Running VAEs on mobile and IOT devices using TFLite.
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Updated
Jun 16, 2021 - Jupyter Notebook
Running VAEs on mobile and IOT devices using TFLite.
Modified inference engine for quantized convolution using product quantization
Protect your Machine Learning model in your Flutter application.
Measure the speed of your machine learning models on real devices!
PyTorch Mobile starter kit.
Realtime image classification in Unity Engine.
Android application recognizing digits using quantized 8-bit MobileNetV3.
A tool to support using classification models in low-power and microcontroller-based embedded systems.
Identification of handwritten digit from images taken by a OV7670 camera module connected to a Raspberry Pi Pico and a 120x160 TFT LCD display. The Pi Pico running CircuitPython handles everything from image acquisition to post-processing and inference. This code is somewhat experimental, but it is fun to play with. For more information, please…
Workshop showcasing how to run defect detection using computer vision at the edge with Amazon SageMaker
Soft Threshold Weight Reparameterization for Learnable Sparsity
Unofficial implementation of MobileNetV3 architecture described in paper Searching for MobileNetV3.
A curated list of awesome edge machine learning resources, including research papers, inference engines, challenges, books, meetups and others.
This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
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