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Video

The scalable annotation platform for videos.

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Use Cases

Computer Vision & Classification

Confidently deploy highly accurate models to detect and track objects, predict and plan behavior, classify videos and more.

Detection & Tracking
Locate and identify objects of various classes and track them frame by frame.
- Product Identification
- Damage and Defect Detection
- Sports Analytics
Prediction & Planning
Understand relationships between objects and predict behavior and intent.
- Autonomous Vehicles and ADAS
- Autonomous Checkout
- Physical Security
Classification
Classify entire videos or events within videos with start and end timestamps.
- Search and Ad Relevance
- Topic Modeling
- Policy Enforcement

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.

Bounding Box
Classification
Polygon
Cuboid

"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 Extraction

ML-Powered Data Labeling
Receive large volumes of high-quality annotated videos via ML-powered pre-labeling and active tooling such as smart interpolation and superpixel segmentation as well as ML-based quality checks.
Automated Quality Pipeline
Have confidence in the quality of annotated data. Quality assurance systems built into the product rapidly monitor and prevent errors. ML model confidence scores trigger varying levels and types of human review.
Data Input Flexibility
Submit videos as a distinct data type or as a sequence of images for annotation. Native video annotation is best suited for user-generated content, sports footage or any video combined with audio.
Multi-Modal Annotation Support
Jointly represent data to understand relationships between different modalities. Scale Video supports integration with audio, voiceovers, transcripts, and text in a single task to link different types of data inputs.
Comprehensive Label Support
Specify geometries for different classes (e.g. polygons for furniture, ellipses for plates). Combine geometries in a singular task and label for events (e.g. music playing) to enhance scene understanding.
Infinitely Long Tasks
Annotate hours-long videos to accurately track objects. Our advanced algorithms enable no limits on video length with tracking accuracy even for objects that leave the camera view for long periods of time.

“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

Enterprise Ready

Custom Annual Plans and SLAs

Get started today with on-demand, or chat with us about an enterprise plan.

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.
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.

Quality Assurance

Best-In-Class Quality Choice

Industry leading quality datasets safe-guarded against ever-changing, messy data.

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.