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.