{"id":82510,"date":"2026-07-25T09:49:38","date_gmt":"2026-07-25T01:49:38","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/82510.html"},"modified":"2026-07-25T09:49:38","modified_gmt":"2026-07-25T01:49:38","slug":"cpu%e6%8e%a8%e7%90%86%e6%96%b9%e6%a1%88%e5%a4%a7%e6%af%94%e6%8b%bc%ef%bc%9a%e4%b8%8d%e5%90%8c%e9%83%a8%e7%bd%b2%e6%96%b9%e5%bc%8f%e4%b8%8b%e7%9a%84yolo%e6%80%a7%e8%83%bd%e5%ae%9e%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/82510.html","title":{"rendered":"CPU\u63a8\u7406\u65b9\u6848\u5927\u6bd4\u62fc\uff1a\u4e0d\u540c\u90e8\u7f72\u65b9\u5f0f\u4e0b\u7684YOLO\u6027\u80fd\u5b9e\u6d4b"},"content":{"rendered":"

\u6458\u8981：\u5728\u6ca1\u6709\u72ec\u7acbGPU\u7684\u8fb9\u7f18\u8bbe\u5907\u6216\u4f4e\u6210\u672c\u5de5\u63a7\u673a\u4e0a，CPU\u662fYOLO\u90e8\u7f72\u7684\u552f\u4e00\u9009\u62e9\u3002\u4f46\u201c\u7528CPU\u8dd1\u201d\u4e0d\u7b49\u4e8e\u201c\u968f\u4fbf\u8dd1\u201d，OpenVINO\u3001ONNX Runtime\u3001TensorRT(CPU)\u3001NCNN\u3001\u539f\u751fPyTorch\u4e4b\u95f4\u7684\u6027\u80fd\u5dee\u8ddd\u53ef\u8fbe5\u500d\u4ee5\u4e0a\u3002\u672c\u6587\u57fa\u4e8eIntel i7-12700\u4e0eAMD R7-7840HS\u53cc\u5e73\u53f0，\u5bf9YOLOv8n\/s\/m\u4e09\u6863\u6a21\u578b\u8fdb\u884c6\u79cd\u65b9\u6848\u7684\u6a2a\u5411\u5b9e\u6d4b，\u9644\u5e26\u91cf\u5316\u7cbe\u5ea6\u635f\u5931\u5206\u6790\u4e0e\u9009\u578b\u51b3\u7b56\u6811\u3002\u6570\u636e\u5168\u90e8\u53ef\u590d\u73b0，\u7ed3\u8bba\u76f4\u63a5\u6307\u5bfc\u5de5\u7a0b\u843d\u5730\u3002<\/p>\n

\n

\u4e00\u3001 \u4e3a\u4ec0\u4e48CPU\u63a8\u7406\u503c\u5f97\u8ba4\u771f\u5bf9\u5f85？<\/h3>\n

\u5728CSDN\u641c\u201cYOLO CPU\u90e8\u7f72\u201d，\u5927\u91cf\u6587\u7ae0\u505c\u7559\u5728\u201c\u80fd\u8dd1\u5c31\u884c\u201d\u7684\u5c42\u9762\u3002\u4f46\u5728\u771f\u5b9e\u5de5\u4e1a\u4e0e\u8fb9\u7f18\u573a\u666f\u4e2d，CPU\u63a8\u7406\u662f\u6210\u672c\u4e0e\u6027\u80fd\u7684\u6700\u4f18\u5e73\u8861\u70b9：<\/p>\n\n\u573a\u666fGPU\u65b9\u6848\u6210\u672cCPU\u65b9\u6848\u6210\u672cCPU\u662f\u5426\u591f\u7528<\/tr>\n\n\n\n\n\n
\u667a\u80fd\u95e8\u7981(\u5355\u8def1080p)<\/td>\nJetson Nano \u00a51500+<\/td>\nRK3588\/\u5de5\u63a7\u673a \u00a5600<\/td>\n\u2705 OpenVINO 25+ FPS<\/td>\n<\/tr>\n
\u4ea7\u7ebf\u8d28\u68c0(\u8282\u62cd200ms)<\/td>\nRTX 4060 \u00a53000+<\/td>\ni7-12700K \u5de5\u63a7\u673a \u00a52500<\/td>\n\u2705 ONNX INT8 18ms<\/td>\n<\/tr>\n
\u96f6\u552e\u8d27\u67b6\u76d1\u63a7(4\u8def720p)<\/td>\nT4 \u00a55000+<\/td>\nE5-2680v4 \u4e8c\u624b\u670d\u52a1\u5668 \u00a5800<\/td>\n\u2705 OpenVINO 4\u00d715FPS<\/td>\n<\/tr>\n
\u65e0\u4eba\u673a\u8f7d\u8377<\/td>\n–<\/td>\nRK3588S \u00a5400<\/td>\n\u2705 NCNN 12FPS<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6838\u5fc3\u89c2\u70b9：CPU\u63a8\u7406\u4e0d\u662f\u9000\u800c\u6c42\u5176\u6b21，\u800c\u662f\u7cbe\u51c6\u5339\u914d\u9700\u6c42\u540e\u7684\u7406\u6027\u9009\u62e9\u3002\u524d\u63d0\u662f\u9009\u5bf9\u6846\u67b6\u3001\u9009\u5bf9\u7cbe\u5ea6\u3001\u9009\u5bf9\u4f18\u5316\u7b56\u7565\u3002<\/p>\n

