Artificial Intelligence
This section introduces AI applications, including the RKNN and RKLLM toolchains, the RKNN Model Zoo examples, and common model deployment workflows.
📄️ RKNN Installation
Start the journey of efficient inference of AI models on Rockchip NPU through RKNN installation, and feel the perfect fusion of technology and humanities
📄️ RKNN Model Zoo
📄️ RKLLM Installation
Start the journey of efficient deployment of intelligent language models with RKLLM installation, bringing technology and human intelligence together
📄️ RKLLM Usage and Deploy LLM
Efficient hardware acceleration of large language models using RKLLM technology for a new chapter in intelligent dialogue
📄️ RKNN Quick Example
📄️ Simulate YOLOv5 Segmentation Inference
Explore simulated inference of AI models with the RKNN toolkit and experience the efficiency and precision of intelligent image segmentation
📄️ Deploy YOLOv5 Object Detection on the Board
📄️ Deploy YOLOv8 Object Detection on the Board
Deploying YOLOv8 on the RK3588 board side opens a new era of intelligent target detection, allowing technology and humanistic care to merge perfectly in accurate identification
📄️ RKNN Ultralytics YOLOv11
Deploying YOLOv11 on the RK3588/356X board side opens a new era of intelligent target detection, allowing technology and humanistic care to merge perfectly in accurate identification
📄️ Convert Custom Trained YOLO Models
Convert Custom Trained YOLO Models
📄️ Stable Diffusion (RKNN)
Convert Stable Diffusion models with RKNN
📄️ RKLLM DeepSeek-R1
📄️ RKLLM Qwen2-VL
Run the Qwen2_VL large language model using RKLLM
📄️ YOLOv8
📄️ PP-YOLOE
📄️ YOLO World
📄️ YOLOv8-Seg
📄️ MobileSAM
📄️ DeepLabV3
📄️ PP-OCR
📄️ LPRNet
📄️ RetinaFace
📄️ ResNet
📄️ MobileNet
📄️ CLIP
📄️ Whisper
📄️ Wav2Vec 2.0
📄️ RKLLM SmolVLM2
Use RKLLM SmolVLM2 on ROCK 5B
📄️ YOLOv8 Multi-stream Recognition
YOLOv8 multi-stream recognition with RKNN