{"id":54729,"date":"2025-08-13T10:39:38","date_gmt":"2025-08-13T02:39:38","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/54729.html"},"modified":"2025-08-13T10:39:38","modified_gmt":"2025-08-13T02:39:38","slug":"%e7%ac%ac10%e7%ab%a0-%e8%ae%a1%e7%ae%97%e6%9c%ba%e8%a7%86%e8%a7%89%e9%a1%b9%e7%9b%ae%e5%ae%9e%e6%88%98","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/54729.html","title":{"rendered":"\u7b2c10\u7ae0 \u8ba1\u7b97\u673a\u89c6\u89c9\u9879\u76ee\u5b9e\u6218"},"content":{"rendered":"

10.1 \u9879\u76ee\u6982\u8ff0\u4e0e\u751f\u547d\u5468\u671f<\/h3>\n

\u5728\u524d\u9762\u7684\u7ae0\u8282\u4e2d，\u6211\u4eec\u5b66\u4e60\u4e86\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u4f17\u591a\u7406\u8bba\u548c\u7b97\u6cd5\u3002\u672c\u7ae0\u5c06\u805a\u7126\u4e8e\u5982\u4f55\u5c06\u8fd9\u4e9b\u77e5\u8bc6\u6574\u5408\u8d77\u6765，\u5b8c\u6210\u4e00\u4e2a\u7aef\u5230\u7aef\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u9879\u76ee\u3002\u4e00\u4e2a\u5178\u578b\u7684CV\u9879\u76ee\u4e0d\u4ec5\u4ec5\u662f\u8bad\u7ec3\u4e00\u4e2a\u6a21\u578b，\u5b83\u6db5\u76d6\u4e86\u4ece\u9700\u6c42\u5206\u6790\u5230\u6700\u7ec8\u90e8\u7f72\u548c\u7ef4\u62a4\u7684\u5168\u8fc7\u7a0b\u3002<\/p>\n

10.1.1 \u8ba1\u7b97\u673a\u89c6\u89c9\u9879\u76ee\u751f\u547d\u5468\u671f<\/h4>\n

\u4e00\u4e2a\u5b8c\u6574\u7684CV\u9879\u76ee\u901a\u5e38\u9075\u5faa\u4ee5\u4e0b\u751f\u547d\u5468\u671f：<\/p>\n

  • \u95ee\u9898\u5b9a\u4e49\u4e0e\u9700\u6c42\u5206\u6790：\u660e\u786e\u9879\u76ee\u7684\u4e1a\u52a1\u76ee\u6807\u3002\u6211\u4eec\u8981\u89e3\u51b3\u4ec0\u4e48\u95ee\u9898？\u6210\u529f\u7684\u6807\u51c6\u662f\u4ec0\u4e48？（\u4f8b\u5982，\u51c6\u786e\u7387\u8fbe\u523095%，\u5904\u7406\u901f\u5ea6\u8fbe\u523030FPS）\u3002<\/li>\n
  • \u6570\u636e\u6536\u96c6\u4e0e\u51c6\u5907：\u8fd9\u662f\u9879\u76ee\u4e2d\u6700\u8017\u65f6\u4f46\u81f3\u5173\u91cd\u8981\u7684\u4e00\u73af\u3002\u5305\u62ec\u6536\u96c6\u539f\u59cb\u56fe\u50cf\/\u89c6\u9891\u6570\u636e，\u8fdb\u884c\u6e05\u6d17\u3001\u7b5b\u9009，\u5e76\u8fdb\u884c\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u6807\u6ce8\u3002<\/li>\n
  • \u6a21\u578b\u9009\u578b\u4e0e\u8bbe\u8ba1：\u6839\u636e\u95ee\u9898\u7c7b\u578b（\u5206\u7c7b\u3001\u68c0\u6d4b\u3001\u5206\u5272\u7b49）\u548c\u6027\u80fd\u8981\u6c42（\u7cbe\u5ea6\u3001\u901f\u5ea6\u3001\u6a21\u578b\u5927\u5c0f），\u9009\u62e9\u5408\u9002\u7684\u57fa\u7840\u6a21\u578b（\u5982ResNet, YOLO, UNet）\u6216\u8bbe\u8ba1\u65b0\u7684\u7f51\u7edc\u67b6\u6784\u3002<\/li>\n
  • \u6a21\u578b\u8bad\u7ec3\u4e0e\u8c03\u4f18：\u4f7f\u7528\u51c6\u5907\u597d\u7684\u6570\u636e\u8bad\u7ec3\u6a21\u578b，\u5e76\u901a\u8fc7\u8c03\u6574\u8d85\u53c2\u6570\u3001\u4f7f\u7528\u6570\u636e\u589e\u5f3a\u3001\u8fc1\u79fb\u5b66\u4e60\u7b49\u6280\u672f\u6765\u4f18\u5316\u6a21\u578b\u6027\u80fd\u3002<\/li>\n
  • \u6a21\u578b\u8bc4\u4f30：\u5728\u72ec\u7acb\u7684\u6d4b\u8bd5\u96c6\u4e0a\u5168\u9762\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd，\u786e\u4fdd\u5176\u6ee1\u8db3\u9879\u76ee\u9700\u6c42，\u5e76\u5206\u6790\u5176\u5728\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u4f18\u7f3a\u70b9\u3002<\/li>\n
  • \u6a21\u578b\u90e8\u7f72：\u5c06\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u96c6\u6210\u5230\u5b9e\u9645\u7684\u5e94\u7528\u73af\u5883\u4e2d\u3002\u8fd9\u53ef\u80fd\u6d89\u53ca\u5c06\u6a21\u578b\u8f6c\u6362\u4e3a\u4f18\u5316\u683c\u5f0f（\u5982ONNX, TensorRT），\u5e76\u5c06\u5176\u90e8\u7f72\u5230\u4e91\u7aef\u670d\u52a1\u5668\u3001\u8fb9\u7f18\u8bbe\u5907\u6216\u79fb\u52a8\u7aef\u3002<\/li>\n
  • \u76d1\u63a7\u4e0e\u8fed\u4ee3：\u5728\u6a21\u578b\u90e8\u7f72\u540e，\u6301\u7eed\u76d1\u63a7\u5176\u5728\u771f\u5b9e\u4e16\u754c\u4e2d\u7684\u8868\u73b0，\u6536\u96c6\u65b0\u7684\u6570\u636e，\u5e76\u6839\u636e\u9700\u8981\u5bf9\u6a21\u578b\u8fdb\u884c\u91cd\u65b0\u8bad\u7ec3\u548c\u8fed\u4ee3\u66f4\u65b0\u3002<\/li>\n

