{"id":33931,"date":"2025-04-28T22:08:24","date_gmt":"2025-04-28T14:08:24","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/33931.html"},"modified":"2025-04-28T22:08:24","modified_gmt":"2025-04-28T14:08:24","slug":"mobilenet%e5%ae%b6%e6%97%8f%ef%bc%9a%e4%bb%8ev1%e5%88%b0v4%e7%9a%84%e6%9e%b6%e6%9e%84%e6%bc%94%e8%bf%9b%e4%b8%8e%e5%8f%91%e5%b1%95%e5%8e%86%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/33931.html","title":{"rendered":"MobileNet\u5bb6\u65cf\uff1a\u4ecev1\u5230v4\u7684\u67b6\u6784\u6f14\u8fdb\u4e0e\u53d1\u5c55\u5386\u7a0b"},"content":{"rendered":"<p>MobileNet \u662f\u4e00\u4e2a\u4e13\u4e3a\u79fb\u52a8\u8bbe\u5907\u548c\u5d4c\u5165\u5f0f\u7cfb\u7edf\u8bbe\u8ba1\u7684\u8f7b\u91cf\u5316\u5377\u79ef\u795e\u7ecf\u7f51\u7edc&#xff08;CNN&#xff09;\u5bb6\u65cf&#xff0c;\u65e8\u5728\u5728\u8d44\u6e90\u53d7\u9650\u7684\u73af\u5883\u4e2d\u5b9e\u73b0\u9ad8\u6548\u7684\u56fe\u50cf\u5206\u7c7b\u3001\u5bf9\u8c61\u68c0\u6d4b\u548c\u8bed\u4e49\u5206\u5272\u7b49\u4efb\u52a1\u3002\u81ea 2017 \u5e74\u9996\u6b21\u63a8\u51fa\u4ee5\u6765&#xff0c;MobileNet \u7ecf\u5386\u4e86\u4ece v1 \u5230 v4 \u7684\u591a\u6b21\u8fed\u4ee3&#xff0c;\u6bcf\u4e00\u4ee3\u90fd\u5728\u8ba1\u7b97\u6548\u7387\u3001\u6a21\u578b\u5927\u5c0f\u548c\u51c6\u786e\u6027\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u8fdb\u6b65\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u63a2\u8ba8 MobileNet v1\u3001v2\u3001v3 \u548c v4 \u7684\u539f\u7406\u3001\u67b6\u6784\u8bbe\u8ba1\u53ca\u5176\u53d1\u5c55\u5386\u7a0b&#xff0c;\u5e76\u5206\u6790\u5176\u5173\u952e\u521b\u65b0\u548c\u6027\u80fd\u8868\u73b0\u3002<\/p>\n<hr \/>\n<h2>MobileNet v1&#xff1a;\u5960\u5b9a\u8f7b\u91cf\u5316\u57fa\u7840<\/h2>\n<p>\u53d1\u5e03\u65f6\u95f4&#xff1a;2017 \u5e74 MobileNet v1 \u662f\u8be5\u7cfb\u5217\u7684\u8d77\u70b9&#xff0c;\u7531 Google \u63d0\u51fa&#xff0c;\u76ee\u6807\u662f\u8bbe\u8ba1\u4e00\u79cd\u9ad8\u6548\u7684 CNN&#xff0c;\u9002\u5408\u5728\u79fb\u52a8\u8bbe\u5907\u4e0a\u8fd0\u884c\u3002\u5176\u6838\u5fc3\u521b\u65b0\u662f\u5f15\u5165\u4e86\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef&#xff08;depthwise separable convolution&#xff09;&#xff0c;\u5927\u5e45\u964d\u4f4e\u4e86\u8ba1\u7b97\u590d\u6742\u5ea6\u548c\u6a21\u578b\u53c2\u6570\u91cf\u3002<\/p>\n<ul>\n<li>\n<p>\u539f\u7406\u4e0e\u521b\u65b0&#xff1a; \u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u5c06\u6807\u51c6\u5377\u79ef\u5206\u89e3\u4e3a\u4e24\u4e2a\u6b65\u9aa4&#xff1a;<\/p>\n<li>\u6df1\u5ea6\u5377\u79ef&#xff08;depthwise