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TensorFlow for Mobile and IoT and TensorFlow.js
Image classification
As of November 2019, the list of available models for pretrained classification is rather
large, and it offers the opportunity to trade space, accuracy, and performance as shown
in Figure 7 (source: https://www.tensorflow.org/lite/guide/hosted_models):
Figure 7: Space, accuracy, and performance trade-offs for various mobile models
MobileNet v1 is a quantized CNN model described in Benoit Jacob [2]. MobileNet
V2 is an advanced model proposed by Google [3]. Online, you can also find floatingpoint
models, which offer the best balance between model size and performance.
Note that GPU acceleration requires the use of floating-point models. Note that
recently AutoML models for mobile have been proposed based an automated mobile
neural architecture search (MNAS) approach [4], beating the models handcrafted by
humans.
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