https://github.com/qengineering/tensorflow_lite_classification_rpi_zero
TensorFlow Lite on a bare Raspberry Pi Zero
https://github.com/qengineering/tensorflow_lite_classification_rpi_zero
armv6 cpp deep-learning mobilenet raspberry-pi-zero raspberry-pi-zero-w tensoflow-lite tensorflow-examples
Last synced: 8 months ago
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TensorFlow Lite on a bare Raspberry Pi Zero
- Host: GitHub
- URL: https://github.com/qengineering/tensorflow_lite_classification_rpi_zero
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2021-07-01T10:35:47.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-11-07T14:16:18.000Z (almost 3 years ago)
- Last Synced: 2025-01-31T03:01:35.473Z (8 months ago)
- Topics: armv6, cpp, deep-learning, mobilenet, raspberry-pi-zero, raspberry-pi-zero-w, tensoflow-lite, tensorflow-examples
- Language: C++
- Homepage: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html
- Size: 836 KB
- Stars: 14
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TensorFlow_Lite_Classification_RPi_zero
## TensorFlow Lite classification running on a bare Raspberry Pi Zero
[](https://opensource.org/licenses/BSD-3-Clause)
A 'fast' C++ implementation of TensorFlow Lite classification on a bare Raspberry Pi zero.
Be noted that we use the zero version here, not the new Raspberry Pi zero 2.
Inference time: 11 sec
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://arxiv.org/pdf/1712.05877.pdf
Training set: COCO with 1000 objects
Size: 224x224
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## 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#Zero)
- 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_Classification_RPi_zero/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:
tabby.jpeg
schoolbus.jpg
grace_hopper.bmp
Labels.txt
TensorFlow_Lite_Mobile.cpb
TensorFlow_Lite_Class.cpp
Next, choose your model from TensorFlow: https://www.tensorflow.org/lite/guide/hosted_models
Download a quantized model, extract the .tflite from the tarball and place it in your *MyDir*.
Now your *MyDir* folder may contain: mobilenet_v1_1.0_224_quant.tflite.
Or: inception_v4_299_quant.tflite. Or both of course.
Enter the .tflite file of your choice on line 54 in TensorFlow_Lite_Class.cpp
The image to be tested is given a line 84, also in TensorFlow_Lite_Class.cpp
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## Running the app.
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).
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[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)