The StyleSnap feature is designed to help people shop better, and that means providing them with the best results possible.
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But the objective isn’t just to display items that look alike. These CNNs were trained with hundreds of thousands of annotated images so that StyleSnap can analyze customer photographs and return results with suggestions similar to the photos. Scientists (from left) Arnau Ramisa, Doug Gray, Mengjiao Wang, and Amit Kumar K C are members of Amazon's Visual Search and Augmented Reality team which helped develop StyleSnap for Fashion and Home by taking advantage of convolutional neural networks trained with hundreds of thousands of images. “We use a system of networks focused on detection and classification, and then another network with a similar architecture, but that is slightly bigger, for comparing the customer and catalog product images.” “We had to choose networks that were lightweight so images could be drawn fast enough to meet our customer response time targets,” said Arnau Ramisa, a senior applied scientist within Amazon’s Visual Search and Augmented Reality (VS&AR) team.
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Both the fashion and home versions of StyleSnap are made possible via the use of multiple CNNs, each with a specific task that spreads the work among the neural networks. StyleSnap was developed with deep learning and computer vision techniques, and takes advantage of convolutional neural networks (CNNs), which were initially developed for image recognition. Customers can even use the Prime filter to find items that can be delivered quickly. If a customer sees a desk they like on social media, instead of scrolling through hundreds of products to find something similar, they simply take a screenshot and upload it to StyleSnap via the Amazon app on their phone, and discover a variety of similar styles.
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StyleSnap for Home can help customers find furnishings to upgrade a living room space or deck out a home office. The StyleSnap for Fashion and Home are one way Amazon is providing customers with faster and easier online shopping experiences. Now, Amazon has launched StyleSnap for Home. The feature first launched in the US in 2019 with fashion as the main target, and has since expanded to Germany, Italy, France, Spain, the United Kingdom, and most recently, India. A customer uploads an image from social media or snaps a photo of a friend’s new dress, and within seconds similar products are displayed that they might be interested in buying.
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This can lead customers to spend unnecessary time scrolling, for example, through hundreds of listings for a new desk, because they’re unaware that the type of desk they’re looking for is called a pedestal desk.Īmazon is addressing this challenge with StyleSnap, an AI-powered feature that helps customers use a photograph or screenshot to find products that inspire them. However, when they sit down at a computer to search for such items, they often might not know the right fashion terms to find the products they are looking for quickly.
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When people go shopping for fashion or home décor items, they mostly have an idea about what they want to purchase.