Products
Scale RapidThe fastest way to production-quality labels.
Scale StudioLabeling infrastructure for your workforce.
Scale 3D Sensor FusionAdvanced annotations for LiDAR + RADAR data.
Scale ImageComprehensive annotations for images.
Scale VideoScalable annotations for video data.
Scale TextSophisticated annotations for text-based data.
Scale AudioAudio Annotation and Speech Annotation for NLP.
Scale MappingThe flexible solution to develop your own maps.
Scale CatalogCreate, enrich, and enhance eCommerce data.
Scale Enterprise AIModels to support your business use cases.
Scale NucleusThe mission control for your data
Scale LaunchShip and track your models in production
Scale Content UnderstandingManage content for better user experiences
Scale InstantMLNext-day machine learning models, without ML expertise
Scale SpellbookThe platform for large language model apps
Scale SyntheticGenerate synthetic data
Solutions
Retail & eCommerce
Defense
Logistics
Autonomous Vehicles
Robotics
AR/VR
Content & Language
Large Language Models
Resources
Resource Library
Blog
Events
Open Datasets
Interviews
Documentation
Guides
Customers
Pricing
Conference
AI Readiness Report 2022
Company
Scaling ground-truth data with Scale AI. Classifying large volumes of images to develop an autonomous checkout system
Industry: Retail
Use Case: Autonomous Checkout
Annotation: Classification
Location: San Francisco, CA
Product:
Overview
About Standard Cognition
Standard Cognition strives to improve everyone’s retail shopping experience by making shopping a seamless and more human interaction. Standard does this by developing an autonomous checkout platform for brick and mortar retailers that can change how people shop in the future. Their approach to autonomous checkout is unique because of their vision-first approach that identifies customers by shape and movement, rather than by facial recognition, and customers can shop in a Standard-powered store with or without their mobile app.
The Problem
Improving the performance of models
“Our system was very naive because it was still very early stage,” said Virginia Puccio, Standard’s Head of Data Operations. To improve the performance of their deep learning models, Standard's team required a large and varied dataset. Despite this need, “we didn’t have a workforce to handle the volume of annotation tasks, nor did we have the tools,” she added. Puccio and the Standard team knew they needed a scalable data labeling solution.
“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.”
Virginia Puccio
Head of Data Operations, Standard Cognition
The Solution
Perfecting the retail shopping experience
The Standard Team selected Scale for their expertise in working with large data sets. “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,” noted Puccio. “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,” Puccio concluded.
By working with Scale, the Standard team have been able to focus on other aspects of their business, such as the collection of data and offering more support services to their customers.
The Result
Experience the future of retail
Those in the San Francisco Bay Area can experience the future of shopping at Standard’s flagship store in downtown San Francisco. As Standard expands its operations globally, more and more shoppers will be able to experience this new paradigm for themselves.