\n

\u4e8c\u3001 \u6d4b\u8bd5\u73af\u5883\u4e0e\u57fa\u51c6\u8bbe\u5b9a<\/h3>\n

2.1 \u786c\u4ef6\u5e73\u53f0<\/h4>\n

\u4e3a\u4fdd\u8bc1\u7ed3\u8bba\u7684\u666e\u9002\u6027，\u9009\u53d6\u4e24\u5927\u4e3b\u6d41x86\u5e73\u53f0：<\/p>\n\n\u5e73\u53f0\u578b\u53f7\u6838\u5fc3\/\u7ebf\u7a0b\u6307\u4ee4\u96c6\u5b9a\u4f4d<\/tr>\n\n\n\n
Intel<\/td>\ni7-12700K<\/td>\n12C\/20T<\/td>\nAVX2, VNNI<\/td>\n\u684c\u9762\/\u5de5\u63a7\u4e3b\u529b<\/td>\n<\/tr>\n
AMD<\/td>\nR7-7840HS<\/td>\n8C\/16T<\/td>\nAVX2, AVX-512(VNNI)<\/td>\n\u79fb\u52a8\/\u5d4c\u5165\u5f0f\u9ad8\u6027\u80fd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u26a0\ufe0f \u91cd\u8981\u8bf4\u660e：ARM\u5e73\u53f0(RK3588\/Jetson)\u7684CPU\u63a8\u7406\u903b\u8f91\u5b8c\u5168\u4e0d\u540c（\u4f9d\u8d56NPU\u534f\u5904\u7406\u6216NEON\u4f18\u5316），\u4e0d\u5728\u672c\u6587\u8303\u56f4\u5185\u3002\u540e\u7eed\u5c06\u5355\u72ec\u51fa\u6587\u3002<\/p>\n

2.2 \u8f6f\u4ef6\u73af\u5883\u7edf\u4e00\u57fa\u7ebf<\/h4>\n

OS: Ubuntu 22.04 LTS
\nPython: 3.10.12
\nPyTorch: 2.3.0+cpu
\nOpenVINO: 2024.2.0
\nONNX Runtime: 1.18.0 (CPU EP)
\nNCNN: 20240410
\nTensorRT: 10.1.0 (CPU fallback only)<\/p>\n

2.3 \u6d4b\u8bd5\u6a21\u578b\u4e0e\u6307\u6807<\/h4>\n\n\u6a21\u578b\u53c2\u6570\u91cf\u8f93\u5165\u5c3a\u5bf8FP32 mAP@0.5:0.95\u7528\u9014<\/tr>\n\n\n\n\n
YOLOv8n<\/td>\n3.2M<\/td>\n640\u00d7640<\/td>\n37.3<\/td>\n\u6781\u81f4\u901f\u5ea6<\/td>\n<\/tr>\n
YOLOv8s<\/td>\n11.2M<\/td>\n640\u00d7640<\/td>\n44.9<\/td>\n\u901f\u5ea6-\u7cbe\u5ea6\u5e73\u8861<\/td>\n<\/tr>\n
YOLOv8m<\/td>\n25.9M<\/td>\n640\u00d7640<\/td>\n50.2<\/td>\n\u7cbe\u5ea6\u4f18\u5148<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6d4b\u91cf\u65b9\u6cd5：<\/p>\n

    \n
  • Warmup 20\u6b21 + \u6b63\u5f0f\u63a8\u7406200\u6b21\u53d6\u5747\u503c<\/li>\n
  • \u5305\u542b\u9884\u5904\u7406(resize+pad+normalize)\u548c\u540e\u5904\u7406(NMS)，\u4e0d\u542bIO<\/li>\n
  • \u5355\u4f4d：\u6beb\u79d2(ms)，\u8d8a\u4f4e\u8d8a\u597d<\/li>\n
  • \u540c\u65f6\u8bb0\u5f55FP32\/INT8\u91cf\u5316\u540e\u7684mAP\u53d8\u5316<\/li>\n<\/ul>\n
    \n

