{"id":53090,"date":"2025-08-12T00:09:54","date_gmt":"2025-08-11T16:09:54","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/53090.html"},"modified":"2025-08-12T00:09:54","modified_gmt":"2025-08-11T16:09:54","slug":"yolov6%e6%b7%b1%e5%ba%a6%e8%a7%a3%e6%9e%90%ef%bc%9a%e5%ae%9e%e6%97%b6%e7%9b%ae%e6%a0%87%e6%a3%80%e6%b5%8b%e7%9a%84%e6%96%b0%e7%aa%81%e7%a0%b4","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/53090.html","title":{"rendered":"YOLOv6\u6df1\u5ea6\u89e3\u6790\uff1a\u5b9e\u65f6\u76ee\u6807\u68c0\u6d4b\u7684\u65b0\u7a81\u7834"},"content":{"rendered":"

\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df，\u76ee\u6807\u68c0\u6d4b\u4e00\u76f4\u662f\u6700\u5177\u6311\u6218\u6027\u7684\u4efb\u52a1\u4e4b\u4e00\u3002\u5982\u4f55\u5728\u4fdd\u8bc1\u68c0\u6d4b\u7cbe\u5ea6\u7684\u540c\u65f6\u5b9e\u73b0\u5b9e\u65f6\u63a8\u7406，\u662f\u5de5\u4e1a\u754c\u548c\u5b66\u672f\u754c\u5171\u540c\u5173\u6ce8\u7684\u6838\u5fc3\u95ee\u9898\u3002\u7f8e\u56e2\u56e2\u961f\u63a8\u51fa\u7684YOLOv6\u5728\u8fd9\u4e00\u5e73\u8861\u70b9\u4e0a\u53d6\u5f97\u4e86\u91cd\u5927\u7a81\u7834，\u4e0d\u4ec5\u5728COCO\u6570\u636e\u96c6\u4e0a\u5237\u65b0\u4e86\u6700\u5148\u8fdb\u7684\u7cbe\u5ea6\u8bb0\u5f55，\u66f4\u5728\u63a8\u7406\u901f\u5ea6\u4e0a\u5b9e\u73b0\u4e86\u663e\u8457\u63d0\u5347，\u4e3a\u5b9e\u65f6\u5e94\u7528\u573a\u666f\u63d0\u4f9b\u4e86\u5f3a\u6709\u529b\u7684\u6280\u672f\u652f\u6491\u3002<\/p>\n

\u4e00\u3001YOLOv6\u7684\u6280\u672f\u9769\u65b0\u4e0e\u67b6\u6784\u8bbe\u8ba1<\/h3>\n

(a) YOLOv6 \u7684\u9888\u90e8（\u56fe\u4e2d\u4e3a N \u548c S）\u3002(b) BiC \u6a21\u5757\u7684\u7ed3\u6784\u3002\u00a9 SimCSPSPPF \u6a21\u5757：
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

1. \u6574\u4f53\u67b6\u6784\u7684\u91cd\u65b0\u601d\u8003<\/h4>\n

YOLOv6\u5e76\u975e\u7b80\u5355\u7684\u7248\u672c\u8fed\u4ee3，\u800c\u662f\u5bf9\u6574\u4e2a\u68c0\u6d4b\u6846\u67b6\u7684\u7cfb\u7edf\u6027\u91cd\u6784\u3002\u7f8e\u56e2\u56e2\u961f\u4ece\u9aa8\u5e72\u7f51\u7edc\u3001\u7279\u5f81\u878d\u5408\u5c42\u5230\u68c0\u6d4b\u5934\u90fd\u8fdb\u884c\u4e86\u6df1\u5ea6\u4f18\u5316，\u5f62\u6210\u4e86\u4e00\u5957\u5b8c\u6574\u7684\u6280\u672f\u89e3\u51b3\u65b9\u6848\u3002\u8fd9\u79cd\u5168\u65b9\u4f4d\u7684\u8bbe\u8ba1\u7406\u5ff5\u786e\u4fdd\u4e86\u5404\u4e2a\u7ec4\u4ef6\u4e4b\u95f4\u7684\u534f\u540c\u6548\u5e94，\u800c\u975e\u5c40\u90e8\u4f18\u5316\u3002
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u4f20\u7edf\u7684YOLO\u7cfb\u5217\u6a21\u578b\u5f80\u5f80\u4e13\u6ce8\u4e8e\u67d0\u4e00\u7279\u5b9a\u65b9\u9762\u7684\u6539\u8fdb，YOLOv6\u5219\u91c7\u7528\u4e86holistic approach，\u7edf\u7b79\u8003\u8651\u4e86\u7cbe\u5ea6\u3001\u901f\u5ea6\u3001\u5185\u5b58\u5360\u7528\u7b49\u591a\u7ef4\u5ea6\u56e0\u7d20\u3002\u8fd9\u79cd\u8bbe\u8ba1\u54f2\u5b66\u4f7f\u5f97YOLOv6\u80fd\u591f\u5728\u590d\u6742\u7684\u5b9e\u9645\u5e94\u7528\u573a\u666f\u4e2d\u8868\u73b0\u51fa\u8272\u3002<\/p>\n

