• Labelbox
• Benefits
• Quickstart
• Uploading Datasets
• Labeling Interfaces
  • Image Segmentation
  • Image Classification
  • Image Segmentation & Classification
  • Text Classification
• Exporting Labels
  • Segmentation Masks
  • COCO format
  • Pascal VOC format
• Creating Custom Labeling Interfaces
  • A Minimal Example
  • Labelbox Pluggable Interface Architecture
  • Using labeling-api.js
  • Hello World Example
  • Full Example
  • Full API Reference
  • Reference Interfaces
  • Local Development of Labeling Interfaces
  • Installing a Labeling Frontend in labelbox.com
• Request features
• Terms of use, privacy and content dispute policy

Labelbox is a data labeling tool that's purpose built for machine learning applications. Start labeling data in minutes using pre-made labeling interfaces, or create your own pluggable interface to suit the needs of your data labeling task. Labelbox is lightweight for single users or small teams and scales up to support large teams and massive data sets.

• Simple image labeling: Labelbox makes it quick and easy to do basic image classification or segmentation tasks. To get started, simply upload your data or a CSV file containing URLs pointing to your data hosted on a server, select a labeling interface, (optional) invite collaborators and start labeling.

• Label just about anything: Create a custom labeling interface to meet the needs of your labeling task. Start by customizing one of the standard Labelbox interfaces or build one from the ground up, just import labeling-api.js in a script tag. See Creating Custom Labeling Interfaces to get started.

• Manage Teams: Invite your team members to help with labeling tasks. Labelbox streamlines your labeling workflows, from micro labeling projects for quick R&D to production grade projects requiring hundreds of collaborators.

• Measure Performance: Maintain the highest quality standards for your data by keeping track of labeling task performance of individuals and teams.

1. Sign up on Labelbox
2. Jump into the example project or create a new one
3. Step through the setup, attach a data set and start labeling
4. Export your labels

From your computer:
Select your data from your local computer to upload. Your data is uploaded in a secure cloud bucket and only you have access to it.

From the cloud:
If your data is hosted in the cloud (e.g. Amazon S3), you can point Labelbox to your data by creating a CSV file with URLs to each file. Checkout an example CSV file containing image URLs.

In Labelbox, a labeling interface is used during the image labeling workflow. There are a few pre-made labeling interfaces available in Labelbox. To configure a labeling interface for your labeling task, navigate to Settings->Labeling Interface->Edit and edit the JSON.

You can also create and install a completely custom labeling interface to suit the needs of your labeling task. See Creating Custom Labeling Interfaces to get started.

Pre-made Labeling Interfaces

• Image Segmentation
• Image Classification
• Text Classification

The Image Segmentation Interface can be used to segment objects in an image. Supported annotation tools are:

• Rectangle
• Polygon
• Line
• Point

For example, let's say we want to configure the labeling interface to segment 2 objects in an image. One object is required to be labeled using a rectangle tool and the other with a polygon tool. To modify the pre-made Labelbox Image Segmentation Interface to support this task, edit the segmentation interface with the JSON snippet below:

{
  "tools": [
    {
      "name": "Car",
      "color": "navy",
      "tool": "polygon"
    },
    {
      "name": "Tree",
      "color": "green",
      "tool": "polygon"
    },
    {
      "name": "Road Sign",
      "color": "orange",
      "tool": "polygon"
    },
    {
      "name": "Person",
      "color": "pink",
      "tool": "rectangle"
    },
    {
      "name": "Corner of Building",
      "color": "red",
      "tool": "point"
    },
        {
      "name": "Lane divider",
      "color": "purple",
      "tool": "line"
    }
  ]
}

Classify an image from a set of categories. Classification interface supports single or multi choice forms which can be configured as below:

[
  {
    "name": "model",
    "instructions": "Select the car model",
    "type": "radio",
    "required": true,
    "options": [
      {
        "value": "model_s",
        "label": "Tesla Model S"
      },
      {
        "value": "model_3",
        "label": "Tesla Model 3"
      },
      {
        "value": "model_x",
        "label": "Tesla Model X"
      }
    ]
  },
  {
    "name": "image_problems",
    "instructions": "Select all that apply",
    "type": "checklist",
    "required": false,
    "options": [
      {
        "value": "blur",
        "label": "Blurry"
      },
      {
        "value": "saturated",
        "label": "Over Saturated"
      },
      {
        "value": "pixelated",
        "label": "Pixelated"
      }
    ]
  }
]

It is also possible to do image segmentation & classification together in the same interface. Just customize your image segmentation interface with JSON input like this:

{
  "tools": [
    {
      "name": "Car",
      "color": "navy",
      "tool": "polygon",
      "classifications": [
        {
          "name": "color",
          "instructions": "Select the car color",
          "type": "radio",
          "options": [
            {
              "value": "red",
              "label": "Red"
            },
            {
              "value": "blue",
              "label": "Blue"
            },
            {
              "value": "green",
              "label": "Green"
            }
          ]
        }
      ]
    },
    {
      "name": "Tree",
      "color": "green",
      "tool": "polygon"
    },
    {
      "name": "Road Sign",
      "color": "orange",
      "tool": "polygon"
    },
    {
      "name": "Person",
      "color": "pink",
      "tool": "rectangle"
    },
    {
      "name": "Corner of Building",
      "color": "red",
      "tool": "point"
    }
  ],
  "classifications": [
    {
      "name": "model",
      "instructions": "Select the car model",
      "type": "radio",
      "options": [
        {
          "value": "model_s",
          "label": "Tesla Model S"
        },
        {
          "value": "model_3",
          "label": "Tesla Model 3"
        },
        {
          "value": "model_x",
          "label": "Tesla Model X"
        }
      ]
    },
    {
      "name": "image_problems",
      "instructions": "Select all that apply",
      "type": "checklist",
      "options": [
        {
          "value": "blur",
          "label": "Blurry"
        },
        {
          "value": "saturated",
          "label": "Over Saturated"
        },
        {
          "value": "pixelated",
          "label": "Pixelated"
        }
      ]
    }
  ]
}

Classify text form a set of categories. Categories can be customized in a fashion similar to the Image Segmentation Interface example above.

