基于C++部署yolov9的tensorrt源码+部署步骤模型.zip

<div align="center"> TensorRT-YOLOv9 =========================== [](https://www.python.org/downloads/release/python-31012/) [](https://developer.nvidia.com/cuda-downloads) [](https://developer.nvidia.com/tensorrt) [](https://github.com/spacewalk01/TensorRT-YOLOv9/tree/main?tab=MIT-1-ov-file#readme) <div align="left"> This repo hosts a C++ and python implementation of the [YOLOv9](https://github.com/WongKinYiu/yolov9) state of the art object detection model, leveraging the TensorRT API for efficient real-time inference. <p align="center" margin: 0 auto;> <img src="assets/traffic.gif" width="360px" /> <img src="assets/parkinglot.gif" width="360px" /> </p> ## 🚀 Usage #### Python ``` shell cd <this project path>/python python yolov9_trt.py --engine yolov9-c.engine --data images --outdir output ``` #### C++ ``` shell cd <this project path>/build/release # infer an image yolov9-tensorrt.exe yolov9-c.engine test.jpg # infer a folder(images) yolov9-tensorrt.exe yolov9-c.engine data # infer a video yolov9-tensorrt.exe yolov9-c.engine test.mp4 # the video path ``` ## 🛠️ Build #### Python The following command will install tensorrt for python: ``` shell cd <tensorrt installation path>/python pip install cuda-python pip install tensorrt-8.6.0-cp310-none-win_amd64.whl pip install opencv-python ``` #### C++ Refer to our [docs/INSTALL.md](https://github.com/spacewalk01/tensorrt-yolov9/blob/main/docs/INSTALL.md) for detailed installation instructions. - Cuda preprocessing: [main branch](https://github.com/spacewalk01/TensorRT-YOLOv9/tree/main) - Cpu preprocessing: [cpu-preprocessing branch](https://github.com/spacewalk01/TensorRT-YOLOv9/tree/cpu_preprocessing) ## 🖥️ Requirement - TensorRT - CUDA, CudaNN - C++ compiler with C++17 or higher support - CMake 3.14 or higher - OpenCV ## 🌱 Examples Parameters for inference: ``` Confidence threshold : 0.2 NMS threshold : 0.3 Model : yolov9-e (with FP32) ``` Inference result: <p align="center" margin: 0 auto;> <img src="assets/street_o.jpg" /> </p> Original image: https://www.flickr.com/photos/nicolelee/19041780 ## 👏 Acknowledgement This project is based on the following awesome projects: - [YOLOv9](https://github.com/WongKinYiu/yolov9) - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. - [TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/8.6/samples) - TensorRT samples and api documentation. - [TensorRTx](https://github.com/wang-xinyu/tensorrtx) - Implementation of popular deep learning networks with TensorRT network definition API. ## 🔗 See also - [Yolov9-Bytetrack](https://github.com/spacewalk01/yolov9-bytetrack-tensorrt) - Integration of YOLOv9 with ByteTracker using the TensorRT API.