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TensorFlow Lite Micro

An Open Source Machine Learning Framework for Everyone.


This is a version of the TensorFlow Lite Micro library for the Raspberry Pi Pico microcontroller. It allows you to run machine learning models to do things like voice recognition, detect people in images, recognize gestures from an accelerometer, and other sensor analysis tasks. This version has scripts to upstream changes from the Google codebase. It also takes advantage of the RP2040's dual cores for increased speed on some operations.

Getting Started

First you'll need to follow the Pico setup instructions to initialize the development environment on your machine. Once that is done, make sure that the PICO_SDK_PATH environment variable has been set to the location of the Pico SDK, either in the shell you're building in, or the CMake configure environment variable setting of the extension if you're using VS Code.

You should then be able to build the library, tests, and examples. The easiest way to build is using VS Code's CMake integration, by loading the project and choosing the build option at the bottom of the window.

Alternatively you can build the entire project, including tests, by running the following commands from a terminal once you're in this repo's directory:

mkdir build
cd build
cmake ..

What's Included

There are several example applications included. The simplest one to begin with is the hello_world project. This demonstrates the fundamentals of deploying an ML model on a device, driving the Pico's LED in a learned sine-wave pattern. Once you have built the project, a UF2 file you can copy to the Pico should be present at build/examples/hello_world/hello_world.uf2.

Another example is the person detector, but since the Pico doesn't come with image inputs you'll need to write some code to hook up your own sensor. You can find a fork of TFLM for the Arducam Pico4ML that does this at


This repository ( is read-only, because it has been automatically generated from the master TensorFlow repository at It's maintained by @petewarden on a best effort basis, so bugs and PRs may not get addressed. You can generate an updated version of this generated project by running the command:


This should create a Pico-compatible project from the latest version of the TensorFlow repository.

Learning More

The TensorFlow website has information on training, tutorials, and other resources.

The TinyML Book is a guide to using TensorFlow Lite Micro across a variety of different systems.

TensorFlowLite Micro: Embedded Machine Learning on TinyML Systems has more details on the design and implementation of the framework.


The TensorFlow source code is covered by the Apache 2 license described in src/tensorflow/LICENSE, components from other libraries have the appropriate licenses included in their third_party folders.


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