{"id":100058,"date":"2026-09-03T20:16:46","date_gmt":"2026-09-03T12:16:46","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/100058.html"},"modified":"2026-09-03T20:16:46","modified_gmt":"2026-09-03T12:16:46","slug":"yolov8-%e5%9c%a8-i5-14600kf-%e4%b8%8a%e4%b8%89%e7%a7%8d%e6%a0%bc%e5%bc%8f%e5%ae%9e%e6%b5%8b%ef%bc%9aonnx-%e7%ab%9f%e6%af%94-pytorch-%e5%bf%ab-1-8-%e5%80%8d%ef%bc%8copenvino-%e4%b8%ba%e4%bd%95%e7%bf%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/100058.html","title":{"rendered":"YOLOv8 \u5728 i5-14600KF \u4e0a\u4e09\u79cd\u683c\u5f0f\u5b9e\u6d4b\uff1aONNX \u7adf\u6bd4 PyTorch \u5feb 1.8 \u500d\uff0cOpenVINO \u4e3a\u4f55\u7ffb\u8f66\uff1f"},"content":{"rendered":"<h3>\u524d\u8a00<\/h3>\n<p>YOLOv8 \u662f Ultralytics \u63a8\u51fa\u7684\u6700\u65b0\u4e00\u4ee3\u76ee\u6807\u68c0\u6d4b\u6a21\u578b&#xff0c;\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5de5\u4e1a\u68c0\u6d4b\u3001\u667a\u80fd\u76d1\u63a7\u3001\u81ea\u52a8\u9a7e\u9a76\u7b49\u9886\u57df\u3002\u5728\u5b9e\u9645\u90e8\u7f72\u65f6&#xff0c;\u6211\u4eec\u901a\u5e38\u9700\u8981\u5c06 PyTorch \u6a21\u578b\u8f6c\u6362\u4e3a ONNX\u3001OpenVINO \u7b49\u63a8\u7406\u683c\u5f0f&#xff0c;\u4ee5\u83b7\u5f97\u66f4\u5feb\u7684\u63a8\u7406\u901f\u5ea6\u548c\u66f4\u5c0f\u7684\u8d44\u6e90\u5360\u7528\u3002<\/p>\n<p>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd YOLOv8 \u6a21\u578b\u7684\u5bfc\u51fa\u6d41\u7a0b&#xff0c;\u5e76\u5728 Intel i5-14600KF CPU \u4e0a\u5bf9 PyTorch\u3001ONNX\u3001OpenVINO \u4e09\u79cd\u683c\u5f0f\u8fdb\u884c\u6027\u80fd\u5b9e\u6d4b\u5bf9\u6bd4&#xff0c;\u5e2e\u52a9\u5927\u5bb6\u9009\u62e9\u6700\u9002\u5408\u81ea\u5df1\u786c\u4ef6\u7684\u90e8\u7f72\u65b9\u6848\u3002<\/p>\n<h3>\u4e00\u3001\u73af\u5883\u642d\u5efa<\/h3>\n<h4>1.1 \u7cfb\u7edf\u73af\u5883<\/h4>\n<ul>\n<li>\u64cd\u4f5c\u7cfb\u7edf&#xff1a;Windows 10\/11<\/li>\n<li>CPU&#xff1a;Intel Core i5-14600KF<\/li>\n<li>Python&#xff1a;3.12<\/li>\n<li>\u5185\u5b58&#xff1a;16GB<\/li>\n<\/ul>\n<h4>1.2 \u5b89\u88c5\u4f9d\u8d56<\/h4>\n<p>pip <span class=\"token function\">install<\/span> ultralytics onnxruntime opencv-python numpy<\/p>\n<p>\u5982\u679c\u9700\u8981 OpenVINO \u683c\u5f0f&#xff0c;\u8fd8\u9700\u8981\u5b89\u88c5&#xff1a;<\/p>\n<p>pip <span class=\"token function\">install<\/span> openvino<\/p>\n<h4>1.3 \u9a8c\u8bc1\u5b89\u88c5<\/h4>\n<p><span class=\"token keyword\">from<\/span> ultralytics <span class=\"token keyword\">import<\/span> YOLO<br \/>\n<span class=\"token keyword\">import<\/span> onnxruntime <span class=\"token keyword\">as<\/span> ort<br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;ultralytics OK&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;onnxruntime OK&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>\u4e8c\u3001\u6a21\u578b\u5bfc\u51fa<\/h3>\n<h4>2.1 \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b<\/h4>\n<p>YOLOv8 \u63d0\u4f9b\u4e86 n\/s\/m\/l\/x \u4e94\u4e2a\u4e0d\u540c\u5927\u5c0f\u7684\u6a21\u578b\u3002\u672c\u6587\u4f7f\u7528\u6700\u8f7b\u91cf\u7684 