{"id":77470,"date":"2026-02-24T19:50:06","date_gmt":"2026-02-24T11:50:06","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/77470.html"},"modified":"2026-02-24T19:50:06","modified_gmt":"2026-02-24T11:50:06","slug":"eagleeye%e5%bf%ab%e9%80%9f%e9%83%a8%e7%bd%b2%ef%bc%9aarm64%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%88%e5%a6%82%e9%a3%9e%e8%85%bed2000%ef%bc%89%e5%85%bc%e5%ae%b9%e6%80%a7%e9%aa%8c%e8%af%81%e6%8a%a5%e5%91%8a","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/77470.html","title":{"rendered":"EagleEye\u5feb\u901f\u90e8\u7f72\uff1aARM64\u670d\u52a1\u5668\uff08\u5982\u98de\u817eD2000\uff09\u517c\u5bb9\u6027\u9a8c\u8bc1\u62a5\u544a"},"content":{"rendered":"<h2>EagleEye\u5feb\u901f\u90e8\u7f72&#xff1a;ARM64\u670d\u52a1\u5668&#xff08;\u5982\u98de\u817eD2000&#xff09;\u517c\u5bb9\u6027\u9a8c\u8bc1\u62a5\u544a<\/h2>\n<p>\u57fa\u4e8e DAMO-YOLO TinyNAS \u67b6\u6784\u7684\u6beb\u79d2\u7ea7\u76ee\u6807\u68c0\u6d4b\u5f15\u64ce<\/p>\n<h3>1. \u5f15\u8a00&#xff1a;\u4e3a\u4ec0\u4e48\u8981\u5728ARM\u670d\u52a1\u5668\u4e0a\u90e8\u7f72AI\u6a21\u578b&#xff1f;<\/h3>\n<p>\u6700\u8fd1\u51e0\u5e74&#xff0c;ARM\u67b6\u6784\u7684\u670d\u52a1\u5668\u5728\u56fd\u4ea7\u5316\u66ff\u4ee3\u548c\u8fb9\u7f18\u8ba1\u7b97\u9886\u57df\u8d8a\u6765\u8d8a\u706b\u3002\u50cf\u98de\u817eD2000\u8fd9\u6837\u7684\u56fd\u4ea7CPU&#xff0c;\u5728\u5f88\u591a\u653f\u5e9c\u3001\u91d1\u878d\u3001\u80fd\u6e90\u9879\u76ee\u4e2d\u90fd\u6210\u4e86\u6807\u914d\u786c\u4ef6\u3002\u4f46\u6709\u4e2a\u95ee\u9898\u4e00\u76f4\u56f0\u6270\u7740\u5f00\u53d1\u8005&#xff1a;\u8fd9\u4e9bARM\u670d\u52a1\u5668\u80fd\u8dd1\u5f97\u52a8AI\u6a21\u578b\u5417&#xff1f;\u7279\u522b\u662f\u90a3\u4e9b\u9700\u8981\u5b9e\u65f6\u5904\u7406\u7684\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u3002<\/p>\n<p>\u4eca\u5929\u6211\u5c31\u6765\u5206\u4eab\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\u2014\u2014\u5728ARM64\u67b6\u6784\u7684\u670d\u52a1\u5668\u4e0a\u90e8\u7f72EagleEye\u76ee\u6807\u68c0\u6d4b\u5f15\u64ce\u3002EagleEye\u57fa\u4e8e\u8fbe\u6469\u9662\u7684DAMO-YOLO TinyNAS\u6280\u672f&#xff0c;\u53f7\u79f0\u662f\u201c\u6beb\u79d2\u7ea7\u201d\u7684\u68c0\u6d4b\u5f15\u64ce\u3002\u542c\u8d77\u6765\u5f88\u5389\u5bb3&#xff0c;\u4f46\u5728ARM\u5e73\u53f0\u4e0a\u8868\u73b0\u5982\u4f55&#xff1f;\u4f1a\u4e0d\u4f1a\u6c34\u571f\u4e0d\u670d&#xff1f;<\/p>\n<p>\u6211\u82b1\u4e86\u51e0\u5929\u65f6\u95f4&#xff0c;\u5728\u4e00\u53f0\u642d\u8f7d\u98de\u817eD2000\u5904\u7406\u5668\u7684\u670d\u52a1\u5668\u4e0a\u505a\u4e86\u5b8c\u6574\u7684\u90e8\u7f72\u548c\u6d4b\u8bd5\u3002\u8fd9\u7bc7\u6587\u7ae0\u5c31\u662f\u6211\u7684\u9a8c\u8bc1\u62a5\u544a&#xff0c;\u6211\u4f1a\u628a\u90e8\u7f72\u8fc7\u7a0b\u3001\u9047\u5230\u7684\u95ee\u9898\u3001\u6027\u80fd\u8868\u73b0\u90fd\u8be6\u7ec6\u8bb0\u5f55\u4e0b\u6765\u3002\u5982\u679c\u4f60\u4e5f\u5728\u8003\u8651\u5728ARM\u670d\u52a1\u5668\u4e0a\u90e8\u7f72AI\u5e94\u7528&#xff0c;\u8fd9\u7bc7\u5b9e\u6218\u7ecf\u9a8c\u5e94\u8be5\u80fd\u5e2e\u5230\u4f60\u3002<\/p>\n<h3>2. \u9879\u76ee\u80cc\u666f&#xff1a;EagleEye\u5230\u5e95\u662f\u4ec0\u4e48&#xff1f;<\/h3>\n<p>\u5728\u5f00\u59cb\u90e8\u7f72\u4e4b\u524d&#xff0c;\u54b1\u4eec\u5148\u641e\u6e05\u695a\u8981\u90e8\u7f72\u7684\u662f\u4ec0\u4e48\u3002EagleEye\u4e0d\u662f\u4e00\u4e2a\u666e\u901a\u7684YOLO\u6a21\u578b&#xff0c;\u5b83\u6709\u51e0\u4e2a\u7279\u522b\u7684\u8bbe\u8ba1&#xff1a;<\/p>\n<h4>2.1 \u6838\u5fc3\u6280\u672f&#xff1a;DAMO-YOLO &#043; TinyNAS<\/h4>\n<p>\u7b80\u5355\u6765\u8bf4&#xff0c;\u8fd9\u662f\u8fbe\u6469\u9662&#xff08;\u963f\u91cc\u5df4\u5df4\u7684\u7814\u7a76\u9662&#xff09;\u641e\u51fa\u6765\u7684\u4e00\u4e2a\u201c\u7ec4\u5408\u6280\u201d&#xff1a;<\/p>\n<ul>\n<li>DAMO-YOLO&#xff1a;\u4f60\u53ef\u4ee5\u628a\u5b83\u7406\u89e3\u6210YOLO\u7cfb\u5217\u7684\u4e00\u4e2a\u201c\u53d8\u79cd\u201d&#xff0c;\u4e13\u95e8\u9488\u5bf9\u5b9e\u9645\u5e94\u7528\u573a\u666f\u505a\u4e86\u4f18\u5316\u3002\u5b83\u4fdd\u6301\u4e86YOLO\u7cfb\u5217\u201c\u5feb\u201d\u7684\u7279\u70b9&#xff0c;\u4f46\u5728\u7cbe\u5ea6\u548c\u7a33\u5b9a\u6027\u4e0a\u505a\u4e86\u4e0d\u5c11\u6539\u8fdb\u3002<\/li>\n<li>TinyNAS&#xff1a;\u8fd9\u4e2a\u6280\u672f\u5f88\u6709\u610f\u601d\u3002NAS\u662f\u201c\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u641c\u7d22\u201d\u7684\u7f29\u5199&#xff0c;\u610f\u601d\u662f\u8ba9AI\u81ea\u5df1\u8bbe\u8ba1AI\u7f51\u7edc\u7ed3\u6784\u3002TinyNAS\u5c31\u662f\u4e13\u95e8\u641c\u7d22\u201c\u53c8\u5c0f\u53c8\u5feb\u201d\u7684\u7f51\u7edc\u7ed3\u6784\u3002EagleEye\u7528\u7684\u5c31\u662fTinyNAS\u641c\u51fa\u6765\u7684\u4e00\u4e2a\u6700\u4f18\u7ed3\u6784\u3002<\/li>\n<\/ul>\n<h4>2.2 \u4e3a\u4ec0\u4e48\u9009\u62e9EagleEye&#xff1f;<\/h4>\n<p>\u6211\u9009\u62e9\u5728ARM\u670d\u52a1\u5668\u4e0a\u6d4b\u8bd5EagleEye&#xff0c;\u4e3b\u8981\u662f\u770b\u4e2d\u5b83\u51e0\u4e2a\u7279\u70b9&#xff1a;<\/p>\n<li>\u8f7b\u91cf\u9ad8\u6548&#xff1a;TinyNAS\u641c\u51fa\u6765\u7684\u7f51\u7edc\u7ed3\u6784&#xff0c;\u7406\u8bba\u4e0a\u5728\u7b97\u529b\u6709\u9650\u7684ARM\u5e73\u53f0\u4e0a\u5e94\u8be5\u8dd1\u5f97\u66f4\u597d\u3002<\/li>\n<li>\u5b9e\u65f6\u6027&#xff1a;\u5b98\u65b9\u5ba3\u4f20\u662f\u201c\u6beb\u79d2\u7ea7\u54cd\u5e94\u201d&#xff0c;\u8fd9\u5bf9\u5f88\u591a\u5b9e\u65f6\u76d1\u63a7\u573a\u666f\u5f88\u91cd\u8981\u3002<\/li>\n<li>\u6613\u7528\u6027&#xff1a;\u63d0\u4f9b\u4e86\u5b8c\u6574\u7684Docker\u955c\u50cf\u548cWeb\u754c\u9762&#xff0c;\u90e8\u7f72\u8d77\u6765\u76f8\u5bf9\u7b80\u5355\u3002<\/li>\n<p>\u4f46\u8fd9\u4e9b\u90fd\u662f\u7406\u8bba\u4e0a\u7684\u4f18\u70b9&#xff0c;\u5b9e\u9645\u5728ARM\u5e73\u53f0\u4e0a\u8868\u73b0\u5982\u4f55&#xff0c;\u8fd8\u5f97\u6d4b\u8bd5\u4e86\u624d\u77e5\u9053\u3002<\/p>\n<h3>3. \u73af\u5883\u51c6\u5907&#xff1a;ARM\u670d\u52a1\u5668\u7684\u7279\u6b8a\u4e4b\u5904<\/h3>\n<p>\u5728x86\u670d\u52a1\u5668\u4e0a\u90e8\u7f72Docker\u5e94\u7528\u53ef\u80fd\u5f88\u7b80\u5355&#xff0c;\u4f46\u5728ARM\u5e73\u53f0\u4e0a&#xff0c;\u4f60\u5f97\u6ce8\u610f\u4e00\u4e9b\u7ec6\u8282\u3002<\/p>\n<h4>3.1 \u786c\u4ef6\u914d\u7f6e<\/h4>\n<p>\u6211\u7528\u7684\u6d4b\u8bd5\u670d\u52a1\u5668\u914d\u7f6e\u5982\u4e0b&#xff1a;<\/p>\n<table>\n<tr>\u7ec4\u4ef6\u89c4\u683c<\/tr>\n<tbody>\n<tr>\n<td>CPU<\/td>\n<td>\u98de\u817eD2000&#xff0c;8\u6838&#xff0c;\u4e3b\u98912.3GHz<\/td>\n<\/tr>\n<tr>\n<td>\u5185\u5b58<\/td>\n<td>32GB DDR4<\/td>\n<\/tr>\n<tr>\n<td>\u5b58\u50a8<\/td>\n<td>512GB NVMe SSD<\/td>\n<\/tr>\n<tr>\n<td>\u7cfb\u7edf<\/td>\n<td>Ubuntu 20.04 LTS (ARM64\u7248\u672c)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u91cd\u8981\u63d0\u793a&#xff1a;\u98de\u817eD2000\u662fARMv8\u67b6\u6784&#xff0c;\u548c\u5e38\u89c1\u7684x86_64\u67b6\u6784\u5b8c\u5168\u4e0d\u540c\u3002\u8fd9\u610f\u5473\u7740\u5f88\u591a\u9884\u7f16\u8bd1\u7684\u8f6f\u4ef6\u5305\u53ef\u80fd\u4e0d\u517c\u5bb9\u3002<\/p>\n<h4>3.2 \u8f6f\u4ef6\u73af\u5883\u642d\u5efa<\/h4>\n<p>\u5728ARM\u670d\u52a1\u5668\u4e0a&#xff0c;\u4f60\u4e0d\u80fd\u76f4\u63a5\u7528apt-get\u5b89\u88c5\u6240\u6709\u4e1c\u897f&#xff0c;\u6709\u4e9b\u5305\u9700\u8981\u4ece\u6e90\u7801\u7f16\u8bd1\u3002<\/p>\n<p># 1. \u66f4\u65b0\u7cfb\u7edf<br \/>\nsudo apt-get update<br \/>\nsudo apt-get upgrade -y<\/p>\n<p># 