# Machine Learning 101 with Lobe and BrainCraft
## Overview
Welcome to the wonderful world of Lobe, where you can train Machine Learning models without writing a single line of code!! Lobe is free and easy-to-use, all you have to do is take pictures and label them and Lobe does the rest.
In this guide we'll walk through training a Lobe model, exporting it, and running it on a Raspberry Pi 4.
This tutorial is part of a series which include the following tutorials:
- [Tutorial 2 - Build an ML Rock Paper Scissors Game with Lobe](https://learn.adafruit.com/lobe-rock-paper-scissors)
- [Tutorial 3 - Build an ML Package Detector with Lobe](https://learn.adafruit.com/build-an-ml-package-detector)
## Background Knowledge
To be successful with this project, you'll need some experience with the following:
1. Setting up and using the Raspberry Pi
2. Some familiarity with using the terminal window
3. Installing the Pi Camera
# Machine Learning 101 with Lobe and BrainCraft
## Required Parts
# Machine Learning 101 with Lobe and BrainCraft
## Train a Model with Lobe
First, we'll train a custom machine learning model using objects on your desk. For my model I used a pen, Lobe sticker, and a succulent plant. Feel free to use any objects you'd like!
**First, download and install Lobe from the link below:**
[Download Lobe](https://lobe.ai/)
## 1. Open Lobe and create a new project.
## 2. In the **Label** tab, select **Import (**top right corner), and **Camera** from the drop down menu.
If this is your first time using Lobe you'll need to give it permission to use your camera.
Info:
## 3. In the bottom left corner, type a label for the first image.
My first label is "Pen" because it's a picture of, well, a pen!
## 4. Take between 10 and 20 pictures of your objects using your computer's camera.
Take pictures from different angles, in different lighting conditions, and with different hand placements to improve model accuracy.
## 5. Repeat steps 3 and 4 for the rest of your objects.
Remember to add a new label for each object (e.g. "Sticker" and "Succulent").
## 6. Add a "Nothing" category.
This improves accuracy of the ML model. See below for more info.
### Why is there always a prediction even when nothing is in the image?
Lobe will always predict one of your labels even if your image does not contain any related content. If you expect your model to see these types of images, create a ‘_None_’ label and add variations of these images as examples. You can use this ‘None’ label as a placeholder when waiting for relevant predictions.
# Machine Learning 101 with Lobe and BrainCraft
## Test your Model
In Lobe, training happens automatically as soon as you add enough images (a minimum of 5 for each label).
When you have taken between 5 and 10 pictures of each object and training is complete, test out the model by following the steps below.
## 1. In Lobe, select the **Use** tab and choose **Camera**.
1a. This is the ML model prediction. The more full this bar is, the more confident the algorithm is with the prediction.
## 2. Try different placements of the objects used for training and see how the model performs.
Try other objects of the same type to check model accuracy. Also try out multiple objects at once. You will notice Lobe only makes one prediction at a time, even if there are multiple objects. See if you can trick your model into predicting the wrong thing and find ways to improve it.
## 3. Improve your model using the buttons in the bottom right.
My model did a great job at identifying sticky notes, but it mixed up the pen and highlighter.
1b. Use these buttons to give Lobe feedback to improve your model.
Click the green button for images that the model predicts correctly. Select the red button for images the model predicts incorrectly.
# Machine Learning 101 with Lobe and BrainCraft
## Export your Model
Next, export your Lobe model to use on the Raspberry Pi. We'll use **TensorFlow Lite** which is a format that is optimized for mobile and edge devices, like the Pi.
In Lobe, navigate to the Use tab and click Export.
Select TensorFlow Lite and select a location to save the model. We'll transfer the model to our Raspberry Pi later in the tutorial.
# Machine Learning 101 with Lobe and BrainCraft
## Set up Raspberry Pi and BrainCraft
Now that the ML model is working on your computer, let's get your Raspberry Pi and BrainCraft HAT ready to make predictions on new images (also called **inferencing** ). Your kit includes all the parts you need for this.