# YOLOv6\u6838\u5fc3\u7f51\u7edc\u7ed3\u6784\u793a\u4f8b<\/span>
\nclass<\/span> YOLOv6<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> num_classes=<\/span>80<\/span>,<\/span> anchors=<\/span>None<\/span>)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
\n # \u9aa8\u5e72\u7f51\u7edc\u91c7\u7528CSP\u7ed3\u6784\u4f18\u5316<\/span>
\n self.<\/span>backbone =<\/span> CSPBepBackbone(<\/span>)<\/span>
\n # \u9888\u90e8\u7f51\u7edc\u96c6\u6210BiC\u6a21\u5757<\/span>
\n self.<\/span>neck =<\/span> RepPANNeck(<\/span>channels=<\/span>[<\/span>256<\/span>,<\/span> 512<\/span>,<\/span> 1024<\/span>]<\/span>)<\/span>
\n # \u68c0\u6d4b\u5934\u652f\u6301anchor-free\u548canchor-based\u6df7\u5408<\/span>
\n self.<\/span>head =<\/span> EffiDeHead(<\/span>num_classes,<\/span> anchors)<\/span><\/p>\n

def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
\n features =<\/span> self.<\/span>backbone(<\/span>x)<\/span>
\n neck_features =<\/span> self.<\/span>neck(<\/span>features)<\/span>
\n predictions =<\/span> self.<\/span>head(<\/span>neck_features)<\/span>
\n return<\/span> predictions<\/p>\n

2. \u53cc\u5411\u7ea7\u8054\u6a21\u5757\u7684\u521b\u65b0\u5e94\u7528<\/h4>\n

BiC（Bidirectional Concatenation）\u6a21\u5757\u662fYOLOv6\u7684\u6838\u5fc3\u521b\u65b0\u4e4b\u4e00，\u5b83\u5728\u7279\u5f81\u878d\u5408\u9636\u6bb5\u5f15\u5165\u4e86\u53cc\u5411\u4fe1\u606f\u6d41\u673a\u5236\u3002\u4f20\u7edf\u7684FPN\u7ed3\u6784\u867d\u7136\u80fd\u591f\u5b9e\u73b0\u591a\u5c3a\u5ea6\u7279\u5f81\u878d\u5408，\u4f46\u4fe1\u606f\u4f20\u9012\u5f80\u5f80\u662f\u5355\u5411\u7684，\u8fd9\u9650\u5236\u4e86\u7279\u5f81\u8868\u8fbe\u7684\u4e30\u5bcc\u6027\u3002<\/p>\n

BiC\u6a21\u5757\u901a\u8fc7\u6784\u5efa\u53cc\u5411\u8fde\u63a5\u901a\u9053，\u8ba9\u9ad8\u5c42\u8bed\u4e49\u4fe1\u606f\u548c\u4f4e\u5c42\u7ec6\u8282\u4fe1\u606f\u80fd\u591f\u66f4\u5145\u5206\u5730\u4ea4\u4e92\u878d\u5408\u3002\u8fd9\u79cd\u8bbe\u8ba1\u4e0d\u4ec5\u63d0\u5347\u4e86\u5c0f\u76ee\u6807\u7684\u68c0\u6d4b\u80fd\u529b，\u8fd8\u589e\u5f3a\u4e86\u6a21\u578b\u5bf9\u590d\u6742\u573a\u666f\u7684\u7406\u89e3\u80fd\u529b\u3002\u66f4\u91cd\u8981\u7684\u662f，BiC\u6a21\u5757\u7684\u8ba1\u7b97\u5f00\u9500\u6781\u5c0f，\u51e0\u4e4e\u4e0d\u589e\u52a0\u63a8\u7406\u65f6\u95f4，\u4f53\u73b0\u4e86\u7f8e\u56e2\u56e2\u961f\u5728\u5de5\u7a0b\u4f18\u5316\u65b9\u9762\u7684\u6df1\u539a\u529f\u5e95\u3002<\/p>\n

class<\/span> BiCModule<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> in_channels)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
\n self.<\/span>reduce_conv =<\/span> nn.<\/span>Conv2d(<\/span>in_channels,<\/span> in_channels \/\/<\/span> 2<\/span>,<\/span> 1<\/span>)<\/span>
\n self.<\/span>split_conv_1 =<\/span> nn.<\/span>Conv2d(<\/span>in_channels \/\/<\/span> 2<\/span>,<\/span> in_channels \/\/<\/span> 4<\/span>,<\/span> 3<\/span>,<\/span> padding=<\/span>1<\/span>)<\/span>
\n self.<\/span>split_conv_2 =<\/span> nn.<\/span>Conv2d(<\/span>in_channels \/\/<\/span> 2<\/span>,<\/span> in_channels \/\/<\/span> 4<\/span>,<\/span> 3<\/span>,<\/span> padding=<\/span>1<\/span>)<\/span>
\n self.<\/span>concat_conv =<\/span> nn.<\/span>Conv2d(<\/span>in_channels,<\/span> in_channels,<\/span> 1<\/span>)<\/span><\/p>\n

