Clarifying Training Time, Startup Launches AI-Assisted Data Annotation

Creating a labeled dataset for training an AI application can hit the brakes on a company’s speed to market. Clarifai, an image and text recognition startup, aims to put that obstacle in the rearview mirror.

The New York City-based company today announced the general availability of its AI-assisted data labeling service, dubbed Clarifai Labeler. The company offers data labeling as a service as well.

Founded in 2013, Clarifai entered the image-recognition market in its early days. Since that time, the number of companies exploiting unstructured data for business advantages has swelled, creating a wave of demand for data scientists. And with industry disruption from image and text recognition spanning agriculture, retail, banking, construction, insurance and beyond, much is at stake.

“High-quality AI models start with high-quality dataset annotation. We’re able to use AI to make labeling data an order of magnitude faster than some of the traditional technologies out there,” said Alfredo Ramos, a senior vice president at Clarifai.

Backed by NVIDIA GPU Ventures, Clarifai is gaining traction in retail, banking and insurance, as well as for applications in federal, state and local agencies, he says.

AI Labeling with Benefits

Clarifai’s Labeler shines at labeling video footage. The tool integrates a statistical method so that an annotated object — one with a bounding box around it — can be tracked as it moves throughout the video.

Since each second of video is made up of multiple frames of images, the tracking capabilities result in increased accuracy and huge improvements in the quantity of annotations per object, as well as a drastic reduction in the time to label large volumes of data.

The new Labeler was most recently used to annotate days of video footage to build a model to detect whether people were wearing face masks, which resulted in a million annotations in less than four days.

Traditionally, this would’ve taken a human workforce six weeks to label the individual frames. With Labeler, they created 1 million annotations 10 times faster, said Ramos.

Clarifai uses an array of NVIDIA V100 Tensor Core GPUs onsite for development of models, and it taps into NVIDIA T4 GPUs in the cloud for inference.

Star-Powered AI 

Ramos reports to one of AI’s academic champions. CEO and founder Matthew Zeiler took the industry by storm when his neural networks dominated the ImageNet Challenge in 2013. That became his launchpad for Clarifai.

Zeiler has since evolved his research into developer-friendly products that allow enterprises to quickly and easily integrate AI into their workflows and customer experiences. The company continues to attract new customers, most recently, with the release of its natural language processing product.

While much has changed in the industry, Clarifai’s focus on research hasn’t.

“We have a sizable team of researchers, and we have become adept at taking some of the best research out there in the academic world and very quickly deploying it for commercial use,” said Ramos.


Clarifai is a member of NVIDIA Inception, a virtual accelerator program that helps startups in AI and data science get to market faster.

Image credit: Chris Curry via Unsplash.

The post Clarifying Training Time, Startup Launches AI-Assisted Data Annotation appeared first on The Official NVIDIA Blog.

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