Train and deploy state-of-the-art mobile image classification models via Cloud TPU

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The open-source implementation provided in the Cloud TPU repository implements saved model export, TensorFlow Lite export, and TensorFlow Lite’s post-training quantization by default. The code also includes a default serving input function that decodes and classifies JPEG images: if your application requires custom input preprocessing, you should consider modifying this example to perform your own input preprocessing (for serving or for on-device deployment via TensorFlow Lite).

With this new open source MnasNet implementation for Cloud TPU, it is easier and faster than ever before to train a state-of-the-art image classification model and deploy it on mobile and embedded devices. Check out our tutorial and Colab to get started.

Many thanks to the Googlers who contributed to this post, including Zak Stone, Xiaodan Song, David Shevitz, Barrett Williams, Russell Power, Adam Kerin, and Quoc Le.

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