convolution&#xff09;&#xff1a;\u5bf9\u6bcf\u4e2a\u8f93\u5165\u901a\u9053\u5355\u72ec\u5e94\u7528\u4e00\u4e2a\u6ee4\u6ce2\u5668&#xff0c;\u51cf\u5c11\u8ba1\u7b97\u91cf\u3002<\/li>\n<li>\u9010\u70b9\u5377\u79ef&#xff08;pointwise convolution&#xff09;&#xff1a;\u4f7f\u7528 1&#215;1 \u5377\u79ef\u878d\u5408\u6df1\u5ea6\u5377\u79ef\u7684\u8f93\u51fa&#xff0c;\u751f\u6210\u65b0\u7684\u7279\u5f81\u56fe\u3002 \u76f8\u6bd4\u6807\u51c6\u5377\u79ef&#xff0c;\u8fd9\u79cd\u65b9\u6cd5\u5c06\u8ba1\u7b97\u6210\u672c\u964d\u4f4e\u4e86\u7ea6 8-9 \u500d&#xff08;\u53d6\u51b3\u4e8e\u6ee4\u6ce2\u5668\u6570\u91cf&#xff09;\u3002 \u6b64\u5916&#xff0c;v1 \u5f15\u5165\u4e86\u5bbd\u5ea6\u4e58\u6570&#xff08;\u03b1&#xff09;\u548c\u5206\u8fa8\u7387\u4e58\u6570&#xff08;\u03c1&#xff09;&#xff0c;\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u8c03\u6574\u6ee4\u6ce2\u5668\u6570\u91cf\u548c\u8f93\u5165\u5206\u8fa8\u7387&#xff0c;\u5728\u6548\u7387\u548c\u51c6\u786e\u6027\u4e4b\u95f4\u7075\u6d3b\u6743\u8861\u3002<\/li>\n<\/li>\n<li>\n<p>\u67b6\u6784\u8bbe\u8ba1&#xff1a; \u7f51\u7edc\u4ee5\u4e00\u4e2a\u6807\u51c6\u7684 3&#215;3 \u5377\u79ef\u5c42\u5f00\u5934&#xff0c;\u968f\u540e\u662f 13 \u4e2a\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u6a21\u5757&#xff0c;\u901a\u8fc7\u6b65\u5e45\u5b9e\u73b0\u964d\u91c7\u6837&#xff0c;\u6700\u540e\u4ee5\u5e73\u5747\u6c60\u5316\u548c\u5168\u8fde\u63a5\u5c42\u5b8c\u6210\u5206\u7c7b\u3002\u8f93\u5165\u5206\u8fa8\u7387\u9ed8\u8ba4\u8bbe\u4e3a 224&#215;224\u3002<\/p>\n<\/li>\n<li>\n<p>\u6027\u80fd\u8868\u73b0&#xff1a; \u5728 ImageNet \u6570\u636e\u96c6\u4e0a&#xff0c;MobileNet v1&#xff08;\u03b1&#061;1&#xff0c;\u03c1&#061;1&#xff09;\u5b9e\u73b0\u4e86 70.6% \u7684 top-1 \u51c6\u786e\u7387&#xff0c;\u62e5\u6709 4.2 \u767e\u4e07\u53c2\u6570\u548c 569 \u767e\u4e07\u4e58\u52a0\u8fd0\u7b97&#xff08;MAdds&#xff09;\u3002\u76f8\u6bd4\u4e4b\u4e0b&#xff0c;VGG-16 \u7684\u53c2\u6570\u91cf&#xff08;138 \u767e\u4e07&#xff09;\u548c\u8ba1\u7b97\u91cf&#xff08;15,300 MAdds&#xff09;\u8fdc\u8d85 v1&#xff0c;\u800c\u51c6\u786e\u7387\u4ec5\u7565\u9ad8&#xff08;71.5%&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u610f\u4e49&#xff1a; MobileNet v1 \u5960\u5b9a\u4e86\u8f7b\u91cf\u5316\u7f51\u7edc\u7684\u57fa\u7840&#xff0c;\u8bc1\u660e\u4e86\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u5728\u79fb\u52a8\u89c6\u89c9\u4efb\u52a1\u4e2d\u7684\u6f5c\u529b\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u53c2\u8003\u6587\u732e&#xff1a;MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications<\/p>\n<hr \/>\n<h2>MobileNet v2&#xff1a;\u5012\u7f6e\u6b8b\u5dee\u4e0e\u7ebf\u6027\u74f6\u9888\u7684\u7a81\u7834<\/h2>\n<p>\u53d1\u5e03\u65f6\u95f4&#xff1a;2018 \u5e74 MobileNet v2 \u5728 v1 \u7684\u57fa\u7840\u4e0a\u5f15\u5165\u4e86\u5012\u7f6e\u6b8b\u5dee&#xff08;inverted residuals&#xff09;\u548c\u7ebf\u6027\u74f6\u9888&#xff08;linear bottlenecks&#xff09;&#xff0c;\u8fdb\u4e00\u6b65\u63d0\u5347\u4e86\u6548\u7387\u548c\u6027\u80fd\u3002<\/p>\n<ul>\n<li>\n<p>\u539f\u7406\u4e0e\u521b\u65b0&#xff1a;<\/p>\n<li>\u5012\u7f6e\u6b8b\u5dee&#xff1a; \u4e0e\u4f20\u7edf\u6b8b\u5dee\u7f51\u7edc&#xff08;\u5982 ResNet&#xff09;\u4e0d\u540c&#xff0c;v2 \u7684\u6b8b\u5dee\u8fde\u63a5\u53d1\u751f\u5728\u4f4e\u7ef4\u74f6\u9888\u5c42\u4e4b\u95f4&#xff0c;\u800c\u975e\u9ad8\u7ef4\u5c42\u3002\u6bcf\u4e2a\u6a21\u5757\u9996\u5148\u901a\u8fc7 1&#215;1 \u5377\u79ef\u6269\u5c55\u901a\u9053&#xff08;\u6269\u5c55\u56e0\u5b50\u901a\u5e38\u4e3a 6&#xff09;&#xff0c;\u7136\u540e\u5e94\u7528\u6df1\u5ea6\u5377\u79ef&#xff0c;\u6700\u540e\u901a\u8fc7 1&#215;1 \u5377\u79ef\u538b\u7f29\u56de\u4f4e\u7ef4\u3002\u8fd9\u79cd\u201c\u6269\u5c55-\u5377\u79ef-\u538b\u7f29\u201d\u7684\u7ed3\u6784\u88ab\u79f0\u4e3a\u5012\u7f6e\u6b8b\u5dee\u3002<\/li>\n<li>\u7ebf\u6027\u74f6\u9888&#xff1a; \u5728\u538b\u7f29\u5c42\u540e&#xff0c;v2 \u79fb\u9664\u975e\u7ebf\u6027\u6fc0\u6d3b&#xff08;\u5982 ReLU&#xff09;&#xff0c;\u4ee5\u907f\u514d\u4f4e\u7ef4\u7279\u5f81\u7684\u4fe1\u606f\u635f\u5931&#xff0c;\u4fdd\u7559\u66f4\u591a\u8868\u793a\u80fd\u529b\u3002<\/li>\n<\/li>\n<li>\n<p>\u67b6\u6784\u8bbe\u8ba1&#xff1a; \u7f51\u7edc\u4ee5\u4e00\u4e2a 32 \u901a\u9053\u7684 3&#215;3 \u5377\u79ef\u5c42\u5f00\u59cb&#xff0c;\u968f\u540e\u662f 19 \u4e2a\u74f6\u9888\u6b8b\u5dee\u5757\u3002\u8fd9\u4e9b\u5757\u5206\u4e3a\u6269\u5c55\u5c42&#xff08;expansion layer&#xff09;\u3001\u6df1\u5ea6\u5377\u79ef\u5c42\u548c\u6295\u5f71\u5c42&#xff08;projection layer&#xff09;&#xff0c;\u901a\u8fc7\u6b8b\u5dee\u8fde\u63a5\u4f18\u5316\u68af\u5ea6\u6d41\u52a8\u3002<\/p>\n<\/li>\n<li>\n<p>\u6027\u80fd\u8868\u73b0&#xff1a; \u5728 ImageNet \u4e0a&#xff0c;MobileNet v2&#xff08;\u03b1&#061;1&#xff09;\u8fbe\u5230 72.0% \u7684 top-1 \u51c6\u786e\u7387&#xff0c;\u53c2\u6570\u91cf\u4e3a 3.4 \u767e\u4e07&#xff0c;MAdds \u4e3a 