yolov8n&#xff08;nano \u7248\u672c&#xff0c;3.2M \u53c2\u6570&#xff0c;6.2MB&#xff09;&#xff0c;\u9002\u5408\u8fb9\u7f18\u8bbe\u5907\u90e8\u7f72\u3002<\/p>\n<p><span class=\"token keyword\">from<\/span> ultralytics <span class=\"token keyword\">import<\/span> YOLO<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b&#xff08;\u9996\u6b21\u8fd0\u884c\u4f1a\u81ea\u52a8\u4e0b\u8f7d&#xff09;<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> YOLO<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n.pt&#039;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u67e5\u770b\u6a21\u578b\u4fe1\u606f<\/span><br \/>\nparams <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span>p<span class=\"token punctuation\">.<\/span>numel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">for<\/span> p <span class=\"token keyword\">in<\/span> model<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u6a21\u578b\u53c2\u6570\u91cf&#xff1a;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>params <span class=\"token operator\">\/<\/span> <span class=\"token number\">1e6<\/span><span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.1f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">M&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>2.2 \u5bfc\u51fa\u4e3a ONNX \u683c\u5f0f<\/h4>\n<p><span class=\"token comment\"># ONNX \u5bfc\u51fa<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span>export<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">format<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;onnx&#039;<\/span><span class=\"token punctuation\">,<\/span> imgsz<span class=\"token operator\">&#061;<\/span><span class=\"token number\">640<\/span><span class=\"token punctuation\">,<\/span> simplify<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u8f93\u51fa&#xff1a;yolov8n.onnx (12.3 MB)<\/span><\/p>\n<p>\u53c2\u6570\u8bf4\u660e&#xff1a;<\/p>\n<ul>\n<li>imgsz&#061;640&#xff1a;\u8f93\u5165\u56fe\u7247\u5c3a\u5bf8<\/li>\n<li>simplify&#061;True&#xff1a;\u7b80\u5316\u6a21\u578b\u7ed3\u6784&#xff0c;\u51cf\u5c11\u5197\u4f59\u7b97\u5b50<\/li>\n<\/ul>\n<h4>2.3 \u5bfc\u51fa\u4e3a OpenVINO \u683c\u5f0f<\/h4>\n<p><span class=\"token comment\"># OpenVINO \u5bfc\u51fa<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span>export<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">format<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;openvino&#039;<\/span><span class=\"token punctuation\">,<\/span> imgsz<span class=\"token operator\">&#061;<\/span><span class=\"token number\">640<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token comment\"># \u8f93\u51fa&#xff1a;yolov8n_openvino_model\/ \u76ee\u5f55&#xff08;12.3 MB&#xff09;<\/span><\/p>\n<p>OpenVINO \u662f Intel \u63a8\u51fa\u7684\u63a8\u7406\u4f18\u5316\u6846\u67b6&#xff0c;\u9488\u5bf9 Intel CPU\/GPU\/NPU \u6709\u4e13\u95e8\u7684\u52a0\u901f\u4f18\u5316\u3002<\/p>\n<h3>\u4e09\u3001\u63a8\u7406\u4ee3\u7801<\/h3>\n<h4>3.1 PyTorch \u63a8\u7406&#xff08;\u6700\u7b80\u5355&#xff09;<\/h4>\n<p><span