{"id":51748,"date":"2025-08-11T06:19:59","date_gmt":"2025-08-10T22:19:59","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/51748.html"},"modified":"2025-08-11T06:19:59","modified_gmt":"2025-08-10T22:19:59","slug":"%e3%80%90%e5%ae%8c%e6%95%b4%e6%ba%90%e7%a0%81%e6%95%b0%e6%8d%ae%e9%9b%86%e9%83%a8%e7%bd%b2%e6%95%99%e7%a8%8b%e3%80%91%e8%9e%ba%e6%a0%93%e5%b0%ba%e5%af%b8%e8%af%86%e5%88%ab%e5%ae%9e%e4%be%8b%e5%88%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/51748.html","title":{"rendered":"\u3010\u5b8c\u6574\u6e90\u7801+\u6570\u636e\u96c6+\u90e8\u7f72\u6559\u7a0b\u3011\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u5b9e\u4f8b\u5206\u5272\u7cfb\u7edf\u6e90\u7801\u548c\u6570\u636e\u96c6\uff1a\u6539\u8fdbyolo11-fasternet-bifpn"},"content":{"rendered":"

\u80cc\u666f\u610f\u4e49<\/h2>\n

\u7814\u7a76\u80cc\u666f\u4e0e\u610f\u4e49<\/p>\n

\u968f\u7740\u5de5\u4e1a\u81ea\u52a8\u5316\u548c\u667a\u80fd\u5236\u9020\u7684\u8fc5\u901f\u53d1\u5c55，\u7cbe\u786e\u7684\u96f6\u90e8\u4ef6\u8bc6\u522b\u4e0e\u5206\u7c7b\u5728\u751f\u4ea7\u6d41\u7a0b\u4e2d\u53d8\u5f97\u6108\u53d1\u91cd\u8981\u3002\u87ba\u6813\u4f5c\u4e3a\u673a\u68b0\u8fde\u63a5\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u57fa\u672c\u5143\u4ef6，\u5176\u5c3a\u5bf8\u8bc6\u522b\u5bf9\u4e8e\u786e\u4fdd\u4ea7\u54c1\u8d28\u91cf\u548c\u88c5\u914d\u7cbe\u5ea6\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002\u4f20\u7edf\u7684\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u65b9\u6cd5\u5f80\u5f80\u4f9d\u8d56\u4e8e\u4eba\u5de5\u68c0\u6d4b\u6216\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f，\u8fd9\u4e0d\u4ec5\u6548\u7387\u4f4e\u4e0b，\u800c\u4e14\u5bb9\u6613\u53d7\u5230\u4eba\u4e3a\u56e0\u7d20\u7684\u5f71\u54cd，\u5bfc\u81f4\u8bc6\u522b\u51c6\u786e\u7387\u4f4e\u4e0b\u3002\u56e0\u6b64，\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u6280\u672f\u5728\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u4e2d\u7684\u5e94\u7528\u663e\u5f97\u5c24\u4e3a\u5fc5\u8981\u3002<\/p>\n

YOLO（You Only Look Once）\u7cfb\u5217\u6a21\u578b\u56e0\u5176\u9ad8\u6548\u7684\u5b9e\u65f6\u76ee\u6807\u68c0\u6d4b\u80fd\u529b\u800c\u5e7f\u53d7\u6b22\u8fce\u3002YOLOv11\u4f5c\u4e3a\u8be5\u7cfb\u5217\u7684\u6700\u65b0\u7248\u672c，\u7ed3\u5408\u4e86\u66f4\u5148\u8fdb\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u7b97\u6cd5\u4f18\u5316，\u80fd\u591f\u5728\u4fdd\u6301\u9ad8\u7cbe\u5ea6\u7684\u540c\u65f6\u663e\u8457\u63d0\u9ad8\u68c0\u6d4b\u901f\u5ea6\u3002\u901a\u8fc7\u5bf9YOLOv11\u8fdb\u884c\u6539\u8fdb，\u7279\u522b\u662f\u5728\u5b9e\u4f8b\u5206\u5272\u4efb\u52a1\u4e2d\u7684\u5e94\u7528，\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u87ba\u6813\u5c3a\u5bf8\u7684\u7cbe\u786e\u8bc6\u522b\u4e0e\u5206\u5272，\u4ece\u800c\u4e3a\u540e\u7eed\u7684\u81ea\u52a8\u5316\u88c5\u914d\u548c\u8d28\u91cf\u63a7\u5236\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002<\/p>\n

\u672c\u7814\u7a76\u57fa\u4e8e\u4e00\u4e2a\u5305\u542b1500\u5f20\u56fe\u50cf\u7684\u87ba\u6813\u6570\u636e\u96c6，\u6db5\u76d6\u4e86M6\u548cM8\u4e24\u79cd\u87ba\u6813\u7c7b\u522b，\u65e8\u5728\u6784\u5efa\u4e00\u4e2a\u9ad8\u6548\u7684\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u5b9e\u4f8b\u5206\u5272\u7cfb\u7edf\u3002\u8be5\u6570\u636e\u96c6\u7ecf\u8fc7\u7cbe\u5fc3\u6807\u6ce8，\u80fd\u591f\u4e3a\u6a21\u578b\u8bad\u7ec3\u63d0\u4f9b\u4e30\u5bcc\u7684\u6837\u672c，\u786e\u4fdd\u6a21\u578b\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u9c81\u68d2\u6027\u548c\u51c6\u786e\u6027\u3002\u901a\u8fc7\u5bf9YOLOv11\u7684\u6539\u8fdb，\u671f\u671b\u80fd\u591f\u63d0\u5347\u6a21\u578b\u5728\u590d\u6742\u80cc\u666f\u4e0b\u7684\u8bc6\u522b\u80fd\u529b，\u8fdb\u800c\u63a8\u52a8\u667a\u80fd\u5236\u9020\u9886\u57df\u7684\u6280\u672f\u8fdb\u6b65\u3002<\/p>\n