300 \u767e\u4e07&#xff0c;Google Pixel 1 \u4e0a\u7684 CPU \u5ef6\u8fdf\u4e3a 75ms\u3002\u76f8\u6bd4 v1&#xff08;70.6% \u51c6\u786e\u7387&#xff0c;575 MAdds&#xff0c;113ms&#xff09;&#xff0c;v2 \u5728\u66f4\u4f4e\u7684\u8ba1\u7b97\u6210\u672c\u4e0b\u63d0\u9ad8\u4e86\u51c6\u786e\u6027\u3002<\/p>\n<\/li>\n<li>\n<p>\u610f\u4e49&#xff1a; \u5012\u7f6e\u6b8b\u5dee\u548c\u7ebf\u6027\u74f6\u9888\u7684\u5f15\u5165\u4f7f MobileNet v2 \u6210\u4e3a\u66f4\u9ad8\u6548\u7684\u6a21\u578b&#xff0c;\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5b9e\u65f6\u4efb\u52a1\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u53c2\u8003\u6587\u732e&#xff1a;MobileNetV2: Inverted Residuals and Linear Bottlenecks<\/p>\n<hr \/>\n<h2>MobileNet v3&#xff1a;\u795e\u7ecf\u67b6\u6784\u641c\u7d22\u4e0e\u786c\u4ef6\u4f18\u5316<\/h2>\n<p>\u53d1\u5e03\u65f6\u95f4&#xff1a;2019 \u5e74 MobileNet v3 \u901a\u8fc7**\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;NAS&#xff09;**\u548c\u786c\u4ef6\u611f\u77e5\u4f18\u5316&#xff0c;\u5c06\u79fb\u52a8\u7f51\u7edc\u8bbe\u8ba1\u63a8\u5411\u65b0\u9ad8\u5ea6\u3002\u5b83\u7ed3\u5408\u4e86 v2 \u7684\u57fa\u7840\u7ed3\u6784&#xff0c;\u5e76\u5f15\u5165\u4e86\u65b0\u7684\u6fc0\u6d3b\u51fd\u6570\u548c\u6a21\u5757\u3002<\/p>\n<ul>\n<li>\n<p>\u539f\u7406\u4e0e\u521b\u65b0&#xff1a;<\/p>\n<li>\u795e\u7ecf\u67b6\u6784\u641c\u7d22&#xff08;NAS&#xff09;&#xff1a; \u4f7f\u7528 NAS \u81ea\u52a8\u641c\u7d22\u7f51\u7edc\u7ed3\u6784&#xff0c;\u7ed3\u5408 NetAdapt \u7b97\u6cd5\u5fae\u8c03\u5c42\u7ea7\u53c2\u6570&#xff0c;\u4f18\u5316\u5ef6\u8fdf\u548c\u51c6\u786e\u6027\u3002<\/li>\n<li>h-swish \u6fc0\u6d3b&#xff1a; \u66ff\u6362\u4f20\u7edf ReLU \u548c swish&#xff0c;h-swish&#xff08;\u57fa\u4e8e\u786c sigmoid&#xff09;\u5728\u6df1\u5c42\u7f51\u7edc\u4e2d\u51cf\u5c11\u8ba1\u7b97\u5f00\u9500&#xff0c;\u540c\u65f6\u4fdd\u6301\u975e\u7ebf\u6027\u8868\u8fbe\u80fd\u529b\u3002<\/li>\n<li>\u6324\u538b-\u6fc0\u52b1&#xff08;SE&#xff09;\u6a21\u5757&#xff1a; \u5728\u74f6\u9888\u5757\u4e2d\u52a0\u5165 SE \u6a21\u5757&#xff0c;\u56fa\u5b9a\u4e3a\u6269\u5c55\u5c42\u7684 1\/4 \u5927\u5c0f&#xff0c;\u589e\u5f3a\u901a\u9053\u95f4\u7684\u4f9d\u8d56\u6027\u3002<\/li>\n<\/li>\n<li>\n<p>\u67b6\u6784\u8bbe\u8ba1&#xff1a; MobileNet v3 \u57fa\u4e8e v2 \u7684\u5012\u7f6e\u6b8b\u5dee\u5757&#xff08;\u79f0\u4e3a MBConv&#xff09;&#xff0c;\u63d0\u4f9b\u4e24\u79cd\u53d8\u4f53&#xff1a;<\/p>\n<ul>\n<li>MobileNetV3-Large&#xff1a;\u9002\u7528\u4e8e\u9ad8\u8d44\u6e90\u573a\u666f&#xff0c;\u76ee\u6807\u5ef6\u8fdf\u7ea6 