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TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It enables on-device machine learning inference with low latency and a small binary size. TensorFlow Lite also supports hardware acceleration with the Android Neural Networks API. TensorFlow Lite uses many techniques for achieving low latency such as optimizing the kernels for mobile apps, pre-fused activations, and quantized kernels that allow smaller and faster (fixed-point math) models. Most of our TensorFlow Lite documentation is on Github for the time being.
Highlights: In this post we are going to show how to build a computer vision model and prepare it for deploying on mobile and embedded devices. Last time, we showed how we can improve a model performance using transfer learning. But why would we only use our model to predict images of cats or dogs on our computer when we can use it on our smartphone or other embedded device. TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It allows us to run machine learning models on mobile devices with low latency, quickly without need for accessing the server.
TensorFlow is an open source software library for numerical computation using data-flow graphs. It was originally developed by the Google Brain Team within Google's Machine Intelligence research organization for machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It reached version 1.0 in February 2017, and has continued rapid development, with 21,000 commits thus far, many from outside contributors. This article introduces TensorFlow, its open source community and ecosystem, and highlights some interesting TensorFlow open sourced models. It runs on nearly everything: GPUs and CPUs--including mobile and embedded platforms--and even tensor processing units (TPUs), which are specialized hardware to do tensor math on.