    10.1.2 Mermaid\u56fe\u8868：CV\u9879\u76ee\u751f\u547d\u5468\u671f<\/h4>\n

    #mermaid-svg-NIOhePQWzWmnLdBA {font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg-NIOhePQWzWmnLdBA .error-icon{fill:#552222;}#mermaid-svg-NIOhePQWzWmnLdBA .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-NIOhePQWzWmnLdBA .edge-thickness-normal{stroke-width:2px;}#mermaid-svg-NIOhePQWzWmnLdBA .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-NIOhePQWzWmnLdBA .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-NIOhePQWzWmnLdBA .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-NIOhePQWzWmnLdBA .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-NIOhePQWzWmnLdBA .marker{fill:#333333;stroke:#333333;}#mermaid-svg-NIOhePQWzWmnLdBA .marker.cross{stroke:#333333;}#mermaid-svg-NIOhePQWzWmnLdBA svg{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-NIOhePQWzWmnLdBA .label{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;color:#333;}#mermaid-svg-NIOhePQWzWmnLdBA .cluster-label text{fill:#333;}#mermaid-svg-NIOhePQWzWmnLdBA .cluster-label span{color:#333;}#mermaid-svg-NIOhePQWzWmnLdBA .label text,#mermaid-svg-NIOhePQWzWmnLdBA span{fill:#333;color:#333;}#mermaid-svg-NIOhePQWzWmnLdBA .node rect,#mermaid-svg-NIOhePQWzWmnLdBA .node circle,#mermaid-svg-NIOhePQWzWmnLdBA .node ellipse,#mermaid-svg-NIOhePQWzWmnLdBA .node polygon,#mermaid-svg-NIOhePQWzWmnLdBA .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-NIOhePQWzWmnLdBA .node .label{text-align:center;}#mermaid-svg-NIOhePQWzWmnLdBA .node.clickable{cursor:pointer;}#mermaid-svg-NIOhePQWzWmnLdBA .arrowheadPath{fill:#333333;}#mermaid-svg-NIOhePQWzWmnLdBA .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-NIOhePQWzWmnLdBA .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-NIOhePQWzWmnLdBA .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid-svg-NIOhePQWzWmnLdBA .edgeLabel rect{opacity:0.5;background-color:#e8e8e8;fill:#e8e8e8;}#mermaid-svg-NIOhePQWzWmnLdBA .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-NIOhePQWzWmnLdBA .cluster text{fill:#333;}#mermaid-svg-NIOhePQWzWmnLdBA .cluster span{color:#333;}#mermaid-svg-NIOhePQWzWmnLdBA div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-NIOhePQWzWmnLdBA :root{–mermaid-font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;}\u90e8\u7f72\u4e0e\u7ef4\u62a4\u6a21\u578b\u5f00\u53d1\u6570\u636e\u51c6\u5907<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u6a21\u578b\u8f6c\u6362\u4e0e\u4f18\u5316\u90e8\u7f72\u5230\u4e91\/\u8fb9\/\u7aef\u6027\u80fd\u76d1\u63a7\u6536\u96c6\u65b0\u6570\u636e\u9009\u62e9\u57fa\u7840\u6a21\u578b\u8bbe\u8ba1\u7f51\u7edc\u67b6\u6784\u6570\u636e\u91c7\u96c6\u6570\u636e\u6e05\u6d17\u6570\u636e\u6807\u6ce81. \u95ee\u9898\u5b9a\u4e492. \u6570\u636e\u6536\u96c6\u4e0e\u51c6\u59073. \u6a21\u578b\u9009\u578b\u4e0e\u8bbe\u8ba14. \u6a21\u578b\u8bad\u7ec3\u4e0e\u8c03\u4f185. \u6a21\u578b\u8bc4\u4f306. \u6a21\u578b\u90e8\u7f727. \u76d1\u63a7\u4e0e\u8fed\u4ee3<\/p>\n