The next few steps walk you through setting up your BrainCraft HAT. We will skip audio setup since we aren't using it for this project, but if you can find the complete setup guide [here](https://learn.adafruit.com/adafruit-braincraft-hat-easy-machine-learning-for-raspberry-pi).
_Please note that the following steps require installing a lot of packages on the Pi and it may take a few hours to download everything._
Info:
As long as you're on the same WiFi as the Pi, you can use the IP address `raspberrypi.local`, as shown below:
```none
ssh pi@raspberrypi.local
```
# Machine Learning 101 with Lobe and BrainCraft
## Raspberry Pi Setup
OK now you have all your parts in order, it's time to get your Raspberry Pi computer set up with the HAT or Bonnet.
# Step 1 - Burn SD Card
[Use Etcher or the Raspberry Pi Imager](https://learn.adafruit.com/adafruit-raspberry-pi-lesson-1-preparing-and-sd-card-for-your-raspberry-pi) to burn the latest **Raspbian Lite to an SD card** (you can use full but we won't be using the desktop software and it takes up a bunch of room.
Primary:
Info:
# Step 2 - Configure log-in access
You'll need to be able to log into your Pi, [either enable SSH access (and use and Ethernet cable)](https://learn.adafruit.com/adafruits-raspberry-pi-lesson-6-using-ssh), use a USB to serial cable, or connect a monitor and keyboard. Basically get it so you can log in.
[We have a quickstart guide here](https://learn.adafruit.com/raspberry-pi-computer-quick-start) and [here that you can follow](https://learn.adafruit.com/raspberry-pi-zero-creation/install-os-on-to-sd-card), or there's dozens of online guides. it is assumed by the next step you are able to log in and type commands in - **ideally from a desktop computer, so you can copy and paste in some of the very long commands**!
# Step 3 - Log in & Enable Internet
Once you've logged in, [enable WiFi (if you have built in WiFi) with **sudo raspi-config**](https://www.raspberrypi.org/documentation/configuration/wireless/wireless-cli.md) so you can ssh in.
Enable SSH as well if you haven't yet, also via **sudo raspi-config**
After you're done, reboot, and verify you can log into your Pi and that it has internet access by running `ping -c 3 raspberrypi.org` and seeing successful responses.
# Step 4 - Update/Upgrade
Now that you are logged in, perform an update/update:
```terminal
sudo apt update
sudo apt-get -y upgrade
```
and
```terminal
sudo apt install --upgrade python3-setuptools
```
# Step 5 - Setup Virtual Environment
If you are installing on the Bookworm version of Raspberry Pi OS or later, you will need to install your python modules in a virtual environment. You can find more information in the [Python Virtual Environment Usage on Raspberry Pi](https://learn.adafruit.com/python-virtual-environment-usage-on-raspberry-pi) guide. To Install and activate the virtual environment, use the following commands:
```terminal
sudo apt install python3.11-venv
python -m venv env --system-site-packages
```
To activate the virtual environment:
```terminal
source env/bin/activate
```
OK you've now got a nice, clean, connected, and up-to-date Pi!
# Machine Learning 101 with Lobe and BrainCraft
## Blinka Setup
Blinka is our CircuitPython library compatibility layer. It allows many of the libraries that were written for CircuitPython to run on CPython for Linux. To learn more about Blinka, you can check out our [CircuitPython Libraries on Linux and Raspberry Pi](https://learn.adafruit.com/circuitpython-on-raspberrypi-linux) guide.
We put together a script to easily make sure your Pi is correctly configured and install Blinka. It requires just a few commands to run. Most of it is installing the dependencies.
Warning:
```terminal
cd ~
sudo pip3 install --upgrade adafruit-python-shell
wget https://raw.githubusercontent.com/adafruit/Raspberry-Pi-Installer-Scripts/master/raspi-blinka.py
sudo python3 raspi-blinka.py
```
When it asks you if you want to reboot, choose **yes**.
Finally, once it reboots, there are just a couple CircuitPython libraries to install for the BrainCraft HAT or Voice Bonnet.
The DotStar library is for controlling the 3 on-board DotStar LEDs and the Motor library is for testing out the GPIO pins.
