Medical image segmentation can be implemented using Deep Learning methods with fast and efficient segmentation networks. Single-board computers (SBCs) are difficult to use to train deep networks due to their memory and processing limitations. Specific hardware such as Google's Edge TPU makes them suitable for real time predictions using complex pre-trained networks. In this work, we study the performance of two SBCs, with and without hardware acceleration for fundus image segmentation, though the conclusions of this study can be applied to the segmentation by deep neural networks of other types of medical images. To test the benefits of hardware acceleration, we use networks and datasets from a previous published work and generalize them by testing with a dataset with ultrasound thyroid images. We measure prediction times in both SBCs and compare them with a cloud based TPU system. The results show the feasibility of Machine Learning accelerated SBCs for optic disc and cup segmentation obtaining times below 25 milliseconds per image using Edge TPUs.

The recent trend in the development of larger and larger Deep Learning models for a slight increase in accuracy raises the concern about their computational efficiency and wide scaled usability. We can not use such huge models on resource-constrained devices like mobiles and embedded devices. Does it mean that such devices have to sacrifice accuracy at the cost of a smaller model? Is it possible at all to deploy these models on devices such as smartphones or a Raspberry Pi or even on Microcontrollers? Optimizing the models using TensorFlow Lite is the answer to these questions.

Covering all the fundamental concepts of using ML models inside React Native applications, this is the most comprehensive React Native ML course available online. The important thing is you don't need to know background working knowledge of Machine learning and computer vision to use ML models inside React Native and train them. Starting from a very simple example course will teach you to use advanced ML models in your React Native ( Android & IOS) Applications. So after completing this course you will be able to use both simple and advanced Tensorflow lite models in your React Native( Android & IOS) applications. We will use React Native CLI but course will also guide you if you just have the expo knowledge.

This post was originally published at thinkmobile.dev Looking for how to automatically test TensorFlow Lite model on a mobile device? Check the 2nd part of this article. Building TensorFlow Lite models and deploying them on mobile applications is getting simpler over time. There is a set of information that needs to be passed between those steps -- model input/output shape, values format, etc.

This tutorial covers how to use Machine Learning with Arduino. The aim of this tutorial is to build a voice controlled car from scratch that uses Tensorflow Machine Learning to recognize voice commands. To do it we will use Arduino Nano 33 BLE sense. The availability of the Tensorflow lite for microcontrollers makes it possible to run machine learning algorithms on microcontrollers such as Arduino. In this tutorial, we will build a Tensorflow model that recognizes voice commands.

For TensorFlow Lite model enhanced with metadata, developers can use the TensorFlow Lite Android wrapper code generator to create platform specific wrapper code. The wrapper code removes the need to interact directly with ByteBuffer. Instead, developers can interact with the TensorFlow Lite model with typed objects such as Bitmap and Rect. The usefulness of the code generator depend on the completeness of the TensorFlow Lite model's metadata entry. Refer to the Codegen usage section under relevant fields in metadata_schema.fbs, to see how the codegen tool parses each field.

Highlights: In this post we are going to show how to build a computer vision model and prepare it for deploying on mobile and embedded devices. Last time, we showed how we can improve a model performance using transfer learning. But why would we only use our model to predict images of cats or dogs on our computer when we can use it on our smartphone or other embedded device. TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It allows us to run machine learning models on mobile devices with low latency, quickly without need for accessing the server.

A guide showing how to train TensorFlow Lite object detection models and run them on Android, the Raspberry Pi, and more! TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications. This guide provides step-by-step instructions for how train a custom TensorFlow Object Detection model, convert it into an optimized format that can be used by TensorFlow Lite, and run it on Android phones or the Raspberry Pi. The guide is broken into three major portions. Each portion will have its own dedicated README file in this repository. This repository also contains Python code for running the newly converted TensorFlow Lite model to perform detection on images, videos, or webcam feeds. I used TensorFlow v1.13 while creating this guide, because TF v1.13 is a stable version that has great support from Anaconda. I will periodically update the guide to make sure it works with newer versions of TensorFlow.

Edge computing devices are becoming the logical destination to run deep learning models. While the public cloud is the preferred environment for training, it is the edge that runs the models for inferencing. Since most of the edge devices have constraints in the form of available CPU and GPU resources, there are purpose-built AI chips designed to accelerate the inferencing. These AI accelerators complement the CPU by speeding up the calculations involved in inferencing. They are designed to optimize the forward propagation of neural networks deployed on the edge.

The rise of industrial Internet of Things (IoT) and artificial intelligence (AI) are making edge computing significant for enterprises. Many industry verticals such as manufacturing, healthcare, automobile, transportation, and aviation are considering an investment in edge computing. Edge computing is fast becoming the conduit between the devices that generate data and the public cloud that processes the data. In the context of machine learning and artificial intelligence, the public cloud is used for training the models and the edge is utilized for inferencing. To accelerate ML training in the cloud, public cloud vendors such as AWS, Azure, and the Google Cloud Platform (GCP) offer GPU-backed virtual machines.