    10.2 \u5b9e\u6218\u6848\u4f8b\u4e00：\u667a\u80fd\u5b89\u9632\u76d1\u63a7\u7cfb\u7edf<\/h3>\n

    \u76ee\u6807：\u5f00\u53d1\u4e00\u4e2a\u80fd\u591f\u5b9e\u65f6\u68c0\u6d4b\u89c6\u9891\u4e2d\u5f02\u5e38\u884c\u4e3a（\u5982\u95ef\u5165\u3001\u6253\u6597\u3001\u6454\u5012）\u5e76\u53d1\u51fa\u8b66\u62a5\u7684\u7cfb\u7edf\u3002<\/p>\n

    \u6280\u672f\u6808：<\/p>\n

      \n
    • \u786c\u4ef6：\u7f51\u7edc\u6444\u50cf\u5934\u3001\u5e26\u6709GPU\u7684\u670d\u52a1\u5668\u3002<\/li>\n
    • \u6838\u5fc3\u7b97\u6cd5：\u591a\u76ee\u6807\u8ddf\u8e2a (DeepSORT) + \u884c\u4e3a\u8bc6\u522b (3D CNN \u6216 \u57fa\u4e8e\u9aa8\u9abc\u70b9\u7684\u65b9\u6cd5)\u3002<\/li>\n<\/ul>\n

      \u5b9e\u73b0\u6b65\u9aa4：<\/p>\n

    • \u6570\u636e\u51c6\u5907：\u6536\u96c6\u6216\u6a21\u62df\u6b63\u5e38\u884c\u4e3a\u548c\u5404\u79cd\u5f02\u5e38\u884c\u4e3a\u7684\u89c6\u9891\u7247\u6bb5，\u5e76\u8fdb\u884c\u6807\u6ce8\u3002<\/li>\n
    • \u76ee\u6807\u8ddf\u8e2a：\u4f7f\u7528YOLOv5\u7b49\u9ad8\u6548\u7684\u68c0\u6d4b\u5668\u7ed3\u5408DeepSORT，\u5bf9\u89c6\u9891\u4e2d\u7684\u6bcf\u4e2a\u4eba\u8fdb\u884c\u5b9e\u65f6\u8ddf\u8e2a，\u4e3a\u6bcf\u4e2a\u4eba\u5206\u914d\u4e00\u4e2a\u552f\u4e00\u7684ID，\u5e76\u8bb0\u5f55\u5176\u8fd0\u52a8\u8f68\u8ff9\u3002<\/li>\n
    • \u884c\u4e3a\u8bc6\u522b：\n
        \n
      • \u5bf9\u4e8e\u6bcf\u4e2a\u88ab\u8ddf\u8e2a\u7684\u4eba，\u63d0\u53d6\u5176\u5728\u8fde\u7eed\u65f6\u95f4\u7a97\u53e3\u5185\u7684\u8f68\u8ff9\u548c\u56fe\u50cf\u5757\u3002<\/li>\n
      • \u65b9\u6cd5A (\u57fa\u4e8e\u9aa8\u9abc\u70b9)：\u5bf9\u6bcf\u4e2a\u4eba\u7684\u56fe\u50cf\u5757\u8fd0\u884c\u59ff\u6001\u4f30\u8ba1\u7b97\u6cd5（\u5982OpenPose），\u63d0\u53d6\u9aa8\u9abc\u70b9\u5e8f\u5217\u3002\u5c06\u8be5\u5e8f\u5217\u8f93\u5165\u5230\u9884\u8bad\u7ec3\u597d\u7684LSTM\u6216GNN\u6a21\u578b\u4e2d，\u5224\u65ad\u5176\u884c\u4e3a\u7c7b\u522b\u3002<\/li>\n
      • \u65b9\u6cd5B (\u57fa\u4e8e3D CNN)：\u5c06\u6bcf\u4e2a\u4eba\u7684\u56fe\u50cf\u5757\u5e8f\u5217\u76f4\u63a5\u8f93\u5165\u5230\u8f7b\u91cf\u7ea7\u76843D CNN（\u5982MobileNet3D）\u4e2d\u8fdb\u884c\u5206\u7c7b\u3002<\/li>\n<\/ul>\n<\/li>\n
      • \u7cfb\u7edf\u96c6\u6210\u4e0e\u8b66\u62a5：\u5f53\u884c\u4e3a\u8bc6\u522b\u6a21\u5757\u68c0\u6d4b\u5230\u5f02\u5e38\u884c\u4e3a\u65f6，\u7cfb\u7edf\u8bb0\u5f55\u4e8b\u4ef6\u53d1\u751f\u7684\u65f6\u95f4\u3001\u5730\u70b9\u548c\u76f8\u5173\u89c6\u9891\u7247\u6bb5，\u5e76\u901a\u8fc7API\u3001\u77ed\u4fe1\u6216\u90ae\u4ef6\u7b49\u65b9\u5f0f\u89e6\u53d1\u8b66\u62a5\u3002<\/li>\n
      • \u90e8\u7f72：\u5c06\u6574\u4e2a\u5904\u7406\u6d41\u7a0b\u5c01\u88c5\u6210\u670d\u52a1，\u90e8\u7f72\u5728\u670d\u52a1\u5668\u4e0a，\u63a5\u6536\u6765\u81ea\u6444\u50cf\u5934\u7684\u5b9e\u65f6\u89c6\u9891\u6d41\u8fdb\u884c\u5206\u6790\u3002<\/li>\n