80ms\u3002<\/li>\n<li>MobileNetV3-Small&#xff1a;\u9002\u7528\u4e8e\u4f4e\u8d44\u6e90\u573a\u666f&#xff0c;\u7ed3\u6784\u66f4\u7d27\u51d1\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6027\u80fd\u8868\u73b0&#xff1a;<\/p>\n<ul>\n<li>MobileNetV3-Large&#xff1a;75.2% top-1 \u51c6\u786e\u7387&#xff0c;5.4 \u767e\u4e07\u53c2\u6570&#xff0c;217 MAdds\u3002<\/li>\n<li>MobileNetV3-Small&#xff1a;67.5% top-1 \u51c6\u786e\u7387&#xff0c;2.5 \u767e\u4e07\u53c2\u6570&#xff0c;57 MAdds\u3002 \u4e0e v2 \u76f8\u6bd4&#xff0c;Large \u6a21\u578b\u51c6\u786e\u7387\u63d0\u5347 3.2%&#xff0c;\u5ef6\u8fdf\u964d\u4f4e\u7ea6 20%\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u610f\u4e49&#xff1a; MobileNet v3 \u901a\u8fc7\u81ea\u52a8\u5316\u8bbe\u8ba1\u548c\u786c\u4ef6\u4f18\u5316&#xff0c;\u6210\u4e3a\u79fb\u52a8\u8bbe\u5907\u4e0a\u7684\u6807\u6746\u6a21\u578b\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u53c2\u8003\u6587\u732e&#xff1a;Searching for MobileNetV3<\/p>\n<hr \/>\n<h2>MobileNet v4&#xff1a;\u901a\u7528\u8bbe\u8ba1\u4e0e\u591a\u786c\u4ef6\u652f\u6301<\/h2>\n<p>\u53d1\u5e03\u65f6\u95f4&#xff1a;2024 \u5e74 MobileNet v4 \u662f\u6700\u65b0\u4e00\u4ee3&#xff0c;\u65e8\u5728\u4e3a\u79fb\u52a8\u751f\u6001\u7cfb\u7edf\u63d0\u4f9b\u901a\u7528\u7684\u9ad8\u6548\u67b6\u6784&#xff0c;\u9488\u5bf9\u591a\u79cd\u786c\u4ef6&#xff08;\u5982 CPU\u3001DSP\u3001GPU \u548c\u4e13\u7528\u52a0\u901f\u5668&#xff09;\u8fdb\u884c\u4e86\u4f18\u5316\u3002<\/p>\n<ul>\n<li>\n<p>\u539f\u7406\u4e0e\u521b\u65b0&#xff1a;<\/p>\n<li>\u901a\u7528\u5012\u7f6e\u74f6\u9888&#xff08;UIB&#xff09;&#xff1a; \u901a\u8fc7\u641c\u7d22\u878d\u5408\u591a\u79cd\u5757\u7c7b\u578b&#xff08;\u5012\u7f6e\u74f6\u9888\u3001ConvNext\u3001FFN \u548c Extra Depthwise&#xff09;&#xff0c;\u751f\u6210\u7075\u6d3b\u7684\u8ba1\u7b97\u5355\u5143&#xff0c;\u9002\u5e94\u4e0d\u540c\u786c\u4ef6\u9700\u6c42\u3002<\/li>\n<li>\u79fb\u52a8\u4f18\u5316\u591a\u67e5\u8be2\u6ce8\u610f\u529b&#xff08;Mobile MQA&#xff09;&#xff1a; \u5f15\u5165\u8f7b\u91cf\u5316\u7684\u6ce8\u610f\u529b\u673a\u5236&#xff0c;\u52a0\u901f\u7ea6 39%&#xff0c;\u63d0\u5347\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002<\/li>\n<li>\u589e\u5f3a\u7684 NAS&#xff1a; \u4f7f\u7528\u66f4\u5148\u8fdb\u7684\u641c\u7d22\u7b56\u7565&#xff0c;\u4f18\u5316\u5ef6\u8fdf\u3001\u5185\u5b58\u548c\u51c6\u786e\u6027\u7684 Pareto \u524d\u6cbf\u3002<\/li>\n<\/li>\n<li>\n<p>\u67b6\u6784\u8bbe\u8ba1&#xff1a; v4 \u7684\u5177\u4f53\u5c42\u7ea7\u7ec6\u8282\u4f9d\u8d56\u4e8e\u641c\u7d22\u7ed3\u679c&#xff0c;\u4f46\u5176\u6838\u5fc3\u662f UIB \u5757\u548c MQA \u6a21\u5757\u7684\u7ec4\u5408&#xff0c;\u652f\u6301\u591a\u79cd\u786c\u4ef6\u52a0\u901f\u5668&#xff08;\u5982 Apple Neural Engine \u548c Google EdgeTPU&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u6027\u80fd\u8868\u73b0&#xff1a; \u5177\u4f53\u6570\u636e\u9700\u53c2\u8003\u6700\u65b0\u8bba\u6587&#xff0c;\u4f46 v4 \u88ab\u8bbe\u8ba1\u4e3a\u5728\u591a\u79cd\u79fb\u52a8\u573a\u666f\u4e0b\u5b9e\u73b0\u6700\u4f18\u6027\u80fd&#xff0c;\u5c24\u5176\u662f\u5728\u8fb9\u7f18\u8ba1\u7b97\u4e2d\u8868\u73b0\u51fa\u8272\u3002<\/p>\n<\/li>\n<li>\n<p>\u610f\u4e49&#xff1a; MobileNet v4 \u7684\u901a\u7528\u6027\u548c\u786c\u4ef6\u9002\u914d\u80fd\u529b\u4f7f\u5176\u6210\u4e3a\u672a\u6765\u79fb\u52a8\u89c6\u89c9\u4efb\u52a1\u7684\u7406\u60f3\u9009\u62e9\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u53c2\u8003\u6587\u732e&#xff1a;MobileNetV4 \u2013 Universal Models for the Mobile Ecosystem<\/p>\n<hr \/>\n<h2>\u53d1\u5c55\u5386\u7a0b\u603b\u7ed3<\/h2>\n<table>\n<tr>\u7248\u672c\u53d1\u5e03\u5e74\u4efd\u6838\u5fc3\u521b\u65b0Top-1 \u51c6\u786e\u7387&#xff08;\u03b1&#061;1&#xff09;\u53c2\u6570\u91cf (M)MAdds (M)<\/tr>\n<tbody>\n<tr>\n<td>MobileNet v1<\/td>\n<td>2017<\/td>\n<td>\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef<\/td>\n<td>70.6%<\/td>\n<td>4.2<\/td>\n<td>569<\/td>\n<\/tr>\n<tr>\n<td>MobileNet v2<\/td>\n<td>2018<\/td>\n<td>\u5012\u7f6e\u6b8b\u5dee&#xff0c;\u7ebf\u6027\u74f6\u9888<\/td>\n<td>72.0%<\/td>\n<td>3.4<\/td>\n<td>300<\/td>\n<\/tr>\n<tr>\n<td>MobileNet v3<\/td>\n<td>2019<\/td>\n<td>NAS&#xff0c;h-swish&#xff0c;SE \u6a21\u5757<\/td>\n<td>75.2% (Large)<\/td>\n<td>5.4<\/td>\n<td>217<\/td>\n<\/tr>\n<tr>\n<td>MobileNet v4<\/td>\n<td>2024<\/td>\n<td>UIB&#xff0c;Mobile MQA&#xff0c;\u589e\u5f3a NAS<\/td>\n<td>\u672a\u516c\u5f00<\/td>\n<td>\u672a\u516c\u5f00<\/td>\n<td>\u672a\u516c\u5f00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h2>\u672a\u6765\u5c55\u671b<\/h2>\n<p>MobileNet \u4ece v1 \u7684\u8f7b\u91cf\u5316\u63a2\u7d22&#xff0c;\u5230 v4 \u7684\u901a\u7528\u786c\u4ef6\u4f18\u5316&#xff0c;\u5c55\u793a\u4e86\u6df1\u5ea6\u5b66\u4e60\u5728\u79fb\u52a8\u8bbe\u5907\u4e0a\u7684\u6f14\u8fdb\u8def\u5f84\u3002\u672a\u6765&#xff0c;\u968f\u7740\u8fb9\u7f18\u8ba1\u7b97\u548c\u4e13\u7528\u786c\u4ef6\u7684\u53d1\u5c55&#xff0c;MobileNet \u53ef\u80fd\u8fdb\u4e00\u6b65\u901a\u8fc7\u81ea\u52a8\u5316\u8bbe\u8ba1\u548c\u8de8\u5e73\u53f0\u4f18\u5316&#xff0c;\u6ee1\u8db3\u66f4\u590d\u6742\u7684\u5b9e\u65f6\u89c6\u89c9\u9700\u6c42\u3002<\/p>\n<hr \/>\n<h2>\u53c2\u8003\u6587\u732e<\/h2>\n<li>Howard, A. G., et al. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861<\/li>\n<li>Sandler, M., et al. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv:1801.04381<\/li>\n<li>Howard, A., et al. (2019). Searching for MobileNetV3. arXiv:1905.02244<\/li>\n<li>Yang, T., et al. (2024). MobileNetV4 \u2013 Universal Models for the Mobile Ecosystem. arXiv:2404.10518<\/li>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb1.1k\u6b21\uff0c\u70b9\u8d5e22\u6b21\uff0c\u6536\u85cf22\u6b21\u3002MobileNet \u662f\u4e00\u4e2a\u4e3a\u79fb\u52a8\u548c\u5d4c\u5165\u5f0f\u8bbe\u5907\u8bbe\u8ba1\u7684\u8f7b\u91cf\u5316\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5bb6\u65cf\uff0c\u4ece v1\uff082017\uff09\u5230 v4\uff082024\uff09\u4e0d\u65ad\u6f14\u8fdb\u3002v1 \u5f15\u5165\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\uff0c\u663e\u8457\u63d0\u5347\u6548\u7387\u3002v2\uff082018\uff09\u91c7\u7528\u5012\u7f6e\u6b8b\u5dee\u548c\u7ebf\u6027\u74f6\u9888\uff0c\u8fdb\u4e00\u6b65\u4f18\u5316\u6027\u80fd\u3002v3\uff082019\uff09\u901a\u8fc7\u795e\u7ecf\u67b6\u6784\u641c\u7d22\u548c h-swish \u6fc0\u6d3b\uff0c\u63d0\u4f9b Large \u548c Small \u53d8\u4f53\uff0c\u9002\u914d\u4e0d\u540c\u573a\u666f\u3002v4\uff082024\uff09\u5f15\u5165\u901a\u7528\u5012\u7f6e\u74f6\u9888\u548c\u79fb\u52a8\u4f18\u5316\u6ce8\u610f\u529b\u673a\u5236\uff0c\u9488\u5bf9\u591a\u786c\u4ef6\u4f18\u5316\uff0c\u5f3a\u8c03\u901a\u7528\u6027\u3002\u5404\u7248\u672c\u5728\u6548\u7387\u4e0e\u51c6\u786e\u6027\u95f4\u6301\u7eed\u6539\u8fdb\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5b9e\u65f6\u89c6\u89c9\u4efb\u52a1\uff0c\u672a\u6765\u6709\u671b\u8fdb\u4e00\u6b65\u652f\u6301\u8fb9\u7f18\u8ba1\u7b97\u9700\u6c42\u3002_mobilenetv4<\/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":[2394,50,2863,207,188],"topic":[],"class_list":["post-33931","post","type-post","status-publish","format-standard","hentry","category-server","tag-cnn","tag-50","tag-2863","tag-207","tag-188"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - 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