TinyML: Machine Learning on Raspberry Pi Pico with Tensorflow Lite Micro and Arducam (Featuring Person Detection)

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The Future of Machine Learning Is Tiny

The future of machine learning is tiny.

From Pete Warden, technical lead of the mobile and embedded TensorFlow group in Google’s Brain team.
the future of machine learning is tiny peter warden 1280
Pete Warden speaking at the O’Reilly Artificial Intelligence Conference in Beijing in 2019. Credit: O’Reilly

Machine learning, seen as a part of artificial intelligence, is the study of computer algorithms that improve automatically through experience. 

Tiny machine learning (tinyML) is the intersection of machine learning and embedded internet of things (IoT) devices. The field has the potential to revolutionize many industries.

Before we start, here is a brief introduction to this emerging field from HarvardX.

Tensorflow Lite Micro (tflmicro)

TensorFlow Lite for Microcontrollers is designed to run machine learning models on microcontrollers and other devices with only a few kilobytes of memory.

It supports microcontroller platforms like Arduino Nano 33 BLE Sense, ESP32, STM32F746 Discovery kit, and so on. Since the release of the $4 Raspberry Pi Pico, which has gained increasing popularity among makers, Arducam has been trying to bring what’s possible on other microcontroller platforms to Pico.

Getting started with Machine Learning with Pico

This article is a tutorial on using the machine learning framework Tensorflow Lite Micro on the Pico for Person Detection.

Components and Supplies

Complete All-in-One Pico Person Detection Bundle

Tiny Machine Learning Person Detection Bundle for Raspberry Pi Pico

$39.99

Components of Pico person detection project

1 × Arducam Mini 2MP Plus UCTRONICS | Amazon
1 × Raspberry Pi PicoRPi | Amazon
1 × USB to TTL ConverterAmazon
Jumper WiresAmazon
1 × Micro USB cableAmazon

See Getting Started with the Raspberry Pi Pico and the README in the pico-sdk for information on getting up and running.

Quick Pico Setup

If you are developing for Raspberry Pi Pico on Raspberry Pi 4B, or the Raspberry Pi 400, most of the installation steps in this Getting Started guide can be skipped by running the setup script. You can get this script by doing the following:

git clone https://github.com/raspberrypi/pico-setup.git

Then run:

 pico-setup/pico_setup.sh

The script will:

  • Create a directory called pico
  • Install required dependencies
  • Download the pico-sdk, pico-examples, pico-extras, and pico-playground repositories
  • Define PICO_SDK_PATH, PICO_EXAMPLES_PATH, PICO_EXTRAS_PATH, and PICO_PLAYGROUND_PATH in your ~/.bashrc
  • Build the blink and hello_world examples in pico-examples/build/blink and pico-examples/build/hello_world
  • Download and build picotool (see Appendix B). Copy it to /usr/local/bin. • Download and build picoprobe (see Appendix A).
  • Download and compile OpenOCD (for debug support)
  • Download and install Visual Studio Code
  • Install the required Visual Studio Code extensions (see Chapter 6 for more details)
  • Configure the Raspberry Pi UART for use with Raspberry Pi Pico

Once it has run, you will need to reboot your Raspberry Pi,

sudo reboot

Person Detection

Person Detection Diagram

Person Detection Diagram
  • Download RPI-Pico-Cam
git clone https://github.com/ArduCAM/RPI-Pico-Cam.git
  • Compile

Tip

If you don’t want to compile, use the pre-built uf2 file below , you only need to wire the hardware and download uf2 to the device.

cd RPI-Pico-Cam/tflmicro
mkdir build 
cd build 
cmake ..
IMAGE ALT TEXT
make


Then you will create some files under RPI-Pico-Cam/tflmicro/build/examples/person_detection path

BinDescription
person_detection_int8.uf2This is the main program of person_detection, which can be dragged onto the RP2040 USB Mass Storage Device.
person_detection_benchmark.uf2This is the benchmark program of person_detection, you can use it to test the performance of person_detection on pico.
image_provider_benchmark.uf2This is the benchmark program of image_provider, you can use it to test the performance of image data acquisition.
IMAGE ALT TEXT

Test Person Detection

AppDescription
person_detection_int8This is a person detection demo.
  • Hardware connection
IMAGE ALT TEXT
  • Load and run person_detection The simplest method to load software onto a RP2040-based board is by mounting it as a USB Mass Storage Device. Doing this allows you to drag a file onto the board to program the flash. Go ahead and connect the Raspberry Pi Pico to your Raspberry Pi using a micro-USB cable, making sure that you hold down the BOOTSEL button to force it into USB Mass Storage Mode. 

If you are logged in via ssh for example, you may have to mount the mass storage device manually:

$ dmesg | tail
[ 371.973555] sd 0:0:0:0: [sda] Attached SCSI removable disk
$ sudo mkdir -p /mnt/pico
$ sudo mount /dev/sda1 /mnt/pico

If you can see the files in /mnt/pico then the USB Mass Storage Device has been mounted correctly:

$ ls /mnt/pico/
INDEX.HTM INFO_UF2.TXT

Copy your person_detection_int8.uf2 onto RP2040:

sudo cp examples/person_detection/person_detection_int8.uf2 /mnt/pico
sudo sync

View output

The person detection example outputs some information through usb, you can use minicom to view:

minicom -b 115200 -o -D /dev/ttyACM0
minicom_output

This person detection example also outputs image data and person detection results to the UART, and we provide a processing program to display them:

Tip:

You can download the Processing here or Processing for Pi.

no-person
person

Learn More


What’s your take on this? Leave your comments below!


6 Comments

Brad Jeff · February 5, 2021 at 10:37 pm

Thanks for sharing this! I was thinking about something similar, and now that you’ve proved that it can be done, I’ll try it as soon as my Pico arrives.

John · February 16, 2021 at 10:40 pm

Why do you need another usb plug. Cant you get the same thing through the on board usb?

    [email protected] · February 18, 2021 at 10:45 pm

    With the on-board USB connector, the Pico will be identified as a ttyACM0 device rather than ttyUSB. Our Windows PC recognizes the ttyACM0, but it can’t send or receive data. Since we’re unable to figure out what the reason is while we were making this tutorial, we had to find another way to communicate to the PC. Using the real serial port on board and a USB-TTL adapter becomes our solution.

Ralph · February 17, 2021 at 5:23 am

This looks really wonderful. I’ve tried it on my brand new Pico though, and for some reason it says it “Can’t find ov2640 sensor”.
I don’t have a USB to TTL converter connected, but that shouldn’t affect it. To ensure that communication over the main USB port does not affect anything, I have adjusted the code of the Arducam demo to indicate camera connectivity with the on-board LED, and alas, it fails.
I have tried with 2 cameras, in case the first one was faulty, and it fails with both.
I have also tried both a local build as well as the pre-built uf2 from here, with the same result.
Any input on what else to try would be highly appreciated!

    [email protected] · February 18, 2021 at 10:51 pm

    Generally speaking, “can’t find ov2640 sensor” results from wiring issues, especially I2C or power ones. However, in this case, we have to use a USB-TTL converter because with the main USB port connected, the Pico is identified as a ttyACM0 device and our Windows PC just won’t talk to it, and we had to use the USB-TTL to set up communication while we made this demo.

eat-sleep-code · February 24, 2021 at 11:35 am

@Kyle —

Is this a limitation of Windows? If you were to run the pico and camera headless — I am thinking where the pico would trip a relay, or light an LED, etc. Would you still need the USB-TTL converter?

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