        10.3 \u5b9e\u6218\u6848\u4f8b\u4e8c：\u533b\u5b66\u5f71\u50cf\u667a\u80fd\u5206\u6790<\/h3>\n

        \u76ee\u6807：\u5f00\u53d1\u4e00\u4e2a\u8f85\u52a9\u533b\u751f\u8bca\u65ad\u80ba\u90e8\u7ed3\u8282\u7684\u7cfb\u7edf，\u80fd\u591f\u81ea\u52a8\u5728CT\u626b\u63cf\u56fe\u50cf\u4e0a\u68c0\u6d4b\u548c\u5206\u5272\u51fa\u7591\u4f3c\u7ed3\u8282\u7684\u533a\u57df\u3002<\/p>\n

        \u6280\u672f\u6808：<\/p>\n

          \n
        • \u6570\u636e\u683c\u5f0f：DICOM (\u533b\u5b66\u6570\u5b57\u6210\u50cf\u548c\u901a\u4fe1\u6807\u51c6)\u3002<\/li>\n
        • \u6838\u5fc3\u7b97\u6cd5：\u56fe\u50cf\u5206\u5272 (U-Net)\u3002<\/li>\n<\/ul>\n

          \u5b9e\u73b0\u6b65\u9aa4：<\/p>\n

        • \u6570\u636e\u51c6\u5907：\u83b7\u53d6\u516c\u5f00\u7684\u533b\u5b66\u5f71\u50cf\u6570\u636e\u96c6（\u5982LUNA16），\u8fd9\u4e9b\u6570\u636e\u901a\u5e38\u5df2\u7ecf\u7531\u4e13\u4e1a\u533b\u751f\u6807\u6ce8\u597d\u4e86\u7ed3\u8282\u7684\u4f4d\u7f6e\u548c\u8f6e\u5ed3\u3002<\/li>\n
        • \u6570\u636e\u9884\u5904\u7406：\n
            \n
          • \u8bfb\u53d6DICOM\u6587\u4ef6，\u63d0\u53d6\u50cf\u7d20\u6570\u636e\u548c\u5143\u4fe1\u606f\u3002<\/li>\n
          • \u8fdb\u884c\u7a97\u5bbd\u7a97\u4f4d\u8c03\u6574，\u4ee5\u7a81\u51fa\u80ba\u90e8\u7ec4\u7ec7\u3002<\/li>\n
          • \u5c063D\u7684CT\u626b\u63cf\u5207\u7247\u5904\u7406\u62102D\u56fe\u50cf，\u6216\u4fdd\u63013D\u6570\u636e\u5757\u3002<\/li>\n<\/ul>\n<\/li>\n
          • \u6a21\u578b\u8bad\u7ec3：\n
              \n
            • \u9009\u62e9U-Net\u6216\u5176\u53d8\u4f53（\u5982U-Net++, Attention U-Net）\u4f5c\u4e3a\u5206\u5272\u6a21\u578b\u3002U-Net\u7684\u7f16\u7801\u5668-\u89e3\u7801\u5668\u7ed3\u6784\u548c\u8df3\u8dc3\u8fde\u63a5\u7279\u522b\u9002\u5408\u4e8e\u533b\u5b66\u5f71\u50cf\u5206\u5272\u4efb\u52a1，\u56e0\u4e3a\u5b83\u80fd\u5f88\u597d\u5730\u878d\u5408\u591a\u5c3a\u5ea6\u7684\u7279\u5f81\u3002<\/li>\n
            • \u4f7f\u7528\u6807\u6ce8\u597d\u7684\u6570\u636e\u5bf9\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u3002\u635f\u5931\u51fd\u6570\u901a\u5e38\u4f7f\u7528Dice Loss\u6216\u5176\u7ec4\u5408，\u56e0\u4e3a\u5b83\u4eec\u5bf9\u7c7b\u522b\u4e0d\u5e73\u8861\u95ee\u9898（\u7ed3\u8282\u533a\u57df\u8fdc\u5c0f\u4e8e\u80cc\u666f）\u66f4\u9c81\u68d2\u3002<\/li>\n<\/ul>\n<\/li>\n
            • \u6a21\u578b\u8bc4\u4f30：\u4f7f\u7528Dice\u7cfb\u6570\u3001IOU\u7b49\u6307\u6807\u8bc4\u4f30\u6a21\u578b\u5206\u5272\u7ed3\u679c\u4e0e\u533b\u751f\u6807\u6ce8\u7684\u543b\u5408\u7a0b\u5ea6\u3002<\/li>\n
            • \u53ef\u89c6\u5316\u4e0e\u96c6\u6210：\u5f00\u53d1\u4e00\u4e2a\u7b80\u5355\u7684\u7528\u6237\u754c\u9762，\u5141\u8bb8\u533b\u751f\u4e0a\u4f20CT\u5f71\u50cf，\u7cfb\u7edf\u8fd0\u884c\u6a21\u578b\u540e，\u5728\u539f\u59cb\u5f71\u50cf\u4e0a\u9ad8\u4eae\u663e\u793a\u51fa\u68c0\u6d4b\u5230\u7684\u7ed3\u8282\u533a\u57df，\u5e76\u7ed9\u51fa\u7f6e\u4fe1\u5ea6\u5206\u6570，\u4f9b\u533b\u751f\u53c2\u8003\u3002<\/li>\n