class=\"token keyword\">from<\/span> ultralytics <span class=\"token keyword\">import<\/span> YOLO<\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> YOLO<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n.pt&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nresults <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>source<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;test.jpg&#039;<\/span><span class=\"token punctuation\">,<\/span> save<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u6253\u5370\u68c0\u6d4b\u7ed3\u679c<\/span><br \/>\n<span class=\"token keyword\">for<\/span> box <span class=\"token keyword\">in<\/span> results<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>boxes<span class=\"token punctuation\">:<\/span><br \/>\n    cls_id <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">int<\/span><span class=\"token punctuation\">(<\/span>box<span class=\"token punctuation\">.<\/span>cls<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    conf <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">float<\/span><span class=\"token punctuation\">(<\/span>box<span class=\"token punctuation\">.<\/span>conf<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u7c7b\u522b&#xff1a;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>model<span class=\"token punctuation\">.<\/span>names<span class=\"token punctuation\">[<\/span>cls_id<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#xff0c;\u7f6e\u4fe1\u5ea6&#xff1a;<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>conf<span class=\"token punctuation\">:<\/span><span class=\"token format-spec\">.2f<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>3.2 ONNX Runtime \u63a8\u7406&#xff08;\u63a8\u8350&#xff09;<\/h4>\n<p>\u4f7f\u7528 ONNX Runtime \u63a8\u7406\u4e0d\u9700\u8981\u4f9d\u8d56 ultralytics \u6846\u67b6&#xff0c;\u90e8\u7f72\u66f4\u8f7b\u91cf&#xff1a;<\/p>\n<p><span class=\"token keyword\">import<\/span> onnxruntime <span class=\"token keyword\">as<\/span> ort<br \/>\n<span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> cv2<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d\u6a21\u578b<\/span><br \/>\nsession <span class=\"token operator\">&#061;<\/span> ort<span class=\"token punctuation\">.<\/span>InferenceSession<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n.onnx&#039;<\/span><span class=\"token punctuation\">,<\/span> providers<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;CPUExecutionProvider&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\ninput_name <span class=\"token operator\">&#061;<\/span> session<span class=\"token punctuation\">.<\/span>get_inputs<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>name<\/p>\n<p><span class=\"token comment\"># \u8bfb\u53d6\u5e76\u9884\u5904\u7406\u56fe\u7247<\/span><br \/>\nimg <span class=\"token operator\">&#061;<\/span> cv2<span class=\"token punctuation\">.