Improving the accuracy of computer vision models, Voxel51 raises $12.5 million

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Computer vision AI models rely on properly labeled data to infer the correct object. The challenge of helping to verify that the data used for a model is accurate is one that Voxel51, a startup based in Ann Arbor, Michigan, aims to solve with open source tools and a commercial service called FiftyOne Teams. /p>

Ann Arbor is home to the University of Michigan, where Voxel51 co-founder and CEO Jason Corso works as a professor, and where he got the idea for the new company. Corso's research focuses on computer vision applications such as the relationship between video and natural language. In recent years, as the adoption of computer vision has grown, so has the size of datasets.

“When I was a graduate student, I had datasets that numbered in the dozens and I could look at every sample,” Corso told VentureBeat. "Now my students have arrived and they can't watch a million samples; it's just not possible, so the need for Voxel51 grew out of that."

This is a need that has resonated with the market and with investors. Today, the company announced that it has raised $12.5 million in Series A funding from Drive Capital, Top Harvest and Shasta Ventures, as well as existing investors eLab Ventures and ID Ventures, and the University of Michigan.

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register here The Challenge and Opportunity of Unstructured Data for Computer Vision

Unstructured data takes many forms and includes any type of data that does not fit into a specific data structure format (e.g. columns and rows).

Among the most common forms of unstructured data is video content, which is growing exponentially as the number of cameras continues to grow globally. The valuation of unstructured video data can be done in different ways. Corso noted that there are technologies that help users extract semantically meaningful information from images, such as simple tools that allow users to search for images taken at a certain location.

While there is no shortage of unstructured image data and large datasets used to help train computer vision models, ensuring accuracy is a challenge.

"All we have to say is that when the datasets grew to over 10 million samples, no one bothered to look at the images anymore," Corso said.

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What Voxel51 does is act as a bridge between what a data engineer does when building datasets and what that same engineer or their partner does when training models. Voxel51 technology supports viewing annotations on image data and can be used to identify potential errors and allow users to compare the performance of different models.

Corso explained that Voxel51 allows users to semantically slice data to understand the accuracy of a model. For example, via a Python API, a user can run a query against a computer vision dataset to find all images where one model outperforms another, for images where there is a child running down the street.

Open source and the enterprise

Voxel51 started out as an open-source product, but alongside the funding announcement, the company is officially launching its FiftyOne Teams enterprise offering, which provides commercial support and additional features.

The Voxel51 open source project was first launched in August 2020 and has grown over the...