Facebook AI Research
Meta's internal AI research division, responsible for open-source tools including the Detectron2 object detection framework.
6 milestones
Facebook AI Research Publishes GrokNet, a Unified Computer Vision Model for Commerce Understanding
In 2020, researchers at Facebook AI published GrokNet, a unified deep learning system for product understanding in commerce settings, capable of recognising object categories, attributes such as colour and material, and brand information from product images at scale across Facebook Shops.
Facebook AI Research Releases Detectron2
In October 2019, Facebook AI Research released Detectron2, an open-source object detection and segmentation framework built on PyTorch, supporting algorithms including Mask R-CNN, DensePose, and panoptic feature pyramid networks, replacing the earlier Caffe2-based Detectron.
Facebook AI Research Published StarSpace: Embed All The Things!
In September 2017, Ledell Wu and colleagues at Facebook AI Research published StarSpace (arXiv:1709.03856), a general-purpose neural embedding model capable of learning entity representations across tasks including text classification, ranking, and collaborative filtering, without task-specific architecture changes.
FAISS: Facebook AI Research Library for Efficient Similarity Search
In 2017, Jeff Johnson, Matthijs Douze, and Hervé Jégou at Facebook AI Research published FAISS (Facebook AI Similarity Search), a library enabling efficient nearest-neighbour search across datasets of billions of vectors, with GPU acceleration substantially reducing search time compared to prior methods.
Caffe2Go: Facebook's On-Device Neural Style Transfer for Mobile Video
In November 2016, researchers at Facebook AI Research published Caffe2Go, a compressed deep-learning framework that ran neural style-transfer models entirely on iOS and Android devices without sending video frames to a server, enabling real-time artistic video effects on mobile hardware.
Facebook Deploys AI-Based Photo and Video Integrity Systems to Detect Nudity and Graphic Violence at Scale
From at least 2016, Facebook applied convolutional neural network-based computer vision systems to automatically detect nudity and graphic violence across photos and videos uploaded to its platform, processing billions of pieces of content as part of its scaled content integrity infrastructure.