              10.4 \u9879\u76ee\u90e8\u7f72\u4e0e\u4f18\u5316<\/h3>\n

              \u5c06\u6a21\u578b\u4ece\u5b9e\u9a8c\u5ba4\u73af\u5883\u63a8\u5411\u751f\u4ea7\u73af\u5883，\u9700\u8981\u5173\u6ce8\u6027\u80fd\u548c\u6548\u7387\u3002<\/p>\n

                \n
              • \u6a21\u578b\u8f6c\u6362：\u5c06PyTorch\u6216TensorFlow\u6a21\u578b\u8f6c\u6362\u4e3a\u66f4\u9002\u5408\u90e8\u7f72\u7684\u683c\u5f0f\u3002\n
                  \n
                • ONNX (Open Neural Network Exchange)：\u4e00\u4e2a\u5f00\u653e\u7684\u6a21\u578b\u8868\u793a\u683c\u5f0f，\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u6846\u67b6\u95f4\u7684\u6a21\u578b\u8f6c\u6362\u3002<\/li>\n
                • TensorRT (NVIDIA)：\u4e00\u4e2a\u7528\u4e8eNVIDIA GPU\u7684\u9ad8\u6027\u80fd\u6df1\u5ea6\u5b66\u4e60\u63a8\u7406\u4f18\u5316\u5668\u548c\u8fd0\u884c\u65f6，\u53ef\u4ee5\u5bf9\u6a21\u578b\u8fdb\u884c\u91cf\u5316\u3001\u5c42\u878d\u5408\u7b49\u4f18\u5316，\u663e\u8457\u63d0\u5347\u63a8\u7406\u901f\u5ea6\u3002<\/li>\n<\/ul>\n<\/li>\n
                • \u670d\u52a1\u5316：\u4f7f\u7528Flask\u3001FastAPI（Python）\u6216Triton Inference Server（NVIDIA）\u7b49\u5de5\u5177\u5c06\u6a21\u578b\u5c01\u88c5\u6210API\u670d\u52a1，\u4f7f\u5176\u53ef\u4ee5\u88ab\u5176\u4ed6\u5e94\u7528\u7a0b\u5e8f\u8c03\u7528\u3002<\/li>\n
                • \u5bb9\u5668\u5316：\u4f7f\u7528Docker\u5c06\u5e94\u7528\u7a0b\u5e8f\u53ca\u5176\u6240\u6709\u4f9d\u8d56\u9879\u6253\u5305\u5230\u4e00\u4e2a\u5bb9\u5668\u4e2d，\u786e\u4fdd\u5728\u4e0d\u540c\u73af\u5883\u4e2d\u7684\u4e00\u81f4\u6027，\u5e76\u7b80\u5316\u90e8\u7f72\u6d41\u7a0b\u3002\u4f7f\u7528Kubernetes\u53ef\u4ee5\u8fdb\u4e00\u6b65\u7ba1\u7406\u548c\u7f16\u6392\u5bb9\u5668\u5316\u7684\u670d\u52a1\u3002<\/li>\n<\/ul>\n

                  10.5 \u5b9e\u6218\u6848\u4f8b\u4e09：\u4ece\u96f6\u6784\u5efa\u56fe\u50cf\u5206\u7c7b\u5668<\/h3>\n