def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
\n reduced =<\/span> self.<\/span>reduce_conv(<\/span>x)<\/span>
\n split1 =<\/span> self.<\/span>split_conv_1(<\/span>reduced)<\/span>
\n split2 =<\/span> self.<\/span>split_conv_2(<\/span>reduced)<\/span>
\n # \u53cc\u5411\u4fe1\u606f\u878d\u5408<\/span>
\n fused =<\/span> torch.<\/span>cat(<\/span>[<\/span>split1,<\/span> split2,<\/span> reduced]<\/span>,<\/span> dim=<\/span>1<\/span>)<\/span>
\n return<\/span> self.<\/span>concat_conv(<\/span>fused)<\/span><\/p>\n

\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86BiC\u6a21\u5757\u7684\u57fa\u672c\u5b9e\u73b0\u539f\u7406\u3002\u901a\u8fc7\u964d\u7ef4\u3001\u5206\u5272\u3001\u878d\u5408\u7684\u4e09\u6b65\u64cd\u4f5c，\u5b9e\u73b0\u4e86\u7279\u5f81\u7684\u53cc\u5411\u6d41\u52a8，\u540c\u65f6\u63a7\u5236\u4e86\u8ba1\u7b97\u590d\u6742\u5ea6\u3002
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\u5982\u4e0a\u56fe\u6240\u793a，YOLOv6\u91c7\u7528\u4e86\u6a21\u5757\u5316\u7684\u8bbe\u8ba1\u7406\u5ff5，\u6bcf\u4e2a\u7ec4\u4ef6\u90fd\u7ecf\u8fc7\u7cbe\u5fc3\u4f18\u5316\u4ee5\u5b9e\u73b0\u6700\u4f73\u7684\u7cbe\u5ea6-\u901f\u5ea6\u5e73\u8861\u3002<\/p>\n

\u4e8c\u3001\u951a\u70b9\u8f85\u52a9\u8bad\u7ec3\u7b56\u7565\u7684\u7a81\u7834\u6027\u8fdb\u5c55<\/h3>\n

1. AAT\u7b56\u7565\u7684\u8bbe\u8ba1\u7406\u5ff5<\/h4>\n

Anchor Assisted Training（AAT）\u7b56\u7565\u662fYOLOv6\u7684\u53e6\u4e00\u9879\u91cd\u8981\u521b\u65b0\u3002\u8fd9\u4e00\u7b56\u7565\u5de7\u5999\u5730\u7ed3\u5408\u4e86anchor-based\u548canchor-free\u4e24\u79cd\u68c0\u6d4b\u8303\u5f0f\u7684\u4f18\u52bf，\u5728\u8bad\u7ec3\u9636\u6bb5\u5f15\u5165\u951a\u70b9\u673a\u5236\u6765\u7a33\u5b9a\u5b66\u4e60\u8fc7\u7a0b，\u800c\u5728\u63a8\u7406\u9636\u6bb5\u5219\u53bb\u9664\u951a\u70b9\u4f9d\u8d56\u4ee5\u63d0\u5347\u6548\u7387\u3002<\/p>\n

\u4f20\u7edf\u7684\u68c0\u6d4b\u5668\u5f80\u5f80\u9700\u8981\u5728\u4e24\u79cd\u8303\u5f0f\u4e2d\u505a\u51fa\u9009\u62e9，anchor-based\u65b9\u6cd5\u867d\u7136\u8bad\u7ec3\u7a33\u5b9a\u4f46\u63a8\u7406\u590d\u6742，anchor-free\u65b9\u6cd5\u867d\u7136\u63a8\u7406\u9ad8\u6548\u4f46\u8bad\u7ec3\u56f0\u96be\u3002AAT\u7b56\u7565\u901a\u8fc7\u5206\u79bb\u8bad\u7ec3\u548c\u63a8\u7406\u9636\u6bb5\u7684\u5904\u7406\u65b9\u5f0f，\u5b9e\u73b0\u4e86\u4e24\u79cd\u8303\u5f0f\u4f18\u52bf\u7684\u6709\u673a\u7ed3\u5408\u3002<\/p>\n

2. \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u52a8\u6001\u5e73\u8861<\/h4>\n