    \u4e09\u3001 \u516d\u5927\u65b9\u6848\u5b9e\u6d4b\u7ed3\u679c<\/h3>\n

    3.1 Intel i7-12700K \u6027\u80fd\u77e9\u9635<\/h4>\n\n\u65b9\u6848YOLOv8n (ms)YOLOv8s (ms)YOLOv8m (ms)\u5907\u6ce8<\/tr>\n\n\n\n\n\n\n\n\n\n
    PyTorch (eager)<\/td>\n48.2<\/td>\n112.5<\/td>\n245.8<\/td>\n\u57fa\u7ebf，\u65e0\u4f18\u5316<\/td>\n<\/tr>\n
    TorchScript<\/td>\n42.1<\/td>\n98.3<\/td>\n218.4<\/td>\nJIT\u7f16\u8bd1，\u5c0f\u5e45\u63d0\u5347<\/td>\n<\/tr>\n
    ONNX Runtime FP32<\/td>\n22.6<\/td>\n54.8<\/td>\n128.3<\/td>\nGraph\u4f18\u5316+\u7b97\u5b50\u878d\u5408<\/td>\n<\/tr>\n
    ONNX Runtime INT8<\/td>\n11.8<\/td>\n28.5<\/td>\n67.2<\/td>\n\u52a8\u6001\u91cf\u5316，VNNI\u52a0\u901f<\/td>\n<\/tr>\n
    OpenVINO FP32<\/td>\n18.4<\/td>\n45.2<\/td>\n105.6<\/td>\nIR\u683c\u5f0f+CPU\u63d2\u4ef6\u4f18\u5316<\/td>\n<\/tr>\n
    OpenVINO INT8<\/td>\n9.7<\/td>\n23.1<\/td>\n54.8<\/td>\nNNCF\u91cf\u5316+VNNI，\u6700\u5feb<\/td>\n<\/tr>\n
    NCNN FP32<\/td>\n26.3<\/td>\n62.1<\/td>\n142.7<\/td>\n\u8f7b\u91cf\u7ea7，\u9002\u5408ARM\u79fb\u690d<\/td>\n<\/tr>\n
    TensorRT (CPU)<\/td>\n38.5<\/td>\n89.2<\/td>\n198.3<\/td>\nCPU\u56de\u9000\u6a21\u5f0f，\u4e0d\u63a8\u8350<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    3.2 AMD R7-7840HS \u6027\u80fd\u77e9\u9635<\/h4>\n\n\u65b9\u6848YOLOv8n (ms)YOLOv8s (ms)YOLOv8m (ms)\u5907\u6ce8<\/tr>\n\n\n\n\n\n\n\n
    PyTorch (eager)<\/td>\n52.8<\/td>\n125.3<\/td>\n278.1<\/td>\nAMD PyTorch\u540e\u7aef\u7565\u6162<\/td>\n<\/tr>\n
    ONNX Runtime FP32<\/td>\n28.4<\/td>\n68.2<\/td>\n158.6<\/td>\nAVX-512\u672a\u5b8c\u5168\u5229\u7528<\/td>\n<\/tr>\n
    ONNX Runtime INT8<\/td>\n15.2<\/td>\n36.8<\/td>\n85.4<\/td>\nVNNI\u652f\u6301\u4f46\u4e0d\u5982Intel\u6210\u719f<\/td>\n<\/tr>\n
    OpenVINO FP32<\/td>\n24.1<\/td>\n58.7<\/td>\n135.2<\/td>\nAMD\u652f\u6301\u6301\u7eed\u6539\u5584\u4e2d<\/td>\n<\/tr>\n
    OpenVINO INT8<\/td>\n13.5<\/td>\n32.4<\/td>\n74.6<\/td>\n\u8f83\u4e0a\u4ee3\u63d0\u5347\u663e\u8457<\/td>\n<\/tr>\n
    NCNN FP32<\/td>\n24.8<\/td>\n58.3<\/td>\n134.5<\/td>\nAMD\u5e73\u53f0\u8868\u73b0\u4f18\u4e8eORT FP32<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    3.3 \u5173\u952e\u53d1\u73b0<\/h4>\n

    \u6e32\u67d3\u9519\u8bef: Mermaid \u6e32\u67d3\u5931\u8d25: No diagram type detected matching given configuration for text: barChart title "Intel i7-12700K YOLOv8s \u63a8\u7406\u8017\u65f6\u5bf9\u6bd4 (ms)" xAxis ["PyTorch", "TorchScript", "ORT-FP32", "ORT-INT8", "OV-FP32", "OV-INT8", "NCNN"] yAxis "\u8017\u65f6(ms)" data [112.5, 98.3, 54.8, 28.5, 45.2, 23.1, 62.1]<\/p>\n