                  \u76ee\u6807：\u901a\u8fc7\u4e00\u4e2a\u5b8c\u6574\u7684\u9879\u76ee，\u6765\u5b9e\u8df5\u524d\u9762\u6240\u5b66\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u77e5\u8bc6\u3002\u6211\u4eec\u7684\u76ee\u6807\u662f\u6784\u5efa\u4e00\u4e2a\u80fd\u591f\u5bf9CIFAR-10\u6570\u636e\u96c6\u4e2d\u7684\u56fe\u50cf\u8fdb\u884c\u5206\u7c7b\u7684\u5206\u7c7b\u5668\u3002<\/p>\n

                  CIFAR-10\u6570\u636e\u96c6: \u5305\u542b10\u4e2a\u7c7b\u522b\u768460000\u5f2032×32\u5f69\u8272\u56fe\u50cf，\u6bcf\u4e2a\u7c7b\u522b\u67096000\u5f20\u56fe\u50cf\u3002<\/p>\n

                  \u9879\u76ee\u6d41\u7a0b<\/p>\n

                • \u6570\u636e\u52a0\u8f7d\u4e0e\u9884\u5904\u7406：\u4f7f\u7528torchvision\u52a0\u8f7d\u5e76\u6807\u51c6\u5316CIFAR-10\u6570\u636e\u96c6\u3002<\/li>\n
                • \u5b9a\u4e49\u5377\u79ef\u795e\u7ecf\u7f51\u7edc (CNN)：\u8bbe\u8ba1\u4e00\u4e2a\u7b80\u5355\u7684CNN\u67b6\u6784\u3002<\/li>\n
                • \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668：\u4f7f\u7528\u4ea4\u53c9\u71b5\u635f\u5931\u548c\u968f\u673a\u68af\u5ea6\u4e0b\u964d (SGD)\u3002<\/li>\n
                • \u8bad\u7ec3\u6a21\u578b：\u5728\u8bad\u7ec3\u6570\u636e\u96c6\u4e0a\u8fed\u4ee3\u8bad\u7ec3\u7f51\u7edc\u3002<\/li>\n
                • \u6d4b\u8bd5\u6a21\u578b：\u5728\u6d4b\u8bd5\u6570\u636e\u96c6\u4e0a\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/li>\n

                  \u4ee3\u7801\u5b9e\u6218<\/p>\n

                  import<\/span> torch
                  \nimport<\/span> torchvision
                  \nimport<\/span> torchvision.<\/span>transforms as<\/span> transforms
                  \nimport<\/span> torch.<\/span>nn as<\/span> nn
                  \nimport<\/span> torch.<\/span>nn.<\/span>functional as<\/span> F
                  \nimport<\/span> torch.<\/span>optim as<\/span> optim
                  \nimport<\/span> matplotlib.<\/span>pyplot as<\/span> plt
                  \nimport<\/span> numpy as<\/span> np<\/p>\n

                  # — 1. \u6570\u636e\u52a0\u8f7d\u4e0e\u9884\u5904\u7406 —<\/span><\/p>\n

                  # \u5bf9\u56fe\u50cf\u8fdb\u884c\u53d8\u6362：\u6807\u51c6\u5316\u5230[-1, 1]\u8303\u56f4<\/span>
                  \ntransform =<\/span> transforms.<\/span>Compose(<\/span>
                  \n [<\/span>transforms.<\/span>ToTensor(<\/span>)<\/span>,<\/span>
                  \n transforms.<\/span>Normalize(<\/span>(<\/span>0.5<\/span>,<\/span> 0.5<\/span>,<\/span> 0.5<\/span>)<\/span>,<\/span> (<\/span>0.5<\/span>,<\/span> 0.5<\/span>,<\/span> 0.5<\/span>)<\/span>)<\/span>]<\/span>)<\/span><\/p>\n

                  # \u4e0b\u8f7d\u5e76\u52a0\u8f7d\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/span>
                  \ntrainset =<\/span> torchvision.<\/span>datasets.<\/span>CIFAR10(<\/span>root=<\/span>'.\/data'<\/span>,<\/span> train=<\/span>True<\/span>,<\/span>
                  \n download=<\/span>True<\/span>,<\/span> transform=<\/span>transform)<\/span>
                  \ntrainloader =<\/span> torch.<\/span>utils.<\/span>data.<\/span>DataLoader(<\/span>trainset,<\/span> batch_size=<\/span>4<\/span>,<\/span>
                  \n shuffle=<\/span>True<\/span>,<\/span> num_workers=<\/span>2<\/span>)<\/span><\/p>\n

                  testset =<\/span> torchvision.<\/span>datasets.<\/span>CIFAR10(<\/span>root=<\/span>'.\/data'<\/span>,<\/span> train=<\/span>False<\/span>,<\/span>
                  \n download=<\/span>True<\/span>,<\/span> transform=<\/span>transform)<\/span>
                  \ntestloader =<\/span> torch.<\/span>utils.<\/span>data.<\/span>DataLoader(<\/span>testset,<\/span> batch_size=<\/span>4<\/span>,<\/span>
                  \n shuffle=<\/span>False<\/span>,<\/span> num_workers=<\/span>2<\/span>)<\/span><\/p>\n