<\/span>imread<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;test.jpg&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\norig_h<span class=\"token punctuation\">,<\/span> orig_w <span class=\"token operator\">&#061;<\/span> img<span class=\"token punctuation\">.<\/span>shape<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">]<\/span><br \/>\nresized <span class=\"token operator\">&#061;<\/span> cv2<span class=\"token punctuation\">.<\/span>resize<span class=\"token punctuation\">(<\/span>img<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">640<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">640<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\nblob <span class=\"token operator\">&#061;<\/span> resized<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">:<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>transpose<span class=\"token punctuation\">(<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>astype<span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span>float32<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">\/<\/span> <span class=\"token number\">255.0<\/span><br \/>\nblob <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>expand_dims<span class=\"token punctuation\">(<\/span>blob<span class=\"token punctuation\">,<\/span> axis<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u63a8\u7406<\/span><br \/>\noutput <span class=\"token operator\">&#061;<\/span> session<span class=\"token punctuation\">.<\/span>run<span class=\"token punctuation\">(<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">{<\/span>input_name<span class=\"token punctuation\">:<\/span> blob<span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u540e\u5904\u7406<\/span><br \/>\npreds <span class=\"token operator\">&#061;<\/span> output<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>T  <span class=\"token comment\"># (8400, 84)<\/span><br \/>\nboxes <span class=\"token operator\">&#061;<\/span> preds<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">:<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">]<\/span><br \/>\nscores <span class=\"token operator\">&#061;<\/span> preds<span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">]<\/span><br \/>\nclass_ids <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span>argmax<span class=\"token punctuation\">(<\/span>scores<span class=\"token punctuation\">,<\/span> axis<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><br \/>\nconfidences <span class=\"token operator\">&#061;<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token builtin\">max<\/span><span class=\"token punctuation\">(<\/span>scores<span class=\"token punctuation\">,<\/span> axis<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># \u8fc7\u6ee4\u4f4e\u7f6e\u4fe1\u5ea6\u7ed3\u679c<\/span><br \/>\nmask <span class=\"token operator\">&#061;<\/span> confidences <span class=\"token operator\">&gt;<\/span> <span class=\"token number\">0.5<\/span><br \/>\nboxes <span class=\"token operator\">&#061;<\/span> boxes<span class=\"token