable Auto-annotation (found at the bottom of the labeling interface). Then enter in the prompt box and press Add. After this, you should receive your predictions. See the video above for a demo.

Using GPU

For the best user experience, it is recommended to use a GPU. To do this, you can update the docker-compose.yml file including the following lines:

environment:
  - NVIDIA_VISIBLE_DEVICES=all
deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia
          count: 1
          capabilities: [gpu]

Using GroundingSAM

If you are looking for GroundingDINO integration with SAM, check this example.