Starred repositories
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Open3D: A Modern Library for 3D Data Processing
Implementation of popular deep learning networks with TensorRT network definition API
Qt编写的一些开源的demo,预计会有100多个,一直持续更新完善,代码简洁易懂注释详细,每个都是独立项目,非常适合初学者,代码随意传播使用,拒绝打赏和捐赠,欢迎留言评论!公众号:Qt实战/Qt入门和进阶/Qt教程
C++ library based on tensorrt integration
🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
🚀 Easier & Faster YOLO Deployment Toolkit for NVIDIA 🛠️
🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎
Qt控件美化:无边框窗口模板,zookeeper可视化操作工具,thrift接口调用工具(支持数据预制,压测,解析pacp文件),ssh终端工具(基本命令可用),数据库工具(查询表数据),qss工具,其他工具集合等)
高性能 高精度 大陆车牌、港澳车牌、台湾车牌 韩国车牌(South Korea LPR)识别 代码开源,方便移植嵌入式和安卓端使用,支持12种车牌识别,支持港澳车牌识别,支持大角度车牌识别,准确率高达99%+
yolov8 hub,cpp with onnxruntime and opencv
Simple thread-based asynchronous TCP & UDP Socket classes in C++.
SuperPoint and SuperGlue with TensorRT. Deploy with C++.
OCR离线图片文字识别命令行windows程序,以JSON字符串形式输出结果,方便别的程序调用。基于 RapidOcrOnnx 。
TensorRT+YOLO系列的 多路 多卡 多实例 并行视频分析处理案例
This repository give a guidline to learn CUDA and TensorRT from the beginning.
Based on tensorrt v8.0+, deploy detection, pose, segment, tracking of YOLO11 with C++ and python api.
Stereo Algorithms (Include:CREStereo,RAFT-Stereo,Hitnet,FastACVNet_plus,Stereo Transformers,RealtimeStereo,DistDepth) with TensorRT,ORT,OpenVINO
LightGlue-OnnxRunner is a repository hosts the C++ inference code of LightGlue in ONNX format,supporting end-to-end/decouple model inference of SuperPoint/DISK + LightGlue
Sample projects for TensorRT in C++
使用Nanodet+YoloV8-Pose实现指针仪表的实时检测、高精度读数识别(借助ncnn框架)
Using OnnxRuntime to inference yolov10,yolov10+SAM ,yolov10+bytetrack , SAM2 and paddleOCR by c++ .
C++ and Python implementations of YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLOv11, YOLOv12, YOLOv13 inference.
The YOLOv11 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT