Category Archives: AI

OpenVINO on Raspberry

OpenVINO is the short term for Open Visual Inference and Neural network Optimization toolkit. There is a port to the Raspberry platform running Rasbian OS.

To setup on a Raspberry Pi, download the latest zip from OpenVINO, and run the commands below.

sudo tar -xf l_openvino_toolkit_runtime_raspbian_p_2019.2.242.tgz --strip 1 -C /opt/intel/openvino
sudo apt install cmake
source /opt/intel/openvino/bin/
echo "source /opt/intel/openvino/bin/" >> ~/.bashrc
sudo usermod -a -G users "$(whoami)"
sh /opt/intel/openvino/install_dependencies/
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino/deployment_tools/inference_engine/samples
make -j2 object_detection_sample_ssd


Once the installation completed, download the pre-built model for facial recognition. The following test will return an output image with the face detected using an input image.

wget --no-check-certificate
wget --no-check-certificate

./armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i barack-obama-12782369-1-402.jpg 





TensorFlow and Keras on RTX2060 for pattern recognition

The MNIST database is a catalog of handwritten digits for image processing. With TensorFlow and Keras training a neural network classifier using the Nvidia RTX206 GPU is a walk in the park.

Using the default import of the MNIST dataset using tf.keras, which comprises of 60,000 handwritten digits images in 28 x 28 pixels, the training of a neural network to learn classifying it could be accomplished in a matter of seconds, depending on the accuracy. The same learning done on ordinary CPU is not as quick as GPU for architectural differences. In this sample run, the digit “eight” is correctly identified using the neural network.

A simple comparison of the training result of the MNIST database on my RTX2060 with varying training samples depicts slight differences in the final accuracy.