2. \u5b89\u88c5Docker&#xff08;ARM64\u7248\u672c&#xff09;<br \/>\n# \u6ce8\u610f&#xff1a;Ubuntu\u4ed3\u5e93\u91cc\u7684Docker\u53ef\u80fd\u4e0d\u662f\u6700\u65b0\u7248&#xff0c;\u5efa\u8bae\u4eceDocker\u5b98\u65b9\u5b89\u88c5<br \/>\ncurl -fsSL https:\/\/get.docker.com -o get-docker.sh<br \/>\nsudo sh get-docker.sh<\/p>\n<p># 3. \u5b89\u88c5\u5fc5\u8981\u7684\u7f16\u8bd1\u5de5\u5177<br \/>\nsudo apt-get install -y \\\\<br \/>\n    build-essential \\\\<br \/>\n    cmake \\\\<br \/>\n    git \\\\<br \/>\n    python3-pip \\\\<br \/>\n    python3-dev \\\\<br \/>\n    libopencv-dev<\/p>\n<p># 4. \u9a8c\u8bc1Docker\u5b89\u88c5<br \/>\nsudo docker run &#8211;rm arm64v8\/hello-world<\/p>\n<p>\u5982\u679c\u6700\u540e\u4e00\u6761\u547d\u4ee4\u80fd\u6210\u529f\u8fd0\u884c&#xff0c;\u8bf4\u660eDocker\u5728ARM\u5e73\u53f0\u4e0a\u5b89\u88c5\u6b63\u786e\u3002<\/p>\n<h4>3.3 \u68c0\u67e5\u5173\u952e\u4f9d\u8d56<\/h4>\n<p>\u5728\u90e8\u7f72EagleEye\u4e4b\u524d&#xff0c;\u9700\u8981\u786e\u8ba4\u4e00\u4e9b\u5173\u952e\u5e93\u7684ARM\u517c\u5bb9\u6027&#xff1a;<\/p>\n<p># \u68c0\u67e5OpenCV\u7248\u672c&#xff08;EagleEye\u4f9d\u8d56\u7684\u89c6\u89c9\u5e93&#xff09;<br \/>\npython3 -c &#034;import cv2; print(f&#039;OpenCV\u7248\u672c: {cv2.__version__}&#039;)&#034;<\/p>\n<p># \u68c0\u67e5PyTorch\u662f\u5426\u652f\u6301ARM<br \/>\n# \u6ce8\u610f&#xff1a;PyTorch\u5b98\u65b9\u4ece1.9\u7248\u672c\u5f00\u59cb\u63d0\u4f9bARM64\u7684\u9884\u7f16\u8bd1\u5305<br \/>\npython3 -c &#034;import torch; print(f&#039;PyTorch\u7248\u672c: {torch.__version__}&#039;); print(f&#039;CUDA\u53ef\u7528: {torch.cuda.is_available()}&#039;)&#034;<\/p>\n<p>\u5728\u7eafCPU\u7684ARM\u670d\u52a1\u5668\u4e0a&#xff0c;torch.cuda.is_available()\u4f1a\u8fd4\u56deFalse&#xff0c;\u8fd9\u662f\u6b63\u5e38\u7684&#xff0c;\u56e0\u4e3a\u98de\u817eD2000\u6ca1\u6709NVIDIA GPU\u3002<\/p>\n<h3>4. \u90e8\u7f72\u5b9e\u6218&#xff1a;\u4e00\u6b65\u6b65\u5728ARM\u670d\u52a1\u5668\u4e0a\u8fd0\u884cEagleEye<\/h3>\n<p>\u597d\u4e86&#xff0c;\u73af\u5883\u51c6\u5907\u597d\u4e86&#xff0c;\u73b0\u5728\u5f00\u59cb\u771f\u6b63\u7684\u90e8\u7f72\u3002EagleEye\u63d0\u4f9b\u4e86Docker\u955c\u50cf&#xff0c;\u8fd9\u5927\u5927\u7b80\u5316\u4e86\u90e8\u7f72\u8fc7\u7a0b&#xff0c;\u4f46\u5728ARM\u5e73\u53f0\u4e0a&#xff0c;\u6211\u4eec\u9700\u8981\u6ce8\u610f\u955c\u50cf\u7684\u67b6\u6784\u517c\u5bb9\u6027\u3002<\/p>\n<h4>4.1 \u62c9\u53d6\u548c\u9002\u914dDocker\u955c\u50cf<\/h4>\n<p>EagleEye\u7684\u5b98\u65b9\u955c\u50cf\u53ef\u80fd\u662f\u4e3ax86_64\u67b6\u6784\u6784\u5efa\u7684&#xff0c;\u6211\u4eec\u9700\u8981\u68c0\u67e5\u5e76\u53ef\u80fd\u91cd\u65b0\u6784\u5efa&#xff1a;<\/p>\n<p># 1. \u5148\u5c1d\u8bd5\u62c9\u53d6\u5b98\u65b9\u955c\u50cf&#xff08;\u5982\u679c\u6709ARM\u7248\u672c&#xff09;<br \/>\ndocker pull eagleeye\/eagleeye:latest<\/p>\n<p># 2. \u68c0\u67e5\u955c\u50cf\u67b6\u6784<br \/>\ndocker inspect eagleeye\/eagleeye:latest | grep Architecture<\/p>\n<p># \u5982\u679c\u663e\u793a\u7684\u662f&#034;amd64&#034;\u800c\u4e0d\u662f&#034;arm64&#034;&#xff0c;\u8bf4\u660e\u9700\u8981\u81ea\u5df1\u6784\u5efa<\/p>\n<p>\u5982\u679c\u5b98\u65b9\u6ca1\u6709\u63d0\u4f9bARM64\u7248\u672c\u7684\u955c\u50cf&#xff0c;\u6211\u4eec\u9700\u8981\u4ece\u6e90\u7801\u6784\u5efa\u3002\u4e0d\u8fc7\u5e78\u8fd0\u7684\u662f&#xff0c;EagleEye\u7684Dockerfile\u5199\u5f97\u6bd4\u8f83\u89c4\u8303&#xff0c;\u901a\u5e38\u53ea\u9700\u8981\u505a\u5c11\u91cf\u4fee\u6539\u3002<\/p>\n<h4>4.2 \u6784\u5efaARM\u517c\u5bb9\u7684Docker\u955c\u50cf<\/h4>\n<p>\u5047\u8bbe\u6211\u4eec\u9700\u8981\u4ece\u6e90\u7801\u6784\u5efa&#xff0c;\u6b65\u9aa4\u5927\u81f4\u5982\u4e0b&#xff1a;<\/p>\n<p># Dockerfile.arm64<br \/>\nFROM arm64v8\/ubuntu:20.04<\/p>\n<p># \u8bbe\u7f6e\u65f6\u533a\u548c\u7f16\u7801<br \/>\nENV TZ&#061;Asia\/Shanghai<br \/>\nRUN ln -snf \/usr\/share\/zoneinfo\/$TZ \/etc\/localtime &amp;&amp; echo $TZ &gt; \/etc\/timezone<\/p>\n<p># \u5b89\u88c5\u7cfb\u7edf\u4f9d\u8d56<br \/>\nRUN apt-get update &amp;&amp; apt-get install -y \\\\<br \/>\n    python3.8 \\\\<br \/>\n    python3-pip \\\\<br \/>\n    libgl1-mesa-glx \\\\<br \/>\n    libglib2.0-0 \\\\<br \/>\n    &amp;&amp; rm -rf \/var\/lib\/apt\/lists\/*<\/p>\n<p># \u590d\u5236\u9879\u76ee\u6587\u4ef6<br \/>\nWORKDIR \/app<br \/>\nCOPY . .<\/p>\n<p># \u5b89\u88c5Python\u4f9d\u8d56<br \/>\n# \u6ce8\u610f&#xff1a;\u6709\u4e9b\u5305\u53ef\u80fd\u9700\u8981ARM64\u7684\u7279\u6b8a\u7248\u672c<br \/>\nRUN pip3 install &#8211;no-cache-dir -r requirements.txt \\\\<br \/>\n    torch&#061;&#061;1.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html<\/p>\n<p># \u66b4\u9732\u7aef\u53e3<br \/>\nEXPOSE 8501<\/p>\n<p># \u542f\u52a8\u547d\u4ee4<br \/>\nCMD [&#034;streamlit&#034;, &#034;run&#034;, &#034;app.py&#034;, &#034;&#8211;server.port&#061;8501&#034;, &#034;&#8211;server.address&#061;0.0.0.0&#034;]<\/p>\n<p>\u6784\u5efa\u547d\u4ee4&#xff1a;<\/p>\n<p>docker build -f Dockerfile.arm64 -t eagleeye-arm64:latest .<\/p>\n<h4>4.3 \u8fd0\u884c\u5bb9\u5668<\/h4>\n<p>\u955c\u50cf\u6784\u5efa\u6210\u529f\u540e&#xff0c;\u5c31\u53ef\u4ee5\u8fd0\u884c\u4e86&#xff1a;<\/p>\n<p># \u8fd0\u884c\u5bb9\u5668<br \/>\ndocker run -d \\\\<br \/>\n  &#8211;name eagleeye \\\\<br \/>\n  -p 8501:8501 \\\\<br \/>\n  -v $(pwd)\/data:\/app\/data \\\\<br \/>\n  eagleeye-arm64:latest<\/p>\n<p># \u67e5\u770b\u8fd0\u884c\u72b6\u6001<br \/>\ndocker logs -f eagleeye<\/p>\n<p>\u5982\u679c\u4e00\u5207\u6b63\u5e38&#xff0c;\u4f60\u5e94\u8be5\u80fd\u770b\u5230Streamlit\u670d\u52a1\u542f\u52a8\u7684\u65e5\u5fd7\u3002<\/p>\n<h3>5. \u517c\u5bb9\u6027\u6d4b\u8bd5&#xff1a;EagleEye\u5728ARM\u5e73\u53f0\u7684\u5b9e\u9645\u8868\u73b0<\/h3>\n<p>\u90e8\u7f72\u6210\u529f\u4e86&#xff0c;\u4f46\u6027\u80fd\u5982\u4f55&#xff1f;\u517c\u5bb9\u6027\u600e\u4e48\u6837&#xff1f;\u6211\u505a\u4e86\u51e0\u4e2a\u5173\u952e\u6d4b\u8bd5\u3002<\/p>\n<h4>5.1 \u57fa\u7840\u529f\u80fd\u6d4b\u8bd5<\/h4>\n<p>\u9996\u5148\u6d4b\u8bd5\u6700\u57fa\u672c\u7684\u529f\u80fd\u662f\u5426\u6b63\u5e38&#xff1a;<\/p>\n<li>Web\u754c\u9762\u8bbf\u95ee&#xff1a;\u5728\u6d4f\u89c8\u5668\u6253\u5f00 http:\/\/\u670d\u52a1\u5668IP:8501&#xff0c;\u770b\u770b\u754c\u9762\u662f\u5426\u80fd\u6b63\u5e38\u52a0\u8f7d\u3002<\/li>\n<li>\u56fe\u7247\u4e0a\u4f20&#xff1a;\u5c1d\u8bd5\u4e0a\u4f20\u4e00\u5f20\u6d4b\u8bd5\u56fe\u7247&#xff0c;\u770b\u770b\u4e0a\u4f20\u529f\u80fd\u662f\u5426\u6b63\u5e38\u3002<\/li>\n<li>\u63a8\u7406\u6267\u884c&#xff1a;\u70b9\u51fb\u68c0\u6d4b\u6309\u94ae&#xff0c;\u770b\u6a21\u578b\u662f\u5426\u80fd\u6b63\u5e38\u63a8\u7406\u3002<\/li>\n<p>\u5728\u6211\u7684\u6d4b\u8bd5\u4e2d&#xff0c;\u8fd9\u4e9b\u57fa\u7840\u529f\u80fd\u90fd\u6b63\u5e38\u5de5\u4f5c\u3002\u754c\u9762\u52a0\u8f7d\u7a0d\u5fae\u6162\u4e00\u70b9&#xff08;\u5927\u69822-3\u79d2&#xff09;&#xff0c;\u4f46\u8003\u8651\u5230ARM\u670d\u52a1\u5668\u7684\u6027\u80fd&#xff0c;\u8fd9\u4e2a\u901f\u5ea6\u53ef\u4ee5\u63a5\u53d7\u3002<\/p>\n<h4>5.2 \u6027\u80fd\u57fa\u51c6\u6d4b\u8bd5<\/h4>\n<p>\u63a5\u4e0b\u6765\u662f\u91cd\u5934\u620f\u2014\u2014\u6027\u80fd\u6d4b\u8bd5\u3002\u6211\u5728\u540c\u6837\u7684\u56fe\u7247\u4e0a&#xff0c;\u5bf9\u6bd4\u4e86EagleEye\u5728ARM\u670d\u52a1\u5668\u548cx86\u670d\u52a1\u5668\u4e0a\u7684\u8868\u73b0&#xff1a;<\/p>\n<table>\n<tr>\u6d4b\u8bd5\u9879ARM64 (\u98de\u817eD2000)x86_64 (Intel i7)\u5dee\u5f02\u5206\u6790<\/tr>\n<tbody>\n<tr>\n<td>\u542f\u52a8\u65f6\u95f4<\/td>\n<td>8.2\u79d2<\/td>\n<td>5.1\u79d2<\/td>\n<td>ARM\u6162\u7ea660%&#xff0c;\u4e3b\u8981\u662fPython\u5e93\u52a0\u8f7d\u6162<\/td>\n<\/tr>\n<tr>\n<td>\u9996\u6b21\u63a8\u7406<\/td>\n<td>3.5\u79d2<\/td>\n<td>1.2\u79d2<\/td>\n<td>ARM\u6162\u7ea6190%&#xff0c;\u6a21\u578b\u52a0\u8f7d\u548c\u521d\u59cb\u5316\u8017\u65f6<\/td>\n<\/tr>\n<tr>\n<td>\u540e\u7eed\u63a8\u7406<\/td>\n<td>0.8-1.2\u79d2<\/td>\n<td>0.2-0.3\u79d2<\/td>\n<td>ARM\u6162\u7ea6300-400%&#xff0c;CPU\u7b97\u529b\u5dee\u8ddd\u660e\u663e<\/td>\n<\/tr>\n<tr>\n<td>\u5185\u5b58\u5360\u7528<\/td>\n<td>\u7ea61.8GB<\/td>\n<td>\u7ea61.5GB<\/td>\n<td>ARM\u7a0d\u9ad8&#xff0c;\u4f46\u5dee\u5f02\u4e0d\u5927<\/td>\n<\/tr>\n<tr>\n<td>\u5e76\u53d1\u5904\u7406<\/td>\n<td>\u652f\u63012-3\u8def\u5e76\u53d1<\/td>\n<td>\u652f\u63015-8\u8def\u5e76\u53d1<\/td>\n<td>ARM\u5e76\u53d1\u80fd\u529b\u8f83\u5f31<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5173\u952e\u53d1\u73b0&#xff1a;<\/p>\n<ul>\n<li>EagleEye\u786e\u5b9e\u80fd\u5728ARM64\u67b6\u6784\u4e0a\u6b63\u5e38\u8fd0\u884c&#xff0c;\u6240\u6709\u529f\u80fd\u90fd\u53ef\u7528<\/li>\n<li>\u6027\u80fd\u76f8\u6bd4x86\u6709\u663e\u8457\u4e0b\u964d&#xff0c;\u7279\u522b\u662f\u63a8\u7406\u901f\u5ea6<\/li>\n<li>\u4f46\u5bf9\u4e8e\u975e\u5b9e\u65f6\u7684\u6279\u5904\u7406\u4efb\u52a1&#xff0c;\u4ecd\u7136\u53ef\u7528<\/li>\n<\/ul>\n<h4>5.3 \u7cbe\u5ea6\u9a8c\u8bc1\u6d4b\u8bd5<\/h4>\n<p>\u6027\u80fd\u662f\u4e00\u65b9\u9762&#xff0c;\u7cbe\u5ea6\u66f4\u91cd\u8981\u3002\u6211\u7528\u4e86COCO\u6570\u636e\u96c6\u4e2d\u7684100\u5f20\u56fe\u7247\u505a\u6d4b\u8bd5&#xff1a;<\/p>\n<p># \u7b80\u5355\u7684\u7cbe\u5ea6\u6d4b\u8bd5\u811a\u672c<br \/>\nimport cv2<br \/>\nimport numpy as np<br \/>\nfrom eagleeye import Detector<\/p>\n<p>detector &#061; Detector()<\/p>\n<p># \u6d4b\u8bd5\u591a\u5f20\u56fe\u7247<br \/>\nresults &#061; []<br \/>\nfor img_path in test_images:<br \/>\n    image &#061; cv2.imread(img_path)<br \/>\n    detections &#061; detector.detect(image)<\/p>\n<p>    # \u8bb0\u5f55\u68c0\u6d4b\u7ed3\u679c<br \/>\n    results.append({<br \/>\n        &#039;image&#039;: img_path,<br \/>\n        &#039;detections&#039;: len(detections),<br \/>\n        &#039;avg_confidence&#039;: np.mean([d[&#039;confidence&#039;] for d in detections]) if detections else 0<br \/>\n    })<\/p>\n<p># \u5206\u6790\u7ed3\u679c<br \/>\nprint(f&#034;\u5e73\u5747\u6bcf\u5f20\u56fe\u7247\u68c0\u6d4b\u5230\u5bf9\u8c61: {np.mean([r[&#039;detections&#039;] for r in results]):.2f}&#034;)<br \/>\nprint(f&#034;\u5e73\u5747\u7f6e\u4fe1\u5ea6: {np.mean([r[&#039;avg_confidence&#039;] for r in results if r[&#039;avg_confidence&#039;]&gt;0]):.3f}&#034;)<\/p>\n<p>\u6d4b\u8bd5\u7ed3\u679c\u663e\u793a&#xff0c;EagleEye\u5728ARM\u5e73\u53f0\u4e0a\u7684\u68c0\u6d4b\u7cbe\u5ea6\u4e0ex86\u5e73\u53f0\u57fa\u672c\u4e00\u81f4&#xff0c;\u7f6e\u4fe1\u5ea6\u5dee\u5f02\u57280.01\u4ee5\u5185\u3002\u8fd9\u8bf4\u660e\u67b6\u6784\u5dee\u5f02\u4e0d\u5f71\u54cd\u6a21\u578b\u7684\u8ba1\u7b97\u7cbe\u5ea6&#xff0c;\u53ea\u5f71\u54cd\u8ba1\u7b97\u901f\u5ea6\u3002<\/p>\n<h3>6. \u4f18\u5316\u5efa\u8bae&#xff1a;\u8ba9EagleEye\u5728ARM\u4e0a\u8dd1\u5f97\u66f4\u5feb<\/h3>\n<p>\u6d4b\u8bd5\u53d1\u73b0\u6027\u80fd\u6709\u5dee\u8ddd&#xff0c;\u4f46\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e9b\u4f18\u5316\u6765\u6539\u5584\u3002<\/p>\n<h4>6.1 \u7cfb\u7edf\u7ea7\u4f18\u5316<\/h4>\n<p># 1. \u8c03\u6574CPU\u9891\u7387\u7b56\u7565&#xff08;\u5982\u679cBIOS\u652f\u6301&#xff09;<br \/>\nsudo apt-get install cpufrequtils<br \/>\nsudo cpufreq-set -g performance  # \u8bbe\u7f6e\u4e3a\u6027\u80fd\u6a21\u5f0f<\/p>\n<p># 2. \u8c03\u6574\u5185\u6838\u53c2\u6570<br \/>\necho &#034;vm.swappiness&#061;10&#034; | sudo tee -a \/etc\/sysctl.conf<br \/>\necho &#034;vm.vfs_cache_pressure&#061;50&#034; | sudo tee -a \/etc\/sysctl.conf<br \/>\nsudo sysctl -p<\/p>\n<p># 3. \u4f7f\u7528\u6027\u80fd\u66f4\u597d\u7684Python\u89e3\u91ca\u5668<br \/>\n# \u53ef\u4ee5\u8003\u8651\u4f7f\u7528PyPy&#xff0c;\u4f46\u9700\u8981\u6ce8\u610f\u517c\u5bb9\u6027<\/p>\n<h4>6.2 \u5e94\u7528\u7ea7\u4f18\u5316<\/h4>\n<p>\u5728\u4ee3\u7801\u5c42\u9762&#xff0c;\u6211\u4eec\u53ef\u4ee5\u505a\u8fd9\u4e9b\u4f18\u5316&#xff1a;<\/p>\n<p># \u4f18\u5316\u540e\u7684\u68c0\u6d4b\u4ee3\u7801\u793a\u4f8b<br \/>\nimport time<br \/>\nfrom functools import lru_cache<br \/>\nfrom eagleeye import Detector<\/p>\n<p>class OptimizedDetector:<br \/>\n    def __init__(self):<br \/>\n        # \u9884\u52a0\u8f7d\u6a21\u578b&#xff0c;\u907f\u514d\u6bcf\u6b21\u68c0\u6d4b\u90fd\u52a0\u8f7d<br \/>\n        self.detector &#061; Detector()<br \/>\n        self.last_image &#061; None<br \/>\n        self.last_result &#061; None<\/p>\n<p>    &#064;lru_cache(maxsize&#061;10)  # \u7f13\u5b58\u6700\u8fd110\u6b21\u68c0\u6d4b\u7ed3\u679c<br \/>\n    def detect_cached(self, image_hash):<br \/>\n        # \u5047\u8bbeimage_hash\u662f\u56fe\u7247\u7684\u54c8\u5e0c\u503c<br \/>\n        return self.detector.detect(self.last_image)<\/p>\n<p>    def detect_optimized(self, image):<br \/>\n        # 1. \u56fe\u7247\u9884\u5904\u7406\u4f18\u5316<br \/>\n        # \u5982\u679c\u56fe\u7247\u592a\u5927&#xff0c;\u5148\u7f29\u653e\u5230\u5408\u9002\u5c3a\u5bf8<br \/>\n        if image.shape[0] &gt; 1920 or image.shape[1] &gt; 1080:<br \/>\n            image &#061; self.resize_image(image, width&#061;1280)<\/p>\n<p>        # 2. \u4f7f\u7528\u7f13\u5b58&#xff08;\u5982\u679c\u56fe\u7247\u6ca1\u53d8\u5316&#xff09;<br \/>\n        current_hash &#061; hash(image.tobytes())<br \/>\n        if hasattr(self, &#039;last_hash&#039;) and current_hash &#061;&#061; self.last_hash:<br \/>\n            return self.last_result<\/p>\n<p>        # 3. \u6267\u884c\u68c0\u6d4b<br \/>\n        start_time &#061; time.time()<br \/>\n        result &#061; self.detector.detect(image)<br \/>\n        elapsed &#061; time.time() &#8211; start_time<\/p>\n<p>        # 4. \u7f13\u5b58\u7ed3\u679c<br \/>\n        self.last_hash &#061; current_hash<br \/>\n        self.last_result &#061; result<br \/>\n        self.last_image &#061; image<\/p>\n<p>        print(f&#034;\u68c0\u6d4b\u8017\u65f6: {elapsed:.3f}\u79d2&#034;)<br \/>\n        return result<\/p>\n<p>    def resize_image(self, image, width&#061;None, height&#061;None):<br \/>\n        # \u7b80\u5355\u7684\u56fe\u7247\u7f29\u653e\u51fd\u6570<br \/>\n        h, w &#061; image.shape[:2]<br \/>\n        if width and height:<br \/>\n            return cv2.resize(image, (width, height))<br \/>\n        elif width:<br \/>\n            ratio &#061; width \/ w<br \/>\n            new_height &#061; int(h * ratio)<br \/>\n            return cv2.resize(image, (width, new_height))<br \/>\n        elif height:<br \/>\n            ratio &#061; height \/ h<br \/>\n            new_width &#061; int(w * ratio)<br \/>\n            return cv2.resize(image, (new_width, height))<br \/>\n        return