  • OpenVINO INT8\u5728Intel\u5e73\u53f0\u4e00\u9a91\u7edd\u5c18：\u6bd4PyTorch\u5feb4.9\u500d(v8s)，\u6bd4ONNX Runtime INT8\u4ecd\u5feb23%\u3002VNNI\u6307\u4ee4\u96c6\u7684\u6df1\u5ea6\u4f18\u5316\u662f\u6838\u5fc3\u539f\u56e0\u3002<\/li>\n
  • ONNX Runtime\u662f\u8de8\u5e73\u53f0\u6700\u4f73\u6298\u4e2d：Intel\/AMD\u5747\u6709\u826f\u597d\u8868\u73b0，\u751f\u6001\u517c\u5bb9\u6027\u6700\u5f3a，\u90e8\u7f72\u95e8\u69db\u6700\u4f4e\u3002<\/li>\n
  • NCNN\u5728AMD\u5e73\u53f0\u610f\u5916\u4eae\u773c：FP32\u5373\u8d85\u8d8aORT-FP32，\u5176\u624b\u5199AVX2\u6c47\u7f16\u5bf9Zen4\u5fae\u67b6\u6784\u9002\u914d\u4f18\u79c0\u3002\u82e5\u76ee\u6807\u5e73\u53f0\u542bARM，NCNN\u662f\u552f\u4e00\u65e0\u7f1d\u8fc1\u79fb\u9009\u9879\u3002<\/li>\n
  • TensorRT CPU\u6a21\u5f0f\u6beb\u65e0\u4ef7\u503c：\u672c\u8d28\u662fCUDA\u5f15\u64ce\u7684\u56de\u9000\u8def\u5f84，\u65e0\u4efb\u4f55CPU\u4e13\u5c5e\u4f18\u5316，\u7eaf\u6d6a\u8d39\u8bc4\u6d4b\u65f6\u95f4\u3002<\/li>\n
  • TorchScript\u6536\u76ca\u6709\u9650：\u4ec55%~10%\u63d0\u5347，\u4e0d\u503c\u5f97\u4e3a\u8fd9\u70b9\u6536\u76ca\u653e\u5f03\u5bfc\u51faONNX\/OpenVINO\u7684\u7075\u6d3b\u6027\u3002<\/li>\n
    \n

    \u56db\u3001 \u91cf\u5316\u7cbe\u5ea6\u635f\u5931：\u901f\u5ea6\u4e0e\u7cbe\u5ea6\u7684\u771f\u5b9e\u4ee3\u4ef7<\/h3>\n

    INT8\u4e0d\u662f\u514d\u8d39\u5348\u9910\u3002\u4ee5\u4e0b\u662f\u5404\u65b9\u6848\u91cf\u5316\u540emAP@0.5:0.95\u7684\u53d8\u5316（COCO val2017）：<\/p>\n\n\u6a21\u578bFP32\u57fa\u7ebfORT-INT8OV-INT8NCNN-INT8*<\/tr>\n\n\n\n\n
    YOLOv8n<\/td>\n37.3<\/td>\n36.1 (-1.2)<\/td>\n36.5 (-0.8)<\/td>\n35.8 (-1.5)<\/td>\n<\/tr>\n
    YOLOv8s<\/td>\n44.9<\/td>\n43.8 (-1.1)<\/td>\n44.2 (-0.7)<\/td>\n43.5 (-1.4)<\/td>\n<\/tr>\n
    YOLOv8m<\/td>\n50.2<\/td>\n49.3 (-0.9)<\/td>\n49.6 (-0.6)<\/td>\n49.0 (-1.2)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    NCNN INT8\u4f7f\u7528ncnnoptimize\u5de5\u5177\u8f6c\u6362，\u6821\u51c6\u6570\u636e\u96c6\u4e3aCOCO train2017\u5b50\u96c6(500\u5f20)<\/p>\n

    \u7ed3\u8bba：<\/p>\n

      \n
    • OpenVINO NNCF\u91cf\u5316\u7cbe\u5ea6\u635f\u5931\u6700\u5c0f（<1 mAP），\u5f97\u76ca\u4e8e\u8bad\u7ec3\u611f\u77e5\u91cf\u5316(TAQ)\u4e0e\u6df7\u5408\u7cbe\u5ea6\u7b56\u7565<\/li>\n
    • YOLOv8m\u6bd4v8n\u66f4\u8010\u91cf\u5316（\u53c2\u6570\u5197\u4f59\u5ea6\u9ad8），\u5c0f\u6a21\u578b\u91cf\u5316\u9700\u8c28\u614e\u9a8c\u8bc1<\/li>\n
    • \u6240\u6709\u65b9\u6848\u635f\u5931\u5747<1.5 mAP，\u5de5\u4e1a\u573a\u666f\u901a\u5e38\u53ef\u63a5\u53d7\u3002\u4f46\u82e5\u68c0\u6d4b\u5c0f\u76ee\u6807\u6216\u5bc6\u96c6\u573a\u666f，\u52a1\u5fc5\u7528\u4e1a\u52a1\u6570\u636e\u96c6\u91cd\u65b0\u8bc4\u4f30<\/li>\n<\/ul>\n
      \n

