Table of Contents

Video classification

Alex Cota Updated by Alex Cota

This document outlines how to import video data, configure the editor for labeling video frames, navigate the video labeling interface, and export video data.

This initial release of video classification supports checklist and radio classification of one or more frames simultaneously. Currently, there are some limitations that we are actively working to fix. Please read the following carefully before using video classification.

  • Only checklist and radio global classifications are supported. Object types (bounding box, polygon, polyline, point, masks), object subclassifications, text classification, dropdown classification, and nested radios are not yet supported. However, you won't be restricted from seeing these unsupported options in the "Configure editor" step.
  • For larger videos, you may experience slow upload times and slow loading times in the labeling interface.
  • Creating video labels via the API is not yet supported.
  • Exports for mixed media is not yet supported. Do not put video data and image data together in a project.
  • Project analytics do not yet support video frames.
  • The import file and export label formats (see below) are subject to change.

How to access Video classification

The video labeling interface is integrated into the existing Editor. Therefore, the steps for importing video data are the same as importing image data.

Import your video data

First, create your project in Labelbox and ensure your videos are in .mp4 format.

By default, you'll be able to load videos with up to 2000 frames and 30fps. Please contact support if you need to work with larger videos.

You have two options for adding your video files to Labelbox:

  1. Direct upload files via app or via Python SDK.
  2. Import URLs via JSON or CSV. Note: private data will require signed URLs. To see the JSON video import format, see Import via JSON - Video import sample.
    1. Upload JSON or CSV containing URLs via app.
    2. Bulk import URLs in JSON via the GraphQL API.
    3. Bulk import URLs in JSON via the Python API.

Configure the Editor for video

After you attach your dataset, select "Editor" as your labeling interface.

This early release only supports global classifications of selected video frames. One frame can have 0 or more global classifications. Users are not limited to any number of classifications in the ontology.

When configuring the ontology, make sure to select checklist or radio as your the global classification type. You will not be restricted from seeing the dropdown and text classification types, however, they are not yet supported.

Label video data

The Labelbox labeling interface will have some additional tools for labeling video data.

Zoom bar

Drag the endpoints on the zoom bar to adjust the amount of frames selected in the timeline. You can also drag the selection across the zoom bar to move to a different part in the timeline.

Timeline

The timeline shows the temporal progression of the video clip. You can hover over a frame in the timeline to preview it.

A single frame can have 0 or more classifications. You can assign a classification by selecting one or more frames in the timeline and choosing a classification from the left sidebar.

Note: when you select a set of frames on the timeline, the left sidebar will only show the active classification state when the classification is true for all frames selected.

Below are some helpful shortcuts for selecting frames.

Shift+click

Select a set of consecutive frames on the timeline by clicking the first and last frame in the set.

Cmd/Ctrl+click

Select individual frames on the timeline.

Left/Right arrows

Seek the previous/next frame.

Shift+Left/Right arrows

Add the previous/next frame to the selection.

Export labels

For instructions on how to access your multi-frame point labels and a sample Video classification label export, see our docs on Video export formats.

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