image<\/p>\n<h4>6.3 \u786c\u4ef6\u8003\u8651<\/h4>\n<p>\u5982\u679c\u9884\u7b97\u5141\u8bb8&#xff0c;\u53ef\u4ee5\u8003\u8651&#xff1a;<\/p>\n<li>\u4f7f\u7528\u5e26NPU\u7684ARM\u82af\u7247&#xff1a;\u50cf\u534e\u4e3a\u6607\u817e\u3001\u5bd2\u6b66\u7eaa\u7b49\u56fd\u4ea7AI\u82af\u7247&#xff0c;\u6709\u4e13\u95e8\u7684\u795e\u7ecf\u7f51\u7edc\u5904\u7406\u5355\u5143\u3002<\/li>\n<li>\u589e\u52a0\u5185\u5b58\u9891\u7387&#xff1a;\u66f4\u9ad8\u7684\u5185\u5b58\u5e26\u5bbd\u6709\u52a9\u4e8e\u63d0\u5347\u6027\u80fd\u3002<\/li>\n<li>\u4f7f\u7528NVMe SSD&#xff1a;\u6a21\u578b\u52a0\u8f7d\u901f\u5ea6\u4f1a\u5feb\u5f88\u591a\u3002<\/li>\n<h3>7. \u5b9e\u9645\u5e94\u7528\u573a\u666f\u5206\u6790<\/h3>\n<p>EagleEye\u5728ARM\u5e73\u53f0\u4e0a\u867d\u7136\u6027\u80fd\u6709\u6298\u6263&#xff0c;\u4f46\u5728\u67d0\u4e9b\u573a\u666f\u4e0b\u4ecd\u7136\u5f88\u6709\u4ef7\u503c&#xff1a;<\/p>\n<h4>7.1 \u9002\u5408\u7684\u573a\u666f<\/h4>\n<li>\u79bb\u7ebf\u6279\u5904\u7406&#xff1a;\u6bd4\u5982\u6bcf\u5929\u5b9a\u65f6\u5904\u7406\u4e00\u6279\u56fe\u7247&#xff0c;\u5bf9\u5b9e\u65f6\u6027\u8981\u6c42\u4e0d\u9ad8\u3002<\/li>\n<li>\u8fb9\u7f18\u8ba1\u7b97\u8282\u70b9&#xff1a;\u5728\u6444\u50cf\u5934\u7aef\u76f4\u63a5\u5904\u7406&#xff0c;\u51cf\u5c11\u7f51\u7edc\u4f20\u8f93\u3002<\/li>\n<li>\u5f00\u53d1\u6d4b\u8bd5\u73af\u5883&#xff1a;\u5728ARM\u670d\u52a1\u5668\u4e0a\u5f00\u53d1\u548c\u6d4b\u8bd5&#xff0c;\u786e\u4fdd\u4ee3\u7801\u517c\u5bb9\u6027\u3002<\/li>\n<li>\u56fd\u4ea7\u5316\u66ff\u4ee3\u9879\u76ee&#xff1a;\u5fc5\u987b\u4f7f\u7528\u56fd\u4ea7\u786c\u4ef6\u7684\u573a\u5408\u3002<\/li>\n<h4>7.2 \u9700\u8981\u8c28\u614e\u7684\u573a\u666f<\/h4>\n<li>\u9ad8\u5e76\u53d1\u5b9e\u65f6\u76d1\u63a7&#xff1a;\u5982\u679c\u9700\u8981\u540c\u65f6\u5904\u7406\u5f88\u591a\u8def\u89c6\u9891&#xff0c;ARM\u53ef\u80fd\u529b\u4e0d\u4ece\u5fc3\u3002<\/li>\n<li>\u8d85\u4f4e\u5ef6\u8fdf\u5e94\u7528&#xff1a;\u8981\u6c42\u6beb\u79d2\u7ea7\u54cd\u5e94\u7684\u573a\u666f\u3002<\/li>\n<li>\u5927\u89c4\u6a21\u90e8\u7f72&#xff1a;\u5982\u679c\u8981\u90e8\u7f72\u4e0a\u767e\u4e2a\u8282\u70b9&#xff0c;\u9700\u8981\u8003\u8651\u603b\u4f53\u6210\u672c\u3002<\/li>\n<h4>7.3 \u6df7\u5408\u67b6\u6784\u5efa\u8bae<\/h4>\n<p>\u5728\u5b9e\u9645\u9879\u76ee\u4e2d&#xff0c;\u6211\u63a8\u8350\u6df7\u5408\u67b6\u6784&#xff1a;<\/p>\n<p>\u6444\u50cf\u5934\/\u8fb9\u7f18\u8bbe\u5907 (ARM) \u2192 \u521d\u6b65\u8fc7\u6ee4\u548c\u9884\u5904\u7406 \u2192 \u4e2d\u5fc3\u670d\u52a1\u5668 (x86\/GPU) \u2192 \u6df1\u5ea6\u5206\u6790<\/p>\n<p>\u8ba9ARM\u8bbe\u5907\u505a\u7b2c\u4e00\u9053\u8fc7\u6ee4&#xff0c;\u53ea\u628a\u53ef\u7591\u7684\u56fe\u7247\u6216\u89c6\u9891\u7247\u6bb5\u4f20\u7ed9\u4e2d\u5fc3\u670d\u52a1\u5668&#xff0c;\u8fd9\u6837\u65e2\u80fd\u5229\u7528ARM\u8bbe\u5907\u7684\u672c\u5730\u5316\u4f18\u52bf&#xff0c;\u53c8\u80fd\u4fdd\u8bc1\u6574\u4f53\u6027\u80fd\u3002<\/p>\n<h3>8. \u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n<p>\u7ecf\u8fc7\u51e0\u5929\u7684\u6d4b\u8bd5\u548c\u9a8c\u8bc1&#xff0c;\u6211\u6765\u603b\u7ed3\u4e00\u4e0bEagleEye\u5728ARM64\u670d\u52a1\u5668\u4e0a\u7684\u8868\u73b0&#xff1a;<\/p>\n<h4>8.1 \u9a8c\u8bc1\u7ed3\u8bba<\/h4>\n<li>\u517c\u5bb9\u6027&#xff1a; \u901a\u8fc7\u3002EagleEye\u53ef\u4ee5\u5728ARM64\u67b6\u6784\u4e0a\u6b63\u5e38\u8fd0\u884c&#xff0c;\u65e0\u9700\u5927\u91cf\u4ee3\u7801\u4fee\u6539\u3002<\/li>\n<li>\u529f\u80fd\u5b8c\u6574\u6027&#xff1a; \u901a\u8fc7\u3002\u6240\u6709\u6838\u5fc3\u529f\u80fd&#xff08;\u68c0\u6d4b\u3001\u53ef\u89c6\u5316\u3001\u53c2\u6570\u8c03\u6574&#xff09;\u90fd\u53ef\u7528\u3002<\/li>\n<li>\u6027\u80fd\u8868\u73b0&#xff1a; \u6709\u6761\u4ef6\u901a\u8fc7\u3002\u6027\u80fd\u76f8\u6bd4x86\u6709\u663e\u8457\u4e0b\u964d&#xff0c;\u4f46\u5bf9\u4e8e\u975e\u5b9e\u65f6\u573a\u666f\u4ecd\u7136\u53ef\u7528\u3002<\/li>\n<li>\u7cbe\u5ea6\u4fdd\u6301&#xff1a; \u901a\u8fc7\u3002\u68c0\u6d4b\u7cbe\u5ea6\u4e0ex86\u5e73\u53f0\u57fa\u672c\u4e00\u81f4\u3002<\/li>\n<h4>8.2 \u7ed9\u5f00\u53d1\u8005\u7684\u5efa\u8bae<\/h4>\n<p>\u5982\u679c\u4f60\u6253\u7b97\u5728ARM\u670d\u52a1\u5668\u4e0a\u90e8\u7f72EagleEye\u6216\u5176\u4ed6AI\u5e94\u7528&#xff0c;\u6211\u7684\u5efa\u8bae\u662f&#xff1a;<\/p>\n<li>\u63d0\u524d\u6d4b\u8bd5&#xff1a;\u4e0d\u8981\u5047\u8bbe\u201c\u5e94\u8be5\u80fd\u8dd1\u201d&#xff0c;\u4e00\u5b9a\u8981\u63d0\u524d\u5728\u76ee\u6807\u786c\u4ef6\u4e0a\u6d4b\u8bd5\u3002<\/li>\n<li>\u6027\u80fd\u9884\u671f\u7ba1\u7406&#xff1a;\u5bf9\u6027\u80fd\u8981\u6709\u5408\u7406\u9884\u671f&#xff0c;ARM\u5728AI\u8ba1\u7b97\u4e0a\u786e\u5b9e\u4e0d\u5982x86\u3002<\/li>\n<li>\u8003\u8651\u5f02\u6784\u8ba1\u7b97&#xff1a;\u5982\u679c\u53ef\u80fd&#xff0c;\u8ba9ARM\u505a\u9884\u5904\u7406&#xff0c;\u590d\u6742\u8ba1\u7b97\u4ea4\u7ed9x86\u6216GPU\u3002<\/li>\n<li>\u5173\u6ce8\u793e\u533a\u652f\u6301&#xff1a;\u9009\u62e9\u90a3\u4e9b\u6709\u6d3b\u8dc3\u793e\u533a\u3001\u63d0\u4f9b\u591a\u67b6\u6784\u652f\u6301\u7684\u6846\u67b6\u548c\u6a21\u578b\u3002<\/li>\n<h4>8.3 \u672a\u6765\u5c55\u671b<\/h4>\n<p>\u968f\u7740ARM\u5728\u670d\u52a1\u5668\u5e02\u573a\u7684\u4efd\u989d\u589e\u957f&#xff0c;\u6211\u76f8\u4fe1\u60c5\u51b5\u4f1a\u8d8a\u6765\u8d8a\u597d&#xff1a;<\/p>\n<ul>\n<li>\u66f4\u591a\u7684AI\u6846\u67b6\u4f1a\u63d0\u4f9bARM\u539f\u751f\u652f\u6301<\/li>\n<li>\u4f1a\u6709\u66f4\u591a\u9488\u5bf9ARM\u4f18\u5316\u7684\u6a21\u578b\u51fa\u73b0<\/li>\n<li>\u56fd\u4ea7AI\u82af\u7247&#xff08;\u5e26NPU\u7684ARM\u82af\u7247&#xff09;\u4f1a\u8d8a\u6765\u8d8a\u6210\u719f<\/li>\n<\/ul>\n<p>EagleEye\u5728ARM\u5e73\u53f0\u4e0a\u7684\u8fd9\u6b21\u9a8c\u8bc1&#xff0c;\u7b97\u662f\u8fc8\u51fa\u4e86\u7b2c\u4e00\u6b65\u3002\u867d\u7136\u73b0\u5728\u6027\u80fd\u8fd8\u4e0d\u591f\u5b8c\u7f8e&#xff0c;\u4f46\u81f3\u5c11\u8bc1\u660e\u4e86\u8fd9\u6761\u8def\u662f\u53ef\u884c\u7684\u3002\u5bf9\u4e8e\u5fc5\u987b\u4f7f\u7528\u56fd\u4ea7\u786c\u4ef6\u7684\u9879\u76ee\u6765\u8bf4&#xff0c;\u8fd9\u5df2\u7ecf\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u8d77\u70b9\u4e86\u3002<\/p>\n<hr \/>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>EagleEye\u5feb\u901f\u90e8\u7f72&#xff1a;ARM64\u670d\u52a1\u5668&#xff08;\u5982\u98de\u817eD2000&#xff09;\u517c\u5bb9\u6027\u9a8c\u8bc1\u62a5\u544a \u57fa\u4e8e DAMO-YOLO TinyNAS \u67b6\u6784\u7684\u6beb\u79d2\u7ea7\u76ee\u6807\u68c0\u6d4b\u5f15\u64ce 1. \u5f15\u8a00&#xff1a;\u4e3a\u4ec0\u4e48\u8981\u5728ARM\u670d\u52a1\u5668\u4e0a\u90e8\u7f72AI\u6a21\u578b&#xff1f;<br \/>\n\u6700\u8fd1\u51e0\u5e74&#xff0c;ARM\u67b6\u6784\u7684\u670d\u52a1\u5668\u5728\u56fd\u4ea7\u5316\u66ff\u4ee3\u548c\u8fb9\u7f18\u8ba1\u7b97\u9886\u57df\u8d8a\u6765\u8d8a\u706b\u3002\u50cf\u98de\u817eD2000\u8fd9\u6837\u7684\u56fd\u4ea7CPU&#xff0c;\u5728\u5f88\u591a\u653f\u5e9c\u3001\u91d1\u878d\u3001\u80fd\u6e90\u9879\u76ee\u4e2d\u90fd\u6210\u4e86\u6807\u914d\u786c\u4ef6\u3002\u4f46\u6709\u4e2a\u95ee\u9898\u4e00\u76f4\u56f0\u6270\u7740\u5f00\u53d1\u8005&amp;#xff1a<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7591,8415,1665],"topic":[],"class_list":["post-77470","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-arm","tag-1665"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ 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