nndeploy是一款模型端到端部署框架。以多端推理以及基于有向无环图模型部署为基础，致力为用户提供跨平台、简单易用、高性能的模型部署体验。

an edge-real-time anchor-free object detector with decent performance

YOLOv5 in PyTorch > ONNX > RKNN

Run your yolov7 object detection with Rockchip NPU platforms (RK3566, RK3568, RK3588, RK3588S, RV1103, RV1106, RK3562).

Inference YOLOv8 segmentation on ONNX, RKNN, Horizon and TensorRT

simple yolov5 rtspserver for rk3588

A demo show how to inference blazeface model on Rockchip RV1126.

RKNN模型推理部署模板

基于u2net网络进行简单修改使其部署到rk3588板子上

Deploy deep learning model on difference hardware and framework. (TensorRT/ONNX/MNN/RKNN)

export any your YOLOv7 model to TensorFlow, TensorFlowJs, ONNX, OpenVINO, RKNN,...

Inference YOLOv8 detection on ONNX, RKNN, Horizon and TensorRT

Web chat front end for rk3588_npu_llm_server / RK3588 LLM chat interface

Superresolution running on Rockchip NPU (RK3588, etc..)

瑞芯微芯片的rknn推理框架部署(yolo模型)

fatigue detect rknn/onnx model deploy in rk3568 npu（ROCK 3A）

Convert and run scikit-learn MLPs on Rockchip NPU.