\u7efc\u4e0a\u6240\u8ff0，\u57fa\u4e8e\u6539\u8fdbYOLOv11\u7684\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u5b9e\u4f8b\u5206\u5272\u7cfb\u7edf\u7684\u7814\u7a76，\u4e0d\u4ec5\u80fd\u591f\u63d0\u5347\u5de5\u4e1a\u751f\u4ea7\u4e2d\u7684\u81ea\u52a8\u5316\u6c34\u5e73，\u8fd8\u80fd\u4e3a\u76f8\u5173\u9886\u57df\u7684\u7814\u7a76\u63d0\u4f9b\u65b0\u7684\u601d\u8def\u548c\u65b9\u6cd5，\u5177\u6709\u91cd\u8981\u7684\u7406\u8bba\u4ef7\u503c\u548c\u5b9e\u9645\u5e94\u7528\u610f\u4e49\u3002<\/p>\n

\u56fe\u7247\u6548\u679c<\/h2>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6570\u636e\u96c6\u4fe1\u606f<\/h2>\n

\u672c\u9879\u76ee\u6570\u636e\u96c6\u4fe1\u606f\u4ecb\u7ecd<\/p>\n

\u672c\u9879\u76ee\u65e8\u5728\u5f00\u53d1\u4e00\u4e2a\u6539\u8fdb\u7248\u7684YOLOv11\u6a21\u578b，\u4ee5\u5b9e\u73b0\u9ad8\u6548\u7684\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u548c\u5b9e\u4f8b\u5206\u5272\u3002\u4e3a\u6b64，\u6211\u4eec\u6784\u5efa\u4e86\u4e00\u4e2a\u4e13\u95e8\u7684\u6570\u636e\u96c6，\u6db5\u76d6\u4e86\u4e24\u79cd\u4e3b\u8981\u7c7b\u522b\u7684\u87ba\u6813：M6\u548cM8\u3002\u8fd9\u4e24\u79cd\u87ba\u6813\u5e7f\u6cdb\u5e94\u7528\u4e8e\u673a\u68b0\u5de5\u7a0b\u548c\u5efa\u7b51\u9886\u57df，\u56e0\u5176\u5728\u8fde\u63a5\u548c\u56fa\u5b9a\u7ed3\u6784\u4e2d\u7684\u91cd\u8981\u6027\u800c\u5907\u53d7\u5173\u6ce8\u3002\u6570\u636e\u96c6\u7684\u6784\u5efa\u8fc7\u7a0b\u7ecf\u8fc7\u7cbe\u5fc3\u8bbe\u8ba1，\u786e\u4fdd\u4e86\u6570\u636e\u7684\u591a\u6837\u6027\u548c\u4ee3\u8868\u6027，\u4ee5\u4fbf\u6a21\u578b\u80fd\u591f\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u8868\u73b0\u51fa\u8272\u3002<\/p>\n

\u6570\u636e\u96c6\u5305\u542b\u4e86\u5927\u91cf\u7684\u56fe\u50cf\u6837\u672c，\u8fd9\u4e9b\u6837\u672c\u5728\u4e0d\u540c\u7684\u5149\u7167\u6761\u4ef6\u3001\u80cc\u666f\u73af\u5883\u548c\u89d2\u5ea6\u4e0b\u62cd\u6444，\u529b\u6c42\u8986\u76d6\u87ba\u6813\u5728\u5b9e\u9645\u4f7f\u7528\u4e2d\u53ef\u80fd\u9047\u5230\u7684\u5404\u79cd\u60c5\u51b5\u3002\u6bcf\u4e2a\u56fe\u50cf\u90fd\u7ecf\u8fc7\u6807\u6ce8，\u660e\u786e\u6307\u51fa\u4e86M6\u548cM8\u87ba\u6813\u7684\u4f4d\u7f6e\u548c\u5c3a\u5bf8\u4fe1\u606f\u3002\u8fd9\u79cd\u7ec6\u81f4\u7684\u6807\u6ce8\u4e0d\u4ec5\u6709\u52a9\u4e8e\u6a21\u578b\u7684\u8bad\u7ec3，\u4e5f\u4e3a\u540e\u7eed\u7684\u5b9e\u4f8b\u5206\u5272\u4efb\u52a1\u63d0\u4f9b\u4e86\u53ef\u9760\u7684\u57fa\u7840\u3002<\/p>\n

\u5728\u6570\u636e\u96c6\u7684\u6784\u5efa\u8fc7\u7a0b\u4e2d，\u6211\u4eec\u7279\u522b\u5173\u6ce8\u4e86\u56fe\u50cf\u7684\u8d28\u91cf\u548c\u6807\u6ce8\u7684\u51c6\u786e\u6027\u3002\u6bcf\u4e2a\u7c7b\u522b\u7684\u6837\u672c\u6570\u91cf\u7ecf\u8fc7\u7cbe\u5fc3\u5e73\u8861，\u4ee5\u907f\u514d\u6a21\u578b\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u504f\u501a\u73b0\u8c61\u3002\u6b64\u5916，\u6570\u636e\u96c6\u8fd8\u5305\u62ec\u4e86\u4e00\u4e9b\u96be\u5ea6\u8f83\u5927\u7684\u6837\u672c，\u4f8b\u5982\u5728\u590d\u6742\u80cc\u666f\u4e0b\u7684\u87ba\u6813\u56fe\u50cf，\u8fd9\u5c06\u6709\u52a9\u4e8e\u63d0\u9ad8\u6a21\u578b\u7684\u9c81\u68d2\u6027\u548c\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n