```terminal
pip3 install --upgrade adafruit-circuitpython-dotstar adafruit-circuitpython-motor adafruit-circuitpython-bmp280
```
That's it for Blinka and CircuitPython libraries.
# Machine Learning 101 with Lobe and BrainCraft
## Fan Service Setup
Info:
We have a really simple fan service that will control the onboard fan. The reason we have it set up as a service instead of keeping the fan on all the time is so that it doesn't drain too much power from the Pi during the initial power on.
The fan service basically controls turning GPIO 4 on at startup, which is what the fan is connected to. Installing the fan service is really simple and we have a script for doing that.
To install, just type **sudo raspi-config**
Select **Performance Options**
Select **Fan**
Select **Yes**
And make sure you put down **GPIO pin 4 for the fan**
You can customize the fan temperature setting
That's it!
You can then 'stress test' by running
- `sudo apt-get install stress`
- `while true; do vcgencmd measure_clock arm; vcgencmd measure_temp; sleep 10; done& stress -c 4 -t 900s`
When the temperature hits the limit you set earlier, the fan should turn on, and cool the pi back down (in this case I set it to 70 C):
# Machine Learning 101 with Lobe and BrainCraft
## Fan Service Troubleshooting
On some newer versions of Raspberry Pi OS the Fan service fails to start. Luckily these newer version of the OS have a built in fan control you can turn on.
In command prompt or terminal connect to the pi using SSH and run the command:
`sudo raspi-config`
Select Performance Options
Select Fan
The default GPIO pin is 14, but the BrainCraft Hat has the fan connected to pin 4.
You can set the temperature to anything between 60-120 degrees Celsius. 80 is a good midway point in that range.
You'll need to reboot after changing the settings.
Now your fan will come on when ever the board is over 80 degrees. To check the current temperature of your board you use this command:
`/opt/vc/bin/vcgencmd measure_temp`
# Machine Learning 101 with Lobe and BrainCraft
## Display Module Install
There's two ways you can use the 1.54" 240x240 display on the BrainCraft HAT. For machine learning purposes, the advanced method is the way to go, so that's what we'll be covering in this guide.
Warning:
**The easy way** is to use 'pure Python 3' and Pillow library to draw to the display from within Python. This is great for showing text, stats, images etc that you design yourself. If you want to do that, the BrainCraft HAT has a pretty close layout to the [Adafruit 1.3" Color TFT Bonnet](https://www.adafruit.com/product/4506) including the same type of display and a joystick, though the pinouts are slightly different. If you choose this option, You can skip this page and view the [Python Setup page](https://learn.adafruit.com/adafruit-1-3-color-tft-bonnet-for-raspberry-pi/python-setup) for instruction for that display.
**The advanced way** is to install a kernel module to add support for the TFT display that will make the console appear on the display. This is cute because you can have any program print text or draw to the framebuffer (or, say, with pygame) and Linux will take care of displaying it for you. If you don't need the console or direct framebuffer access, please consider using the 'pure Python' technique instead as it is not as delicate.
**If you plan on using the Pi Camera for vision projects, you will need to go with the advanced route!**
# Installing The 1.54" Kernel Module
We have tried to make this as easy as possible for you by providing a script that takes care of everything. You will need to set up a Virtual Environment first and there's only a couple of additional dependencies needed.
## Setup Virtual Environment
If you are installing on the Bookworm version of Raspberry Pi OS or later, you will need to install your python modules in a virtual environment. You can find more information in the [Python Virtual Environment Usage on Raspberry Pi](https://learn.adafruit.com/python-virtual-environment-usage-on-raspberry-pi) guide. To Install and activate the virtual environment, use the following commands:
```terminal
sudo apt install python3-venv
python -m venv env --system-site-packages
```
To activate the virtual environment:
```terminal
source env/bin/activate
```
With the virtual environment set up, just run the following at the terminal:
```terminal
cd ~
pip3 install --upgrade adafruit-python-shell click
sudo apt-get install -y git
git clone https://github.com/adafruit/Raspberry-Pi-Installer-Scripts.git
cd Raspberry-Pi-Installer-Scripts
sudo -E env PATH=$PATH python3 adafruit-pitft.py --display=st7789v_bonnet_240x240 --rotation=0 --install-type=mirror
```
Warning:
When you get asked to reboot, reboot!
That's it! You will now have the BrainCraft HAT with a console display on it
# Machine Learning 101 with Lobe and BrainCraft
## Camera Test
## Install the Pi Camera module
Make sure you have the Pi camera module by running the following command:
```none
pip3 install picamera
```
Now that you have everything set up, it's time to do an initial test with the camera. This should display what the camera sees on the display.
`raspistill -t 0`
Exit the camera test by pressing `CTRL + C`
# Machine Learning 101 with Lobe and BrainCraft
## Set up an FTP Connection
Since we're using the Pi in a headless configuration, we'll use an FTP connection to transfer files between our computer and the Pi.
## Windows Instructions
Download and install [WinSCP](https://winscp.net/eng/index.php)
Open WinSCP and start a New Session
Select an SFTP connection, fill in the IP address of your Pi, set the username to Pi, and put in your password.
Info:
## Mac Instructions
Download and install [FileZilla](https://filezilla-project.org/download.php?type=client). When it's done installing, open the program.
Type `sftp://` followed by the IP address of your Pi. Set the username to `pi` and put in your password.
Info:
# Machine Learning 101 with Lobe and BrainCraft
## Get Predictions on the Pi
## 1. Connect your Pi to a power source and wait for it boot up.
You should see a solid red light and an intermittently flashing green light.
## 2. Open **command prompt** on a PC or terminal on Mac/Linux and connect to your Pi using SSH.
Type the following command but replace the bolded number below with IP address of your Pi:
`ssh pi@192.168.0.22`
Info:
## 3. Download the GitHub folder.
Run the following commands to download the sample code from GitHub:
```none
cd ~
git clone https://github.com/lobe/lobe-adafruit-kit.git
```
## 4. Create a new folder called `model` in the home directory.
Type the following commands:
```none
cd ~
mkdir model
```
## 5. Open the FTP connection from the previous step.
## 6. Copy `saved_model.tflite` and `signature.json` from your exported Lobe model to the `model` directory on the Pi.
## 7. In terminal on the Pi, run the following script to install Lobe and all it's dependences:
```none
cd ~
wget https://raw.githubusercontent.com/lobe/lobe-python/master/scripts/lobe-rpi-install.sh
sudo bash lobe-rpi-install.sh
```
## 8. In terminal on the Pi, run the Python program `lobe-basic-prediction.py`
Type the following commands:
```none
cd ~
cd lobe-adafruit-kit
python3 lobe-basic-prediction.py
```
# Machine Learning 101 with Lobe and BrainCraft
## Going Further
Keep testing the model on the Pi and see how it works. If you find that the prediction is consistently wrong, you can add more images to the model to improve its performance.
You can train an ML model to recognize all sorts of objects and then use the BrainCraft to trigger actions in the physical world!
To learn how to do this, check out these more advanced projects:
- [Tutorial 2: Build a rock paper scissor game](https://learn.adafruit.com/lobe-rock-paper-scissors)
- [Tutorial 3: Build a package detector](https://learn.adafruit.com/build-an-ml-package-detector)
## Primary Products
### Microsoft Machine Learning Kit for Lobe - Pi 4 Not Included
[Microsoft Machine Learning Kit for Lobe - Pi 4 Not Included](https://www.adafruit.com/product/4949)
Machine learning is a transformative tool that’s redefining how we build software—but up until now, it was only accessible to a small group of experts. At Adafruit, we think machine learning should be accessible to everyone, that’s why today we are partnering with
No Longer Stocked
[Buy Now](https://www.adafruit.com/product/4949)
[Related Guides to the Product](https://learn.adafruit.com/products/4949/guides)
### Microsoft Machine Learning Kit for Lobe with Raspberry Pi 4 4GB
[Microsoft Machine Learning Kit for Lobe with Raspberry Pi 4 4GB](https://www.adafruit.com/product/4963)
Machine learning is a transformative tool that’s redefining how we build software—but up until now, it was only accessible to a small group of experts. At Adafruit, we think machine learning should be accessible to everyone, that’s why today we are partnering with
No Longer Stocked
[Buy Now](https://www.adafruit.com/product/4963)
[Related Guides to the Product](https://learn.adafruit.com/products/4963/guides)
## Featured Products
### Adafruit BrainCraft HAT - Machine Learning for Raspberry Pi 4
[Adafruit BrainCraft HAT - Machine Learning for Raspberry Pi 4](https://www.adafruit.com/product/4374)
The idea behind the BrainCraft HAT is that you’d be able to “craft brains” for Machine Learning on the EDGE, with Microcontrollers & Microcomputers. On ASK AN ENGINEER, our founder & engineer chatted with Pete Warden, the technical lead of the mobile,...
