York Yang is a hardcore millennial: He doesn’t wait for restaurants or mass transit — he orders on-demand meals and wheels. And when it comes to grocery checkouts, he takes matters into his own hands.
Yang and three co-founders started Caper, a maker of automated shopping carts, to solve their generation’s distaste for waiting in line to pay at the supermarket.
Sure, there’s Instacart or other delivery services, but when you want the instant gratification of a kombucha, those options are too slow, said Yang, the company’s CTO.
Caper speeds things along. Its smart carts feature barcode scanners, three cameras for image recognition, scales and point-of-sale card readers. Shoppers can use them for self-checkout to bypass cashier lines. They pack into a shopping cart similar technology as that of Amazon Go self-service stores.
“The technology enables shoppers to scan directly on the shopping cart and pay directly on the shopping cart so they don’t have to wait in line to get to the cashiers,” Yang said.
New York-based Caper recently landed $10 million in financing. The Y Combinator-accelerated company is also a member of the NVIDIA Inception program, which helps startups scale markets faster.
Bagging Store Interest
Grocers are getting comfortable with Caper offering new convenience without the cost of a store overhaul.
Caper’s convenience goes beyond self-checkouts. Its smart carts can suggest items to buy based off of previous purchases, as well as direct consumers by map to items in the store.
Also, Caper remotely updates prices and deals hourly on smart carts to match store databases.
It’s a flood of streaming data. To handle all that, the company deploys edge computing from NVIDIA GPUs in servers inside of stores to enable its smart carts.
Caper’s carts are in pilot tests with Sobeys, Canada’s second-largest food retailer with more than 1,500 locations. “Right now the whole team of executives is really excited with our solution. “They want to help us grow this out and help us succeed in this area,” said Yang.
Training AI Carts
Stores stock a lot of items. Caper is developing image-recognition models to see as many as 50,000 items for some stores, so it can avoid and cost and time in using product photography to produce labeled datasets.
It takes 100 to 1,000 images to positively identify each item, but Caper accelerates this process using simulation for data augmentation. It can take five images of each product and then run 3D simulations to capture different angles of it to synthetically expand the training set to 100 to 1,000 images of each product.
Caper runs these graphics-intensive simulations and its model training on NVIDIA GPUs in the cloud and on local machines.
Cartloads of Training
Caper has a lot of data pipelines going. It’s a big undertaking on-boarding each store for image recognition. But its barcode reader can scan items while it develops the capability. In the interim, it’s using sensor fusion between its scales and cameras to verify items by using weight and images. But many at Caper are busy perfecting its image models for retailers.
The fast-growing startup — it’s launched in a number of grocers this year since its January debut — has a lot of AI know-how in play across its teams. Keeping everybody up to speed, especially new hires, has been aided by NVIDIA’s Inception program, which offers free access to Deep Learning Institute courses on the latest AI topics.
“As we hire more people, our new hires might not always have all the skills we require — NVIDIA’s Deep Learning Institute courses are very useful and have helped train them,” Yang said.
Photo credit: David Shankbone, licensed under Creative Commons 3.0