\u901a\u8fc7\u5bf9\u8be5\u6570\u636e\u96c6\u7684\u8bad\u7ec3，\u6211\u4eec\u671f\u671b\u6539\u8fdbYOLOv11\u5728\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u548c\u5b9e\u4f8b\u5206\u5272\u4efb\u52a1\u4e2d\u7684\u6027\u80fd，\u4f7f\u5176\u80fd\u591f\u66f4\u51c6\u786e\u5730\u8bc6\u522b\u548c\u5206\u5272\u4e0d\u540c\u5c3a\u5bf8\u7684\u87ba\u6813\u3002\u8fd9\u5c06\u4e3a\u5de5\u4e1a\u81ea\u52a8\u5316\u548c\u667a\u80fd\u5236\u9020\u63d0\u4f9b\u5f3a\u6709\u529b\u7684\u6280\u672f\u652f\u6301，\u63a8\u52a8\u76f8\u5173\u9886\u57df\u7684\u8fdb\u4e00\u6b65\u53d1\u5c55\u3002
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6838\u5fc3\u4ee3\u7801<\/h2>\n

\u4ee5\u4e0b\u662f\u4ee3\u7801\u4e2d\u6700\u6838\u5fc3\u7684\u90e8\u5206，\u5e76\u9644\u4e0a\u8be6\u7ec6\u7684\u4e2d\u6587\u6ce8\u91ca：<\/p>\n

import torch
\nimport torch.nn as nn<\/p>\n

\u5b9a\u4e49\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u6279\u91cf\u5f52\u4e00\u5316\u7c7b
\nclass RepBN(nn.Module):
\ndef init(self, channels):
\nsuper(RepBN, self).init()
\n# \u521d\u59cb\u5316\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u53c2\u6570 alpha，\u521d\u59cb\u503c\u4e3a 1
\nself.alpha = nn.Parameter(torch.ones(1))
\n# \u521d\u59cb\u5316\u4e00\u4e2a\u4e00\u7ef4\u6279\u91cf\u5f52\u4e00\u5316\u5c42
\nself.bn = nn.BatchNorm1d(channels)<\/p>\n

def forward(self, x):
\n # \u5c06\u8f93\u5165\u7684\u7ef4\u5ea6\u8fdb\u884c\u8f6c\u7f6e，\u4ea4\u6362\u7b2c1\u7ef4\u548c\u7b2c2\u7ef4
\n x = x.transpose(1, 2)
\n # \u8fdb\u884c\u6279\u91cf\u5f52\u4e00\u5316，\u5e76\u52a0\u4e0a alpha \u4e58\u4ee5\u8f93\u5165 x
\n x = self.bn(x) + self.alpha * x
\n # \u518d\u6b21\u8f6c\u7f6e\u56de\u539f\u6765\u7684\u7ef4\u5ea6
\n x = x.transpose(1, 2)
\n return x<\/p>\n

\u5b9a\u4e49\u4e00\u4e2a\u7ebf\u6027\u5f52\u4e00\u5316\u7c7b
\nclass LinearNorm(nn.Module):
\ndef init(self, dim, norm1, norm2, warm=0, step=300000, r0=1.0):
\nsuper(LinearNorm, self).init()
\n# \u6ce8\u518c\u7f13\u51b2\u533a，\u7528\u4e8e\u5b58\u50a8 warm-up \u8ba1\u6570\u5668
\nself.register_buffer(\u2018warm\u2019, torch.tensor(warm))
\n# \u6ce8\u518c\u7f13\u51b2\u533a，\u7528\u4e8e\u5b58\u50a8\u8fed\u4ee3\u6b21\u6570
\nself.register_buffer(\u2018iter\u2019, torch.tensor(step))
\n# \u6ce8\u518c\u7f13\u51b2\u533a，\u7528\u4e8e\u5b58\u50a8\u603b\u7684\u8fed\u4ee3\u6b65\u6570
\nself.register_buffer(\u2018total_step\u2019, torch.tensor(step))
\nself.r0 = r0 # \u521d\u59cb\u6bd4\u4f8b\u56e0\u5b50
\n# \u521d\u59cb\u5316\u4e24\u4e2a\u5f52\u4e00\u5316\u5c42
\nself.norm1 = norm1(dim)
\nself.norm2 = norm2(dim)<\/p>\n

def forward(self, x):
\n # \u5982\u679c\u6a21\u578b\u5904\u4e8e\u8bad\u7ec3\u6a21\u5f0f
\n if self.training:
\n # \u5982\u679c\u8fd8\u6709 warm-up \u6b65\u9aa4
\n if self.warm > 0:
\n # \u51cf\u5c11 warm-up \u6b65\u9aa4\u8ba1\u6570
\n self.warm.copy_(self.warm – 1)
\n # \u8fdb\u884c\u7b2c\u4e00\u6b21\u5f52\u4e00\u5316
\n x = self.norm1(x)
\n else:
\n # \u8ba1\u7b97\u5f53\u524d\u7684\u6bd4\u4f8b\u56e0\u5b50 lamda
\n lamda = self.r0 * self.iter \/ self.total_step
\n # \u51cf\u5c11\u8fed\u4ee3\u6b21\u6570
\n if self.iter > 0:
\n self.iter.copy_(self.iter – 1)
\n # \u8fdb\u884c\u4e24\u6b21\u5f52\u4e00\u5316
\n x1 = self.norm1(x)
\n x2 = self.norm2(x)
\n # \u6309\u7167 lamda \u52a0\u6743\u5408\u5e76\u4e24\u4e2a\u5f52\u4e00\u5316\u7ed3\u679c
\n x = lamda * x1 + (1 – lamda) * x2
\n else:
\n # \u5982\u679c\u6a21\u578b\u5904\u4e8e\u8bc4\u4f30\u6a21\u5f0f，\u76f4\u63a5\u4f7f\u7528\u7b2c\u4e8c\u4e2a\u5f52\u4e00\u5316
\n x = self.norm2(x)
\n return x<\/p>\n

