https://github.com/qengineering/tensorflow_lite_segmentation_rpi_32-bit
TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 4.2 FPS
https://github.com/qengineering/tensorflow_lite_segmentation_rpi_32-bit
armv7 armv8 cpp deep-learning raspberry-pi-4 segmentation semantic-segmentation tensorflow-examples tensorflow-lite unet unet-image-segmentation unet-segmentation unet-tensorflow
Last synced: 12 months ago
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TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 4.2 FPS
- Host: GitHub
- URL: https://github.com/qengineering/tensorflow_lite_segmentation_rpi_32-bit
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2020-03-14T17:03:51.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-01-25T09:45:17.000Z (about 3 years ago)
- Last Synced: 2025-03-27T09:23:14.564Z (about 1 year ago)
- Topics: armv7, armv8, cpp, deep-learning, raspberry-pi-4, segmentation, semantic-segmentation, tensorflow-examples, tensorflow-lite, unet, unet-image-segmentation, unet-segmentation, unet-tensorflow
- Language: C++
- Homepage: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html
- Size: 11.5 MB
- Stars: 8
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TensorFlow_Lite_Segmentation_RPi_32
## TensorFlow Lite Segmentation on a bare Raspberry Pi 32-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 1900 MHz, the app runs at 4.0 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 @ 1850 MHz - 64 bits OS)
------------
## Dependencies.
To run the application, you have to:
- TensorFlow Lite framework installed. [Install TensorFlow Lite](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html)
- OpenCV installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-pi-4.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_32/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 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).
------------
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)