https://github.com/qengineering/tensorflow_lite_segmentation_rpi_64-bit
TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 7.2 FPS with 64-bit OS
https://github.com/qengineering/tensorflow_lite_segmentation_rpi_64-bit
armv7 armv8 cpp deep-learning raspberry-pi-4 segmentation semantic-segmentation tensorflow-examples tensorflow-lite ubuntu1804 unet unet-image-segmentation unet-segmentation unet-tensorflow
Last synced: 2 months ago
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TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 7.2 FPS with 64-bit OS
- Host: GitHub
- URL: https://github.com/qengineering/tensorflow_lite_segmentation_rpi_64-bit
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2020-03-16T13:59:54.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-25T09:44:34.000Z (over 2 years ago)
- Last Synced: 2025-03-27T09:23:43.540Z (6 months ago)
- Topics: armv7, armv8, cpp, deep-learning, raspberry-pi-4, segmentation, semantic-segmentation, tensorflow-examples, tensorflow-lite, ubuntu1804, unet, unet-image-segmentation, unet-segmentation, unet-tensorflow
- Language: C++
- Homepage: https://qengineering.eu/install-ubuntu-18.04-on-raspberry-pi-4.html
- Size: 11.5 MB
- Stars: 19
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
 Find this example on our [SD-image](https://github.com/Qengineering/RPi-image)
# TensorFlow_Lite_Segmentation_RPi_64-bit
## TensorFlow Lite Segmentation running on bare Raspberry Pi 4 with 64-bit OS
[](https://opensource.org/licenses/BSD-3-Clause)
A fast C++ implementation of TensorFlow Lite Unet on a bare Raspberry Pi 4.
Once overclocked to 1850 MHz, the app runs at 7.2 FPS!
Special made for a bare Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)
------------
Papers: https://arxiv.org/abs/1606.00915
Training set: VOC2017
Size: 257x257
------------
## Benchmark.
Frame rate Unet Lite : 4.0 FPS (RPi 4 @ 1900 MHz - 32 bits OS)
Frame rate Unet Lite : 7.2 FPS (RPi 4 @ 1875 MHz - 64 bits OS)
------------
## Dependencies.
To run the application, you have to:
- A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. [Install 64-bit OS](https://qengineering.eu/install-raspberry-64-os.html)
- TensorFlow Lite framework installed. [Install TensorFlow Lite](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-64-os.html)
- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html)
- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)
------------
## Installing the app.
To extract and run the network in Code::Blocks
$ mkdir *MyDir*
$ cd *MyDir*
$ wget https://github.com/Qengineering/TensorFlow_Lite_Segmentation_RPi_64-bit/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:
cat.jpg.mp4
deeplabv3_257_mv_gpu.tflite
TestUnet.cpb
Unet.cpp
------------
## Running the app.
Run TestUnet.cpb withCode::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://www.youtube.com/watch?v=Kh9DLMgCIIE
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[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)