\u4ee3\u7801\u6838\u5fc3\u90e8\u5206\u8bf4\u660e：
\nRepBN \u7c7b：\u5b9e\u73b0\u4e86\u4e00\u4e2a\u81ea\u5b9a\u4e49\u7684\u6279\u91cf\u5f52\u4e00\u5316\u5c42，\u589e\u52a0\u4e86\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u53c2\u6570 alpha，\u7528\u4e8e\u8c03\u6574\u5f52\u4e00\u5316\u7684\u8f93\u51fa\u3002
\nLinearNorm \u7c7b：\u5b9e\u73b0\u4e86\u4e00\u4e2a\u7ebf\u6027\u5f52\u4e00\u5316\u673a\u5236，\u652f\u6301 warm-up \u548c\u52a8\u6001\u8c03\u6574\u5f52\u4e00\u5316\u7684\u65b9\u5f0f\u3002\u6839\u636e\u8bad\u7ec3\u7684\u8fdb\u5ea6\u52a8\u6001\u9009\u62e9\u4f7f\u7528\u4e24\u79cd\u4e0d\u540c\u7684\u5f52\u4e00\u5316\u65b9\u6cd5\u7684\u52a0\u6743\u7ec4\u5408\u3002
\n\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6\u5b9a\u4e49\u4e86\u4e24\u4e2a\u795e\u7ecf\u7f51\u7edc\u6a21\u5757，\u5206\u522b\u662f RepBN \u548c LinearNorm，\u5b83\u4eec\u90fd\u7ee7\u627f\u81ea PyTorch \u7684 nn.Module \u7c7b\u3002<\/p>\n

RepBN \u7c7b\u5b9e\u73b0\u4e86\u4e00\u79cd\u81ea\u5b9a\u4e49\u7684\u6279\u91cf\u5f52\u4e00\u5316（Batch Normalization）\u65b9\u6cd5\u3002\u5b83\u7684\u6784\u9020\u51fd\u6570\u63a5\u53d7\u4e00\u4e2a\u53c2\u6570 channels，\u8868\u793a\u8f93\u5165\u6570\u636e\u7684\u901a\u9053\u6570\u3002\u5728\u6784\u9020\u51fd\u6570\u4e2d，\u5b9a\u4e49\u4e86\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u53c2\u6570 alpha，\u521d\u59cb\u503c\u4e3a 1，\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a\u6807\u51c6\u7684\u6279\u91cf\u5f52\u4e00\u5316\u5c42 bn\u3002\u5728\u524d\u5411\u4f20\u64ad\u65b9\u6cd5 forward \u4e2d，\u8f93\u5165 x \u9996\u5148\u8fdb\u884c\u7ef4\u5ea6\u8f6c\u6362，\u5c06\u901a\u9053\u7ef4\u548c\u65f6\u95f4\u7ef4\u8fdb\u884c\u4ea4\u6362，\u7136\u540e\u901a\u8fc7\u6279\u91cf\u5f52\u4e00\u5316\u5c42\u8fdb\u884c\u5904\u7406，\u6700\u540e\u5c06\u5f52\u4e00\u5316\u540e\u7684\u7ed3\u679c\u4e0e alpha \u4e58\u4ee5\u539f\u59cb\u8f93\u5165\u76f8\u52a0，\u518d\u6b21\u8fdb\u884c\u7ef4\u5ea6\u8f6c\u6362，\u8fd4\u56de\u5904\u7406\u540e\u7684\u7ed3\u679c\u3002\u8fd9\u79cd\u8bbe\u8ba1\u4f7f\u5f97\u6a21\u578b\u5728\u8fdb\u884c\u6279\u91cf\u5f52\u4e00\u5316\u7684\u540c\u65f6，\u4fdd\u7559\u4e86\u8f93\u5165\u4fe1\u606f\u7684\u67d0\u4e9b\u7279\u5f81\u3002<\/p>\n

LinearNorm \u7c7b\u5219\u5b9e\u73b0\u4e86\u4e00\u79cd\u7ebf\u6027\u5f52\u4e00\u5316\u7684\u65b9\u6cd5\u3002\u5b83\u7684\u6784\u9020\u51fd\u6570\u63a5\u53d7\u591a\u4e2a\u53c2\u6570，\u5305\u62ec dim（\u7ef4\u5ea6）\u3001norm1 \u548c norm2（\u5206\u522b\u662f\u4e24\u79cd\u5f52\u4e00\u5316\u65b9\u6cd5）\u3001warm（\u9884\u70ed\u6b65\u6570）\u3001step（\u5f53\u524d\u6b65\u6570）\u548c r0（\u521d\u59cb\u6bd4\u4f8b）\u3002\u5728\u6784\u9020\u51fd\u6570\u4e2d，\u4f7f\u7528 register_buffer \u65b9\u6cd5\u6ce8\u518c\u4e86\u4e00\u4e9b\u7f13\u51b2\u533a，\u7528\u4e8e\u5b58\u50a8\u9884\u70ed\u6b65\u6570\u548c\u5f53\u524d\u6b65\u6570\u7b49\u4fe1\u606f\u3002\u524d\u5411\u4f20\u64ad\u65b9\u6cd5 forward \u4e2d，\u9996\u5148\u68c0\u67e5\u6a21\u578b\u662f\u5426\u5904\u4e8e\u8bad\u7ec3\u72b6\u6001\u3002\u5982\u679c\u662f\u8bad\u7ec3\u72b6\u6001\u4e14\u9884\u70ed\u6b65\u6570\u5927\u4e8e 0，\u5219\u8fdb\u884c\u7b2c\u4e00\u6b21\u5f52\u4e00\u5316 norm1，\u5e76\u51cf\u5c11\u9884\u70ed\u6b65\u6570\u3002\u5982\u679c\u9884\u70ed\u7ed3\u675f，\u5219\u6839\u636e\u5f53\u524d\u6b65\u6570\u8ba1\u7b97\u4e00\u4e2a\u6bd4\u4f8b lamda，\u5e76\u6839\u636e\u8fd9\u4e2a\u6bd4\u4f8b\u5bf9\u8f93\u5165 x \u8fdb\u884c\u4e24\u79cd\u5f52\u4e00\u5316\u65b9\u6cd5 norm1 \u548c norm2 \u7684\u52a0\u6743\u7ec4\u5408\u3002\u5982\u679c\u6a21\u578b\u4e0d\u5728\u8bad\u7ec3\u72b6\u6001，\u5219\u76f4\u63a5\u4f7f\u7528 norm2 \u5bf9\u8f93\u5165\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\u3002\u8fd9\u6837\u8bbe\u8ba1\u7684\u76ee\u7684\u662f\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u9010\u6b65\u8c03\u6574\u5f52\u4e00\u5316\u7684\u65b9\u5f0f，\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n