In Stock
[Buy Now](https://www.adafruit.com/product/4374)
[Related Guides to the Product](https://learn.adafruit.com/products/4374/guides)
### Raspberry Pi 4 Model B - 4 GB RAM
[Raspberry Pi 4 Model B - 4 GB RAM](https://www.adafruit.com/product/4296)
The Raspberry Pi 4 Model B is the newest Raspberry Pi computer made, and the Pi Foundation knows you can always make a good thing _better_! And what could make the Pi 4 better than the 3? How about a _faster_ processor, USB 3.0 ports, and updated Gigabit Ethernet chip with...
Out of Stock
[Buy Now](https://www.adafruit.com/product/4296)
[Related Guides to the Product](https://learn.adafruit.com/products/4296/guides)
## Related Guides
- [Raspberry Pi Care and Troubleshooting](https://learn.adafruit.com/raspberry-pi-care-and-troubleshooting.md)
- [Adafruit BrainCraft HAT - Easy Machine Learning for Raspberry Pi](https://learn.adafruit.com/adafruit-braincraft-hat-easy-machine-learning-for-raspberry-pi.md)
- [Build an ML Rock Paper Scissors Game with Lobe](https://learn.adafruit.com/lobe-rock-paper-scissors.md)
- [Basic TensorFlow Object Recognition on any Computer or iOS device with Google Colab](https://learn.adafruit.com/basic-tensorflow-object-recognition-in-the-cloud-google-colab.md)
- [Build an ML Package Detector with Lobe](https://learn.adafruit.com/build-an-ml-package-detector.md)
- [diy lofi hip hop raspberry pi radio](https://learn.adafruit.com/lofi-hip-hop-raspberry-pi-radio-braincraft.md)
- [BrainCraft Camera Case](https://learn.adafruit.com/braincraft-camera-case.md)
- [Using Google Assistant on the BrainCraft HAT or Voice Bonnet](https://learn.adafruit.com/using-google-assistant-on-the-braincraft-hat.md)
- [Running TensorFlow Lite Object Recognition on the Raspberry Pi 4 or Pi 5](https://learn.adafruit.com/running-tensorflow-lite-on-the-raspberry-pi-4.md)
- [Raspberry Pi + Teachable Machine = Teachable Pi](https://learn.adafruit.com/teachable-machine-raspberry-pi-tensorflow-camera.md)
- [Raspberry Pi YouTube Boombox](https://learn.adafruit.com/youtube-radio.md)
- [Python Edge Speech Recognition with Voice2JSON](https://learn.adafruit.com/edge-speech-recognition-with-voice2json.md)
- [3D Scans for Low Poly Statues](https://learn.adafruit.com/low-poly-3d-scans-for-3d-printing.md)
- [Adafruit DPS310 Precision Barometric Pressure and Altitude Sensor](https://learn.adafruit.com/adafruit-dps310-precision-barometric-pressure-sensor.md)
- [Adafruit VL53L4CD Time of Flight Distance Sensor](https://learn.adafruit.com/adafruit-vl53l4cd-time-of-flight-distance-sensor.md)
- [Adafruit ATECC608 Breakout](https://learn.adafruit.com/adafruit-atecc608-breakout.md)