punctuation\">[<\/span>mask<span class=\"token punctuation\">]<\/span><br \/>\nconfidences <span class=\"token operator\">&#061;<\/span> confidences<span class=\"token punctuation\">[<\/span>mask<span class=\"token punctuation\">]<\/span><br \/>\nclass_ids <span class=\"token operator\">&#061;<\/span> class_ids<span class=\"token punctuation\">[<\/span>mask<span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u68c0\u6d4b\u5230 <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span><span class=\"token builtin\">len<\/span><span class=\"token punctuation\">(<\/span>boxes<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\"> \u4e2a\u76ee\u6807&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>3.3 OpenVINO \u63a8\u7406<\/h4>\n<p><span class=\"token keyword\">from<\/span> ultralytics <span class=\"token keyword\">import<\/span> YOLO<\/p>\n<p><span class=\"token comment\"># \u52a0\u8f7d OpenVINO \u6a21\u578b<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> YOLO<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n_openvino_model\/&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nresults <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>source<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;test.jpg&#039;<\/span><span class=\"token punctuation\">,<\/span> save<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">True<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>\u56db\u3001\u6027\u80fd\u5bf9\u6bd4<\/h3>\n<p>\u4e3a\u4e86\u5c3d\u91cf\u516c\u5e73&#xff0c;\u4e09\u79cd\u683c\u5f0f\u5747\u5728\u540c\u4e00\u53f0 Intel i5-14600KF \u4e3b\u673a\u4e0a\u8fdb\u884c\u6d4b\u8bd5&#xff0c;\u7edf\u4e00\u8f93\u5165\u5c3a\u5bf8 640\u00d7640\u3001\u6279\u91cf\u5927\u5c0f batch&#061;1&#xff1b;\u6bcf\u79cd\u683c\u5f0f\u5148\u9884\u70ed 2 \u6b21&#xff0c;\u518d\u8fde\u7eed\u63a8\u7406 10 \u6b21\u53d6\u5e73\u5747\u8017\u65f6\u3002\u6d4b\u8bd5\u65f6 CPU \u672a\u5f00\u542f\u8d85\u9891&#xff0c;\u5e76\u5c3d\u91cf\u5173\u95ed\u540e\u53f0\u9ad8\u5360\u7528\u7a0b\u5e8f&#xff0c;\u4ee5\u51cf\u5c11\u5076\u7136\u6ce2\u52a8&#xff1a;<\/p>\n<table>\n<tr>\u683c\u5f0f\u63a8\u7406\u8017\u65f6FPS\u6a21\u578b\u5927\u5c0f\u5907\u6ce8<\/tr>\n<tbody>\n<tr>\n<td>PyTorch<\/td>\n<td>29.4ms<\/td>\n<td>34<\/td>\n<td>6.2 MB<\/td>\n<td>\u57fa\u51c6<\/td>\n<\/tr>\n<tr>\n<td>ONNX<\/td>\n<td>16.6ms<\/td>\n<td>60<\/td>\n<td>12.3 MB<\/td>\n<td>\u901f\u5ea6\u6700\u5feb<\/td>\n<\/tr>\n<tr>\n<td>OpenVINO<\/td>\n<td>30.7ms<\/td>\n<td>32<\/td>\n<td>12.3 MB<\/td>\n<td>\u5927\u6a21\u578b\u4f18\u52bf\u660e\u663e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>4.1 \u7ed3\u8bba<\/h4>\n<ul>\n<li>\u5c0f\u6a21\u578b&#xff08;yolov8n&#xff09;&#xff1a;ONNX Runtime \u6700\u5feb&#xff0c;\u6bd4 PyTorch \u5feb 1.8 \u500d&#xff0c;\u6bd4 OpenVINO \u5feb 1.9 \u500d<\/li>\n<li>\u5927\u6a21\u578b&#xff08;yolov8s\/m\/l\/x&#xff09;&#xff1a;OpenVINO \u4f18\u52bf\u66f4\u660e\u663e&#xff0c;\u5b98\u65b9\u6570\u636e\u663e\u793a\u53ef\u83b7\u5f97 2-3 \u500d\u52a0\u901f<\/li>\n<li>\u8fb9\u7f18\u8bbe\u5907&#xff08;\u6811\u8393\u6d3e\u7b49&#xff09;&#xff1a;\u63a8\u8350\u4f7f\u7528 ONNX Runtime&#xff0c;\u517c\u5bb9\u6027\u6700\u597d<\/li>\n<\/ul>\n<h4>4.2 \u4e3a\u4ec0\u4e48 yolov8n \u4e0a OpenVINO \u6ca1\u6709\u4f18\u52bf&#xff1f;<\/h4>\n<p>OpenVINO \u7684\u4f18\u5316\u4e3b\u8981\u9488\u5bf9\u5927\u89c4\u6a21\u8ba1\u7b97\u56fe&#xff1a;\u5b83\u901a\u8fc7\u5c42\u878d\u5408\u3001\u7cbe\u5ea6\u538b\u7f29&#xff08;\u5982 FP32\u2192FP16\/INT8&#xff09;\u3001\u5185\u5b58\u5e03\u5c40\u91cd\u6392\u548c\u5185\u6838\u8c03\u5ea6\u6765\u51cf\u5c11\u8ba1\u7b97\u5f00\u9500\u3002\u4f46 yolov8n \u53ea\u6709 3.2M \u53c2\u6570&#xff0c;\u6a21\u578b\u672c\u8eab\u5f88\u5c0f&#xff0c;\u5355\u6b21\u63a8\u7406\u7684\u6d6e\u70b9\u8fd0\u7b97\u91cf\u8f83\u4f4e&#xff0c;\u53ef\u88ab\u878d\u5408\u3001\u538b\u7f29\u7684\u7b97\u5b50\u6709\u9650&#xff0c;\u4f18\u5316\u6536\u76ca\u4f1a\u88ab\u660e\u663e\u7a00\u91ca\u3002\u4e0e\u6b64\u540c\u65f6&#xff0c;OpenVINO Runtime \u5728\u9996\u6b21\u63a8\u7406\u524d\u9700\u8981\u5b8c\u6210\u6a21\u578b\u52a0\u8f7d\u3001\u7f16\u8bd1\u548c\u5185\u6838\u9009\u62e9\u7b49\u521d\u59cb\u5316\u5de5\u4f5c&#xff0c;\u8fd9\u90e8\u5206\u56fa\u5b9a\u5f00\u9500\u5728\u5c0f\u6a21\u578b\u4e0a\u663e\u5f97\u66f4\u52a0\u7a81\u51fa&#xff0c;\u6700\u7ec8\u5bfc\u81f4\u6574\u4f53\u8017\u65f6\u53cd\u800c\u7565\u9ad8\u4e8e PyTorch \u548c ONNX Runtime\u3002\u6362\u53e5\u8bdd\u8bf4&#xff0c;OpenVINO \u66f4\u50cf\u662f\u201c\u4e3a\u91cd\u578b\u6a21\u578b\u51c6\u5907\u7684\u52a0\u901f\u5668\u201d&#xff1b;\u5728 yolov8s\/m\/l\/x \u7b49\u66f4\u5927\u6a21\u578b\u4e0a&#xff0c;\u8ba1\u7b97\u5bc6\u96c6\u5ea6\u63d0\u5347\u540e&#xff0c;\u5b83\u7684\u4f18\u52bf\u624d\u4f1a\u771f\u6b63\u4f53\u73b0\u51fa\u6765\u3002<\/p>\n<h3>\u4e94\u3001YOLOv8 \u4e0d\u540c\u6a21\u578b\u5927\u5c0f\u5bf9\u6bd4<\/h3>\n<table>\n<tr>\u6a21\u578b\u53c2\u6570\u91cf\u6a21\u578b\u5927\u5c0fmAP&#064;50\u9002\u7528\u573a\u666f<\/tr>\n<tbody>\n<tr>\n<td>yolov8n<\/td>\n<td>3.2M<\/td>\n<td>6.2 MB<\/td>\n<td>37.3<\/td>\n<td>\u8fb9\u7f18\u8bbe\u5907\u3001\u5b9e\u65f6\u68c0\u6d4b<\/td>\n<\/tr>\n<tr>\n<td>yolov8s<\/td>\n<td>11.2M<\/td>\n<td>22.5 MB<\/td>\n<td>44.9<\/td>\n<td>\u8f7b\u91cf\u670d\u52a1\u5668<\/td>\n<\/tr>\n<tr>\n<td>yolov8m<\/td>\n<td>25.9M<\/td>\n<td>52.2 MB<\/td>\n<td>50.2<\/td>\n<td>\u901a\u7528\u573a\u666f<\/td>\n<\/tr>\n<tr>\n<td>yolov8l<\/td>\n<td>43.7M<\/td>\n<td>87.7 MB<\/td>\n<td>52.9<\/td>\n<td>\u9ad8\u7cbe\u5ea6\u9700\u6c42<\/td>\n<\/tr>\n<tr>\n<td>yolov8x<\/td>\n<td>68.2M<\/td>\n<td>136 MB<\/td>\n<td>53.9<\/td>\n<td>\u6781\u81f4\u7cbe\u5ea6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>\u516d\u3001\u90e8\u7f72\u5230\u8fb9\u7f18\u8bbe\u5907<\/h3>\n<h4>6.1 \u6811\u8393\u6d3e\u90e8\u7f72<\/h4>\n<p><span class=\"token comment\"># \u6811\u8393\u6d3e\u4e0a\u5b89\u88c5 ONNX Runtime<\/span><br \/>\npip <span class=\"token function\">install<\/span> onnxruntime<\/p>\n<p><span class=\"token comment\"># \u5c06 yolov8n.onnx \u590d\u5236\u5230\u6811\u8393\u6d3e<\/span><br \/>\n<span class=\"token