FOMO is a TinyML neural network for real-time object detection

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very small computation and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications. Most object-detection deep learning models have memory and computation requirements that are beyond the capacity of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have limited or no internet connectivity. TinyML has made great progress in image classification, where the machine learning model must only predict the presence of a certain type of object in an image.