More importantly: TensorFlow Lite runs off of the device itself, ... Follow this link, for example, and you will be able to download a starter model capable of basic image classification.

TensorFlow Lite (TFLite) was announced in 2017 and Google is now calling it “LiteRT” to reflect how it supports third-party models. TensorFlow Lite for mobile on-device AI has “grown beyond ...

TensorFlow Lite is the “evolution” of Google’s previous efforts to get TensorFlow AI onto mobile devices. Its existing TensorFlow Mobile API will remain operational but is no longer the ...

TensorFlow Lite works by the model being created as a C array which is then parsed and run by an interpreter on the microcontroller. This is a little beyond the capabilities of the mighty 64, ...

TensorFlow Lite architecture. Google has just released a new solution, the developer preview of TensofFlow Lite for iOS and Android and announced plans to support Raspberry Pi 3.

TensorFlow lite drives home the point that Google cares about the nexus of AI and mobile devices. The next phase of Google’s work in this space will require dedicated hardware to maximize the ...

TensorFlow Lite can run on Raspberry Pi and new Coral Dev boards unveiled a few days ago by Google. Google has also released the TensorFlow 2.0 alpha, TensorFlow.js 1.0 and TensorFlow 0.2 for ...

Prior versions of the image captioning model took three seconds per training step on an Nvidia G20 GPU, but the version open sourced today can do the same task in a quarter of that time, or just 0 ...

Android Studio 4.1: easier to add on-device TensorFlow Lite models, run Android Emulator directly, more foldable form factors, and Database Inspector.