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 Enterprise AIModels to support your business use cases.
Scale NucleusThe mission control for your data
Scale ValidateCompare and understand your models
Scale LaunchShip and track your models in production
Scale Document AITemplate-free ML document processing
Scale Content UnderstandingManage content for better user experiences
Scale SyntheticGenerate synthetic data
Solutions
Resources
Resource Library
Blog
Events
Open Datasets
Interviews
Documentation
Guides
Customers
Pricing
Conference
AI Readiness Report 2022
Company
Creating large volumes of training data without sacrificing quality.
Company Size: 300+
Industry: Autonomous Vehicles
Location: Los Altos, CA
Products:
Overview
Toyota Research Institute’s mission is to build a new approach to mobility and pioneering the technologies that drive its future.
As part of this mission and vision, one of TRI’s key research areas is automated driving, developing two different automated driving modes in parallel - Guardian and Chauffeur.
Guardian monitors a human’s driving task, intervening only when necessary. Chauffeur mode, on the other hand, takes all responsibility for driving, leaving occupants purely as passengers.
The Problem
Large volumes of data, but limited ability to label this data
Led by Adrien Gaidon, TRI’s Machine Learning team found itself with large volumes of data, but limited ability to label this data.
The need for large volumes of annotated data was obvious, but with a commitment to safety being non-negotiable, the TRI team did not want to trade off quality for quantity.
To support the research teams, David Garber and Ashmi Wadhwani were tasked with managing the data annotation pipeline, data infrastructure and machine learning infrastructure.
Very quickly, they recognized the need for a labeling provider they could rely on long term to meet the ever-changing needs of TRI’s researchers.
“Imagine a future where mobility is truly for all. Regardless of physical faculties, age, or income, you can participate in this world because technology has progressed to enable you to move as you want.”
David Garber
Product Manager, TRI
The Solution
Scale’s Sensor Fusion Cuboids, Sensor Fusion Segmentation and Semantic Segmentation
TRI looked into various solutions for its data annotation problem, but made Scale its main provider. This close working relationship led to TRI participating in the private beta of Sensor Fusion Segmentation, a challenging annotation type in which every point in a 3D point cloud needs to be painted.
Beyond new annotation types and features, working with Scale also gives the TRI team greater flexibility and the ability to amend workflows. “One of the things we love about Scale is the fact that we can fully label the world. We can label 2D bounding boxes, 3D bounding boxes, but also semantic segmentation, including in 3D, to understand as much as possible, including scenarios we don’t foresee today,” says Gaidon. “The ability to go to Scale for multi-modal annotation gives TRI an advantage in the automated driving space,” Garber concluded.
Since starting its work with Scale, Garber’s team has been able to support four large annotation pipelines without significantly increasing the size of their team.
The Result
Leveraging latest annotations and features
TRI will continue to leverage Scale's latest annotations and features, such as Sensor Fusion Segmentation.
“One of the things we love about Scale is the fact that we can fully label the world [...] to understand as much as possible, including scenarios we don’t foresee today.”
Adrien Gaidon
Machine Learning Lead, Toyota Research