This article aims to give general information about the x-linux-ai tool. (il faut developper encore un petit peu)
1. Description[edit source]
1.1. What is X-LINUX-AI Tool ?[edit source]
The X-LINUX-AI tool is a software designed to verify the current version of X-Linux-AI and to display a comprehensive list of all the available features and applications.
2. X-LINUX-AI Tool installation[edit source]
After having configured the AI OpenSTLinux package you can install the X-LINUX-AI components.
apt-get install x-linux-ai-tool
And restart the demo launcher:
systemctl restart weston-graphical-session.service
3. How to use X-LINUX-AI Tool:[edit source]
Usage:x-linux-ai
-v --version : Show X-LINUX-AI current version if it is installed
-f --supported-features : Print all supported frameworks in this X-LINUX-AI version
-a --supported-appli : Print all delivered applications in this X-LINUX-AI version
-h --help : Show this help
3.1. Usage Example[edit source]
x-linux-ai -v
X-LINUX-AI version: v5.0.0
x-linux-ai -f
Features: * TensorFlow™ Lite 2.11.0 with XNNPACK delegate activated * ONNX Runtime 1.14.0 with XNNPACK execution engine activated * OpenCV 4.7.x * Python™ 3.10.x (enabling Pillow module) * Coral Edge TPU™ accelerator native support * libedgetpu 2.0.0 (Gouper) aligned with TensorFlow™ Lite 2.11.0 * libcoral 2.0.0 (Gouper) aligned with TensorFlow™ Lite 2.11.0 * PyCoral 2.0.0 (Gouper) aligned with TensorFlow™ Lite 2.11.0 * Support for the OpenSTLinux AI package repository allowing the installation of a prebuilt package using apt-* utilities Find more informations on the wiki page: https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_OpenSTLinux_Expansion_Package
x-linux-ai -a
Applications: * C++ / Python™ image classification example using TensorFlow™ Lite based on the MobileNet v1 quantized model * C++ / Python™ object detection example using TensorFlow™ Lite based on the COCO SSD MobileNet v1 quantized model * C++ / Python™ image classification example using Coral Edge TPU™ based on the MobileNet v1 quantized model and compiled for the Edge TPU™ * C++ / Python™ object detection example using Coral Edge TPU™ based on the COCO SSD MobileNet v1 quantized model and compiled for the Edge TPU™ * C++ face recognition application using proprietary model capable of recognizing the face of a known (enrolled) user. Contact the local STMicroelectronics support for more information about this application or send a request to edge.ai@st.com * Python™ image classification example using ONNX Runtime based on the MobileNet v1 quantized model * C++ / Python™ object detection example using ONNX Runtime based on the COCO SSD MobileNet v1 quantized model