Project Management Overview

The Data Capture Lab includes several features that help you organize and update the files in your project through the Project Explorer.

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File Organization

At the top of the Project Explorer there are several key columns that help organize your files so that you can better manage your project. You can organize and filter the files in your project by these columns. Let’s take a look at how these columns are used.

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  • Status: File server status (Synced locally and in the cloud, Saved locally, or Saved in the cloud)

  • Video: Indicates if the file has a video file linked

  • Time: File length based on sample rate

  • Segments: Total number of segments in the current selected session

  • Label Distribution: Distribution of labels ranked from high to low

  • Uploaded: Date uploaded to SensiML servers

Label Distribution

The label distribution column provides a quick way for you to view a breakdown of the labels in a file.

Hover over the label distribution icons to see a deeper view of the label distribution in a file.

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Highlight a list of files and Right + Click → Label Distribution to see a total label distribution across multiple files in your project.

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Custom Metadata Columns

You can create custom file metadata properties to describe attributes about the subject or object in the file. See more details on how to create metadata in the Setting Up Labels and Metadata Documentation.

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File Management Tools

In the Project Explorer menu options there are several useful features to manage files, metadata, and labels across your entire project.

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  • File Storage: Upload, Download, or Delete files

  • Export: Export the selected files into the DCLI file format

  • Segments: Add, Edit, Copy, or Clear all segments in the selected files

  • Metadata: Add, Edit, or Clear all metadata in the selected files

  • Compare Files: Select multiple files to open side-by-side

  • Video Management: Link video files (MP4 or MOV) to be played during labeling

Session Management

The Project Explorer lets you create multiple sessions for labeling the same dataset. For example, you can label your data in one session and then create a brand new session for new labels. This allows you to work on the same set of data without having to re-import your files.

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