\u6574\u4f53\u6765\u770b，\u8fd9\u4e2a\u6587\u4ef6\u5b9e\u73b0\u4e86\u4e24\u79cd\u81ea\u5b9a\u4e49\u7684\u5f52\u4e00\u5316\u65b9\u6cd5，\u5206\u522b\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u573a\u666f，\u589e\u5f3a\u4e86\u6a21\u578b\u7684\u7075\u6d3b\u6027\u548c\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

10.4 afpn.py
\n\u4ee5\u4e0b\u662f\u4ee3\u7801\u4e2d\u6700\u6838\u5fc3\u7684\u90e8\u5206，\u5e76\u9644\u4e0a\u8be6\u7ec6\u7684\u4e2d\u6587\u6ce8\u91ca：<\/p>\n

import torch
\nimport torch.nn as nn
\nimport torch.nn.functional as F
\nfrom \u2026modules.conv import Conv<\/p>\n

class BasicBlock(nn.Module):
\n\u201c\u201d\u201c\u57fa\u672c\u5757，\u5305\u542b\u4e24\u4e2a\u5377\u79ef\u5c42\u548c\u6b8b\u5dee\u8fde\u63a5\u201d\u201c\u201d
\ndef init(self, filter_in, filter_out):
\nsuper(BasicBlock, self).init()
\n# \u7b2c\u4e00\u4e2a\u5377\u79ef\u5c42，\u4f7f\u75283×3\u5377\u79ef
\nself.conv1 = Conv(filter_in, filter_out, 3)
\n# \u7b2c\u4e8c\u4e2a\u5377\u79ef\u5c42，\u4f7f\u75283×3\u5377\u79ef，\u4e0d\u4f7f\u7528\u6fc0\u6d3b\u51fd\u6570
\nself.conv2 = Conv(filter_out, filter_out, 3, act=False)<\/p>\n

def forward(self, x):
\n residual = x # \u4fdd\u5b58\u8f93\u5165\u4ee5\u4fbf\u8fdb\u884c\u6b8b\u5dee\u8fde\u63a5
\n out = self.conv1(x) # \u901a\u8fc7\u7b2c\u4e00\u4e2a\u5377\u79ef\u5c42
\n out = self.conv2(out) # \u901a\u8fc7\u7b2c\u4e8c\u4e2a\u5377\u79ef\u5c42
\n out += residual # \u6dfb\u52a0\u6b8b\u5dee
\n return self.conv1.act(out) # \u901a\u8fc7\u6fc0\u6d3b\u51fd\u6570\u8fd4\u56de\u7ed3\u679c<\/p>\n

class Upsample(nn.Module):
\n\u201c\u201d\u201c\u4e0a\u91c7\u6837\u6a21\u5757，\u4f7f\u75281×1\u5377\u79ef\u548c\u53cc\u7ebf\u6027\u63d2\u503c\u201d\u201c\u201d
\ndef init(self, in_channels, out_channels, scale_factor=2):
\nsuper(Upsample, self).init()
\nself.upsample = nn.Sequential(
\nConv(in_channels, out_channels, 1), # 1×1\u5377\u79ef
\nnn.Upsample(scale_factor=scale_factor, mode=\u2018bilinear\u2019) # \u53cc\u7ebf\u6027\u63d2\u503c\u4e0a\u91c7\u6837
\n)<\/p>\n

def forward(self, x):
\n return self.upsample(x) # \u8fd4\u56de\u4e0a\u91c7\u6837\u540e\u7684\u7ed3\u679c<\/p>\n

class Downsample_x2(nn.Module):
\n\u201c\u201d\u201c\u4e0b\u91c7\u6837\u6a21\u5757，\u4f7f\u75282×2\u5377\u79ef\u201d\u201c\u201d
\ndef init(self, in_channels, out_channels):
\nsuper(Downsample_x2, self).init()
\nself.downsample = Conv(in_channels, out_channels, 2, 2, 0) # 2×2\u5377\u79ef，\u6b65\u5e45\u4e3a2<\/p>\n

def forward(self, x):
\n return self.downsample(x) # \u8fd4\u56de\u4e0b\u91c7\u6837\u540e\u7684\u7ed3\u679c<\/p>\n

class ASFF_2(nn.Module):
\n\u201c\u201d\u201c\u81ea\u9002\u5e94\u7279\u5f81\u878d\u5408\u6a21\u5757，\u878d\u5408\u4e24\u4e2a\u8f93\u5165\u7279\u5f81\u56fe\u201d\u201c\u201d
\ndef init(self, inter_dim=512):
\nsuper(ASFF_2, self).init()
\nself.inter_dim = inter_dim
\ncompress_c = 8 # \u538b\u7f29\u901a\u9053\u6570<\/p>\n

# \u7528\u4e8e\u8ba1\u7b97\u6743\u91cd\u7684\u5377\u79ef\u5c42
\n self.weight_level_1 = Conv(self.inter_dim, compress_c, 1)
\n self.weight_level_2 = Conv(self.inter_dim, compress_c, 1)
\n self.weight_levels = nn.Conv2d(compress_c * 2, 2, kernel_size=1, stride=1, padding=0)
\n self.conv = Conv(self.inter_dim, self.inter_dim, 3) # \u6700\u540e\u7684\u5377\u79ef\u5c42<\/p>\n

def forward(self, input1, input2):
\n # \u8ba1\u7b97\u8f93\u5165\u7279\u5f81\u56fe\u7684\u6743\u91cd
\n level_1_weight_v = self.weight_level_1(input1)
\n level_2_weight_v = self.weight_level_2(input2)<\/p>\n

