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<rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:media="http://search.yahoo.com/mrss/" version="2.0"><channel><title>个人笔记</title><link>http://localhost:8090</link><atom:link href="http://localhost:8090/rss.xml" rel="self" type="application/rss+xml"/><description>个人笔记</description><generator>Halo v2.21.10</generator><language>zh-cn</language><image><url>http://fnos.20220430.xyz:40027/i/2025/12/12/693baad9a143e.png</url><title>个人笔记</title><link>http://localhost:8090</link></image><lastBuildDate>Sun, 5 Apr 2026 15:22:20 GMT</lastBuildDate><item><title><![CDATA[TensoRrt推理环境搭建]]></title><link>http://localhost:8090/archives/tensorrt%E6%8E%A8%E7%90%86%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=TensoRrt%E6%8E%A8%E7%90%86%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA&amp;url=/archives/tensorrt%E6%8E%A8%E7%90%86%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA" width="1" height="1" alt="" style="opacity:0;">TensoRrt推理环境搭建 第一步:安装cuda 1、安装cuda_12.2.2_537.13_windows.exe]]></description><guid isPermaLink="false">/archives/tensorrt%E6%8E%A8%E7%90%86%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA</guid><dc:creator>Administrator</dc:creator><category>C#</category><pubDate>Fri, 12 Dec 2025 05:24:40 GMT</pubDate></item><item><title><![CDATA[miniconda基本指令]]></title><link>http://localhost:8090/archives/miniconda-basic-instructions-z1mkjap</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=miniconda%E5%9F%BA%E6%9C%AC%E6%8C%87%E4%BB%A4&amp;url=/archives/miniconda-basic-instructions-z1mkjap" width="1" height="1" alt="" style="opacity:0;">miniconda基本指令 1、安装Miniconda： 下载Miniconda安装程序，根据操作系统的不同选择适当的版本。 运行安装程序，并按照指示进行安装。可以选择安装路径和添加到系统路径。 2、创建一个新的环境： conda create --name 环境名称 可以使用 -n 或 --nam]]></description><guid isPermaLink="false">/archives/miniconda-basic-instructions-z1mkjap</guid><dc:creator>Administrator</dc:creator><pubDate>Tue, 9 Dec 2025 08:17:44 GMT</pubDate></item><item><title><![CDATA[Python脚本自动发送电子邮件]]></title><link>http://localhost:8090/archives/pythonjiao-ben-zi-dong-fa-song-dian-zi-you-jian</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=Python%E8%84%9A%E6%9C%AC%E8%87%AA%E5%8A%A8%E5%8F%91%E9%80%81%E7%94%B5%E5%AD%90%E9%82%AE%E4%BB%B6&amp;url=/archives/pythonjiao-ben-zi-dong-fa-song-dian-zi-you-jian" width="1" height="1" alt="" style="opacity:0;">要编写一个Python脚本来自动发送电子邮件，你可以使用smtplib库来处理SMTP协议，以及email库来构建邮件内容。 安装必要的库 通常情况下，smtplib和email库是Python标准库的一部分，因此不需要额外安装。如果你使用的是较旧的Python版本，可能需要确保这些库已安装。 编写]]></description><guid isPermaLink="false">/archives/pythonjiao-ben-zi-dong-fa-song-dian-zi-you-jian</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 09:32:10 GMT</pubDate></item><item><title><![CDATA[Linux常用命令大全]]></title><link>http://localhost:8090/archives/wei-ming-ming-wen-zhang</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=Linux%E5%B8%B8%E7%94%A8%E5%91%BD%E4%BB%A4%E5%A4%A7%E5%85%A8&amp;url=/archives/wei-ming-ming-wen-zhang" width="1" height="1" alt="" style="opacity:0;">mv详解目录 Linux 常用命令大全 1. ls 指令 2. touch 指令 3. pwd 指令 4. mkdir 指令 5. cd 指令 6. rmdir 和 rm 指令 7. man 指令 8. cp 指令 9. mv 指令 10. cat 指令 11. more 指令 12. less 指]]></description><guid isPermaLink="false">/archives/wei-ming-ming-wen-zhang</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 09:26:38 GMT</pubDate></item><item><title><![CDATA[【C语言程序设计——入门】C语言入门与基础语法]]></title><link>http://localhost:8090/archives/wei-ming-ming-wen-zhang</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=%E3%80%90C%E8%AF%AD%E8%A8%80%E7%A8%8B%E5%BA%8F%E8%AE%BE%E8%AE%A1%E2%80%94%E2%80%94%E5%85%A5%E9%97%A8%E3%80%91C%E8%AF%AD%E8%A8%80%E5%85%A5%E9%97%A8%E4%B8%8E%E5%9F%BA%E7%A1%80%E8%AF%AD%E6%B3%95&amp;url=/archives/wei-ming-ming-wen-zhang" width="1" height="1" alt="" style="opacity:0;">目录😋 ⚙️C语言环境配置：Windows配置C语言环境(超级详细) &lt;第1关：程序改错&gt;]]></description><guid isPermaLink="false">/archives/wei-ming-ming-wen-zhang</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 09:20:53 GMT</pubDate></item><item><title><![CDATA[YOLOV8实现目标追踪]]></title><link>http://localhost:8090/archives/wei-ming-ming-wen-zhang</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=YOLOV8%E5%AE%9E%E7%8E%B0%E7%9B%AE%E6%A0%87%E8%BF%BD%E8%B8%AA&amp;url=/archives/wei-ming-ming-wen-zhang" width="1" height="1" alt="" style="opacity:0;">主要是学习一下实现目标追踪的原理，并测试一下效果。 