Adventures in TensorFlow Lite
A collection of Jupyter notebooks demonstrating TensorFlow Lite model quantization, conversion, and optimization techniques for deep neural networks.
What is Adventures in TensorFlow Lite?
Adventures in TensorFlow Lite is a collection of Jupyter notebooks that demonstrate how to optimize and deploy deep learning models using TensorFlow Lite. It provides practical examples of quantization techniques, model conversion workflows, and optimization strategies to make neural networks efficient for edge and mobile devices. The repository covers various computer vision models including segmentation, style transfer, and text detection.
Machine learning engineers and developers who need to deploy TensorFlow models to resource-constrained environments like mobile devices or edge hardware. It's particularly useful for those working on model optimization, quantization, and TensorFlow Lite conversion.
This repository offers hands-on, practical examples with real-world models rather than theoretical explanations. It covers both quantization-aware training and post-training quantization, includes Edge TPU compatibility, and demonstrates complete workflows from model conversion to inference.
Overview
This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.
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