# \u5408\u5e76\u6743\u91cd\u5e76\u8ba1\u7b97\u6700\u7ec8\u6743\u91cd
\n levels_weight_v = torch.cat((level_1_weight_v, level_2_weight_v), 1)
\n levels_weight = self.weight_levels(levels_weight_v)
\n levels_weight = F.softmax(levels_weight, dim=1) # \u5f52\u4e00\u5316\u6743\u91cd<\/p>\n

# \u878d\u5408\u7279\u5f81\u56fe
\n fused_out_reduced = input1 * levels_weight[:, 0:1, :, :] + \\\\
\n input2 * levels_weight[:, 1:2, :, :]<\/p>\n

out = self.conv(fused_out_reduced) # \u901a\u8fc7\u5377\u79ef\u5c42
\n return out # \u8fd4\u56de\u878d\u5408\u540e\u7684\u7279\u5f81\u56fe<\/p>\n

class BlockBody_P345(nn.Module):
\n\u201c\u201d\u201c\u7279\u5f81\u5757\u4f53，\u5904\u7406\u4e09\u4e2a\u4e0d\u540c\u5c3a\u5ea6\u7684\u7279\u5f81\u56fe\u201d\u201c\u201d
\ndef init(self, channels=[64, 128, 256, 512]):
\nsuper(BlockBody_P345, self).init()<\/p>\n

# \u521d\u59cb\u5316\u4e0d\u540c\u5c3a\u5ea6\u7684\u5377\u79ef\u5757
\n self.blocks_scalezero1 = nn.Sequential(Conv(channels[0], channels[0], 1))
\n self.blocks_scaleone1 = nn.Sequential(Conv(channels[1], channels[1], 1))
\n self.blocks_scaletwo1 = nn.Sequential(Conv(channels[2], channels[2], 1))<\/p>\n

# \u4e0b\u91c7\u6837\u548c\u4e0a\u91c7\u6837\u6a21\u5757
\n self.downsample_scalezero1_2 = Downsample_x2(channels[0], channels[1])
\n self.upsample_scaleone1_2 = Upsample(channels[1], channels[0], scale_factor=2)<\/p>\n

# \u81ea\u9002\u5e94\u7279\u5f81\u878d\u5408\u6a21\u5757
\n self.asff_scalezero1 = ASFF_2(inter_dim=channels[0])
\n self.asff_scaleone1 = ASFF_2(inter_dim=channels[1])<\/p>\n

# \u5b9a\u4e49\u66f4\u591a\u7684\u5377\u79ef\u5757\u548c\u4e0b\u91c7\u6837\/\u4e0a\u91c7\u6837\u64cd\u4f5c…<\/p>\n

def forward(self, x):
\n x0, x1, x2 = x # \u8f93\u5165\u4e09\u4e2a\u4e0d\u540c\u5c3a\u5ea6\u7684\u7279\u5f81\u56fe<\/p>\n

# \u5904\u7406\u6bcf\u4e2a\u5c3a\u5ea6\u7684\u7279\u5f81\u56fe
\n x0 = self.blocks_scalezero1(x0)
\n x1 = self.blocks_scaleone1(x1)
\n x2 = self.blocks_scaletwo1(x2)<\/p>\n

# \u878d\u5408\u7279\u5f81\u56fe
\n scalezero = self.asff_scalezero1(x0, self.upsample_scaleone1_2(x1))
\n scaleone = self.asff_scaleone1(self.downsample_scalezero1_2(x0), x1)<\/p>\n

# \u8fd4\u56de\u5904\u7406\u540e\u7684\u7279\u5f81\u56fe
\n return x0, x1, x2<\/p>\n

class AFPN_P345(nn.Module):
\n\u201c\u201d\u201c\u81ea\u9002\u5e94\u7279\u5f81\u91d1\u5b57\u5854\u7f51\u7edc，\u5904\u7406\u4e09\u4e2a\u8f93\u5165\u7279\u5f81\u56fe\u201d\u201c\u201d
\ndef init(self, in_channels=[256, 512, 1024], out_channels=256, factor=4):
\nsuper(AFPN_P345, self).init()<\/p>\n

# \u521d\u59cb\u5316\u5377\u79ef\u5c42
\n self.conv0 = Conv(in_channels[0], in_channels[0] \/\/ factor, 1)
\n self.conv1 = Conv(in_channels[1], in_channels[1] \/\/ factor, 1)
\n self.conv2 = Conv(in_channels[2], in_channels[2] \/\/ factor, 1)<\/p>\n

# \u7279\u5f81\u5757\u4f53
\n self.body = BlockBody_P345([in_channels[0] \/\/ factor, in_channels[1] \/\/ factor, in_channels[2] \/\/ factor])<\/p>\n

# \u8f93\u51fa\u5377\u79ef\u5c42
\n self.conv00 = Conv(in_channels[0] \/\/ factor, out_channels, 1)
\n self.conv11 = Conv(in_channels[1] \/\/ factor, out_channels, 1)
\n self.conv22 = Conv(in_channels[2] \/\/ factor, out_channels, 1)<\/p>\n

def forward(self, x):
\n x0, x1, x2 = x # \u8f93\u5165\u4e09\u4e2a\u7279\u5f81\u56fe<\/p>\n

# \u901a\u8fc7\u5377\u79ef\u5c42\u5904\u7406\u7279\u5f81\u56fe
\n x0 = self.conv0(x0)
\n x1 = self.conv1(x1)
\n x2 = self.conv2(x2)<\/p>\n

# \u901a\u8fc7\u7279\u5f81\u5757\u4f53\u5904\u7406\u7279\u5f81\u56fe
\n out0, out1, out2 = self.body([x0, x1, x2])<\/p>\n