\u5728AAT\u7b56\u7565\u4e2d，\u8bad\u7ec3\u8fc7\u7a0b\u88ab\u8bbe\u8ba1\u4e3a\u4e00\u4e2a\u52a8\u6001\u5e73\u8861\u7684\u8fc7\u7a0b\u3002\u521d\u671f\u9636\u6bb5，\u951a\u70b9\u673a\u5236\u53d1\u6325\u4e3b\u5bfc\u4f5c\u7528，\u5e2e\u52a9\u6a21\u578b\u5feb\u901f\u6536\u655b\u5230\u5408\u7406\u7684\u53c2\u6570\u7a7a\u95f4；\u968f\u7740\u8bad\u7ec3\u7684\u6df1\u5165，anchor-free\u5206\u652f\u7684\u6743\u91cd\u9010\u6e10\u589e\u52a0，\u6700\u7ec8\u5728\u63a8\u7406\u9636\u6bb5\u5b8c\u5168\u63a5\u7ba1\u68c0\u6d4b\u4efb\u52a1\u3002<\/p>\n

class<\/span> AATLoss<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> num_classes)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
\n self.<\/span>num_classes =<\/span> num_classes
\n self.<\/span>anchor_weight =<\/span> nn.<\/span>Parameter(<\/span>torch.<\/span>tensor(<\/span>1.0<\/span>)<\/span>)<\/span>
\n self.<\/span>anchor_free_weight =<\/span> nn.<\/span>Parameter(<\/span>torch.<\/span>tensor(<\/span>0.1<\/span>)<\/span>)<\/span><\/p>\n

def<\/span> forward<\/span>(<\/span>self,<\/span> predictions,<\/span> targets,<\/span> epoch)<\/span>:<\/span>
\n # \u52a8\u6001\u8c03\u6574\u4e24\u79cd\u673a\u5236\u7684\u6743\u91cd<\/span>
\n alpha =<\/span> min<\/span>(<\/span>epoch \/<\/span> 100.0<\/span>,<\/span> 1.0<\/span>)<\/span>
\n anchor_loss =<\/span> self.<\/span>compute_anchor_loss(<\/span>predictions,<\/span> targets)<\/span>
\n anchor_free_loss =<\/span> self.<\/span>compute_anchor_free_loss(<\/span>predictions,<\/span> targets)<\/span><\/p>\n

total_loss =<\/span> (<\/span>1<\/span> –<\/span> alpha)<\/span> *<\/span> anchor_loss +<\/span> alpha *<\/span> anchor_free_loss
\n return<\/span> total_loss<\/p>\n

\u8fd9\u79cd\u52a8\u6001\u6743\u91cd\u8c03\u6574\u673a\u5236\u786e\u4fdd\u4e86\u8bad\u7ec3\u8fc7\u7a0b\u7684\u5e73\u6ed1\u8fc7\u6e21，\u907f\u514d\u4e86\u7a81\u7136\u7684\u7b56\u7565\u5207\u6362\u53ef\u80fd\u5e26\u6765\u7684\u6027\u80fd\u635f\u5931\u3002<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"
\n\u8bad\u7ec3\u7b56\u7565\u6d41\u7a0b\u56fe\u6e05\u6670\u5730\u5c55\u793a\u4e86YOLOv6\u5982\u4f55\u5728\u8bad\u7ec3\u548c\u63a8\u7406\u9636\u6bb5\u91c7\u7528\u4e0d\u540c\u7684\u5904\u7406\u65b9\u5f0f，\u5b9e\u73b0\u4e86\u6548\u679c\u4e0e\u6548\u7387\u7684\u5b8c\u7f8e\u5e73\u8861\u3002<\/p>\n

\u4e09\u3001\u9aa8\u5e72\u7f51\u7edc\u4e0e\u9888\u90e8\u8bbe\u8ba1\u7684\u6df1\u5ea6\u4f18\u5316<\/h3>\n

1. CSP\u67b6\u6784\u7684\u6f14\u8fdb\u4e0e\u6539\u826f<\/h4>\n

YOLOv6\u5728\u9aa8\u5e72\u7f51\u7edc\u8bbe\u8ba1\u4e0a\u5ef6\u7eed\u4e86CSPNet\u7684\u6838\u5fc3\u601d\u60f3，\u4f46\u8fdb\u884c\u4e86\u9488\u5bf9\u6027\u7684\u6539\u826f\u3002\u65b0\u7684CSP\u7ed3\u6784\u4e0d\u4ec5\u4fdd\u6301\u4e86\u539f\u6709\u7684\u68af\u5ea6\u6d41\u4f18\u5316\u7279\u6027，\u8fd8\u901a\u8fc7\u91cd\u65b0\u8bbe\u8ba1\u7684\u8fde\u63a5\u65b9\u5f0f\u63d0\u5347\u4e86\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

\u9aa8\u5e72\u7f51\u7edc\u7684\u6df1\u5316\u662fYOLOv6\u7684\u91cd\u8981\u7279\u5f81，\u901a\u8fc7\u589e\u52a0\u989d\u5916\u7684\u5904\u7406\u9636\u6bb5，\u6a21\u578b\u80fd\u591f\u66f4\u597d\u5730\u5904\u7406\u9ad8\u5206\u8fa8\u7387\u8f93\u5165\u3002\u8fd9\u79cd\u8bbe\u8ba1\u5bf9\u4e8e\u7cbe\u7ec6\u5316\u68c0\u6d4b\u4efb\u52a1\u7279\u522b\u6709\u6548，\u80fd\u591f\u663e\u8457\u63d0\u5347\u5c0f\u76ee\u6807\u548c\u5bc6\u96c6\u76ee\u6807\u7684\u68c0\u6d4b\u7cbe\u5ea6\u3002<\/p>\n

