tensorflow for Microcontrollers TinyML 单片机版资料 arduino esp32 stm32

简介

TensorFlow Lite for Microcontrollers is designed to run machine learning models on microcontrollers and other devices with only few kilobytes of memory. The core runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic models. It doesn't require operating system support, any standard C or C++ libraries, or dynamic memory allocation.

https://www.tensorflow.org/lite/microcontrollers#supported_platforms

https://www.tinyml.org/home/meetups/bay-area-situnayake.pdf

入门教程:

https://www.tensorflow.org/lite/microcontrollers/get_started
 

 

Supported platforms

TensorFlow Lite for Microcontrollers is written in C++ 11 and requires a 32-bit platform. It has been tested extensively with many processors based on the Arm Cortex-M Series architecture, and has been ported to other architectures including ESP32. The framework is available as an Arduino library. It can also generate projects for development environments such as Mbed. It is open source and can be included in any C++ 11 project.

The following development boards are supported:

  • Arduino Nano 33 BLE Sense
  • SparkFun Edge
  • STM32F746 Discovery kit
  • Adafruit EdgeBadge
  • Adafruit TensorFlow Lite for Microcontrollers Kit
  • Adafruit Circuit Playground Bluefruit
  • Espressif ESP32-DevKitC
  • Espressif ESP-EYE

 

使用过程

tensorflow for Microcontrollers TinyML 单片机版资料 arduino esp32 stm32_第1张图片

tensorflow for Microcontrollers TinyML 单片机版资料 arduino esp32 stm32_第2张图片

资源需求

tensorflow for Microcontrollers TinyML 单片机版资料 arduino esp32 stm32_第3张图片

环境:

 

Development environment

● C++ 11, no standard libraries

● TensorFlow Lite flatbuffer

● Generate projects for Make, Mbed, Keil

● Arduino library

 

 

例子里用的单片机

tensorflow for Microcontrollers TinyML 单片机版资料 arduino esp32 stm32_第4张图片

 

Person detection example

This example shows how you can use Tensorflow Lite to run a 250 kilobyte neural
network to recognize people in images captured by a camera. It is designed to
run on systems with small amounts of memory such as microcontrollers and DSPs.

https://code.ihub.org.cn/projects/124/repository/revisions/master/entry/tensorflow/lite/micro/examples/person_detection/README.md

 

水果识别

https://blog.tensorflow.org/2019/11/fruit-identification-using-arduino-and-tensorflow.html

魔法棒教程:

https://codelabs.developers.google.com/codelabs/ai-magicwand/index.html?index=..%2F..index#0

https://codelabs.developers.google.com/

 

其他示例

https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples

https://code.ihub.org.cn/projects/124/repository/revisions/master/show/tensorflow/lite/micro/examples

 

博客教程

https://www.digikey.com/en/maker/projects/intro-to-tinyml-part-1-training-a-model-for-arduino-in-tensorflow/8f1fc8c0b83d417ab521c48864d2a8ec

https://eloquentarduino.github.io/2020/01/easy-tinyml-on-esp32-and-arduino/

 

arduino ai:

https://blog.arduino.cc/2019/10/15/get-started-with-machine-learning-on-arduino/

 

 

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