# \u901a\u8fc7\u8f93\u51fa\u5377\u79ef\u5c42\u5904\u7406\u7279\u5f81\u56fe
\n out0 = self.conv00(out0)
\n out1 = self.conv11(out1)
\n out2 = self.conv22(out2)
\n return [out0, out1, out2] # \u8fd4\u56de\u5904\u7406\u540e\u7684\u7279\u5f81\u56fe<\/p>\n

\u4ee3\u7801\u8bf4\u660e：
\nBasicBlock: \u8fd9\u662f\u4e00\u4e2a\u57fa\u672c\u7684\u5377\u79ef\u5757，\u5305\u542b\u4e24\u4e2a\u5377\u79ef\u5c42\u548c\u4e00\u4e2a\u6b8b\u5dee\u8fde\u63a5\u3002
\nUpsample: \u8fd9\u4e2a\u6a21\u5757\u7528\u4e8e\u4e0a\u91c7\u6837\u7279\u5f81\u56fe，\u4f7f\u75281×1\u5377\u79ef\u548c\u53cc\u7ebf\u6027\u63d2\u503c\u3002
\nDownsample_x2: \u8fd9\u4e2a\u6a21\u5757\u7528\u4e8e\u4e0b\u91c7\u6837\u7279\u5f81\u56fe，\u4f7f\u75282×2\u5377\u79ef\u3002
\nASFF_2: \u81ea\u9002\u5e94\u7279\u5f81\u878d\u5408\u6a21\u5757，\u7528\u4e8e\u878d\u5408\u4e24\u4e2a\u8f93\u5165\u7279\u5f81\u56fe，\u8ba1\u7b97\u5b83\u4eec\u7684\u6743\u91cd\u5e76\u8fdb\u884c\u52a0\u6743\u878d\u5408\u3002
\nBlockBody_P345: \u5904\u7406\u4e09\u4e2a\u4e0d\u540c\u5c3a\u5ea6\u7279\u5f81\u56fe\u7684\u6a21\u5757，\u5305\u542b\u591a\u4e2a\u5377\u79ef\u5757\u548c\u81ea\u9002\u5e94\u7279\u5f81\u878d\u5408\u6a21\u5757\u3002
\nAFPN_P345: \u81ea\u9002\u5e94\u7279\u5f81\u91d1\u5b57\u5854\u7f51\u7edc，\u5904\u7406\u4e09\u4e2a\u8f93\u5165\u7279\u5f81\u56fe\u5e76\u8f93\u51fa\u878d\u5408\u540e\u7684\u7279\u5f81\u56fe\u3002
\n\u4ee5\u4e0a\u662f\u6838\u5fc3\u90e8\u5206\u7684\u4ee3\u7801\u548c\u8be6\u7ec6\u6ce8\u91ca，\u5e2e\u52a9\u7406\u89e3\u6bcf\u4e2a\u6a21\u5757\u7684\u529f\u80fd\u548c\u4f5c\u7528\u3002<\/p>\n

\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6afpn.py\u5b9e\u73b0\u4e86\u4e00\u79cd\u7279\u5f81\u91d1\u5b57\u5854\u7f51\u7edc（FPN）\u7684\u7ed3\u6784，\u4e3b\u8981\u7528\u4e8e\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1，\u5c24\u5176\u662f\u76ee\u6807\u68c0\u6d4b\u548c\u5206\u5272\u3002\u8be5\u7f51\u7edc\u901a\u8fc7\u591a\u5c3a\u5ea6\u7279\u5f81\u878d\u5408\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u6587\u4ef6\u4e2d\u5b9a\u4e49\u4e86\u591a\u4e2a\u7c7b，\u6bcf\u4e2a\u7c7b\u5b9e\u73b0\u4e86\u7f51\u7edc\u7684\u4e0d\u540c\u90e8\u5206\u548c\u529f\u80fd\u3002<\/p>\n

\u9996\u5148，\u6587\u4ef6\u5bfc\u5165\u4e86\u4e00\u4e9b\u5fc5\u8981\u7684\u5e93，\u5305\u62ectorch\u548ctorch.nn，\u4ee5\u53ca\u4e00\u4e9b\u81ea\u5b9a\u4e49\u7684\u6a21\u5757，\u5982Conv\u548c\u4e0d\u540c\u7684\u5757（C2f, C3, C3Ghost, C3k2）\u3002\u8fd9\u4e9b\u6a21\u5757\u63d0\u4f9b\u4e86\u6784\u5efa\u7f51\u7edc\u6240\u9700\u7684\u57fa\u672c\u64cd\u4f5c\u3002<\/p>\n

BasicBlock\u7c7b\u662f\u4e00\u4e2a\u57fa\u672c\u7684\u5377\u79ef\u5757，\u5305\u542b\u4e24\u4e2a\u5377\u79ef\u5c42\u548c\u4e00\u4e2a\u6b8b\u5dee\u8fde\u63a5\u3002\u901a\u8fc7\u6b8b\u5dee\u8fde\u63a5，\u8f93\u5165\u53ef\u4ee5\u76f4\u63a5\u52a0\u5230\u8f93\u51fa\u4e0a，\u6709\u52a9\u4e8e\u7f13\u89e3\u6df1\u5c42\u7f51\u7edc\u7684\u8bad\u7ec3\u95ee\u9898\u3002<\/p>\n

Upsample\u548cDownsample_x2, Downsample_x4, Downsample_x8\u7c7b\u5206\u522b\u5b9e\u73b0\u4e86\u4e0a\u91c7\u6837\u548c\u4e0d\u540c\u500d\u6570\u7684\u4e0b\u91c7\u6837\u64cd\u4f5c\u3002\u8fd9\u4e9b\u64cd\u4f5c\u5728\u7279\u5f81\u878d\u5408\u65f6\u975e\u5e38\u91cd\u8981，\u56e0\u4e3a\u5b83\u4eec\u5141\u8bb8\u7f51\u7edc\u5728\u4e0d\u540c\u7684\u5c3a\u5ea6\u4e0a\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u548c\u878d\u5408\u3002<\/p>\n

