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Create a dataset

Instructions for uploading a dataset to Labelbox via the application.

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Import specifications: image | video | text | geospatial | audio | documents | HTML | DICOM

Python tutorials: datasets | data rows | data row metadata

In Labelbox, a data row represents an asset and all of its relevant information. A dataset is a collection of data rows imported to Labelbox.

Key definitions

TermDefinition
Data rowContains all of the following information for a single asset:
- URL to your cloud-hosted file
- Metadata
- Media attributes (e.g., data type, size, etc.)
- Attachments (files that provide context for your labelers)
DatasetA set of data rows from a single domain or source
AssetA single cloud-hosted file to be labeled (e.g., an image, a video, or a text file).
AttachmentSupplementary information you can attach to an asset that provides contextual information used as an aid during labeling. Learn more about attachments and image layers.
Global keyA customer-specified ID for each data row asset. It is an optional field, but it is a good practice to use global keys to map your external database/file path to your Labelbox assets for easy retrieval.

Global keys are uniquely enforced at the Catalog (organization) level, so it helps prevent duplicate data upload. This is the preferred ID to use to identify all your assets.

Supported data types

NameKindsImport specs
ImagesPNG, JPEG, BMPImage import format
VideoMP4Video import format
TextTXT (UTF-8)Text import format
Geospatial imageryTile Map ServerGeospatial import format
Simple tiledTile Map ServerSimple tiled import format
AudioMP3, WAV, M4AAudio import format
DocumentsPDFDocuments import format
HTMLHTMLHTML import format
DICOMDCMDICOM import format

Create a dataset via the app UI

Although uploading a dataset via the Python SDK is recommended, you can still upload datasets via to Labelbox via the app UI.

Step 1: Create your JSON file

  1. Create a JSON file containing data formatted as per data type.

  2. Go to the Create a dataset page.

  3. Drag and drop your JSON file onto the page.

When you upload your JSON file, your data is able to remain in your cloud bucket. See Integrations to learn how to set up IAM delegated access.

Give it a try using the examples below. Copy and paste the content into a text editor and save it as a JSON file (.json extension)

[
  {
    "row_data": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/image-sample-1.jpg",
    "global_key": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/image-sample-1.jpg",
    "media_type": "IMAGE",
    "metadata_fields": [{"schema_id": "cko8s9r5v0001h2dk9elqdidh", "value": "tag_string"}],
    "attachments": [{"type": "IMAGE_OVERLAY", "value": "https://storage.googleapis.com/labelbox-sample-datasets/Docs/rgb.jpg", "name": "RGB" }]
  },
  {
    "row_data": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/image-sample-2.jpg",
    "global_key": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/image-sample-2.jpg",
    "media_type": "IMAGE",
    "metadata_fields": [{"schema_id": "cko8s9r5v0001h2dk9elqdidh", "value": "tag_string"}],
    "attachments": [{"type": "TEXT_URL", "value": "https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt"}]
  }
]

[
  {
    "row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-1.mp4",
    "global_key": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-1.mp4",
    "media_type": "VIDEO",
    "metadata_fields": [{"schema_id": "cko8s9r5v0001h2dk9elqdidh", "value": "tag_string"}],
    "attachments": [{"type": "VIDEO", "value": "https://storage.googleapis.com/labelbox-sample-datasets/Docs/drone_video.mp4" }]
  },
  {
    "row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4",
    "global_key": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4",
    "media_type": "VIDEO",
    "metadata_fields": [{"schema_id": "cko8s9r5v0001h2dk9elqdidh", "value": "tag_string"}],
    "attachments": [{"type": "TEXT_URL", "value": "https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt"}]
  }
]

[
  {
    "row_data": {
      "pdf_url": "https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf",
      "text_layer_url": "https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483-lb-textlayer.json"
    },
    "global_key": "https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf",
    "media_type": "PDF",
    "metadata_fields": [{"schema_id": "cko8s9r5v0001h2dk9elqdidh", "value": "tag_string"}],
    "attachments": [{"type": "HTML", "value": "https://www.wikipedia.org/" }]
  }
]

[
  {
    "row_data":{
      "tile_layer_url": "https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png",
      "bounds": [
        [
          19.405662413477728,
          -99.21052827588443
        ],
        [
          19.400498983095076,
          -99.20534818927473
        ]
      ],
      "min_zoom": 12,
      "max_zoom": 20,
      "epsg": "EPSG4326",
      "alternative_layers": [
        {
          "tile_layer_url": "https://api.mapbox.com/styles/v1/mapbox/satellite-streets-v11/tiles/{z}/{x}/{y}?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw",
          "name": "Satellite"
        },
        {
          "tile_layer_url": "https://api.mapbox.com/styles/v1/mapbox/navigation-guidance-night-v4/tiles/{z}/{x}/{y}?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw",
          "name": "Guidance"
        }
      ]
    },
    "global_key": "https://s3-us-west-1.amazonaws.com/lb-tiler-layers/mexico_city/{z}/{x}/{y}.png",
    "media_type": "TMS_GEO",
    "metadata_fields": [{"schema_id": "cko8s9r5v0001h2dk9elqdidh", "value": "tag_string"}],
    "attachments": [{"type": "TEXT_URL", "value": "https://storage.googleapis.com/labelbox-sample-datasets/Docs/text_attachment.txt"}]
  }
]

Step 2: Upload your JSON file

  1. Log into Labelbox.

  2. Go to the Catalog and select New dataset.

  3. Upload any supported data types.

📘

Limits

See this page to learn about the limits for uploading data to Labelbox.

Best practices

It is best to put data from a single domain or source into a single dataset. Organizing your data this way will make it easier to set up your labeling workflows. For example, it would be easiest to organize a set of images coming from a particular type of medical device into a single dataset. You can then use metadata to better organize and filter the Data Rows within that dataset.

Clear names that explain the source and purpose of a dataset are best. For example medical-device-type-1 would help identify this dataset as data relating to a particular version of a device. You can use the dataset description to include more context.

Append to an existing dataset

You can also append data rows to a dataset in the UI. Go to Catalog, select your dataset from the left, then click Append to dataset.

For instructions on how to append to a dataset using the Python SDK, see Dataset.

Copy the dataset ID

Each dataset has a unique dataset ID. You can find this dataset ID in the UI of Labelbox:

  1. Go to Catalog
  2. Select your dataset
  3. Copy the ID from the URL

Updated about 2 months ago

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