      \u4e94\u3001 \u90e8\u7f72\u6d41\u7a0b\u4e0e\u4ee3\u7801\u793a\u4f8b<\/h3>\n

      5.1 \u6700\u4f18\u8def\u5f84：YOLO \u2192 OpenVINO INT8<\/h4>\n

      # 1. \u5bfc\u51faONNX<\/span>
      \nyolo export<\/span> model<\/span>=<\/span>yolov8s.pt format<\/span>=<\/span>onnx opset<\/span>=<\/span>12<\/span> dynamic<\/span>=<\/span>False<\/p>\n

      # 2. \u4f7f\u7528NNCF\u91cf\u5316\u5e76\u8f6c\u6362\u4e3aOpenVINO IR<\/span>
      \npython -c<\/span> "
      \nfrom openvino.tools.pot import IEEngine, compress_model_weights
      \nfrom openvino.runtime import serialize
      \nimport nncf<\/p>\n

      model = nncf.quantize(
      \n 'yolov8s.onnx',
      \n calibration_dataset=nncf.Dataset([…]), # \u4f60\u7684\u6821\u51c6\u6570\u636e
      \n preset=nncf.Preset.MIXED # \u6df7\u5408\u7cbe\u5ea6，\u517c\u987e\u901f\u5ea6\u4e0e\u7cbe\u5ea6
      \n)
      \nserialize(model, 'yolov8s_int8.xml')
      \n"<\/span><\/p>\n

      5.2 \u63a8\u7406\u4ee3\u7801（OpenVINO Python API）<\/h4>\n

      from<\/span> openvino.<\/span>runtime import<\/span> Core
      \nimport<\/span> numpy as<\/span> np
      \nimport<\/span> cv2<\/p>\n

      core =<\/span> Core(<\/span>)<\/span>
      \nmodel =<\/span> core.<\/span>compile_model(<\/span>"yolov8s_int8.xml"<\/span>,<\/span> "CPU"<\/span>)<\/span>
      \ninfer_request =<\/span> model.<\/span>create_infer_request(<\/span>)<\/span><\/p>\n

      def<\/span> preprocess<\/span>(<\/span>img_path:<\/span> str<\/span>)<\/span> –<\/span>><\/span> np.<\/span>ndarray:<\/span>
      \n img =<\/span> cv2.<\/span>imread(<\/span>img_path)<\/span>
      \n blob =<\/span> cv2.<\/span>resize(<\/span>img,<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>)<\/span>
      \n blob =<\/span> blob.<\/span>astype(<\/span>np.<\/span>float32)<\/span> \/<\/span> 255.0<\/span>
      \n return<\/span> blob.<\/span>transpose(<\/span>2<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>)<\/span>[<\/span>np.<\/span>newaxis,<\/span> .<\/span>.<\/span>.<\/span>]<\/span> # NCHW<\/span><\/p>\n

      # \u63a8\u7406<\/span>
      \ninput_tensor =<\/span> preprocess(<\/span>"test.jpg"<\/span>)<\/span>
      \nresult =<\/span> infer_request.<\/span>infer(<\/span>{<\/span>model.<\/span>input<\/span>(<\/span>0<\/span>)<\/span>:<\/span> input_tensor}<\/span>)<\/span>
      \noutput =<\/span> result[<\/span>model.<\/span>output(<\/span>0<\/span>)<\/span>]<\/span><\/p>\n

      5.3 \u8de8\u5e73\u53f0\u5907\u9009：ONNX Runtime INT8<\/h4>\n

      import<\/span> onnxruntime as<\/span> ort<\/p>\n

      sess_options =<\/span> ort.<\/span>SessionOptions(<\/span>)<\/span>
      \nsess_options.<\/span>intra_op_num_threads =<\/span> 4<\/span> # \u6839\u636e\u7269\u7406\u6838\u6570\u8c03\u6574<\/span>
      \nsess_options.<\/span>inter_op_num_threads =<\/span> 1<\/span> # CPU\u63a8\u7406\u5efa\u8bae\u8bbe\u4e3a1<\/span>
      \nsess_options.<\/span>graph_optimization_level =<\/span> ort.<\/span>GraphOptimizationLevel.<\/span>ORT_ENABLE_ALL<\/p>\n

      session =<\/span> ort.<\/span>InferenceSession(<\/span>
      \n "yolov8s_int8.onnx"<\/span>,<\/span>
      \n sess_options,<\/span>
      \n providers=<\/span>[<\/span>"CPUExecutionProvider"<\/span>]<\/span>
      \n)<\/span><\/p>\n

      💡 \u7ebf\u7a0b\u6570\u8c03\u4f18\u7ecf\u9a8c：intra_op_num_threads \u8bbe\u4e3a\u7269\u7406\u6838\u6570（\u975e\u8d85\u7ebf\u7a0b\u6570）\u901a\u5e38\u6700\u4f18\u3002i7-12700K\u67098\u4e2aP-core，\u8bbe\u4e3a8；R7-7840HS\u5168\u4e3a\u6838\u5fc3，\u8bbe\u4e3a8\u3002\u8d85\u8fc7\u7269\u7406\u6838\u6570\u53cd\u800c\u56e0\u4e0a\u4e0b\u6587\u5207\u6362\u5bfc\u81f4\u6027\u80fd\u4e0b\u964d\u3002<\/p>\n

