Use Cases

Computer Vision

  • Object Detection

    Locate and precisely identify objects of various classes.

    • • Autonomous Vehicles and ADAS

    • • Product Identification

    • • Damage and Defect Detection

    • • Health Diagnostics

  • Classification

    Classify images according to their visual contents.

    • • Search and Ad Relevance

    • • Recommendation Systems

    • • Precision Agrigulture

    • • Policy Enforcement

  • Text Recognition

    Identify and extract structured text from images.

    • • License Plate Identification

    • • ID Verification

    • • Product Cataloging

How it works

Easy to Start, Optimize and Scale

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

    Machine learning powered pre-labeling and active tooling such as superpixel segmentation as well as ML-based quality checks accurately annotate large volumes of images efficiently and at high quality.

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    Automated Quality Pipeline

    Have confidence in the quality of annotated data. Quality assurance systems monitor and prevent errors. Confidence scores trigger varying levels and types of human review.

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    Comprehensive Label Support

    Specify geometries for different classes (e.g. boxes for people, polygons for furniture, ellipses for plates). Enhance object detection by combining different geometries in a singular task.

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    Operational Excellence

    Taxonomy changes can be implemented rapidly via automated personalized training and evaluation for flexibility on quality and annotation requirements.

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    Data Input Flexibility

    Submit images regardless of file format (e.g. jpg, tiff, png). Scale’s Data Engine even supports annotations for fisheye or extra-wide panoramic images.

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    Configurable Tasks

    Image tasks are composable. Configure image tasks to dynamically generate a classification task with consensus if a target object is unknown.

Quality Assurance

Best-In-Class Quality

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.

Scale's DashboardScale's Dashboard

Get Started Today

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