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Chapter 13In this section, we will discuss all the optimized pretrained models available inTensorFlow Lite out-of-the-box as of November 2019. These models can be used for alarge number of mobile and edge computing use cases. Compiling the example codeis pretty simple.You just import a new project from each example directory and Android Studio willuse Gradle (https://gradle.org/) for synching the code with the latest version inthe repo and for compiling. If you compile all the examples, you should be able tosee them in the emulator (see Figure 6). Remember to select Build | Make Project,and Android Studio will do the rest.Edge computing is a distributed computing model that bringscomputation and data closer to the location where it is needed.Figure 6: Emulated Google Pixel 3 XL with TensorFlow Lite example applications[ 469 ]
TensorFlow for Mobile and IoT and TensorFlow.jsImage classificationAs of November 2019, the list of available models for pretrained classification is ratherlarge, and it offers the opportunity to trade space, accuracy, and performance as shownin Figure 7 (source: https://www.tensorflow.org/lite/guide/hosted_models):Figure 7: Space, accuracy, and performance trade-offs for various mobile modelsMobileNet v1 is a quantized CNN model described in Benoit Jacob [2]. MobileNetV2 is an advanced model proposed by Google [3]. Online, you can also find floatingpointmodels, which offer the best balance between model size and performance.Note that GPU acceleration requires the use of floating-point models. Note thatrecently AutoML models for mobile have been proposed based an automated mobileneural architecture search (MNAS) approach [4], beating the models handcrafted byhumans.[ 470 ]
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Chapter 13
In this section, we will discuss all the optimized pretrained models available in
TensorFlow Lite out-of-the-box as of November 2019. These models can be used for a
large number of mobile and edge computing use cases. Compiling the example code
is pretty simple.
You just import a new project from each example directory and Android Studio will
use Gradle (https://gradle.org/) for synching the code with the latest version in
the repo and for compiling. If you compile all the examples, you should be able to
see them in the emulator (see Figure 6). Remember to select Build | Make Project,
and Android Studio will do the rest.
Edge computing is a distributed computing model that brings
computation and data closer to the location where it is needed.
Figure 6: Emulated Google Pixel 3 XL with TensorFlow Lite example applications
[ 469 ]
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