How this Helps

This feature enables teams to streamline repetitive or manual tasks, reducing time spent on video review and dataset cleanup. It’s particularly valuable for computer vision teams managing large-scale video datasets across multiple environments.
[Feature Name] allows users to [brief description of what this feature enables]. At Visual Layer, this is especially useful for [describe role, e.g., ML engineers or data scientists] looking to [solve a specific challenge, like identify duplicates, improve annotation quality, or prepare datasets for fine-tuning]. Common use cases include:
  • [Use case 1]: [brief description]
  • [Use case 2]: [brief description]
  • [Use case 3]: [brief description]

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[Feature Architecture / How It Works]

The diagram below outlines the key components of this feature in Visual Layer:
[Alt text for diagram]
Below is a breakdown of each component:
ComponentDescription
1TriggersEvents or user actions that initiate the workflow or feature process.
2StepsSequential actions or evaluations taken on the video or image data.
3Flow ControlsLogic that governs conditional execution of steps, such as filters or decision trees.
4OutputsFinal outputs such as metadata exports, updated video versions, or metrics.
You can use Visual Layer Copilot to accelerate the setup process and generate optimized flows automatically.

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Using [Feature Name]

With [Feature Name], you can:
  • [action 1]
  • [action 2]
  • [action 3]
  • [action 4]

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[First sub-heading — e.g., How It Works]

The following is an overview of how the process works:
1

Upload your video dataset

Upload a dataset using the Visual Layer UI or API. Once uploaded, the system will begin metadata indexing.
2

Enable frame quality checks

Enable frame quality filters from the configuration panel.
Frame Quality UI Configuration
3

Define thresholds for blur detection

Set acceptable ranges for visual clarity, based on your project’s needs.
4

Review flagged frames

A preview set of flagged frames will be shown for validation. You can confirm removals or override false positives.
5

Commit clean dataset version

Once validated, save the filtered dataset as a new version for downstream tasks like labeling or fine-tuning.

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Recommendations and Limitations

  • Works best on datasets larger than 500 frames
  • Thresholds may need adjustment for infrared or thermal footage
  • Currently supports .mp4, .avi, and .mov formats
  • GPU-accelerated processing is only available on Enterprise plans

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Related Articles

Working with Dataset Versions

Learn how to manage different versions of your video datasets.

Using Visual Layer Copilot

Leverage AI assistance to automate common workflows.

Preprocessing Before Labeling

See best practices for cleaning and preparing your data.