Video

The scalable annotation platform for videos.

Why Scale

Accelerate the Development of Artificial Intelligence

Scale Video is the most scalable annotation platform for video data. Capable of supporting annotations for a wide range of computer vision and classification applications, machine learning teams trust Scale Video to accelerate the development of high-quality models.

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    Automated Quality Pipeline

    Tech-enabled quality assurance systems built into the product to reduce costly human review without sacrificing quality. Customers are also provided transparency in regards to quality with a QA tool and dashboard metrics.

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    Operational Excellence

    Operational excellence and expertise means extreme flexibility on quality and annotation requirements. Flexibly ramp up or down large-volume data pipelines without sacrificing quality.

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    ML-Powered Data Labeling

    The Scale AI Platform is built by machine learning engineers for machine learning engineers. Scale Video leverages machine learning to power pre-labeling and tooling to annotate large volumes of videos efficiently.

Use Cases

Computer Vision & Classification

Develop accurate models to detect and track objects, predict behavior, classify videos and more.

Detection & Tracking

Locate, identify and track objects of various classes.

  • Product Identification

  • Damage and Defect Detection

  • Sports Analytics

Detection & Tracking
Prediction & Planning
Prediction & Planning

Understand relationships between objects and predict behavior and intent.

  • Autonomous Vehicles and ADAS

  • Autonomous Checkout

  • Physical Security

Classification

Classify entire videos according to its content.

  • Search and Ad Relevance

  • Topic Modeling

  • Policy Enforcement

Classification
How It Works

Easy to Start, Optimize and Scale

Build models you can trust while maximizing operational efficiency and reducing the cost of ML projects.

"Annotate all the vehicles, pedestrians and traffic lights in the video."
client.createVideoannotationTask(
  {
    callback_url: 'http://www.example.com/callback',
    instruction: 'Annotate all the vehicles, pedestrians and traffic lights in the video.',
    attachment_type: 'video',
    attachment: 'http://example.com/video.mp4',
    objects_to_annotate: ['person'],
  },
  (err, task) => {
    // do something with task
  }
);
Run code

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