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The scalable annotation platform for videos.

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    Detection & Tracking

    • Product Identification

    • Damage and Defect Detection

    • Sports Analytics

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    Prediction & Planning

    • Autonomous Vehicles and ADAS

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    • Physical Security

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    • Search and Ad Relevance

    • Topic Modeling

    • Policy Enforcement

Annotate all the vehicles, pedestrians and traffic lights in the video.

2    callback_url: '',
3    instruction: 'Annotate all the vehicles, pedestrians and traffic lights in the video.',
4    attachment_type: 'video',
5    attachment: '',
6    objects_to_annotate: ['person'],
7  },
8  (err, task) => {
9    // do something with task
10  }
Run Extraction
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    ML-Powered Data Labeling

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    Automated Quality Pipeline

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    Data Input Flexibility

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    Multi-Modal Annotation Support

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    Comprehensive Label Support

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    Infinitely Long Tasks

“Scale AI has been a critical part of our AI development cycle: its powerful data platform fits into our data pipeline seamlessly providing efficient annotations for large volumes of data, its talented engineering and QA teams help delivering high quality of challenging data, and its customer operations team work very closely with us to help reaching our development goal successfully and smoothly.”

Fiona Hua

Lead Perception Engineer, Sea Machines Robotics

  • 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

Video 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 the ML model confidence scores. All tasks receive additional layers of both ML-based checks and human review.

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

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