About Voxel51

We’re on a mission to bring transparency and clarity to the world’s data. We empower hundreds of thousands of AI builders to unlock data insights to maximize model performance and make visual AI a reality.

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By the numbers

2018

Founded

$45.4M

Total funding

2.81M+

FiftyOne OSS downloads

250+

Enterprise customers

Global Top 10

Enterprises across industries build with FiftyOne

Who we are

We unlock breakthroughs in visual AI and transform industries from automotive to robotics

OUR NAME

Where does the name Voxel51 come from?

What is a voxel, you ask? A voxel is to a video what a pixel is to an image. It is literally a “volume element” in the space-time volume of a video.

Why 51? A few reasons: there are many voxels; can we help you pinpoint the right one? Does the voxel you need to find exist in the video?

Our values

Achieve more with FiftyOne

Build better models

Increase model accuracy up to 30% by refining your data and models together

Accelerate time to value

Bring data and models into a unified development environment to get to production faster

Increase productivity

Increase productivity as much as 50% by enabling teams to deliver more innovation and impact

Our Founders

Professor meets PhD student. AI magic happens. Voxel51 begins.

At UMich, Prof Corso and PhD student Brian Moore spotted a serious gap in visual AI tooling. They decided to build the tools they dreamed of—and Voxel51 was born.

Jason Corso

Founder

Jason Corso has spent the last 15+ years identifying and tackling problems in computer vision and machine learning. At Voxel51 and in his role as Professor at the University of Michigan, his mission is to inspire everyone around him to think big, develop a deep appreciation for the critical role data plays in machine learning, and make an impact with our work.

Brian Moore

Founder

While doing his PhD at the University of Michigan, Brian Moore designed algorithms for problems in computer vision and machine learning. But when he realized that there was a serious lack of ML tooling available for visual datasets, he decided to pivot and focus on building the tools that he wished he had when training his computer vision models. Enter Voxel51.

Our People

Meet our AI/ML pros and enthusiasts

Testimonials

Here’s what our customers say

“Everything we do from the MLOps side interacts with FiftyOne Teams. It’s becoming the hub where all the spokes are connected. It’s like GitHub for code, but for our datasets.”

Matt Shaffer

VP of Artificial Intelligence and Co-Founder | RIOS

“At Allstate, my team works on auto vehicle damage inspection. Verifying the damage to a vehicle can take an insurance claim agent hours to verify, but using computer vision and FiftyOne, we can segment the parts of vehicles first, then detect the damages, and finally match the damage to repair costs and generate reports for the adjusters.”

Pavan Nanjundappa

Data Science Manager | Allstate India

“From vehicle safety and autonomy to security systems and robotics, Bosch is a leader in artificial intelligence solutions utilizing computer vision. Voxel51’s solutions help us organize, evaluate, and refine our data and models, enabling us to develop robust, reliable AI applications across multiple teams and projects.”

Arvind Kumar Shekar

Lead Expert AI Validation | Bosch

“We use FiftyOne Teams to organize, select, display, and share our data which has led to better collaboration with and understanding of our large volume of data. FiftyOne Teams enables us to gain insights such as identifying and understanding data problems early, hypothesis validation, and dataset management overall. This has led to better solution engineering and better testing for the products and services we deliver to our customers.”

Lanny Lin

Sr. Director of AI and Data Science | Vivint

“It’s a great thing to have centralized dataset management in the form of FiftyOne Teams, which really makes our lives easier when curating datasets and training new models. All the team members have the same view on the datasets, which ensures that everyone understands the data used to train the models. This really saves a few hours of back and forth between team members.”