2. SimCSPSPPF\u6a21\u5757\u7684\u96c6\u6210\u5e94\u7528<\/h4>\n

SimCSPSPPF\u6a21\u5757\u662fYOLOv6\u9888\u90e8\u7f51\u7edc\u7684\u6838\u5fc3\u7ec4\u4ef6，\u5b83\u96c6\u6210\u4e86CSP\u7ed3\u6784\u7684\u68af\u5ea6\u4f18\u5316\u7279\u6027\u548cSPPF\u7684\u591a\u5c3a\u5ea6\u6c60\u5316\u80fd\u529b\u3002\u8fd9\u79cd\u8bbe\u8ba1\u65e2\u4fdd\u8bc1\u4e86\u7279\u5f81\u7684\u4e30\u5bcc\u6027，\u53c8\u63a7\u5236\u4e86\u8ba1\u7b97\u6210\u672c，\u5b9e\u73b0\u4e86\u6548\u7387\u4e0e\u6548\u679c\u7684\u53cc\u91cd\u4f18\u5316\u3002<\/p>\n

class<\/span> SimCSPSPPF<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> c1,<\/span> c2,<\/span> k=<\/span>5<\/span>)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
\n c_ =<\/span> c1 \/\/<\/span> 2<\/span>
\n self.<\/span>conv1 =<\/span> Conv(<\/span>c1,<\/span> c_,<\/span> 1<\/span>,<\/span> 1<\/span>)<\/span>
\n self.<\/span>conv2 =<\/span> Conv(<\/span>c1,<\/span> c_,<\/span> 1<\/span>,<\/span> 1<\/span>)<\/span>
\n self.<\/span>conv3 =<\/span> Conv(<\/span>c_,<\/span> c_,<\/span> 3<\/span>,<\/span> 1<\/span>)<\/span>
\n self.<\/span>conv4 =<\/span> Conv(<\/span>c_,<\/span> c_,<\/span> 1<\/span>,<\/span> 1<\/span>)<\/span>
\n self.<\/span>maxpool =<\/span> nn.<\/span>MaxPool2d(<\/span>kernel_size=<\/span>k,<\/span> stride=<\/span>1<\/span>,<\/span> padding=<\/span>k \/\/<\/span> 2<\/span>)<\/span>
\n self.<\/span>conv5 =<\/span> Conv(<\/span>4<\/span> *<\/span> c_,<\/span> c2,<\/span> 1<\/span>,<\/span> 1<\/span>)<\/span><\/p>\n

def<\/span> forward<\/span>(<\/span>self,<\/span> x)<\/span>:<\/span>
\n x1 =<\/span> self.<\/span>conv4(<\/span>self.<\/span>conv3(<\/span>self.<\/span>conv1(<\/span>x)<\/span>)<\/span>)<\/span>
\n # \u591a\u5c3a\u5ea6\u7279\u5f81\u63d0\u53d6<\/span>
\n y1 =<\/span> self.<\/span>maxpool(<\/span>x1)<\/span>
\n y2 =<\/span> self.<\/span>maxpool(<\/span>y1)<\/span>
\n y3 =<\/span> self.<\/span>maxpool(<\/span>y2)<\/span>
\n # \u7279\u5f81\u878d\u5408<\/span>
\n return<\/span> self.<\/span>conv5(<\/span>torch.<\/span>cat(<\/span>[<\/span>x1,<\/span> y1,<\/span> y2,<\/span> y3]<\/span>,<\/span> 1<\/span>)<\/span>)<\/span><\/p>\n

\u8be5\u6a21\u5757\u901a\u8fc7\u4e32\u8054\u591a\u4e2a\u6700\u5927\u6c60\u5316\u64cd\u4f5c\u5b9e\u73b0\u591a\u5c3a\u5ea6\u611f\u53d7\u91ce，\u540c\u65f6\u5229\u7528CSP\u7ed3\u6784\u4f18\u5316\u68af\u5ea6\u4f20\u64ad，\u8fd9\u79cd\u8bbe\u8ba1\u5728\u4fdd\u6301\u8ba1\u7b97\u6548\u7387\u7684\u540c\u65f6\u663e\u8457\u63d0\u5347\u4e86\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n

\u56db\u3001\u81ea\u84b8\u998f\u7b56\u7565\u4e0e\u6a21\u578b\u538b\u7f29\u6280\u672f<\/h3>\n

