🚀🚀🚀 A collection of some awesome public YOLO object detection series projects and the related object detection datasets.

Easy-to-use, high-performance, multi-platform inference deployment framework

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).

simple yolov5 rtspserver for rk3588

Inference YOLOv8 segmentation on ONNX, RKNN, Horizon and TensorRT

Automated script to convert Huggingface and GGUF models to rkllm format for running on Rockchip NPU

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

Inference YOLOv8 detection on ONNX, RKNN, Horizon and TensorRT

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

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

瑞芯微芯片的rknn推理框架部署(yolo模型)

Web chat front end for rk3588_npu_llm_server / RK3588 LLM chat interface

This project includes implementations of YOLOv8, RT-DETR-V2(RTDETR), MobileSAM, and NanoSAM on TensorRT, ONNX Runtime, and RKNN, along with support for asynchronous inference workflows. It provides a user-friendly deep learning deployment tool for seamless algorithm migration across different inference frameworks.