{"id":71116,"date":"2026-02-03T08:57:02","date_gmt":"2026-02-03T00:57:02","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/71116.html"},"modified":"2026-02-03T08:57:02","modified_gmt":"2026-02-03T00:57:02","slug":"yolov13%e5%85%a8%e9%9d%a2%e8%a7%a3%e6%9e%90%e4%b8%8e%e5%ae%89%e5%8d%93%e5%b9%b3%e5%8f%b0ncnn%e9%83%a8%e7%bd%b2%e5%ae%9e%e6%88%98%ef%bc%9a%e8%b6%85%e5%9b%be%e8%a7%86%e8%a7%89%e9%87%8d%e5%a1%91%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/71116.html","title":{"rendered":"YOLOv13\u5168\u9762\u89e3\u6790\u4e0e\u5b89\u5353\u5e73\u53f0NCNN\u90e8\u7f72\u5b9e\u6218\uff1a\u8d85\u56fe\u89c6\u89c9\u91cd\u5851\u5b9e\u65f6\u76ee\u6807\u68c0\u6d4b\u7684\u7cbe\u5ea6\u4e0e\u6548\u7387\u8fb9\u754c"},"content":{"rendered":"<p>\u63d0\u793a&#xff1a;\u6587\u7ae0\u5199\u5b8c\u540e&#xff0c;\u76ee\u5f55\u53ef\u4ee5\u81ea\u52a8\u751f\u6210&#xff0c;\u5982\u4f55\u751f\u6210\u53ef\u53c2\u8003\u53f3\u8fb9\u7684\u5e2e\u52a9\u6587\u6863<\/p>\n<\/p>\n<h4>\u6587\u7ae0\u76ee\u5f55<\/h4>\n<ul>\n<li>\u4e00\u3001\u524d\u8a00<\/li>\n<li>\u4e8c\u3001YOLOv13\u7684\u6838\u5fc3\u521b\u65b0<\/li>\n<li>\n<ul>\n<li>1. \u81ea\u9002\u5e94\u8d85\u56fe\u76f8\u5173\u6027\u589e\u5f3a&#xff08;HyperACE&#xff09;<\/li>\n<li>2. \u5168\u6d41\u7a0b\u805a\u5408\u4e0e\u5206\u53d1&#xff08;FullPAD&#xff09;\u8303\u5f0f<\/li>\n<li>3. \u57fa\u4e8e\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u7684\u5168\u6d41\u7a0b\u8f7b\u91cf\u5316<\/li>\n<\/ul>\n<\/li>\n<li>\u4e09\u3001YOLOv13\u6027\u80fd\u57fa\u51c6<\/li>\n<li>\u56db\u3001\u73af\u5883\u642d\u5efa&#xff08;windows\u7248\u672c&#xff09;<\/li>\n<li>\n<ul>\n<li>1\u3001\u57fa\u7840\u73af\u5883\u642d\u5efa<\/li>\n<li>2. requirements.txt\u5b89\u88c5<\/li>\n<li>3. \u5b89\u88c5flash-attention<\/li>\n<li>4. \u73af\u5883\u9a8c\u8bc1<\/li>\n<\/ul>\n<\/li>\n<li>\u4e94\u3001\u91cf\u5316\u6d41\u7a0b<\/li>\n<li>\n<ul>\n<li>1. \u5728\u4e0a\u8ff0\u73af\u5883\u4e2d\u5b89\u88c5\u91cf\u5316\u4f9d\u8d56\u5e93<\/li>\n<li>2. \u5bfc\u51fa yolov13 torchscript<\/li>\n<li>3. \u8f6c\u6362\u9759\u6001\u5c3a\u5bf8\u8f93\u5165\u7684 torchscript<\/li>\n<li>4. \u4fee\u6539 yolov13n_pnnx.py \u6a21\u578b\u811a\u672c&#xff0c;\u652f\u6301\u52a8\u6001\u5c3a\u5bf8\u8f93\u5165<\/li>\n<li>5. \u6ce8\u610f\u529b\u6a21\u5757&#xff0c;\u4fee\u6539\u652f\u6301\u52a8\u6001\u5c3a\u5bf8\u8f93\u5165<\/li>\n<li>6. HGNN\u6a21\u5757&#xff0c;\u4fee\u6539\u652f\u6301\u52a8\u6001\u5c3a\u5bf8\u8f93\u5165<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<hr \/>\n<h2>\u4e00\u3001\u524d\u8a00<\/h2>\n<p>YOLOv13\u4e8e2025\u5e746\u6708\u91cd\u78c5\u53d1\u5e03&#xff0c;\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u8d85\u56fe\u589e\u5f3a\u81ea\u9002\u5e94\u89c6\u89c9\u611f\u77e5\u7684\u5168\u65b0\u67b6\u6784&#xff0c;\u8fd9\u5f7b\u5e95\u8df3\u8131\u4e86YOLOv12\u6240\u4f9d\u8d56\u7684\u6ce8\u610f\u529b\u673a\u5236\u548cYOLOv11\u53ca\u66f4\u65e9\u7248\u672c\u7684\u5377\u79ef\u8303\u5f0f\u3002\u8be5\u6a21\u578b\u901a\u8fc7\u5f15\u5165\u8d85\u56fe\u7406\u8bba\u6765\u5efa\u6a21\u590d\u6742\u7684\u5168\u5c40\u9ad8\u9636\u8bed\u4e49\u5173\u8054&#xff0c;\u5728\u663e\u8457\u63d0\u5347\u76ee\u6807\u68c0\u6d4b\u51c6\u786e\u6027\u7684\u540c\u65f6&#xff0c;\u5de7\u5999\u5730\u63a7\u5236\u4e86\u8ba1\u7b97\u5f00\u9500&#xff0c;\u7ef4\u6301\u4e86\u51fa\u8272\u7684\u5b9e\u65f6\u63a8\u7406\u6027\u80fd\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005650-698147d2b48c9.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u672c\u6587\u5c06\u6df1\u5165\u89e3\u6790YOLOv13\u7684\u67b6\u6784\u521b\u65b0&#xff0c;\u91cd\u70b9\u4ecb\u7ecd\u5176\u81ea\u9002\u5e94\u8d85\u56fe\u76f8\u5173\u6027\u589e\u5f3a&#xff08;HyperACE&#xff09; \u673a\u5236\u548c\u5168\u6d41\u7a0b\u805a\u5408\u5206\u53d1&#xff08;FullPAD&#xff09; \u8303\u5f0f&#xff0c;\u5982\u4f55\u901a\u8fc7\u8f7b\u91cf\u5316\u8bbe\u8ba1\u5b9e\u73b0\u6548\u7387\u4e0e\u7cbe\u5ea6\u7684\u53cc\u91cd\u7a81\u7834&#xff0c;\u7136\u540e\u8be6\u7ec6\u5c55\u793a\u5982\u4f55\u5c06YOLOv13\u6a21\u578b\u901a\u8fc7PNNX\u8f6c\u6362\u5e76\u90e8\u7f72\u5230NCNN\u6846\u67b6&#xff0c;\u5b9e\u73b0\u9ad8\u6027\u80fd\u76ee\u6807\u68c0\u6d4b\u3002<\/p>\n<p>YOLOv13\u4ee3\u7801\u4ed3\u5e93&#xff1a; https:\/\/github.com\/iMoonLab\/yolov13 YOLOv13\u8bba\u6587&#xff1a; https:\/\/arxiv.org\/abs\/2506.17733 