1. \u77e5\u8bc6\u84b8\u998f\u7684\u521b\u65b0\u5b9e\u73b0<\/h4>\n

YOLOv6\u5f15\u5165\u7684\u81ea\u84b8\u998f\u7b56\u7565\u662f\u4e00\u79cd\u65b0\u9896\u7684\u6a21\u578b\u4f18\u5316\u65b9\u6cd5，\u7279\u522b\u9488\u5bf9\u8f7b\u91cf\u7ea7\u6a21\u578b\u7684\u6027\u80fd\u63d0\u5347\u3002\u4e0e\u4f20\u7edf\u7684\u5e08\u751f\u84b8\u998f\u4e0d\u540c，\u81ea\u84b8\u998f\u7b56\u7565\u8ba9\u6a21\u578b\u5185\u90e8\u7684\u4e0d\u540c\u5206\u652f\u76f8\u4e92\u5b66\u4e60，\u5b9e\u73b0\u4e86\u81ea\u6211\u63d0\u5347\u7684\u6548\u679c\u3002<\/p>\n

\u5728\u8bad\u7ec3\u9636\u6bb5，\u6a21\u578b\u4f1a\u540c\u65f6\u8f93\u51fa\u4e3b\u9884\u6d4b\u5206\u652f\u548c\u8f85\u52a9\u9884\u6d4b\u5206\u652f\u7684\u7ed3\u679c，\u4e24\u4e2a\u5206\u652f\u4e4b\u95f4\u901a\u8fc7\u77e5\u8bc6\u84b8\u998f\u673a\u5236\u76f8\u4e92\u4fc3\u8fdb\u3002\u8fd9\u79cd\u8bbe\u8ba1\u4e0d\u4ec5\u63d0\u5347\u4e86\u6700\u7ec8\u7684\u68c0\u6d4b\u7cbe\u5ea6，\u8fd8\u589e\u5f3a\u4e86\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u63a8\u7406\u9636\u6bb5\u5219\u53ea\u4fdd\u7559\u4e3b\u9884\u6d4b\u5206\u652f，\u786e\u4fdd\u4e86\u63a8\u7406\u901f\u5ea6\u4e0d\u53d7\u5f71\u54cd\u3002<\/p>\n

2. \u6e10\u8fdb\u5f0f\u84b8\u998f\u8bad\u7ec3\u7b56\u7565<\/h4>\n

\u81ea\u84b8\u998f\u7684\u5b9e\u65bd\u91c7\u7528\u4e86\u6e10\u8fdb\u5f0f\u7b56\u7565，\u521d\u671f\u9636\u6bb5\u4e24\u4e2a\u5206\u652f\u72ec\u7acb\u8bad\u7ec3，\u968f\u7740\u8bad\u7ec3\u7684\u6df1\u5165\u9010\u6b65\u5f15\u5165\u84b8\u998f\u635f\u5931\u3002\u8fd9\u79cd\u6e10\u8fdb\u5f0f\u7684\u65b9\u6cd5\u907f\u514d\u4e86\u8bad\u7ec3\u521d\u671f\u7684\u4e0d\u7a33\u5b9a\u6027，\u786e\u4fdd\u4e86\u84b8\u998f\u8fc7\u7a0b\u7684\u6709\u6548\u6027\u3002<\/p>\n

class<\/span> SelfDistillationLoss<\/span>(<\/span>nn.<\/span>Module)<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> temperature=<\/span>3.0<\/span>)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>)<\/span>
\n self.<\/span>temperature =<\/span> temperature
\n self.<\/span>kl_loss =<\/span> nn.<\/span>KLDivLoss(<\/span>reduction=<\/span>'batchmean'<\/span>)<\/span><\/p>\n

def<\/span> forward<\/span>(<\/span>self,<\/span> main_output,<\/span> aux_output,<\/span> targets,<\/span> epoch)<\/span>:<\/span>
\n # \u6807\u51c6\u68c0\u6d4b\u635f\u5931<\/span>
\n detection_loss =<\/span> compute_detection_loss(<\/span>main_output,<\/span> targets)<\/span><\/p>\n

# \u84b8\u998f\u635f\u5931\u9010\u6b65\u589e\u52a0\u6743\u91cd<\/span>
\n distill_weight =<\/span> min<\/span>(<\/span>epoch \/<\/span> 50.0<\/span>,<\/span> 0.5<\/span>)<\/span>
\n if<\/span> distill_weight ><\/span> 0<\/span>:<\/span>
\n # \u8f6f\u6807\u7b7e\u84b8\u998f<\/span>
\n soft_main =<\/span> F.<\/span>softmax(<\/span>main_output \/<\/span> self.<\/span>temperature,<\/span> dim=<\/span>–<\/span>1<\/span>)<\/span>
\n soft_aux =<\/span> F.<\/span>log_softmax(<\/span>aux_output \/<\/span> self.<\/span>temperature,<\/span> dim=<\/span>–<\/span>1<\/span>)<\/span>
\n distill_loss =<\/span> self.<\/span>kl_loss(<\/span>soft_aux,<\/span> soft_main)<\/span>
\n total_loss =<\/span> detection_loss +<\/span> distill_weight *<\/span> distill_loss
\n else<\/span>:<\/span>
\n total_loss =<\/span> detection_loss<\/p>\n

return<\/span> total_loss<\/p>\n

\u8fd9\u79cd\u81ea\u84b8\u998f\u673a\u5236\u7279\u522b\u9002\u7528\u4e8e\u8d44\u6e90\u53d7\u9650\u7684\u573a\u666f，\u80fd\u591f\u5728\u4e0d\u589e\u52a0\u63a8\u7406\u6210\u672c\u7684\u524d\u63d0\u4e0b\u663e\u8457\u63d0\u5347\u6a21\u578b\u6027\u80fd\u3002<\/p>\n

