There's a LOT of software to install, this can take up to an hour

Install requirements

For TensorFlow, there are a few dependency requirements to install in the Python Environment:

pip3 install virtualenv Pillow numpy pygame

Install rpi-vision

Now to install an Adafruit fork of a program originally written by Leigh Johnson that uses the MobileNet V2 model to detect objects. This part will take a few minutes to complete.

cd ~
git clone --depth 1
cd rpi-vision
python3 -m virtualenv -p $(which python3) .venv
source .venv/bin/activate

Install TensorFlow 2.x

You should now be inside a virtual environment. You can tell by the (.venv) on the left side of the command prompt. While in the virtual environment, you may download and install Tensorflow 2.3.1


chmod a+x ./


pip3 install --upgrade setuptools

pip3 install tensorflow-*-linux_armv7l.whl

pip3 install -e .

After this, go ahead and reboot the Pi.

sudo reboot

Running the Graphic Labeling Demo

Finally you are ready to run the detection software. First you want to run as root so that Python can access the Frame Buffer of the display.

sudo bash

Then activate the virtual environment again:

cd rpi-vision && . .venv/bin/activate

To run a program that will display the object it sees on screen type in the following:

python3 tests/ --tflite

You should see a bunch of text scrolling in your SSH window.

On your display, if you notice everything is sideways, you can add a rotation parameter.

For instance, if you want to rotate everything by 90 degrees, you can type:

python3 tests/ --tflite --rotation=90

Now start holding up various items in front of the camera and it should display what it thinks it sees, which isn't actually what the item may be. Some items that it's pretty good about identifying are coffee mugs and animals.

Speech Output

As an added bonus, you can hook up a pair of headphones or a speaker to the Raspberry Pi and it will actually tell you what it is detecting. Make sure you don't have any HDMI cords plugged in though or the audio will go through the monitor.

This guide was first published on Sep 04, 2019. It was last updated on Sep 04, 2019.

This page (TensorFlow Lite 2 Setup) was last updated on Nov 23, 2020.