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Quick Start Guide: Raspberry Pi with Ultralytics YOLOv8

This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on Raspberry Pi devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLOv8 on these small and powerful devices.



Watch: Raspberry Pi 5 updates and improvements.

Note

This guide has been tested with Raspberry Pi 4 and Raspberry Pi 5 running the latest Raspberry Pi OS Bookworm (Debian 12). Using this guide for older Raspberry Pi devices such as the Raspberry Pi 3 is expected to work as long as the same Raspberry Pi OS Bookworm is installed.

What is Raspberry Pi?

Raspberry Pi is a small, affordable, single-board computer. It has become popular for a wide range of projects and applications, from hobbyist home automation to industrial uses. Raspberry Pi boards are capable of running a variety of operating systems, and they offer GPIO (General Purpose Input/Output) pins that allow for easy integration with sensors, actuators, and other hardware components. They come in different models with varying specifications, but they all share the same basic design philosophy of being low-cost, compact, and versatile.

Raspberry Pi Series Comparison

Raspberry Pi 3 Raspberry Pi 4 Raspberry Pi 5
CPU Broadcom BCM2837, Cortex-A53 64Bit SoC Broadcom BCM2711, Cortex-A72 64Bit SoC Broadcom BCM2712, Cortex-A76 64Bit SoC
CPU Max Frequency 1.4GHz 1.8GHz 2.4GHz
GPU Videocore IV Videocore VI VideoCore VII
GPU Max Frequency 400Mhz 500Mhz 800Mhz
Memory 1GB LPDDR2 SDRAM 1GB, 2GB, 4GB, 8GB LPDDR4-3200 SDRAM 4GB, 8GB LPDDR4X-4267 SDRAM
PCIe N/A N/A 1xPCIe 2.0 Interface
Max Power Draw 2.5A@5V 3A@5V 5A@5V (PD enabled)

What is Raspberry Pi OS?

Raspberry Pi OS (formerly known as Raspbian) is a Unix-like operating system based on the Debian GNU/Linux distribution for the Raspberry Pi family of compact single-board computers distributed by the Raspberry Pi Foundation. Raspberry Pi OS is highly optimized for the Raspberry Pi with ARM CPUs and uses a modified LXDE desktop environment with the Openbox stacking window manager. Raspberry Pi OS is under active development, with an emphasis on improving the stability and performance of as many Debian packages as possible on Raspberry Pi.

Flash Raspberry Pi OS to Raspberry Pi

The first thing to do after getting your hands on a Raspberry Pi is to flash a micro-SD card with Raspberry Pi OS, insert into the device and boot into the OS. Follow along with detailed Getting Started Documentation by Raspberry Pi to prepare your device for first use.

Set Up Ultralytics

There are two ways of setting up Ultralytics package on Raspberry Pi to build your next Computer Vision project. You can use either of them.

Start with Docker

The fastest way to get started with Ultralytics YOLOv8 on Raspberry Pi is to run with pre-built docker image for Raspberry Pi.

Execute the below command to pull the Docker container and run on Raspberry Pi. This is based on arm64v8/debian docker image which contains Debian 12 (Bookworm) in a Python3 environment.

t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host $t

After this is done, skip to Use NCNN on Raspberry Pi section.

Start without Docker

Install Ultralytics Package

Here we will install Ultralyics package on the Raspberry Pi with optional dependencies so that we can export the PyTorch models to other different formats.

  1. Update packages list, install pip and upgrade to latest

    sudo apt update
    sudo apt install python3-pip -y
    pip install -U pip
    
  2. Install ultralytics pip package with optional dependencies

    pip install ultralytics[export]
    
  3. Reboot the device

    sudo reboot
    

Use NCNN on Raspberry Pi

Out of all the model export formats supported by Ultralytics, NCNN delivers the best inference performance when working with Raspberry Pi devices because NCNN is highly optimized for mobile/ embedded platforms (such as ARM architecture). Therefore our recommendation is to use NCNN with Raspberry Pi.