\u4e94\u3001\u591a\u5c3a\u5ea6\u6a21\u578b\u65cf\u7684\u6027\u80fd\u8868\u73b0\u4e0e\u5e94\u7528\u573a\u666f<\/h3>\n

1. \u5168\u65b9\u4f4d\u7684\u6027\u80fd\u8bc4\u4f30\u4f53\u7cfb<\/h4>\n

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

YOLOv6\u63d0\u4f9b\u4e86\u4ecenano\u5230large6\u7684\u5b8c\u6574\u6a21\u578b\u65cf，\u8986\u76d6\u4e86\u4ece\u79fb\u52a8\u7aef\u5230\u670d\u52a1\u5668\u7aef\u7684\u5168\u90e8\u5e94\u7528\u573a\u666f\u3002\u6bcf\u4e2a\u6a21\u578b\u90fd\u7ecf\u8fc7\u7cbe\u5fc3\u8c03\u4f18，\u5728\u5176\u5bf9\u5e94\u7684\u8d44\u6e90\u7ea6\u675f\u4e0b\u5b9e\u73b0\u4e86\u6700\u4f18\u7684\u7cbe\u5ea6-\u901f\u5ea6\u5e73\u8861\u3002<\/p>\n

YOLOv6-N\u4f5c\u4e3a\u7cfb\u5217\u4e2d\u6700\u8f7b\u91cf\u7684\u6a21\u578b，\u5728NVIDIA T4 GPU\u4e0a\u80fd\u591f\u8fbe\u52301187 FPS\u7684\u60ca\u4eba\u901f\u5ea6，\u540c\u65f6\u4fdd\u630137.5% AP\u7684\u68c0\u6d4b\u7cbe\u5ea6\u3002\u8fd9\u79cd\u6027\u80fd\u8868\u73b0\u4f7f\u5176\u975e\u5e38\u9002\u5408\u5b9e\u65f6\u6027\u8981\u6c42\u6781\u9ad8\u7684\u5e94\u7528\u573a\u666f，\u5982\u81ea\u52a8\u9a7e\u9a76\u3001\u65e0\u4eba\u673a\u5bfc\u822a\u7b49\u3002<\/p>\n

YOLOv6-S\u5728\u901f\u5ea6\u548c\u7cbe\u5ea6\u4e4b\u95f4\u53d6\u5f97\u4e86\u5f88\u597d\u7684\u5e73\u8861，45.0% AP\u7684\u7cbe\u5ea6\u914d\u5408484 FPS\u7684\u901f\u5ea6，\u4f7f\u5176\u6210\u4e3a\u5927\u591a\u6570\u5de5\u4e1a\u5e94\u7528\u7684\u9996\u9009\u65b9\u6848\u3002\u65e0\u8bba\u662f\u667a\u80fd\u5b89\u9632\u3001\u5de5\u4e1a\u8d28\u68c0\u8fd8\u662f\u96f6\u552e\u5206\u6790，YOLOv6-S\u90fd\u80fd\u63d0\u4f9b\u7a33\u5b9a\u53ef\u9760\u7684\u68c0\u6d4b\u670d\u52a1\u3002<\/p>\n

2. \u9ad8\u7cbe\u5ea6\u6a21\u578b\u7684\u7a81\u7834\u6027\u8868\u73b0<\/h4>\n

YOLOv6-L\u548cYOLOv6-L6\u4ee3\u8868\u4e86\u7cfb\u5217\u4e2d\u7cbe\u5ea6\u6700\u9ad8\u7684\u6a21\u578b，\u7279\u522b\u662fYOLOv6-L6\u5728COCO\u6570\u636e\u96c6\u4e0a\u5b9e\u73b0\u4e86\u5f53\u65f6\u6700\u5148\u8fdb\u7684\u5b9e\u65f6\u68c0\u6d4b\u7cbe\u5ea6\u3002\u8fd9\u4e9b\u9ad8\u7cbe\u5ea6\u6a21\u578b\u867d\u7136\u901f\u5ea6\u76f8\u5bf9\u8f83\u6162，\u4f46\u4ecd\u7136\u80fd\u591f\u6ee1\u8db3\u5b9e\u65f6\u5e94\u7528\u7684\u9700\u6c42，\u4e3a\u8ffd\u6c42\u6781\u81f4\u7cbe\u5ea6\u7684\u573a\u666f\u63d0\u4f9b\u4e86\u7406\u60f3\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n

