วันพฤหัสบดีที่ 25 มกราคม พ.ศ. 2567

Raspberry pi5 TensorFlow-lite Object Detection

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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