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The comprehensive annotation platform for images.

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  • Object Detection

    • Autonomous Vehicles and ADAS

    • Product Identification

    • Damage and Defect Detection

    • Health Diagnostics

  • Classification

    • Search and Ad Relevance

    • Recommendation Systems

    • Precision Agrigulture

    • Policy Enforcement

  • Text Recognition

    • License Plate Identification

    • ID Verification

    • Product Cataloging

Draw a box around each rooftop and pool.

1client.createAnnotationTask({
2  callback_url: 'http://www.example.com/callback',
3  instruction: 'Draw a box around each rooftop and pool.',
4  attachment: 'http://i.imgur.com/XOJbalC.jpg',
5  objects_to_annotate: ['pool', 'rooftop'],
6  with_labels: true,
7  min_width: 30,
8  min_height: 30
9}, (err, task) => {
10    // do something with task
11});
Run Extraction
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    ML-Powered Data Labeling

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    Automated Quality Pipeline

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    Comprehensive Label Support

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    Operational Excellence

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    Data Input Flexibility

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    Configurable Tasks

“It was easy to get started with Scale AI because they had the platform and the people with the skillset to perform the annotation tasks we needed. While we’re much bigger now and could potentially handle the volume of annotation tasks, why would we want to? We believe we should leave data annotation to experts like Scale so we can focus on perfecting the retail shopping experience with regard to the use of AI.”

Virginia Puccio

Head of Data Operations, Standard Cognition

  • Guaranteed Task Completion Time

    Enterprise-grade SLAs include task completion times and tasks can be rapidly scaled up and down to meet your requirements.

  • 24/7 Development Support

    Each enterprise customer is paired with a dedicated engagement manager who will ensure smooth on-boarding and continued data delivery.

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  • Cost Effective

    Enterprise engagements provide upfront and volume-based discounts, and is the most cost-effective solution for high-quality labels. Plus with Scale AI, there are no platform fees.

Super Human Quality

Image tasks submitted to the platform are first pre-labeled by our proprietary ML models, then manually annotated and reviewed by highly trained workers depending on heuristic and learned error detectors, ml model confidence scores, and other quality metrics.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently.

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