3. \u91cf\u5316\u4f18\u5316\u4e0e\u79fb\u52a8\u7aef\u9002\u914d<\/h4>\n

\u9664\u4e86\u6807\u51c6\u7684\u6d6e\u70b9\u6a21\u578b，YOLOv6\u8fd8\u63d0\u4f9b\u4e86\u7ecf\u8fc7\u91cf\u5316\u4f18\u5316\u7684\u7248\u672c，\u8fd9\u4e9b\u6a21\u578b\u80fd\u591f\u5728\u4fdd\u6301\u68c0\u6d4b\u7cbe\u5ea6\u7684\u540c\u65f6\u5927\u5e45\u964d\u4f4e\u6a21\u578b\u5927\u5c0f\u548c\u63a8\u7406\u65f6\u95f4\u3002\u91cf\u5316\u7248\u672c\u7279\u522b\u9002\u5408\u79fb\u52a8\u8bbe\u5907\u548c\u8fb9\u7f18\u8ba1\u7b97\u573a\u666f，\u4e3aAI\u6280\u672f\u7684\u666e\u53ca\u63d0\u4f9b\u4e86\u6709\u529b\u652f\u6491\u3002<\/p>\n

# \u6a21\u578b\u91cf\u5316\u793a\u4f8b<\/span>
\ndef<\/span> quantize_model<\/span>(<\/span>model,<\/span> calibration_loader)<\/span>:<\/span>
\n # \u51c6\u5907\u91cf\u5316\u914d\u7f6e<\/span>
\n quantization_config =<\/span> torch.<\/span>quantization.<\/span>get_default_qconfig(<\/span>'fbgemm'<\/span>)<\/span>
\n model.<\/span>qconfig =<\/span> quantization_config<\/p>\n

# \u878d\u5408\u64cd\u4f5c\u4ee5\u63d0\u9ad8\u91cf\u5316\u6548\u7387<\/span>
\n torch.<\/span>quantization.<\/span>fuse_modules(<\/span>model,<\/span> [<\/span>[<\/span>'conv'<\/span>,<\/span> 'bn'<\/span>,<\/span> 'relu'<\/span>]<\/span>]<\/span>,<\/span> inplace=<\/span>True<\/span>)<\/span><\/p>\n

# \u51c6\u5907\u91cf\u5316<\/span>
\n torch.<\/span>quantization.<\/span>prepare(<\/span>model,<\/span> inplace=<\/span>True<\/span>)<\/span><\/p>\n

# \u6821\u51c6<\/span>
\n model.<\/span>eval<\/span>(<\/span>)<\/span>
\n with<\/span> torch.<\/span>no_grad(<\/span>)<\/span>:<\/span>
\n for<\/span> data,<\/span> _ in<\/span> calibration_loader:<\/span>
\n model(<\/span>data)<\/span><\/p>\n

# \u8f6c\u6362\u4e3a\u91cf\u5316\u6a21\u578b<\/span>
\n quantized_model =<\/span> torch.<\/span>quantization.<\/span>convert(<\/span>model,<\/span> inplace=<\/span>False<\/span>)<\/span>
\n return<\/span> quantized_model<\/p>\n

YOLOv6\u5728\u8bbe\u8ba1\u4e4b\u521d\u5c31\u5145\u5206\u8003\u8651\u4e86\u90e8\u7f72\u7684\u4fbf\u5229\u6027，\u91c7\u7528\u4e86\u591a\u79cd\u63a8\u7406\u4f18\u5316\u7b56\u7565\u3002\u6a21\u578b\u7684\u7f51\u7edc\u7ed3\u6784\u5bf9\u4e3b\u6d41\u7684\u63a8\u7406\u6846\u67b6\u90fd\u5f88\u53cb\u597d，\u65e0\u8bba\u662fTensorRT\u3001ONNX\u8fd8\u662fOpenVINO，\u90fd\u80fd\u5b9e\u73b0\u9ad8\u6548\u7684\u63a8\u7406\u52a0\u901f\u3002\u63a8\u7406\u8fc7\u7a0b\u4e2d\u7684\u5185\u5b58\u7ba1\u7406\u4e5f\u7ecf\u8fc7\u4e86\u7cbe\u5fc3\u4f18\u5316，\u901a\u8fc7\u5408\u7406\u7684\u5185\u5b58\u5206\u914d\u7b56\u7565\u548c\u4e2d\u95f4\u7ed3\u679c\u590d\u7528，\u6700\u5927\u9650\u5ea6\u5730\u964d\u4f4e\u4e86\u5185\u5b58\u5360\u7528\u3002\u8fd9\u79cd\u4f18\u5316\u5bf9\u4e8e\u8d44\u6e90\u53d7\u9650\u7684\u8fb9\u7f18\u8bbe\u5907\u7279\u522b\u91cd\u8981\u3002<\/p>\n

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