ASFF_2, ASFF_3, \u548c ASFF_4\u7c7b\u5b9e\u73b0\u4e86\u81ea\u9002\u5e94\u7279\u5f81\u878d\u5408\u6a21\u5757（ASFF），\u5b83\u4eec\u6839\u636e\u8f93\u5165\u7279\u5f81\u7684\u6743\u91cd\u8fdb\u884c\u52a0\u6743\u878d\u5408\u3002\u6bcf\u4e2a\u7c7b\u5bf9\u5e94\u4e0d\u540c\u6570\u91cf\u7684\u8f93\u5165\u7279\u5f81\u56fe（2\u30013\u62164\u4e2a），\u901a\u8fc7\u5b66\u4e60\u7684\u6743\u91cd\u6765\u51b3\u5b9a\u5982\u4f55\u878d\u5408\u8fd9\u4e9b\u7279\u5f81\u56fe，\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

BlockBody_P345\u548cBlockBody_P2345\u7c7b\u5206\u522b\u5b9e\u73b0\u4e86\u5305\u542b\u591a\u4e2a\u5377\u79ef\u5757\u548c\u81ea\u9002\u5e94\u7279\u5f81\u878d\u5408\u6a21\u5757\u7684\u7f51\u7edc\u4e3b\u4f53\u3002\u5b83\u4eec\u4f7f\u7528\u4e0d\u540c\u7684\u901a\u9053\u6570\u548c\u5c42\u6b21\u7ed3\u6784\u6765\u5904\u7406\u4e0d\u540c\u6570\u91cf\u7684\u8f93\u5165\u7279\u5f81\u56fe（3\u4e2a\u62164\u4e2a）\u3002\u5728\u8fd9\u4e9b\u7c7b\u4e2d，\u7279\u5f81\u56fe\u7ecf\u8fc7\u591a\u4e2a\u5377\u79ef\u5757\u548cASFF\u6a21\u5757\u7684\u5904\u7406，\u6700\u7ec8\u8f93\u51fa\u878d\u5408\u540e\u7684\u7279\u5f81\u56fe\u3002<\/p>\n

AFPN_P345\u548cAFPN_P2345\u7c7b\u662f\u7f51\u7edc\u7684\u9876\u5c42\u7ed3\u6784，\u8d1f\u8d23\u5c06\u8f93\u5165\u7279\u5f81\u56fe\u8fdb\u884c\u5904\u7406\u5e76\u8f93\u51fa\u6700\u7ec8\u7684\u7279\u5f81\u56fe\u3002\u5b83\u4eec\u9996\u5148\u901a\u8fc7\u5377\u79ef\u5c42\u964d\u4f4e\u901a\u9053\u6570，\u7136\u540e\u5c06\u5904\u7406\u540e\u7684\u7279\u5f81\u56fe\u4f20\u9012\u7ed9\u5bf9\u5e94\u7684BlockBody\u7c7b\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u3002\u6700\u7ec8，\u8f93\u51fa\u7684\u7279\u5f81\u56fe\u901a\u8fc7\u5377\u79ef\u5c42\u8c03\u6574\u5230\u6307\u5b9a\u7684\u8f93\u51fa\u901a\u9053\u6570\u3002<\/p>\n

\u6b64\u5916，AFPN_P345_Custom\u548cAFPN_P2345_Custom\u7c7b\u5141\u8bb8\u7528\u6237\u81ea\u5b9a\u4e49\u5377\u79ef\u5757\u7684\u7c7b\u578b，\u63d0\u4f9b\u4e86\u66f4\u5927\u7684\u7075\u6d3b\u6027，\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u4efb\u52a1\u9700\u6c42\u3002<\/p>\n

\u6574\u4f53\u6765\u770b，\u8fd9\u4e2a\u7a0b\u5e8f\u6587\u4ef6\u5b9e\u73b0\u4e86\u4e00\u4e2a\u7075\u6d3b\u4e14\u9ad8\u6548\u7684\u7279\u5f81\u91d1\u5b57\u5854\u7f51\u7edc\u7ed3\u6784，\u80fd\u591f\u5728\u591a\u5c3a\u5ea6\u4e0a\u63d0\u53d6\u548c\u878d\u5408\u7279\u5f81，\u9002\u7528\u4e8e\u5404\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u3002<\/p>\n

\u6e90\u7801\u6587\u4ef6<\/h2>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6e90\u7801\u83b7\u53d6<\/h2>\n

\u6b22\u8fce\u5927\u5bb6\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u5173\u6ce8\u3001\u8bc4\u8bba\u5566 \u3001\u67e5\u770b👇🏻\u83b7\u53d6\u8054\u7cfb\u65b9\u5f0f<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb254\u6b21\u3002\u3010\u5b8c\u6574\u6e90\u7801+\u6570\u636e\u96c6+\u90e8\u7f72\u6559\u7a0b\u3011\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u5b9e\u4f8b\u5206\u5272\u7cfb\u7edf\u6e90\u7801\u548c\u6570\u636e\u96c6\uff1a\u6539\u8fdbyolo11-fasternet-bifpn<\/p>\n","protected":false},"author":2,"featured_media":51739,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[81,156,2842,5134,1908,5135,523,5132],"topic":[],"class_list":["post-51748","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-python","tag-yolo","tag-yolo11","tag-5134","tag-1908","tag-5135","tag-523","tag-5132"],"yoast_head":"\n\u3010\u5b8c\u6574\u6e90\u7801+\u6570\u636e\u96c6+\u90e8\u7f72\u6559\u7a0b\u3011\u87ba\u6813\u5c3a\u5bf8\u8bc6\u522b\u5b9e\u4f8b\u5206\u5272\u7cfb\u7edf\u6e90\u7801\u548c\u6570\u636e\u96c6\uff1a\u6539\u8fdbyolo11-fasternet-bifpn - 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