Raspberry pi5 TensorFlow-lite Object Detection

Hardware : Raspberry pi5

Specification

Broadcom BCM2712 2.4GHz quad-core 64-bit Arm Cortex-A76 CPU, with cryptography extensions, 512KB per-core L2 caches and a 2MB shared L3 cache
VideoCore VII GPU, supporting OpenGL ES 3.1, Vulkan 1.2
Dual 4Kp60 HDMI® display output with HDR support
4Kp60 HEVC decoder
LPDDR4X-4267 SDRAM (4GB and 8GB SKUs available at launch)
Dual-band 802.11ac Wi-Fi®
Bluetooth 5.0 / Bluetooth Low Energy (BLE)
microSD card slot, with support for high-speed SDR104 mode
2 × USB 3.0 ports, supporting simultaneous 5Gbps operation
2 × USB 2.0 ports
Gigabit Ethernet, with PoE+ support (requires separate PoE+ HAT)
2 × 4-lane MIPI camera/display transceivers
PCIe 2.0 x1 interface for fast peripherals (requires separate M.2 HAT or other adapter)
5V/5A DC power via USB-C, with Power Delivery support
Raspberry Pi standard 40-pin header
Real-time clock (RTC), powered from external battery
Power button

Software

Raspberry pi OS : 64 bit

tflite-support

The new Raspberry Pi OS uses Python 3.11 by default, but TensorFlow Lite currently only supports Python 3.9 on ARM aarch64 architecture. To resolve this, you can create a separate virtual environment with Python 3.8 or 3.9 using

conda create -n name python=3.8 or conda create -n name python=3.9

Conda Python 3.8

Install mini conda

https://docs.conda.io/projects/miniconda/en/latest/miniconda-other-installer-links.html

conda create -n tflite python=3.8.18

conda activate tflite

TensorFlow-lite Example

Install Code

git clone https://github.com/tensorflow/examples --depth 1

cd examples/lite/examples/object_detection/raspberry_pi
sh setup.sh

Run Image Object Detection

Run Video File Object Detection

Source Code

https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/raspberry_pi

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Raspberry pi5 Yolov8 Object Detection

YOLOv8 from Ultralytics

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

Raspberry pi 5

Specification

Broadcom BCM2712 2.4GHz quad-core 64-bit Arm Cortex-A76 CPU, with cryptography extensions, 512KB per-core L2 caches and a 2MB shared L3 cache
VideoCore VII GPU, supporting OpenGL ES 3.1, Vulkan 1.2
Dual 4Kp60 HDMI® display output with HDR support
4Kp60 HEVC decoder
LPDDR4X-4267 SDRAM (4GB and 8GB SKUs available at launch)
Dual-band 802.11ac Wi-Fi®
Bluetooth 5.0 / Bluetooth Low Energy (BLE)
microSD card slot, with support for high-speed SDR104 mode
2 × USB 3.0 ports, supporting simultaneous 5Gbps operation
2 × USB 2.0 ports
Gigabit Ethernet, with PoE+ support (requires separate PoE+ HAT)
2 × 4-lane MIPI camera/display transceivers
PCIe 2.0 x1 interface for fast peripherals (requires separate M.2 HAT or other adapter)
5V/5A DC power via USB-C, with Power Delivery support
Raspberry Pi standard 40-pin header
Real-time clock (RTC), powered from external battery
Power button

Raspberry Pi 3, 4, and 5 compare

Feature	Raspberry Pi 3	Raspberry Pi 4	Raspberry Pi 5
CPU	1.2GHz Quad-Core ARM Cortex-A53	1.5GHz Quad-core 64-bit ARM Cortex-A72	2.4GHz Quad-core 64-bit Arm Cortex-A76
RAM	1GB LPDDR2	2GB, 4GB or 8GB LPDDR4	Details not yet available
USB Ports	4 x USB 2.0	2 x USB 2.0, 2 x USB 3.0	2 x USB 3.0, 2 x USB 2.0
Network	Ethernet & Wi-Fi 802.11n	Gigabit Ethernet & Wi-Fi 802.11ac	Gigabit Ethernet with PoE+ support, Dual-band 802.11ac Wi-Fi®
Performance	Slower, may require lighter YOLO models	Faster, can run complex YOLO models	Details not yet available
Power Requirement	2.5A power supply	3.0A USB-C power supply	Details not yet available
Official Documentation	Link	Link	Link

Raspberry pi OS

Install Library

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Run Test Image with Command Line

YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:

yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

Run Video File

yolo predict model=yolov8n.pt source=/home/pi/Video/your_video.mp4 show=True

Reference

Quick Start Guide: Raspberry Pi and Pi Camera with YOLOv5 and YOLOv8

https://docs.ultralytics.com/guides/raspberry-pi/#choose-your-yolo-version-yolov5-or-yolov8

https://github.com/ultralytics/ultralytics