                  # \u7c7b\u522b\u6807\u7b7e<\/span>
                  \nclasses =<\/span> (<\/span>'plane'<\/span>,<\/span> 'car'<\/span>,<\/span> 'bird'<\/span>,<\/span> 'cat'<\/span>,<\/span>
                  \n 'deer'<\/span>,<\/span> 'dog'<\/span>,<\/span> 'frog'<\/span>,<\/span> 'horse'<\/span>,<\/span> 'ship'<\/span>,<\/span> 'truck'<\/span>)<\/span><\/p>\n

                  # — 2. \u5b9a\u4e49\u5377\u79ef\u795e\u7ecf\u7f51\u7edc (CNN) —<\/span>
                  \nclass<\/span> Net<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
                  \n def<\/span> __init__<\/span>(<\/span>self)<\/span>:<\/span>
                  \n super<\/span>(<\/span>Net,<\/span> self)<\/span>.<\/span>__init__(<\/span>)<\/span>
                  \n self.<\/span>conv1 =<\/span> nn.<\/span>Conv2d(<\/span>3<\/span>,<\/span> 6<\/span>,<\/span> 5<\/span>)<\/span>
                  \n self.<\/span>pool =<\/span> nn.<\/span>MaxPool2d(<\/span>2<\/span>,<\/span> 2<\/span>)<\/span>
                  \n self.<\/span>conv2 =<\/span> nn.<\/span>Conv2d(<\/span>6<\/span>,<\/span> 16<\/span>,<\/span> 5<\/span>)<\/span>
                  \n self.<\/span>fc1 =<\/span> nn.<\/span>Linear(<\/span>16<\/span> *<\/span> 5<\/span> *<\/span> 5<\/span>,<\/span> 120<\/span>)<\/span>
                  \n self.<\/span>fc2 =<\/span> nn.<\/span>Linear(<\/span>120<\/span>,<\/span> 84<\/span>)<\/span>
                  \n self.<\/span>fc3 =<\/span> nn.<\/span>Linear(<\/span>84<\/span>,<\/span> 10<\/span>)<\/span><\/p>\n

                  def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
                  \n x =<\/span> self.<\/span>pool(<\/span>F.<\/span>relu(<\/span>self.<\/span>conv1(<\/span>x)<\/span>)<\/span>)<\/span>
                  \n x =<\/span> self.<\/span>pool(<\/span>F.<\/span>relu(<\/span>self.<\/span>conv2(<\/span>x)<\/span>)<\/span>)<\/span>
                  \n x =<\/span> x.<\/span>view(<\/span>–<\/span>1<\/span>,<\/span> 16<\/span> *<\/span> 5<\/span> *<\/span> 5<\/span>)<\/span>
                  \n x =<\/span> F.<\/span>relu(<\/span>self.<\/span>fc1(<\/span>x)<\/span>)<\/span>
                  \n x =<\/span> F.<\/span>relu(<\/span>self.<\/span>fc2(<\/span>x)<\/span>)<\/span>
                  \n x =<\/span> self.<\/span>fc3(<\/span>x)<\/span>
                  \n return<\/span> x<\/p>\n

                  net =<\/span> Net(<\/span>)<\/span><\/p>\n

                  # — 3. \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668 —<\/span>
                  \ncriterion =<\/span> nn.<\/span>CrossEntropyLoss(<\/span>)<\/span>
                  \noptimizer =<\/span> optim.<\/span>SGD(<\/span>net.<\/span>parameters(<\/span>)<\/span>,<\/span> lr=<\/span>0.001<\/span>,<\/span> momentum=<\/span>0.9<\/span>)<\/span><\/p>\n

                  # — 4. \u8bad\u7ec3\u6a21\u578b —<\/span>
                  \nprint<\/span>(<\/span>'\u5f00\u59cb\u8bad\u7ec3…'<\/span>)<\/span>
                  \nfor<\/span> epoch in<\/span> range<\/span>(<\/span>2<\/span>)<\/span>:<\/span> # \u5728\u6570\u636e\u96c6\u4e0a\u5faa\u73af\u591a\u6b21，\u8fd9\u91cc\u53ea\u6f14\u793a2\u6b21<\/span><\/p>\n

                  running_loss =<\/span> 0.0<\/span>
                  \n for<\/span> i,<\/span> data in<\/span> enumerate<\/span>(<\/span>trainloader,<\/span> 0<\/span>)<\/span>:<\/span>
                  \n # \u83b7\u53d6\u8f93\u5165<\/span>
                  \n inputs,<\/span> labels =<\/span> data<\/p>\n

                  # \u68af\u5ea6\u6e05\u96f6<\/span>
                  \n optimizer.<\/span>zero_grad(<\/span>)<\/span><\/p>\n

                  # \u524d\u5411\u4f20\u64ad -> \u53cd\u5411\u4f20\u64ad -> \u4f18\u5316<\/span>
                  \n outputs =<\/span> net(<\/span>inputs)<\/span>
                  \n loss =<\/span> criterion(<\/span>outputs,<\/span> labels)<\/span>
                  \n loss.<\/span>backward(<\/span>)<\/span>
                  \n optimizer.<\/span>step(<\/span>)<\/span><\/p>\n

