Which Type of Data Are Included in Amazon SageMaker Ground Truth Manifest File?

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Heather Bennett

Which Type of Data Are Included in Amazon SageMaker Ground Truth Manifest File?

When working with Amazon SageMaker Ground Truth, it is essential to understand the structure of the manifest file and the types of data it includes. The manifest file is a crucial component for labeling tasks and ensures that the data is properly organized for machine learning workflows.

What is a Manifest File?

A manifest file is a JSON-formatted file that serves as a reference for the data used in labeling tasks. It contains information about the input data, such as its location, format, and metadata. The manifest file acts as a central repository that helps track and manage the labeled data throughout the labeling process.

Data Types in a Manifest File

The Amazon SageMaker Ground Truth manifest file can include various types of data, depending on your specific use case. These include:

  • Text: The manifest file can include text data, such as sentences or paragraphs that require annotation or labeling.
  • Images: Images are commonly used in computer vision tasks. The manifest file can contain references to image files stored in S3 buckets or other locations.
  • Videos: For video-related tasks, you can include video files in the manifest file. Similar to images, these references point to videos stored externally.
  • Audio: If your machine learning task involves audio analysis or speech recognition, you can include audio files in the manifest file.

Sample Structure of a Manifest File

The structure of a typical Amazon SageMaker Ground Truth manifest file includes several key fields:

  • “source-ref”: This field contains the reference to the data source, such as an S3 URI or a local path. It specifies the location of the data that requires labeling.
  • “attribute-names”: This field defines the attribute names associated with the data.

    For example, if you are labeling images, this field could include attributes like “object_type” or “bounding_box. “

  • “attribute-values”: This field contains the values corresponding to the attribute names defined above. These values provide additional information about the labeled data.

Here is an example of a manifest file structure:

{
    "source-ref": "s3://your-bucket-name/image1.jpg",
    "attribute-names": ["object_type", "bounding_box"],
    "attribute-values": ["car", [10, 20, 100, 200]]
}

Conclusion

The Amazon SageMaker Ground Truth manifest file serves as a critical component for organizing and managing labeled data. It allows you to include various types of data such as text, images, videos, and audio files. Understanding the structure of the manifest file is essential for effectively utilizing Amazon SageMaker Ground Truth in your machine learning workflows.

By properly structuring your manifest files and incorporating relevant attributes and values, you can streamline your labeling tasks and improve the accuracy of your machine learning models.

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