TensorFlow_Lite_Classification_RPi_zero

TensorFlow Lite classification running on a bare Raspberry Pi Zero

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

---

Papers: https://arxiv.org/pdf/1712.05877.pdf
Training set: COCO with 1000 objects
Size: 224x224

---

Dependencies.

To run the application, you have to:

• TensorFlow Lite framework installed. Install TensorFlow Lite
  
• OpenCV installed. Install OpenCV 4.5
  
• 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

---

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.

---