function\">scp<\/span> yolov8n.onnx pi&#064;raspberrypi:~\/<\/p>\n<p><span class=\"token comment\"># \u8fd0\u884c\u63a8\u7406&#xff08;\u4f7f\u7528\u672c\u6587\u7684 ONNX \u63a8\u7406\u4ee3\u7801&#xff09;<\/span><br \/>\npython detect_onnx.py<\/p>\n<h4>6.2 Jetson Nano \u90e8\u7f72&#xff08;NVIDIA GPU&#xff09;<\/h4>\n<p><span class=\"token comment\"># \u5bfc\u51fa\u4e3a TensorRT \u683c\u5f0f&#xff08;\u5728 Jetson \u4e0a\u6267\u884c&#xff09;<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> YOLO<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n.pt&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span>export<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">format<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;engine&#039;<\/span><span class=\"token punctuation\">,<\/span> imgsz<span class=\"token operator\">&#061;<\/span><span class=\"token number\">640<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>6.3 RK3568\/RK3588 \u90e8\u7f72&#xff08;\u745e\u82af\u5fae NPU&#xff09;<\/h4>\n<p><span class=\"token comment\"># RK3568\/RK3588 \u90e8\u7f72&#xff08;\u745e\u82af\u5fae NPU&#xff09;<\/span><br \/>\n<span class=\"token comment\"># \u5148\u5bfc\u51fa ONNX&#xff0c;\u518d\u4f7f\u7528 rknn-toolkit2 \u8f6c\u6362\u4e3a RKNN<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> YOLO<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n.pt&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span>export<span class=\"token punctuation\">(<\/span><span class=\"token builtin\">format<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;onnx&#039;<\/span><span class=\"token punctuation\">,<\/span> imgsz<span class=\"token operator\">&#061;<\/span><span class=\"token number\">640<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token comment\"># rknn-toolkit2 \u8f6c\u6362\u793a\u4f8b\u89c1&#xff1a;<\/span><br \/>\n<span class=\"token comment\"># https:\/\/github.com\/airockchip\/rknn-toolkit2<\/span><\/p>\n<h3>\u4e03\u3001\u5e38\u89c1\u95ee\u9898<\/h3>\n<h4>Q1&#xff1a;\u5bfc\u51fa ONNX \u65f6\u62a5\u9519\u7f3a\u5c11 onnx \u4f9d\u8d56\u600e\u4e48\u529e&#xff1f;<\/h4>\n<p>pip <span class=\"token function\">install<\/span> onnx onnxslim<\/p>\n<h4>Q2&#xff1a;CPU \u4e0a\u63a8\u7406\u592a\u6162\u600e\u4e48\u4f18\u5316&#xff1f;<\/h4>\n<ul>\n<li>\u4f7f\u7528 ONNX Runtime \u66ff\u4ee3 PyTorch<\/li>\n<li>\u964d\u4f4e\u8f93\u5165\u5206\u8fa8\u7387&#xff08;imgsz&#061;320&#xff09;<\/li>\n<li>\u4f7f\u7528 INT8 \u91cf\u5316&#xff08;\u9700\u8981\u6821\u51c6\u6570\u636e\u96c6&#xff09;<\/li>\n<\/ul>\n<h4>Q3&#xff1a;\u5982\u4f55\u4f7f\u7528\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u8bad\u7ec3&#xff1f;<\/h4>\n<p><span class=\"token keyword\">from<\/span> ultralytics <span class=\"token keyword\">import<\/span> YOLO<\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> YOLO<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#039;yolov8n.pt&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span>train<span class=\"token