                  # \u6253\u5370\u7edf\u8ba1\u4fe1\u606f<\/span>
                  \n running_loss +=<\/span> loss.<\/span>item(<\/span>)<\/span>
                  \n if<\/span> i %<\/span> 2000<\/span> ==<\/span> 1999<\/span>:<\/span> # \u6bcf2000\u4e2amini-batches\u6253\u5370\u4e00\u6b21<\/span>
                  \n print<\/span>(<\/span>'[%d, %5d] loss: %.3f'<\/span> %<\/span>
                  \n (<\/span>epoch +<\/span> 1<\/span>,<\/span> i +<\/span> 1<\/span>,<\/span> running_loss \/<\/span> 2000<\/span>)<\/span>)<\/span>
                  \n running_loss =<\/span> 0.0<\/span><\/p>\n

                  print<\/span>(<\/span>'\u8bad\u7ec3\u5b8c\u6210'<\/span>)<\/span><\/p>\n

                  # — 5. \u6d4b\u8bd5\u6a21\u578b —<\/span>
                  \ncorrect =<\/span> 0<\/span>
                  \ntotal =<\/span> 0<\/span>
                  \nwith<\/span> torch.<\/span>no_grad(<\/span>)<\/span>:<\/span>
                  \n for<\/span> data in<\/span> testloader:<\/span>
                  \n images,<\/span> labels =<\/span> data
                  \n outputs =<\/span> net(<\/span>images)<\/span>
                  \n _,<\/span> predicted =<\/span> torch.<\/span>max<\/span>(<\/span>outputs.<\/span>data,<\/span> 1<\/span>)<\/span>
                  \n total +=<\/span> labels.<\/span>size(<\/span>0<\/span>)<\/span>
                  \n correct +=<\/span> (<\/span>predicted ==<\/span> labels)<\/span>.<\/span>sum<\/span>(<\/span>)<\/span>.<\/span>item(<\/span>)<\/span><\/p>\n

                  print<\/span>(<\/span>'Accuracy of the network on the 10000 test images: %d %%'<\/span> %<\/span> (<\/span>
                  \n 100<\/span> *<\/span> correct \/<\/span> total)<\/span>)<\/span><\/p>\n

                  10.6 \u603b\u7ed3<\/h3>\n

                  \u672c\u7ae0\u901a\u8fc7\u4e09\u4e2a\u5177\u4f53\u7684\u5b9e\u6218\u6848\u4f8b\u2014\u2014\u667a\u80fd\u5b89\u9632\u76d1\u63a7\u3001\u533b\u5b66\u5f71\u50cf\u5206\u6790\u548c\u4ece\u96f6\u6784\u5efa\u56fe\u50cf\u5206\u7c7b\u5668，\u4e32\u8054\u8d77\u4e86\u8ba1\u7b97\u673a\u89c6\u89c9\u9879\u76ee\u7684\u6574\u4e2a\u751f\u547d\u5468\u671f\u3002\u6211\u4eec\u4e0d\u4ec5\u5b66\u4e60\u4e86\u5982\u4f55\u6839\u636e\u4e1a\u52a1\u9700\u6c42\u9009\u62e9\u548c\u7ec4\u5408\u4e0d\u540c\u7684CV\u6280\u672f，\u8fd8\u4e86\u89e3\u4e86\u9879\u76ee\u4ece\u6570\u636e\u51c6\u5907\u5230\u6700\u7ec8\u90e8\u7f72\u7684\u5b8c\u6574\u6d41\u7a0b\u3002\u66f4\u91cd\u8981\u7684\u662f，\u6211\u4eec\u8ba4\u8bc6\u5230，\u4e00\u4e2a\u6210\u529f\u7684CV\u9879\u76ee\u4e0d\u4ec5\u9700\u8981\u9ad8\u8d85\u7684\u7b97\u6cd5\u80fd\u529b，\u540c\u6837\u9700\u8981\u624e\u5b9e\u7684\u5de5\u7a0b\u5b9e\u8df5\u80fd\u529b，\u5305\u62ec\u6570\u636e\u5904\u7406\u3001\u7cfb\u7edf\u96c6\u6210\u3001\u6027\u80fd\u4f18\u5316\u548c\u90e8\u7f72\u7ef4\u62a4\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u7ae0\u7684\u5b66\u4e60，\u4f60\u80fd\u591f\u5c06\u4e4b\u524d\u6240\u5b66\u7684\u7406\u8bba\u77e5\u8bc6\u878d\u4f1a\u8d2f\u901a，\u5e76\u6709\u4fe1\u5fc3\u548c\u80fd\u529b\u53bb\u5f00\u542f\u81ea\u5df1\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u9879\u76ee\u4e4b\u65c5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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