https://github.com/qengineering/tensorflow_lite_pose_jetson-nano
TensorFlow Lite Posenet on a Jetson Nano at 15.2 FPS
https://github.com/qengineering/tensorflow_lite_pose_jetson-nano
aarch64 cpp gpu-acceleration gpu-delegate jetson-nano posenet tensorflow-examples tensorflow-lite
Last synced: 5 months ago
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TensorFlow Lite Posenet on a Jetson Nano at 15.2 FPS
- Host: GitHub
- URL: https://github.com/qengineering/tensorflow_lite_pose_jetson-nano
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2021-02-02T12:26:34.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-11-20T09:24:14.000Z (over 1 year ago)
- Last Synced: 2025-01-31T03:01:35.505Z (about 1 year ago)
- Topics: aarch64, cpp, gpu-acceleration, gpu-delegate, jetson-nano, posenet, tensorflow-examples, tensorflow-lite
- Language: C++
- Homepage: https://qengineering.eu/install-tensorflow-2-lite-on-jetson-nano.html
- Size: 12.5 MB
- Stars: 6
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TensorFlow_Lite_Pose_Jetson-Nano
## TensorFlow Lite Posenet running on a Jetson Nano
[](https://opensource.org/licenses/BSD-3-Clause)
A fast C++ implementation of TensorFlow Lite Posenet on a Jetson Nano.
Once overclocked to 2015 MHz, the app runs at 15.2 FPS.
Special made for a Jetson Nano see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)
------------
Papers: https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5
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## Benchmark.
| CPU 2015 MHz | GPU 2015 MHz | CPU 1479 MHz | GPU 1479 MHZ | RPi 4 64os 1950 MHz |
| :------------: | :-------------: | :-------------: | :-------------: | :-------------: |
| 15.2 FPS | 11.8 FPS | 12 FPS | 11 FPS | 9.4 FPS |
------------
## Dependencies.
To run the application, you have to:
- TensorFlow Lite framework installed. [Install TensorFlow Lite](https://qengineering.eu/install-tensorflow-2-lite-on-jetson-nano.html)
- Optional OpenCV installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-jetson-nano.html)
- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)
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## Installing the app.
To extract and run the network in Code::Blocks
$ mkdir *MyDir*
$ cd *MyDir*
$ wget https://github.com/Qengineering/TensorFlow_Lite_Pose_Jetson-Nano/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your *MyDir* folder must now look like this:
Dance.mp4
posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite
TestTensorFlow_Lite_Pose.cpb
Pose_single.cpp
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## Running the app.
Run TestTensorFlow_Lite.cpb with Code::Blocks.
You may need to adapt the specified library locations in *TestTensorFlow_Lite.cpb* to match your directory structure.
With the `#define GPU_DELEGATE` uncommented, the TensorFlow Lite will deploy GPU delegates, if you have, of course, the appropriate libraries compiled by bazel. [Install GPU delegates](https://qengineering.eu/install-tensorflow-2-lite-on-jetson-nano.html)
See the RPi 4 movie at: https://www.youtube.com/watch?v=LxSR5JJRBoI
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