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C # Model Deployment Platform: Video Detection Based on YOLOv8 Object Detection Model
基于.NET Framework 4.8 开发的深度学习模型部署测试平台,提供了YOLO框架的主流系列模型,包 YOLOv8~v9,以及其系列下的Det、Seg、Pose、Obb、Cls等应用场景,同时支持图像与视频检测。模型部署引擎使用的是OpenVINO、TensorRT、ONNX runtime以及OpenCV DNN,支持CPU、IGPU以及GPU多种设备推理。
Based on The deep learning model deployment testing platform developed with NET Framework 4.8 provides the mainstream series of YOLO framework models, including YOLOv8~v9, as well as its application scenarios such as Det, Seg, Pose, Obb, Cls, etc., and supports image and video detection. The model deployment engine uses OpenVINO, TensorRT, ONNX runtime, and OpenCV DNN, supporting CPU, IGPU, and GPU device inference.
其中,OpenVINO以及TensorRT的C#接口均为自行开发,项目链接为:
OpenVINO C# API :https://github.com/guojin-yan/OpenVINO-CSharp-API.git
TensorRT C# API :https://github.com/guojin-yan/TensorRT-CSharp-API.git
项目源码目前已经在GitHub上开源,项目链接为:https://github.com/guojin-yan/YoloDeployCsharp.git
各位开发者可以根据自己情况加逆行下载,并进行项目配置,其中一些内容的配置可以参考一下文章:
[最新发布!TensorRT C# API :基于C#与TensorRT部署深度学习模型](https://mp.weixin.qq.com/s/Sw9ukiM9ZKOuzzGePVV4mg)
[在 Windows 上使用 OpenVINO
C# API 部署 Yolov8-obb 实现任意方向的目标检测 | 开发者实战](https://mp.weixin.qq.com/s/Cj18ih8G1aw4lrOUqW-06w)
Among them, the C # interfaces of OpenVINO and TensorRT are both self-developed, and the project link is:
OpenVINO C# API : https://github.com/guojin-yan/OpenVINO-CSharp-API.git
TensorRT C# API : https://github.com/guojin-yan/TensorRT-CSharp-API.git
The project source code is currently open source on GitHub, and the project link is: https://github.com/guojin-yan/YoloDeployCsharp.git
Developers can download in reverse according to their own situation and configure the project. Some of the content configuration can refer to the following article:
Latest Release! TensorRT C # API: Deploying Deep Learning Models Based on C # and TensorRT(https://mp.weixin.qq.com/s/Sw9ukiM9ZKOuzzGePVV4mg)
Using OpenVINO on Windows
C # API Deployment Yolov8 obb for Object Detection in Any Direction | Developer Practical( https://mp.weixin.qq.com/s/Cj18ih8G1aw4lrOUqW-06w )
Видео C # Model Deployment Platform: Video Detection Based on YOLOv8 Object Detection Model канала 椒颜皮皮虾
Based on The deep learning model deployment testing platform developed with NET Framework 4.8 provides the mainstream series of YOLO framework models, including YOLOv8~v9, as well as its application scenarios such as Det, Seg, Pose, Obb, Cls, etc., and supports image and video detection. The model deployment engine uses OpenVINO, TensorRT, ONNX runtime, and OpenCV DNN, supporting CPU, IGPU, and GPU device inference.
其中,OpenVINO以及TensorRT的C#接口均为自行开发,项目链接为:
OpenVINO C# API :https://github.com/guojin-yan/OpenVINO-CSharp-API.git
TensorRT C# API :https://github.com/guojin-yan/TensorRT-CSharp-API.git
项目源码目前已经在GitHub上开源,项目链接为:https://github.com/guojin-yan/YoloDeployCsharp.git
各位开发者可以根据自己情况加逆行下载,并进行项目配置,其中一些内容的配置可以参考一下文章:
[最新发布!TensorRT C# API :基于C#与TensorRT部署深度学习模型](https://mp.weixin.qq.com/s/Sw9ukiM9ZKOuzzGePVV4mg)
[在 Windows 上使用 OpenVINO
C# API 部署 Yolov8-obb 实现任意方向的目标检测 | 开发者实战](https://mp.weixin.qq.com/s/Cj18ih8G1aw4lrOUqW-06w)
Among them, the C # interfaces of OpenVINO and TensorRT are both self-developed, and the project link is:
OpenVINO C# API : https://github.com/guojin-yan/OpenVINO-CSharp-API.git
TensorRT C# API : https://github.com/guojin-yan/TensorRT-CSharp-API.git
The project source code is currently open source on GitHub, and the project link is: https://github.com/guojin-yan/YoloDeployCsharp.git
Developers can download in reverse according to their own situation and configure the project. Some of the content configuration can refer to the following article:
Latest Release! TensorRT C # API: Deploying Deep Learning Models Based on C # and TensorRT(https://mp.weixin.qq.com/s/Sw9ukiM9ZKOuzzGePVV4mg)
Using OpenVINO on Windows
C # API Deployment Yolov8 obb for Object Detection in Any Direction | Developer Practical( https://mp.weixin.qq.com/s/Cj18ih8G1aw4lrOUqW-06w )
Видео C # Model Deployment Platform: Video Detection Based on YOLOv8 Object Detection Model канала 椒颜皮皮虾
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4 июня 2024 г. 13:15:39
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