Convert Model to NCNN and Run Inference

The YOLOv8n model in PyTorch format is converted to NCNN to run inference with the exported model.

Example

from ultralytics import YOLO

# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")

# Export the model to NCNN format
model.export(format="ncnn")  # creates 'yolov8n_ncnn_model'

# Load the exported NCNN model
ncnn_model = YOLO("yolov8n_ncnn_model")

# Run inference
results = ncnn_model("https://ultralytics.com/images/bus.jpg")
# Export a YOLOv8n PyTorch model to NCNN format
yolo export model=yolov8n.pt format=ncnn  # creates 'yolov8n_ncnn_model'

# Run inference with the exported model
yolo predict model='yolov8n_ncnn_model' source='https://ultralytics.com/images/bus.jpg'

Tip

For more details about supported export options, visit the Ultralytics documentation page on deployment options.

Raspberry Pi 5 vs Raspberry Pi 4 YOLOv8 Benchmarks

YOLOv8 benchmarks were run by the Ultralytics team on nine different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF Graphdef, TF Lite, PaddlePaddle, NCNN. Benchmarks were run on both Raspberry Pi 5 and Raspberry Pi 4 at FP32 precision with default input image size of 640.

Note

We have only included benchmarks for YOLOv8n and YOLOv8s models because other models sizes are too big to run on the Raspberry Pis and does not offer decent performance.

Comparison Chart

Performance

NVIDIA Jetson Ecosystem

NVIDIA Jetson Ecosystem

Detailed Comparison Table

The below table represents the benchmark results for two different models (YOLOv8n, YOLOv8s) across nine different formats (PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF Graphdef, TF Lite, PaddlePaddle, NCNN), running on both Raspberry Pi 4 and Raspberry Pi 5, giving us the status, size, mAP50-95(B) metric, and inference time for each combination.

Performance

Format Status Size on disk (MB) mAP50-95(B) Inference time (ms/im)
PyTorch 6.2 0.6381 508.61
TorchScript 12.4 0.6092 558.38
ONNX 12.2 0.6092 198.69
OpenVINO 12.3 0.6092 704.70
TF SavedModel 30.6 0.6092 367.64
TF GraphDef 12.3 0.6092 473.22
TF Lite 12.3 0.6092 380.67
PaddlePaddle 24.4 0.6092 703.51
NCNN 12.2 0.6034 94.28
Format Status Size on disk (MB) mAP50-95(B) Inference time (ms/im)
PyTorch 21.5 0.6967 969.49
TorchScript 43.0 0.7136 1110.04
ONNX 42.8 0.7136 451.37
OpenVINO 42.9 0.7136 873.51
TF SavedModel 107.0 0.7136 658.15
TF GraphDef 42.8 0.7136 946.01
TF Lite 42.8 0.7136 1013.27
PaddlePaddle 85.5 0.7136 1560.23
NCNN 42.7 0.7204 211.26
Format Status Size on disk (MB) mAP50-95(B) Inference time (ms/im)
PyTorch 6.2 0.6381 1068.42
TorchScript 12.4 0.6092 1248.01
ONNX 12.2 0.6092 560.04
OpenVINO 12.3 0.6092 534.93
TF SavedModel 30.6 0.6092 816.50
TF GraphDef 12.3 0.6092 1007.57
TF Lite 12.3 0.6092 950.29
PaddlePaddle 24.4 0.6092 1507.75
NCNN 12.2 0.6092 414.73
Format Status Size on disk (MB) mAP50-95(B) Inference time (ms/im)
PyTorch 21.5 0.6967 2589.58
TorchScript 43.0 0.7136 2901.33
ONNX 42.8 0.7136 1436.33
OpenVINO 42.9 0.7136 1225.19
TF SavedModel 107.0 0.7136 1770.95
TF GraphDef 42.8 0.7136 2146.66
TF Lite 42.8 0.7136 2945.03
PaddlePaddle 85.5 0.7136 3962.62
NCNN 42.7 0.7136 1042.39

Reproduce Our Results

To reproduce the above Ultralytics benchmarks on all export formats, run this code:

Example

from ultralytics import YOLO

# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")

# Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
results = model.benchmarks(data="coco8.yaml", imgsz=640)
# Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all all export formats
yolo benchmark model=yolov8n.pt data=coco8.yaml imgsz=640

Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. data='coco8.yaml' (4 val images), ordata='coco.yaml'` (5000 val images).

