★ Embedded AI Series – Deploying YOLOv5 Model on RK Platform RV1126 ” 1. Introduction The official RK example code includes a deployment example for YOLOv5, so we do not need to start from scratch as we did in previous articles for deploying YOLOv3. We can first run the official YOLOv5 example code from RK … Read more
★ Embedded AI Series – Code Analysis of YOLOv3 Deployment on RV1126 – Post-Processing 1 ” 1. Introduction After executing inference and obtaining the inference results in the previous article, the next step is to parse and process the inference results, commonly referred to as “post-processing.” The knowledge points involved in the post-processing section are … Read more
Description Deploying YOLOv8 on Wildfire RK3588, we will use yolov8s.pt (downloaded from the YOLOv8 official website) as an example. 1. pt->onnx Do not use the official YOLOv8 code; instead, use the Rockchip YOLOv8 code available at the following address: https://github.com/airockchip/ultralytics_yolov8 After downloading the code, execute the model conversion with the following code: from ultralytics import … Read more
★ Embedded AI Series – Analysis of YOLOv3 Model Deployment Code – Overall Process ” 1. Introduction In the previous article “Embedded AI Series – Deploying YOLOv3 Model on RV1126”, we provided the C++ source code project for deploying the YOLOv3 model in RKNN format on the RV1126 hardware platform and demonstrated the detection capabilities … Read more
Description Deploying YOLOv8 on Wildfire RK3588, we use yolov8s.pt (downloaded from the YOLOv8 official website) as an example. 1. pt->onnx Do not use the official YOLOv8 code; instead, use the Rockchip YOLOv8 code, available at https://github.com/airockchip/ultralytics_yolov8 After downloading the code, execute the model conversion with the following code: from ultralytics import YOLO model = YOLO('yolov8s.pt') … Read more
Description Deploying YOLOv8 on Wildfire RK3588, we use yolov8s.pt (downloaded from the YOLOv8 official website) as an example. 1. pt->onnx Do not use the official YOLOv8 code; instead, use the Rockchip YOLOv8 code, available at https://github.com/airockchip/ultralytics_yolov8 After downloading the code, execute the model conversion with the following code: from ultralytics import YOLO model = YOLO('yolov8s.pt') … Read more
Objective of This Article This article will detail how to configure the RKNN Docker environment on an Android development board equipped with the RK3588 chip in a Windows 11 system environment, and how to run the YOLOv5 object detection model accelerated by NPU on the development board using the adb tool. Development Environment Description Host … Read more
The Objective of This Article This article will practice the MobileNet image classification example in the RKNN Docker environment and deploy it to the RK3588 development board using the adb tool. For an introduction to MobileNet, please refer to the previous article. Development Environment Description Host System: Windows 11 Target Device: Android development board equipped … Read more
For more content, you can join the Linux system knowledge base package (tutorials + videos + Q&A). Table of Contents 1. Download rknn-llm and DeepSeek model 2. Install RKLLM-Toolkit 2.1 Install miniforge3 tool 2.2 Download miniforge3 installation package 2.3 Install miniforge3 3. Create RKLLM-Toolkit Conda environment 3.1 Enter Conda base environment 3.2 Create a Conda … Read more
With the development of AI intelligence, many chips more suitable for AI learning have been introduced following traditional CPUs and GPUs. This article introduces how to develop NPU chips based on the SDK provided by the RK platform. 1. Introduction to NPU Chips NPU stands for Neural Network Processing Unit. 2. Using RKNN 1. SDK … Read more