punctuation\">(<\/span>data<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;your_dataset.yaml&#039;<\/span><span class=\"token punctuation\">,<\/span> epochs<span class=\"token operator\">&#061;<\/span><span class=\"token number\">100<\/span><span class=\"token punctuation\">,<\/span> imgsz<span class=\"token operator\">&#061;<\/span><span class=\"token number\">640<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u6570\u636e\u96c6 YAML \u914d\u7f6e\u6587\u4ef6\u683c\u5f0f\u53c2\u8003 COCO \u6570\u636e\u96c6\u3002<\/p>\n<h3>\u516b\u3001\u8d44\u6e90\u4e0b\u8f7d<\/h3>\n<p>\u672c\u6587\u7684\u5b8c\u6574\u4ee3\u7801\u3001\u6a21\u578b\u6587\u4ef6\u548c\u90e8\u7f72\u6587\u6863\u5df2\u4e0a\u4f20\u81f3 CSDN \u6587\u5e93&#xff1a;<\/p>\n<p>YOLOv8\u591a\u683c\u5f0f\u90e8\u7f72\u8d44\u6e90\u5305&#xff08;PyTorch&#043;ONNX&#043;OpenVINO\u63a8\u7406\u4ee3\u7801&#043;\u6027\u80fd\u5bf9\u6bd4&#043;\u8fb9\u7f18\u8bbe\u5907\u90e8\u7f72\u6307\u5357&#xff09;<\/p>\n<p>\u8d44\u6e90\u5305\u542b&#xff1a;<\/p>\n<ul>\n<li>\u4e09\u79cd\u683c\u5f0f\u7684\u9884\u8bad\u7ec3\u6a21\u578b&#xff08;PyTorch \/ ONNX \/ OpenVINO&#xff09;<\/li>\n<li>\u56db\u5957\u5b8c\u6574\u7684\u4e2d\u6587\u6ce8\u91ca\u63a8\u7406\u4ee3\u7801<\/li>\n<li>\u6027\u80fd\u5bf9\u6bd4\u6d4b\u8bd5\u811a\u672c<\/li>\n<li>\u4e0d\u4f9d\u8d56 ultralytics \u7684\u72ec\u7acb\u63a8\u7406\u4ee3\u7801&#xff08;\u53ef\u76f4\u63a5\u90e8\u7f72\u5230\u8fb9\u7f18\u8bbe\u5907&#xff09;<\/li>\n<\/ul>\n<h3>\u603b\u7ed3<\/h3>\n<li>YOLOv8 \u6a21\u578b\u5bfc\u51fa\u975e\u5e38\u65b9\u4fbf&#xff0c;\u4e00\u6761\u547d\u4ee4\u5373\u53ef\u5b8c\u6210\u683c\u5f0f\u8f6c\u6362<\/li>\n<li>\u5c0f\u6a21\u578b\u5728 CPU \u4e0a\u63a8\u8350\u4f7f\u7528 ONNX Runtime&#xff0c;\u63a8\u7406\u901f\u5ea6\u6700\u5feb<\/li>\n<li>\u5927\u6a21\u578b\u5728 Intel CPU \u4e0a\u63a8\u8350\u4f7f\u7528 OpenVINO&#xff0c;\u53ef\u83b7\u5f97 2-3 \u500d\u52a0\u901f<\/li>\n<li>\u8fb9\u7f18\u8bbe\u5907\u90e8\u7f72\u63a8\u8350 ONNX \u683c\u5f0f&#xff0c;\u517c\u5bb9\u6027\u6700\u597d<\/li>\n<li>\u5b9e\u9645\u90e8\u7f72\u65f6\u5e94\u7ed3\u5408\u786c\u4ef6\u5e73\u53f0\u548c\u6a21\u578b\u5927\u5c0f\u9009\u62e9\u5408\u9002\u7684\u63a8\u7406\u683c\u5f0f<\/li>\n","protected":false},"excerpt":{"rendered":"<p>\u524d\u8a00<br \/>\nYOLOv8 \u662f Ultralytics \u63a8\u51fa\u7684\u6700\u65b0\u4e00\u4ee3\u76ee\u6807\u68c0\u6d4b\u6a21\u578b&#xff0c;\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5de5\u4e1a\u68c0\u6d4b\u3001\u667a\u80fd\u76d1\u63a7\u3001\u81ea\u52a8\u9a7e\u9a76\u7b49\u9886\u57df\u3002\u5728\u5b9e\u9645\u90e8\u7f72\u65f6&#xff0c;\u6211\u4eec\u901a\u5e38\u9700\u8981\u5c06 PyTorch \u6a21\u578b\u8f6c\u6362\u4e3a ONNX\u3001OpenVINO \u7b49\u63a8\u7406\u683c\u5f0f&#xff0c;\u4ee5\u83b7\u5f97\u66f4\u5feb\u7684\u63a8\u7406\u901f\u5ea6\u548c\u66f4\u5c0f\u7684\u8d44\u6e90\u5360\u7528\u3002<br \/>\n\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd YOLOv8 \u6a21\u578b\u7684\u5bfc\u51fa\u6d41\u7a0b&#xff0c;\u5e76\u5728 Intel i5-14600KF CPU \u4e0a\u5bf9 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