Is it Possible to Deploy a Deep Learning Model on Raspberry Pi? Yes, It Is!

Learn how to deploy your deep learning model on Raspberry Pi and make it run real-time inference with this step-by-step guide. From setting up the environment to optimizing your model for Raspberry Pi …


Updated September 23, 2023

Need help with your Raspberry Pi?
Contact Me!

Do you love silly Raspberry Pi Projects?
Check out my this YouTube Channel!


Learn how to deploy your deep learning model on Raspberry Pi and make it run real-time inference with this step-by-step guide. From setting up the environment to optimizing your model for Raspberry Pi, we’ll cover everything you need to know.

Introduction

Raspberry Pi has become a popular choice among developers looking to experiment with deep learning models and deploy them on a smaller scale. The ability to run inference at the edge is a powerful feature that can help save resources and reduce latency in many applications. In this article, we’ll go through the process of deploying a deep learning model on Raspberry Pi step-by-step.

Prerequisites

Before you start, make sure you have the following:

  1. A Raspberry Pi device (any model will do)
  2. An SD card with Raspbian OS installed (you can download it from the official website)
  3. A power supply and peripherals (keyboard, mouse, monitor, etc.)
  4. Basic knowledge of Python and deep learning frameworks like TensorFlow or PyTorch
  5. Your trained deep learning model in Keras, TensorFlow, PyTorch, or ONNX format

Setting up the Environment

The first step is to set up your Raspberry Pi environment with all the required tools and libraries. You can follow these steps:

  1. Install the latest version of Raspbian OS on your SD card following the official documentation.
  2. Enable SSH by creating an empty file named ssh in the boot partition of the SD card (this will allow you to connect to your Pi remotely).
  3. Connect your Raspberry Pi to a monitor, keyboard, and power supply, then boot it up.
  4. Log in with username pi and password raspberry.
  5. Open a terminal and update the system:
sudo apt-get update && sudo apt-get upgrade -y
  1. Install Python 3 and pip:
sudo apt-get install python3 python3-pip -y
  1. Install required deep learning libraries, such as TensorFlow or PyTorch:
# For TensorFlow
pip3 install tensorflow

# Or, for PyTorch
pip3 install torch torchvision
  1. Clone your deep learning model repository and transfer it to the Raspberry Pi using SCP or SFTP (we’ll assume you have a Keras model in HDF5 format).

Optimizing Your Model for Raspberry Pi

To make your model run efficiently on Raspberry Pi, you need to optimize it. Here are some steps you can follow:

  1. Convert the model to ONNX format if it’s not already (TensorFlow and PyTorch both support exporting models to ONNX).
  2. Use a quantization tool like tf.lite.optimize_for_inference in TensorFlow or torch.quantization.quantize_dynamic in PyTorch to reduce the model’s size and speed up inference.
  3. Prune your model using techniques like tfmot.sparsity.keras.prune_low_magnitude for TensorFlow or torch.nn.utils.prune for PyTorch to remove unnecessary weights.
  4. Use a lighter-weight deep learning framework like TinyYOLOv3 or MobileNet if your model is too large.
  5. Limit the input size of your model, as larger inputs can slow down inference on Raspberry Pi’s limited resources.
  6. Test your optimized model on your computer to ensure it works correctly before deploying it on the Pi.

Deploying Your Model

Now that you have optimized your model and set up the environment, it’s time to deploy it on Raspberry Pi. Here are the steps:

  1. Install OpenCV for image processing:
sudo apt-get install libopencv-dev python3-opencv -y
  1. Install the ONNX Runtime or TensorFlow Lite Interpreter depending on your model format:
# For ONNX Runtime
pip3 install onnxruntime

# Or, for TensorFlow Lite
pip3 install tflite_runtime
  1. Write a Python script to load the model and perform inference on an input image or video stream:
import cv2
import onnxruntime as ort # or import tensorflow as tf (for TFLite)

# Load the model
session = ort.InferenceSession("your_model.onnx") # or tf.lite.Interpreter("your_model.tflite")

# Get input and output names
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

# Start the camera
cap = cv2.VideoCapture(0)

while True:
    # Read frame from camera
    ret, frame = cap.read()

    # Preprocess the input (resize and normalize)
    input_data = preprocess_input(frame)

    # Perform inference
    outputs = session.run([output_name], {input_name: input_data})

    # Postprocess the output (e.g., thresholding, non-maximum suppression)
    predictions = postprocess_output(outputs)

    # Draw the results on the frame and display it
    draw_predictions(frame, predictions)
    cv2.imshow("Output", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
  1. Run your script on Raspberry Pi:
python3 deploy_model.py

And that’s it! Your deep learning model is now deployed and running real-time inference on Raspberry Pi. You can further optimize the model or add features like edge computing to improve its performance and functionality.