2022 AI Readiness Report is here. 1,300+ ML professionals share their biggest challenges & best practices.

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Image

The comprehensive annotation platform for images.

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Use Cases

Computer Vision

Develop highly accurate perception models to locate and identify objects of various classes, classify entire images, extract structured text from images, and more. Scale Image supports a range of use cases from autonomous vehicles to robotics to retail.

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

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

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

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.
Automated Quality Pipeline
Have confidence in the quality of annotated data. Quality assurance systems built into the product monitor and prevent errors. Confidence scores trigger varying levels and types of human review.
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.
Operational Excellence
Taxonomy changes can be implemented rapidly via automated personalized training and evaluation for flexibility on quality and annotation requirements.
Data Input Flexibility
Submit images to the platform regardless of file format (e.g. jpg, tiff, png). Scale Image even supports annotations for fish-eye or extra-wide panoramic images.
Configurable Tasks
Scale Image tasks are composable. Configure image tasks to dynamically generate a classification task with consensus if a target object is unknown.

“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

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

ML-accelerated, human-in-the-loop data annotation for rapid turnaround and industry-leading 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.