/TensorFlow_Lite_SSD_Jetson-Nano

TensorFlow Lite SSD on a Jetson Nano 28.5 FPS

Primary LanguageC++BSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

TensorFlow_Lite_SSD_Jetson-Nano

output image

TensorFlow Lite SSD running on a Jetson Nano

License

A fast C++ implementation of TensorFlow Lite SSD on a Jetson Nano.
Once overclocked to 2015 MHz, the app runs at 28.5 FPS.

https://arxiv.org/abs/1611.10012
Training set: COCO
Size: 300x300

Benchmark.

CPU 2015 MHz GPU 2015 MHz CPU 1479 MHz GPU 1479 MHZ RPi 4 64os 1950 MHz
28.5 FPS -- FPS 21.8 FPS -- FPS 24 FPS

Special made for a Jetson Nano see Q-engineering deep learning examples

Dependencies.

To run the application, you have to:

Running the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/TensorFlow_Lite_SSD_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:
James.mp4
COCO_labels.txt
detect.tflite
TestTensorFlow_Lite.cpb
MobileNetV1.cpp

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

See the RPi 4 movie at: https://vimeo.com/393889226


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