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https://github.com/raspberrypi/pico-tflmicro

Pico TensorFlow Lite Port
https://github.com/raspberrypi/pico-tflmicro

Last synced: 3 days ago
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Pico TensorFlow Lite Port

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README 

        
# TensorFlow Lite Micro



An Open Source Machine Learning Framework for Everyone.



## Introduction



This is a version of the [TensorFlow Lite Micro library](https://www.tensorflow.org/lite/microcontrollers)

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:



```bash

mkdir build

cd build

cmake ..

make

```



## 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 [arducam.com/pico4ml-an-rp2040-based-platform-for-tiny-machine-learning/](https://www.arducam.com/pico4ml-an-rp2040-based-platform-for-tiny-machine-learning/).



## Contributing



This repository (https://github.com/raspberrypi/pico-tflmicro) is read-only,

because it has been automatically generated from the master TensorFlow 

repository at https://github.com/tensorflow/tensorflow. 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:



```

sync/sync_with_upstream.sh

```



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

TensorFlow repository.



## Learning More



The [TensorFlow website](https://www.tensorflow.org/lite/microcontrollers) has

information on training, tutorials, and other resources.



The [TinyML Book](https://tinymlbook.com) is a guide to using TensorFlow Lite Micro

across a variety of different systems.



[TensorFlowLite Micro: Embedded Machine Learning on TinyML Systems](https://arxiv.org/pdf/2010.08678.pdf)

has more details on the design and implementation of the framework.



## Licensing



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.