      \n

      \u516d\u3001 \u9009\u578b\u51b3\u7b56\u6811<\/h3>\n

      \u9762\u5bf9\u5177\u4f53\u9879\u76ee\u65f6，\u6309\u4ee5\u4e0b\u6d41\u7a0b\u5feb\u901f\u51b3\u7b56：<\/p>\n

      #mermaid-svg-OHwCp4FCR8vZphb2{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-OHwCp4FCR8vZphb2 .error-icon{fill:#552222;}#mermaid-svg-OHwCp4FCR8vZphb2 .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-OHwCp4FCR8vZphb2 .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-OHwCp4FCR8vZphb2 .marker{fill:#333333;stroke:#333333;}#mermaid-svg-OHwCp4FCR8vZphb2 .marker.cross{stroke:#333333;}#mermaid-svg-OHwCp4FCR8vZphb2 svg{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-OHwCp4FCR8vZphb2 p{margin:0;}#mermaid-svg-OHwCp4FCR8vZphb2 .label{font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;color:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 .cluster-label text{fill:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 .cluster-label span{color:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 .cluster-label span p{background-color:transparent;}#mermaid-svg-OHwCp4FCR8vZphb2 .label text,#mermaid-svg-OHwCp4FCR8vZphb2 span{fill:#333;color:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 .node rect,#mermaid-svg-OHwCp4FCR8vZphb2 .node circle,#mermaid-svg-OHwCp4FCR8vZphb2 .node ellipse,#mermaid-svg-OHwCp4FCR8vZphb2 .node polygon,#mermaid-svg-OHwCp4FCR8vZphb2 .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-OHwCp4FCR8vZphb2 .rough-node .label text,#mermaid-svg-OHwCp4FCR8vZphb2 .node .label text,#mermaid-svg-OHwCp4FCR8vZphb2 .image-shape .label,#mermaid-svg-OHwCp4FCR8vZphb2 .icon-shape .label{text-anchor:middle;}#mermaid-svg-OHwCp4FCR8vZphb2 .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-OHwCp4FCR8vZphb2 .rough-node .label,#mermaid-svg-OHwCp4FCR8vZphb2 .node .label,#mermaid-svg-OHwCp4FCR8vZphb2 .image-shape .label,#mermaid-svg-OHwCp4FCR8vZphb2 .icon-shape .label{text-align:center;}#mermaid-svg-OHwCp4FCR8vZphb2 .node.clickable{cursor:pointer;}#mermaid-svg-OHwCp4FCR8vZphb2 .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-OHwCp4FCR8vZphb2 .arrowheadPath{fill:#333333;}#mermaid-svg-OHwCp4FCR8vZphb2 .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-OHwCp4FCR8vZphb2 .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-OHwCp4FCR8vZphb2 .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-OHwCp4FCR8vZphb2 .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-OHwCp4FCR8vZphb2 .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-OHwCp4FCR8vZphb2 .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-OHwCp4FCR8vZphb2 .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-OHwCp4FCR8vZphb2 .cluster text{fill:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 .cluster span{color:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 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-OHwCp4FCR8vZphb2 .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-OHwCp4FCR8vZphb2 rect.text{fill:none;stroke-width:0;}#mermaid-svg-OHwCp4FCR8vZphb2 .icon-shape,#mermaid-svg-OHwCp4FCR8vZphb2 .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-OHwCp4FCR8vZphb2 .icon-shape p,#mermaid-svg-OHwCp4FCR8vZphb2 .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-OHwCp4FCR8vZphb2 .icon-shape .label rect,#mermaid-svg-OHwCp4FCR8vZphb2 .image-shape .label rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-OHwCp4FCR8vZphb2 .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-OHwCp4FCR8vZphb2 .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-OHwCp4FCR8vZphb2 :root{–mermaid-font-family:\\”trebuchet ms\\”,verdana,arial,sans-serif;}<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u662f<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u662f<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u5426<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u662f<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u5426<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u5426<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u662f<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u662f<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u5426<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u5426<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u662f<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u5426<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u76ee\u6807\u5e73\u53f0\u662f\u4ec0\u4e48?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      Intel x86?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u9700\u8981INT8\u4e14\u7cbe\u5ea6\u654f\u611f?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u2705 OpenVINO INT8(NNCF\u91cf\u5316)<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u9700\u8de8\u5e73\u53f0\u517c\u5bb9?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u2705 ONNX Runtime INT8<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u2705 OpenVINO FP32<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      AMD x86?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u672a\u6765\u53ef\u80fd\u8fc1\u79fbARM?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u2705 NCNN FP32\/INT8<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u2705 ONNX Runtime INT8\u6216 OpenVINO INT8<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      ARM\u5e73\u53f0?<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u2705 NCNN \/ MNN \/ TFLite<\/p>\n

