X-LINUX-AI OpenSTLinux Expansion Package - v5.1.0

1. X-LINUX-AI v5.1.0[edit source]

Information

This version is compatible with Yocto Project^® build system Mickledore (v4.2.2) and has been validated against the OpenSTLinux ecosystem release v5.1.0 and validated on STM32MP1 series' boards and STM32MP2 series' boards

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1.1. Contents[edit source]

TensorFlow™ Lite^[1] 2.11.0 with XNNPACK delegate activated
Coral Edge TPU™^[2] accelerator native support
- libedgetpu 2.0.0 (Grouper) aligned with TensorFlow™ Lite 2.11.0
- libcoral 2.0.0 (Grouper) aligned with TensorFlow™ Lite 2.11.0
- PyCoral 2.0.0 (Grouper) aligned with TensorFlow™ Lite 2.11.0
ONNX Runtime™ ^[3] 1.14.0 with XNNPACK execution engine activated
OpenCV^[4] 4.7.x
Python™^[5] 3.11.x (enabling Pillow module)
Support of Sony™ IMX335 5Mpx sensor with use of DCMIPP and internal ISP
TensorFlow™ Lite application samples :
- GPU Python human pose estimation using TensorFlow™ Lite based on Movenet SinglePose Lightning quantized model
- NPU Python semantic segmentation using TensorFlow™ Lite based on DeepLabV3 quantized model
- NPU C++ / Python image classification using TensorFlow™ Lite based on MobileNet v3 quantized model
- NPU C++ object detection using TensorFlow™ Lite based on COCO SSD MobileNet v1 quantized model
- NPU Python object detection using TensorFlow™ Lite based on Tiny YOLOv4 quantized model
- NPU C++ face recognition using TensorFlow™ Lite models capable of recognizing the face of a known (enrolled) user (available on demand)

Warning

The face recognition binary is available on demand. Please contact the local STMicroelectronics support for more information about this application or send a request to edge.ai@st.com

Coral Edge TPU™ based application samples :
- C++ / Python image classification using Coral Edge TPU™ based on MobileNet v1 quantized model and compiled for the Coral Edge TPU™
- C++ / Python object detection using Coral Edge TPU™ based on COCO SSD MobileNet v1 quantized model and compiled for the Coral Edge TPU™
ONNX Runtime™ application samples :
- Python image classification using ONNX Runtime™ based on MobileNet v3 quantized model
- C++ / Python object detection using ONNX Runtime™ based on COCO SSD MobileNet v1 quantized model
OpenVX™ based application samples :
- NPU C++ image classification using Network Binary Graph based on MobileNet v3 quantized model
Tools :
- STM32AI MPU offline tool dedicated to deploy NN on the stm32mp25
- NBG benchmark application

1.2. Validated hardware[edit source]

X-LINUX-AI STM32MP25 beta is supported on all STM32MP25 Series and it has been validated on the following boards:

STM32MP257F-EV1 Evaluation board

[tensorflowlite_url-1] TensorFlow Lite

[edgetpu_url-2] Coral Edge TPU

[onnx_runtime_url-3] ONNX Runtime

[opencv_url-4] OpenCV

[python_url-5] Python

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