Use Raspberry Pi Camera

When using Raspberry Pi for Computer Vision projects, it can be essentially to grab real-time video feeds to perform inference. The onboard MIPI CSI connector on the Raspberry Pi allows you to connect official Raspberry PI camera modules. In this guide, we have used a Raspberry Pi Camera Module 3 to grab the video feeds and perform inference using YOLOv8 models.

Note

Raspberry Pi 5 uses smaller CSI connectors than the Raspberry Pi 4 (15-pin vs 22-pin), so you will need a 15-pin to 22pin adapter cable to connect to a Raspberry Pi Camera.

Test the Camera

Execute the following command after connecting the camera to the Raspberry Pi. You should see a live video feed from the camera for about 5 seconds.

rpicam-hello

Inference with Camera

There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.

Usage

We can use picamera2which comes pre-installed with Raspberry Pi OS to access the camera and inference YOLOv8 models.

Example

import cv2
from picamera2 import Picamera2
from ultralytics import YOLO

# Initialize the Picamera2
picam2 = Picamera2()
picam2.preview_configuration.main.size = (1280, 720)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()

# Load the YOLOv8 model
model = YOLO("yolov8n.pt")

while True:
    # Capture frame-by-frame
    frame = picam2.capture_array()

    # Run YOLOv8 inference on the frame
    results = model(frame)

    # Visualize the results on the frame
    annotated_frame = results[0].plot()

    # Display the resulting frame
    cv2.imshow("Camera", annotated_frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) == ord("q"):
        break

# Release resources and close windows
cv2.destroyAllWindows()

We need to initiate a TCP stream with rpicam-vid from the connected camera so that we can use this stream URL as an input when we are inferencing later. Execute the following command to start the TCP stream.

rpicam-vid -n -t 0 --inline --listen -o tcp://127.0.0.1:8888

Learn more about rpicam-vid usage on official Raspberry Pi documentation

Example

from ultralytics import YOLO

# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")

# Run inference
results = model("tcp://127.0.0.1:8888")
yolo predict model=yolov8n.pt source="tcp://127.0.0.1:8888"

Tip

Check our document on Inference Sources if you want to change the image/ video input type

Best Practices when using Raspberry Pi

There are a couple of best practices to follow in order to enable maximum performance on Raspberry Pis running YOLOv8.

  1. Use an SSD

    When using Raspberry Pi for 24x7 continued usage, it is recommended to use an SSD for the system because an SD card will not be able to withstand continuous writes and might get broken. With the onboard PCIe connector on the Raspberry Pi 5, now you can connect SSDs using an adapter such as the NVMe Base for Raspberry Pi 5.

  2. Flash without GUI

    When flashing Raspberry Pi OS, you can choose to not install the Desktop environment (Raspberry Pi OS Lite) and this can save a bit of RAM on the device, leaving more space for computer vision processing.

Next Steps

Congratulations on successfully setting up YOLO on your Raspberry Pi! For further learning and support, visit Ultralytics YOLOv8 Docs and Kashmir World Foundation.

Acknowledgements and Citations

This guide was initially created by Daan Eeltink for Kashmir World Foundation, an organization dedicated to the use of YOLO for the conservation of endangered species. We acknowledge their pioneering work and educational focus in the realm of object detection technologies.

For more information about Kashmir World Foundation's activities, you can visit their website.



Created 2023-11-12, Updated 2024-05-22
Authors: lakshanthad (2), glenn-jocher (4)

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