Note

This is the documentation for the latest development branch and may refer to features that are not available in released versions. If you are looking for the documentation for a specific release, use the drop-down menu on the left and select the desired version.

AI Application Development Guide

This section focuses on AI application development on RT-Smart, from low-level inference primitives to multi-task pipelines, multi-camera flows, and packaged scenario demos.

Detailed Guides

• NNCASE Guide
• AI2D Application Development Guide
• KPU Application Development Guide
• Single-Model Application Development Guide
• Double-Model Application Development Guide
• Triple-Camera AI Application Development Guide
• UVC + AI Application Development Guide
• YOLO Application Guide
• Deploying Kendryte Training-Platform Models on RT-Smart
• AI Demo Guide
• Multi-Object Tracking (MOT) Application Development Guide

AI application examples for RT-Smart are mainly located in src/rtsmart/examples/ai. A practical way to approach the stack is to split the examples into three groups:

Core Acceleration Basics

These examples explain the hardware-accelerated building blocks used by almost every deployment:

Directory	Purpose	Why It Matters
usage_ai2d	Demonstrates the five AI2D preprocessing modes: crop, shift, resize, pad, and affine	Learn how to offload preprocessing from the CPU
usage_kpu	Shows the full flow from model load to preprocess, inference, and postprocess	Learn the KPU runtime APIs and tensor handling

Reusable Task Templates

These examples are the main references when you want to build your own application:

Directory	Type	Use Case
single_model_example	Single-model pipeline	A basic AI application that runs one model
double_model_example	Multi-model pipeline	Cascaded tasks such as detection plus recognition
triple_camera_ai_example	Multi-camera pipeline	One application using three camera streams
uvc_face_detection	UVC-based pipeline	AI on a USB camera input
yolo	Unified YOLO wrapper	YOLOv5, YOLOv8, and YOLO11 tasks
cloudplat_deploy	Platform model deployment	Running models exported from the Kendryte training platform

Scenario Demos

These examples package lower-level media and AI components into more complete scenario applications:

Directory	Purpose
aidemo	50+ scenario demos spanning detection, recognition, OCR, speech, gesture, and more
multi_object_tracking	MOT demos integrating several tracking algorithms