Labels are exported in CSV or JSON format. For geometic labels (segmentation, key point, etc...) the coordinates are exported in X-Y coordinates or WKT strings. The coordinate origin for geometric labels is the bottom left point of the image.

For parsing the WKT format, We recommend using python Shapely.

from shapely.wkt import loads as wkt_loads
polygon_wkt= wkt_loads(polygon)

You can also convert Labelbox output to COCO or Pascal VOC format using our scripts.

• COCO format
• Pascal VOC format

Labelbox can also export image masks from labels created using the image segmentation tools. Using image masks often means less data preparation is needed to use labels when training machine learning models.

Labelbox generates an image mask per label class (i.e. if there are 2 car and 2 tree labels in an image, 2 image masks in total - a car and a tree - will be created). White pixels in the image mask represent the object.

You can create custom labeling interfaces to suit the needs of your labeling tasks. All of the pre-made labeling interfaces are open source.

<script src="https://api.labelbox.com/static/labeling-api.js"></script>
<div id="form"></div>
<script>
function label(label){
  Labelbox.setLabelForAsset(label).then(() => {
    Labelbox.fetchNextAssetToLabel();
  });
}

Labelbox.currentAsset().subscribe((asset) => {
  if (asset){
    drawItem(asset.data);
  }
})
function drawItem(dataToLabel){
  const labelForm = `
    <img src="${dataToLabel}" style="width: 300px;"></img>
    <div style="display: flex;">
      <button onclick="label('bad')">Bad Quality</button>
      <button onclick="label('good')">Good Quality</button>
    </div>
  `;
  document.querySelector('#form').innerHTML = labelForm;
}

</script>

Labelbox allows the use of custom labeling interfaces. Custom labeling interfaces minimally define a labeling ontology and optionally the look and feel of the labeling interface. A minimal labeling interface imports labeling-api.js and uses the fetch and submit functions to integrate with Labelbox. While Labelbox makes it simple to do basic labeling of images and text, there are a variety of other data types such as point clouds, maps, videos or medical DICOM imagery that require bespoke labeling interfaces. With this in mind, Labelbox is designed to facilitate the creation, installation, and maintenance of custom labeling frontends.

To develop a Labelbox frontend, import labeling-api.js and use the 2 APIs described below to fetch the next data and then submit the label against the data. Note that multiple data can be loaded in a single fetch if a row in the CSV file contains an array of data pointers.

Attach the Labelbox Client Side API

<script src="https://api.labelbox.com/static/labeling-api.js"></script>

Get a row to label

Labelbox.fetchNextAssetToLabel().then((dataToLabel) => {
  // ... draw to screen for user to view and label
});

Save the label for a row

Labelbox.setLabelForAsset(label); // labels the asset currently on the screen

Try it in your browser
(The project must be setup first)

<script src="https://api.labelbox.com/static/labeling-api.js"></script>
<div id="form"></div>
<script>
function label(label){
  Labelbox.setLabelForAsset(label).then(() => {
    Labelbox.fetchNextAssetToLabel();
  });
}

Labelbox.currentAsset().subscribe((asset) => {
  if (asset){
    drawItem(asset.data);
  }
})
function drawItem(dataToLabel){
  const labelForm = `
    <img src="${dataToLabel}" style="width: 300px;"></img>
    <div style="display: flex;">
      <button onclick="label('bad')">Bad Quality</button>
      <button onclick="label('good')">Good Quality</button>
    </div>
  `;
  document.querySelector('#form').innerHTML = labelForm;
}

</script>

Labeling interfaces are developed locally. Once the interface is ready to use, it is installed in Labelbox by pointing to a hosted version of the interface.

Run localhost server

1. Start the localhost server in a directory containing your labeling frontend files. For example, run the server inside templates/hello-world to run the hello world labeling interface locally.

python -m SimpleHTTPServer

2. Open your browser and navigate to the localhost endpoint provided by the server.

3. Customize the labeling frontend by making changes to index.html. Restart the server and refresh the browser to see the updates.

When you are ready to use your custom labeling interface on Labelbox, upload your index.html file to a cloud service that exposes a URL for Labelbox to fetch the file. If you don't have a hosting service on-hand, you can quickly get setup with Now from Zeit.

Custom Interface Hosting Quickstart with Now

• Create an account at Zeit, download and install Now here: https://zeit.co/download

• With Now installed, navigate to the directory with your labeling interface (where your index.html is located) and launch Now in your terminal by typing now. The Now service will provide a link to your hosted labeling interface file.

• Within the Labeling Interface menu of the Settings tab of your Labelbox project, choose Custom and paste the link in the URL to labeling frontend field as shown in the video below.

Have a feature request? Need a custom labeling interface built for your labeling task? We can help.

Create an issue here: https://github.com/Labelbox/Labelbox/issues or contact us at [email protected]

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