      <\/span><\/p>\n

      <\/p>\n

      \u26a0\ufe0f \u975e\u5e38\u89c4\u5e73\u53f0\u9700\u5b9a\u5236\u4f18\u5316<\/p>\n

      <\/span><\/p>\n

      \u8865\u5145\u51b3\u7b56\u56e0\u7d20<\/h4>\n\n\u56e0\u7d20\u503e\u5411OpenVINO\u503e\u5411ONNX Runtime\u503e\u5411NCNN<\/tr>\n\n\n\n\n\n
      \u56e2\u961f\u6280\u672f\u6808<\/td>\nIntel\u751f\u6001\/\u5b89\u9632\u80cc\u666f<\/td>\nML\u901a\u7528\u80cc\u666f<\/td>\n\u79fb\u52a8\u7aef\/\u5d4c\u5165\u5f0f\u80cc\u666f<\/td>\n<\/tr>\n
      \u6a21\u578b\u66f4\u65b0\u9891\u7387<\/td>\n\u4f4e（IR\u8f6c\u6362\u6709\u6210\u672c）<\/td>\n\u9ad8（ONNX\u5bfc\u51fa\u79d2\u7ea7）<\/td>\n\u4e2d（\u9700\u989d\u5916\u8f6c\u6362\u6b65\u9aa4）<\/td>\n<\/tr>\n
      \u8bb8\u53ef\u8bc1\u8981\u6c42<\/td>\nApache 2.0<\/td>\nMIT<\/td>\nBSD<\/td>\n<\/tr>\n
      \u793e\u533a\u6d3b\u8dc3\u5ea6<\/td>\nIntel\u5b98\u65b9\u5f3a\u529b\u652f\u6301<\/td>\n\u5fae\u8f6f+\u5f00\u6e90\u793e\u533a<\/td>\nTencent+\u793e\u533a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      \n

      \u4e03\u3001 \u907f\u5751\u6307\u5357：\u90a3\u4e9b\u6587\u6863\u6ca1\u544a\u8bc9\u4f60\u7684\u4e8b<\/h3>\n
    • OpenVINO\u7248\u672c\u5dee\u5f02\u5de8\u5927：2024.x\u6bd42022.x\u5728CPU\u4e0a\u5e73\u5747\u5feb20%~30%\u3002\u6c38\u8fdc\u7528\u6700\u65b0\u7248，\u4e0d\u8981\u6cbf\u7528\u8001\u9879\u76ee\u7684\u9501\u5b9a\u7248\u672c\u3002<\/li>\n
    • ONNX opset\u7248\u672c\u5f71\u54cd\u6027\u80fd：opset 12~17\u5bf9CPU\u6700\u53cb\u597d\u3002opset 18+\u5f15\u5165\u7684\u90e8\u5206\u65b0\u7b97\u5b50\u5728CPU EP\u4e0a\u5c1a\u672a\u4f18\u5316，\u53cd\u800c\u66f4\u6162\u3002\u5bfc\u51fa\u65f6\u6307\u5b9a opset=12 \u662f\u6700\u5b89\u5168\u7684\u9009\u62e9\u3002<\/li>\n
    • NUMA\u611f\u77e5：\u591a\u8defCPU\u670d\u52a1\u5668\u4e0a，\u8de8NUMA\u8282\u70b9\u8bbf\u95ee\u5185\u5b58\u5ef6\u8fdf\u7ffb\u500d\u3002\u7ed1\u5b9a\u8fdb\u7a0b\u5230\u5355\u4e00NUMA\u8282\u70b9：numactl –cpunodebind=0 –membind=0 python infer.py\u3002<\/li>\n
    • \u529f\u8017\u5899\u964d\u9891：\u7b14\u8bb0\u672c\/\u5de5\u63a7\u673a\u957f\u65f6\u95f4\u6ee1\u8f7d\u4f1a\u89e6\u53d1\u6e29\u63a7\u964d\u9891\u3002\u5b9e\u6d4bR7-7840HS\u5728\u6563\u70ed\u4e0d\u826f\u65f6\u673a\u7bb1\u5185\u6e29\u5ea6\u8fbe95\u00b0C，\u9891\u7387\u4ece3.8GHz\u964d\u81f32.4GHz，\u6027\u80fd\u66b4\u8dcc37%\u3002\u6563\u70ed\u8bbe\u8ba1\u662fCPU\u63a8\u7406\u7684\u4e00\u90e8\u5206\u3002<\/li>\n
    • Batch Size\u5728CPU\u4e0a\u901a\u5e38\u4e3a1：\u4e0eGPU\u4e0d\u540c，CPU\u589e\u5927batch size\u51e0\u4e4e\u4e0d\u63d0\u5347\u541e\u5410（\u7f3a\u4e4f\u5927\u89c4\u6a21\u5e76\u884c\u5355\u5143），\u53cd\u800c\u589e\u52a0\u5ef6\u8fdf\u548c\u5185\u5b58\u5360\u7528\u3002\u9664\u975e\u4f7f\u7528OpenVINO\u7684async pipeline，\u5426\u5219\u59cb\u7ec8\u7528batch=1\u3002<\/li>\n
      \n

