The handful of models included in the detection API include heavy duty inception-based convolutional neural networks and streamlined models designed to operate on less sophisticated machines — a MobileNets single shot detector comes optimized to run in real-time on a smartphone.
Earlier this week Google announced its MobileNets family of lightweight computer vision models. These models can handle tasks like object detection, facial recognition and landmark recognition.
Google, Facebook and Apple have been pouring resources into these mobile models. Last fall, Facebook announced its Caffe2Go framework for building models to run on smartphones — the first big implementation of this was Facebook’s Style Transfer. This spring at I/O, Google released TensorFlow lite, it’s version of a streamlined machine learning framework. And most recently at WWDC, Apple pushed out CoreML, its attempt to reduce the difficulty of running machine learning models on iOS devices.
Today’s TensorFlow object detection API can be found here.
