https://github.com/qengineering/tensorflow_lite_ssd_rpi_64-bits

TensorFlow Lite SSD on bare Raspberry Pi 4 with 64-bit OS at 24 FPS
https://github.com/qengineering/tensorflow_lite_ssd_rpi_64-bits

aarch64 armv7 armv8 bare-raspberry-pi cpp deep-learning frame-rate high-fps jamesbond lite raspberry-pi-4 ssd-mobilenet tensorflow-examples tensorflow-lite testtensorflow-lite ubuntu ubuntu1804

Last synced: 3 months ago
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TensorFlow Lite SSD on bare Raspberry Pi 4 with 64-bit OS at 24 FPS

• Host: GitHub
• URL: https://github.com/qengineering/tensorflow_lite_ssd_rpi_64-bits
• Owner: Qengineering
• License: bsd-3-clause
• Created: 2020-02-26T09:44:19.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-05-03T19:15:17.000Z (about 3 years ago)
• Last Synced: 2025-01-31T03:11:39.548Z (3 months ago)
• Topics: aarch64, armv7, armv8, bare-raspberry-pi, cpp, deep-learning, frame-rate, high-fps, jamesbond, lite, raspberry-pi-4, ssd-mobilenet, tensorflow-examples, tensorflow-lite, testtensorflow-lite, ubuntu, ubuntu1804
• Language: C++
• Homepage: https://qengineering.eu/install-ubuntu-18.04-on-raspberry-pi-4.html
• Size: 21.1 MB
• Stars: 43
• Watchers: 3
• Forks: 7
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![output image](https://qengineering.eu/images/SDcard16GB_tiny.jpg) Find this example on our [SD-image](https://github.com/Qengineering/RPi-image)

# TensorFlow_Lite_SSD_RPi_64-bits

![output image]( https://qengineering.eu/images/James_24.jpg )


## TensorFlow Lite SSD running at 24 FPS on a bare Raspberry Pi 4 64-OS 


[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)



A fast C++ implementation of TensorFlow Lite on a bare Raspberry Pi 4 64-bit OS.

Once overclocked to 1925 MHz, the app runs a whopping 24 FPS!

Without any hardware accelerator, just you and your Pi.

https://arxiv.org/abs/1611.10012 


Training set: COCO 


Size: 300x300 


Frame rate V1 Lite : 28 FPS (RPi 4 @ 1925 MHz - 64 bits Bullseye OS) 


Frame rate V1 Lite : 17 FPS (RPi 4 @ 2000 MHz - 32 bits OS) see [32-OS](https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits)





Special made for a Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) 





To extract and run the network in Code::Blocks 


$ mkdir *MyDir* 


$ cd *MyDir* 


$ wget https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits/archive/refs/heads/master.zip 


$ unzip -j master.zip 


Remove master.zip and README.md as they are no longer needed. 
 

$ rm master.zip 


$ rm README.md 
 


Your *MyDir* folder must now look like this: 
 

James.mp4 


COCO_labels.txt 


detect.tflite 


TestTensorFlow_Lite.cpb 


MobileNetV1.cpp


 


Run TestTensorFlow_Lite.cpb with Code::Blocks.  More info or
 

if you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).


I fact you can run this example on any aarch64 Linux system. 



See the movie at: https://vimeo.com/393889226

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