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