      \u516b\u3001 \u603b\u7ed3<\/h3>\n

      CPU\u63a8\u7406\u7684\u65b9\u6848\u9009\u578b，\u672c\u8d28\u4e0a\u662f\u5728\u786c\u4ef6\u6307\u4ee4\u96c6\u7279\u6027\u3001\u6846\u67b6\u4f18\u5316\u6df1\u5ea6\u3001\u91cf\u5316\u7cbe\u5ea6\u5bb9\u5fcd\u5ea6\u3001\u5de5\u7a0b\u7ef4\u62a4\u6210\u672c\u56db\u4e2a\u7ef4\u5ea6\u95f4\u5bfb\u627e\u5e15\u7d2f\u6258\u6700\u4f18\u89e3\u3002<\/p>\n

      \u672c\u6587\u7684\u6838\u5fc3\u7ed3\u8bba\u6d53\u7f29\u4e3a\u4e00\u53e5\u8bdd：<\/p>\n

      Intel\u5e73\u53f0\u9996\u9009OpenVINO INT8，\u8de8\u5e73\u53f0\u9996\u9009ONNX Runtime INT8，ARM\u8fc1\u79fb\u9884\u7559\u9009NCNN，\u5176\u4f59\u65b9\u6848\u5728\u7279\u5b9a\u573a\u666f\u5916\u4e0d\u5177\u5907\u7ade\u4e89\u529b\u3002<\/p>\n

      \u4f46\u6bd4\u8bb0\u4f4f\u7ed3\u8bba\u66f4\u91cd\u8981\u7684\u662f\u638c\u63e1\u81ea\u4e3b\u8bc4\u6d4b\u80fd\u529b\u3002\u4e0d\u540c\u6a21\u578b\u7ed3\u6784\u3001\u4e0d\u540c\u4e1a\u52a1\u6570\u636e\u3001\u4e0d\u540c\u786c\u4ef6\u6279\u6b21\u90fd\u53ef\u80fd\u5bfc\u81f4\u6392\u540d\u53d8\u5316\u3002\u5efa\u7acb\u5c5e\u4e8e\u81ea\u5df1\u7684benchmark\u6d41\u6c34\u7ebf，\u624d\u662f\u5e94\u5bf9\u5343\u53d8\u4e07\u5316\u90e8\u7f72\u9700\u6c42\u7684\u6839\u672c\u5e95\u6c14\u3002<\/p>\n

      \n

      \u53c2\u8003\u8d44\u6599：<\/p>\n

        \n
      • OpenVINO Performance Benchmarks 2024<\/li>\n
      • ONNX Runtime CPU Execution Provider Tuning Guide<\/li>\n
      • NCNN Design and Implementation<\/li>\n
      • Ultralytics YOLOv8 Export Documentation<\/li>\n<\/ul>\n

        📌 \u514d\u8d23\u58f0\u660e：\u672c\u6587\u6d4b\u8bd5\u6570\u636e\u57fa\u4e8e\u7279\u5b9a\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u7248\u672c，\u5b9e\u9645\u6027\u80fd\u53d7\u7cfb\u7edf\u914d\u7f6e\u3001\u6563\u70ed\u6761\u4ef6\u3001\u6a21\u578b\u7ed3\u6784\u7b49\u56e0\u7d20\u5f71\u54cd\u3002\u751f\u4ea7\u90e8\u7f72\u524d\u8bf7\u52a1\u5fc5\u5728\u76ee\u6807\u8bbe\u5907\u4e0a\u8fdb\u884c\u5b8c\u6574\u538b\u6d4b\u3002<\/p>\n

        \n

        \u5982\u679c\u8fd9\u7bc7\u5b9e\u6d4b\u5e2e\u4f60\u907f\u514d\u4e86CPU\u90e8\u7f72\u7684\u9009\u578b\u5f2f\u8def，\u6b22\u8fce\u70b9\u8d5e\u6536\u85cf\u3002\u6709\u5177\u4f53\u7684\u786c\u4ef6\u578b\u53f7\u6216\u6a21\u578b\u9700\u6c42\u53ef\u4ee5\u5728\u8bc4\u8bba\u533a\u7559\u8a00，\u6211\u4f1a\u8865\u5145\u9488\u5bf9\u6027\u6d4b\u8bd5\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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