![]() The image understanding engine is a deep neural network with millions of learnable parameters. Each of the public photos uploaded to Facebook is processed by a distributed real-time system called the image understanding engine. Applied research has produced cutting-edge deep learning techniques capable of processing billions of photos to extract searchable semantic meaning at enormous scale. Understanding photos at Facebook’s scale presents different challenge as compared with demonstrating low image-recognition error rates in the Imagenet Challenge competition. What search needs to understand about photos ![]() To augment this approach, the Photo Search team applied deep neural networks to improve the accuracy of image searches based on visual content in the photo and searchable text. Graph Search was built to retrieve objects from the social graph based on the relationships between them, such as “My friends who live in San Francisco.” This has proven to be effective but presents engineering challenges when constraining the query to a relevant subset, sorting and scoring the results for relevancy, and then delivering the most relevant results. Created a few years ago to power the social graph-aware Graph Search, Unicorn supports billions of queries per day powering multiple components in Facebook. Photo Search was built with Unicorn, an in-memory and flash storage indexing system designed to search trillions of edges between tens of billions of users and entities. ![]() ![]() To help people to find the photos they’re looking for more easily, Facebook’s Photo Search team applied machine learning techniques to better understand what’s in an image as well as improve the search and retrieval process. ![]() On Facebook, people share billions of photos every day, making it challenging to scroll backward in time to find photos posted a few days ago, let alone months or years ago. It is difficult for one person to categorize their own repository of smartphone photos, much less to define a structured taxonomy for everyone’s photos. Today, the volume of photos taken by people with smartphone cameras challenges the limits of structured categorization. ![]()
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