Artificial Intelligence

This section mainly introduces application development using NPU for hardware-accelerated artificial intelligence

📄️ RK3582 NPU Platform Specific Instructions

RK3582 NPU Platform Specific Instructions

📄️ RKNN Installation

Start your efficient AI model inference journey on Rockchip NPU through RKNN installation, and experience the perfect fusion of technology and humanity

📄️ RKNN Quick Examples

Verify NPU availability through simple examples

📄️ Simulate YOLOv5 Segmentation Inference

Using the RKNN toolkit to explore AI model simulation inference, experience the efficiency and precision of intelligent image segmentation

📄️ Deploy YOLOv5 Object Detection on the Board

Deploy YOLOv5 on RK3582 board to usher in a new era of intelligent object detection, where technology and humanity perfectly blend in precise recognition

📄️ Deploy YOLOv8 Object Detection on the Board

Deploy YOLOv8 on RK3582 board to usher in a new era of intelligent object detection, where technology and humanity perfectly blend in precise recognition

📄️ RKNN Ultralytics YOLOv11

Deploy YOLOv11 on RK3582 board to usher in a new era of intelligent object detection, where technology and humanity perfectly blend in precise recognition

📄️ RKLLM Installation

Through RKLLM installation, embark on an efficient inference journey of LLM models on Rockchip NPU, and experience the perfect fusion of technology and humanity

📄️ RKLLM Usage and Deploy LLM

Detailed RKLLM usage documentation

📄️ RKLLM DeepSeek-R1

Running DeepSeek-R1 large language model with RKLLM

📄️ RKLLM Qwen2-VL

Run the Qwen2_VL large language model using RKLLM

📄️ Python Virtual Environment Usage

Using virtual environments to isolate system environments