目的是通过YOLOV8实现人员检测，并实现人员追踪，没个人员给分配一个ID，实现追踪的效果。 也可以统计人数。在小区办公楼的出入场所，这类很常见。 一、简介 追踪任务是指识别和跟踪特定目标在视频序列中的运动和位置，一般用唯一ID或固定颜色检测框表示]]></description><guid isPermaLink="false">/archives/wei-ming-ming-wen-zhang</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 09:18:02 GMT</pubDate></item><item><title><![CDATA[使用YOLOv8完成对图像的目标检测任务（从数据准备到训练测试部署的完整流程）]]></title><link>http://localhost:8090/archives/shi-yong-yolov8wan-cheng-dui-tu-xiang-de-mu-biao-jian-ce-ren-wu-cong-shu-ju-zhun-bei-dao-xun-lian-ce-shi-bu-shu-de-wan-zheng-liu-cheng</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=%E4%BD%BF%E7%94%A8YOLOv8%E5%AE%8C%E6%88%90%E5%AF%B9%E5%9B%BE%E5%83%8F%E7%9A%84%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%E4%BB%BB%E5%8A%A1%EF%BC%88%E4%BB%8E%E6%95%B0%E6%8D%AE%E5%87%86%E5%A4%87%E5%88%B0%E8%AE%AD%E7%BB%83%E6%B5%8B%E8%AF%95%E9%83%A8%E7%BD%B2%E7%9A%84%E5%AE%8C%E6%95%B4%E6%B5%81%E7%A8%8B%EF%BC%89&amp;url=/archives/shi-yong-yolov8wan-cheng-dui-tu-xiang-de-mu-biao-jian-ce-ren-wu-cong-shu-ju-zhun-bei-dao-xun-lian-ce-shi-bu-shu-de-wan-zheng-liu-cheng" width="1" height="1" alt="" style="opacity:0;">一、目标检测介绍 目标检测（Object Detection）是计算机视觉领域的一项重要技术，旨在识别图像或视频中的特定目标并确定其位置。通过训练深度学习模型，如卷积神经网络（CNN），可以实现对各种目标的精确检测。常见的目标检测任务包括：人脸检测、行人检测、车辆检测等。目标检测在安防监控、自动驾驶]]></description><guid isPermaLink="false">/archives/shi-yong-yolov8wan-cheng-dui-tu-xiang-de-mu-biao-jian-ce-ren-wu-cong-shu-ju-zhun-bei-dao-xun-lian-ce-shi-bu-shu-de-wan-zheng-liu-cheng</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 09:00:21 GMT</pubDate></item><item><title><![CDATA[yolov8_obb旋转框训练]]></title><link>http://localhost:8090/archives/wei-ming-ming-wen-zhang</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=yolov8_obb%E6%97%8B%E8%BD%AC%E6%A1%86%E8%AE%AD%E7%BB%83&amp;url=/archives/wei-ming-ming-wen-zhang" width="1" height="1" alt="" style="opacity:0;">一、训练 1、环境搭建 使用的是AUTODL环境，yolov8-obb数据集不大，也可以使用cpu。 2、创建虚拟环境 # 创建虚拟环境 conda create -n yolov8_env python=3.8 # 初始化 source activate # 激活 conda activate y]]></description><guid isPermaLink="false">/archives/wei-ming-ming-wen-zhang</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 08:58:58 GMT</pubDate></item><item><title><![CDATA[ONNX 优化技巧：加速模型推理]]></title><link>http://localhost:8090/archives/wei-ming-ming-wen-zhang</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=ONNX%20%E4%BC%98%E5%8C%96%E6%8A%80%E5%B7%A7%EF%BC%9A%E5%8A%A0%E9%80%9F%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86&amp;url=/archives/wei-ming-ming-wen-zhang" width="1" height="1" alt="" style="opacity:0;">概述 ONNX (Open Neural Network Exchange) 是一个开放格式，用于表示机器学习模型，使模型能够在多种框架之间进行转换。ONNX Runtime (ORT) 是一个高效的推理引擎，旨在加速模型的部署。本文将介绍如何使用 ONNX Runtime 和相关工具来优化模型的推]]></description><guid isPermaLink="false">/archives/wei-ming-ming-wen-zhang</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 08:56:55 GMT</pubDate></item><item><title><![CDATA[YOlOv8 ONNXRunTime-gpu 推理]]></title><link>http://localhost:8090/archives/yolov8-onnxruntime-gpu-tui-li</link><description><![CDATA[<img src="http://localhost:8090/plugins/feed/assets/telemetry.gif?title=YOlOv8%20ONNXRunTime-gpu%20%E6%8E%A8%E7%90%86&amp;url=/archives/yolov8-onnxruntime-gpu-tui-li" width="1" height="1" alt="" style="opacity:0;">YOLO(You Only Look Once）是一种流行的物体检测和图像分割模型，由华盛顿大学的约瑟夫-雷德蒙（Joseph Redmon）和阿里-法哈迪（Ali Farhadi）开发。YOLO 于 2015 年推出，因其高速度和高精确度而迅速受到欢迎。 YOLOv8是YOLO 的最新版本，由Ul]]></description><guid isPermaLink="false">/archives/yolov8-onnxruntime-gpu-tui-li</guid><dc:creator>Administrator</dc:creator><pubDate>Wed, 15 Jan 2025 08:54:37 GMT</pubDate></item></channel></rss>