ncnn-android-yolov13&#xff1a; https:\/\/github.com\/LeeeFu\/ncnn-android-yolov13<\/p>\n<h2>\u4e8c\u3001YOLOv13\u7684\u6838\u5fc3\u521b\u65b0<\/h2>\n<h3>1. \u81ea\u9002\u5e94\u8d85\u56fe\u76f8\u5173\u6027\u589e\u5f3a&#xff08;HyperACE&#xff09;<\/h3>\n<p>\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df&#xff0c;\u65e0\u8bba\u662f\u4f20\u7edf\u7684\u5377\u79ef\u64cd\u4f5c\u8fd8\u662fYOLOv12\u5f15\u5165\u7684\u533a\u57df\u6ce8\u610f\u529b&#xff0c;\u90fd\u53d7\u9650\u4e8e\u5c40\u90e8\u4fe1\u606f\u805a\u5408\u6216\u6210\u5bf9&#xff08;pairwise&#xff09;\u50cf\u7d20\u5173\u7cfb\u5efa\u6a21\u3002\u5b83\u4eec\u96be\u4ee5\u6355\u6349\u56fe\u50cf\u4e2d\u771f\u5b9e\u5b58\u5728\u7684\u3001\u8d85\u8d8a\u4e24\u4e24\u4ea4\u4e92\u7684\u591a\u5bf9\u591a\u9ad8\u9636\u8bed\u4e49\u5173\u8054&#xff08;\u4f8b\u5982&#xff0c;\u4e00\u8f86\u8f66\u7531\u8f66\u8eab\u3001\u8f6e\u6bc2\u3001\u8f66\u7a97\u7b49\u591a\u4e2a\u90e8\u4ef6\u534f\u540c\u6784\u6210&#xff09;\u3002YOLOv13\u901a\u8fc7\u5f15\u5165\u81ea\u9002\u5e94\u8d85\u56fe\u76f8\u5173\u6027\u589e\u5f3a&#xff08;Hypergraph-based Adaptive Correlation Enhancement, HyperACE&#xff09; \u673a\u5236\u6765\u89e3\u51b3\u8fd9\u4e00\u6839\u672c\u6027\u95ee\u9898\u3002 \u8be5\u673a\u5236\u5c06\u7279\u5f81\u56fe\u4e2d\u7684\u50cf\u7d20\u89c6\u4e3a\u9876\u70b9&#xff08;vertices&#xff09;&#xff0c;\u5e76\u901a\u8fc7\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u6a21\u5757\u52a8\u6001\u751f\u6210\u8d85\u8fb9&#xff08;hyperedges&#xff09;\u3002\u6bcf\u6761\u8d85\u8fb9\u53ef\u4ee5\u8fde\u63a5\u4efb\u610f\u6570\u91cf\u7684\u9876\u70b9&#xff0c;\u4ece\u800c\u4e00\u6b21\u6027\u5efa\u6a21\u7fa4\u7ec4\u95f4\u7684\u590d\u6742\u5173\u7cfb\u3002\u4e0d\u540c\u4e8e\u4f9d\u8d56\u624b\u5de5\u9608\u503c\u6784\u5efa\u8d85\u56fe\u7684\u8106\u5f31\u65b9\u6cd5&#xff0c;HyperACE\u901a\u8fc7\u8ba1\u7b97\u6bcf\u4e2a\u9876\u70b9\u5bf9\u6bcf\u6761\u8d85\u8fb9\u7684\u8fde\u7eed\u53c2\u4e0e\u5ea6\u6743\u91cd&#xff0c;\u5f62\u6210\u4e86\u4e00\u4e2a\u9c81\u68d2\u4e14\u8868\u8fbe\u529b\u5f3a\u7684\u5173\u8054\u77e9\u9635\u3002\u5176\u5185\u90e8\u91c7\u7528\u201c\u9876\u70b9\u2192\u8d85\u8fb9\u805a\u5408\u201d\u548c\u201c\u8d85\u8fb9\u2192\u9876\u70b9\u5206\u53d1\u201d\u7684\u4e24\u9636\u6bb5\u6d88\u606f\u4f20\u9012&#xff0c;\u5b9e\u73b0\u4e86\u9ad8\u6548\u7684\u5168\u5c40\u7279\u5f81\u878d\u5408&#xff0c;\u5e76\u901a\u8fc7\u7ebf\u6027\u590d\u6742\u5ea6\u4f18\u5316\u786e\u4fdd\u4e86\u5b9e\u65f6\u6027\u3002 \u5728COCO\u6570\u636e\u96c6\u4e0a&#xff0c;YOLOv13-N&#xff08;Nano\u7248\u672c&#xff09;\u5b9e\u73b0\u4e8642.1% mAP&#xff0c;\u8f83YOLOv12-N\u768440.6% \u63d0\u5347\u4e861.5%\u3002\u5173\u952e\u4f18\u52bf\u5728\u4e8e\u5176\u9ad8\u9636\u8bed\u4e49\u7406\u89e3\u80fd\u529b&#xff1a;\u53ef\u89c6\u5316\u7ed3\u679c\u8868\u660e&#xff0c;YOLOv13\u80fd\u66f4\u51c6\u786e\u5730\u5904\u7406\u906e\u6321&#xff08;\u5982\u82b1\u74f6\u540e\u7684\u690d\u7269&#xff09;\u3001\u533a\u5206\u76ee\u6807\u4e0e\u9634\u5f71&#xff08;\u5982\u7f51\u7403\u62cd&#xff09;&#xff0c;\u5e76\u5728\u590d\u6742\u573a\u666f\u4e2d\u4ea7\u751f\u66f4\u53ef\u9760\u7684\u68c0\u6d4b\u7ed3\u679c&#xff0c;\u8bef\u68c0\u7387\u663e\u8457\u964d\u4f4e\u3002<\/p>\n<h3>2. \u5168\u6d41\u7a0b\u805a\u5408\u4e0e\u5206\u53d1&#xff08;FullPAD&#xff09;\u8303\u5f0f<\/h3>\n<p>\u4f20\u7edf\u7684YOLO\u67b6\u6784\u9075\u5faa\u4e25\u683c\u7684\u5355\u5411\u4fe1\u606f\u6d41&#xff1a;\u9aa8\u5e72\u7f51\u7edc \u2192 \u9888\u90e8\u7f51\u7edc \u2192 \u68c0\u6d4b\u5934\u3002\u8fd9\u79cd\u8bbe\u8ba1\u5bfc\u81f4\u9ad8\u5c42\u8bed\u4e49\u4fe1\u606f\u96be\u4ee5\u6709\u6548\u56de\u6eaf\u81f3\u5e95\u5c42\u7279\u5f81&#xff0c;\u9020\u6210\u5c0f\u76ee\u6807\u7ec6\u8282\u5728\u6df1\u5c42\u7f51\u7edc\u4e2d\u4e22\u5931\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e00\u74f6\u9888&#xff0c;YOLOv13\u63d0\u51fa\u4e86\u5168\u6d41\u7a0b\u805a\u5408\u4e0e\u5206\u53d1&#xff08;Full-Pipeline Aggregation-and-Distribution, FullPAD&#xff09; \u8303\u5f0f\u3002 \u8be5\u8303\u5f0f\u5c06HyperACE\u6a21\u5757\u751f\u6210\u7684\u3001\u5bcc\u542b\u5168\u5c40\u9ad8\u9636\u8bed\u4e49\u7684\u589e\u5f3a\u7279\u5f81&#xff0c;\u901a\u8fc7\u53ef\u5b66\u4e60\u7684\u95e8\u63a7\u878d\u5408\u673a\u5236&#xff0c;\u7cbe\u51c6\u5730\u53cd\u5411\u6ce8\u5165\u5230\u7f51\u7edc\u7684\u4e09\u4e2a\u5173\u952e\u8282\u70b9&#xff1a;\u9aa8\u5e72\u4e0e\u9888\u90e8\u8fde\u63a5\u5904\u3001\u9888\u90e8\u5185\u90e8\u4ee5\u53ca\u9888\u90e8\u4e0e\u68c0\u6d4b\u5934\u8fde\u63a5\u5904\u3002\u8fd9\u79cd\u201c\u805a\u5408-\u5206\u53d1\u201d\u7684\u95ed\u73af\u8bbe\u8ba1&#xff0c;\u6253\u7834\u4e86\u5355\u5411\u4fe1\u606f\u6d41\u7684\u58c1\u5792&#xff0c;\u4e0d\u4ec5\u6781\u5927\u5730\u6539\u5584\u4e86\u68af\u5ea6\u6d41\u52a8&#xff0c;\u7f13\u89e3\u4e86\u68af\u5ea6\u6d88\u5931\u95ee\u9898&#xff0c;\u8fd8\u5b9e\u73b0\u4e86\u5168\u7f51\u7edc\u8303\u56f4\u5185\u7684\u7cbe\u7ec6\u5316\u4fe1\u606f\u534f\u540c\u3002\u5b9e\u9a8c\u8868\u660e&#xff0c;FullPAD\u8303\u5f0f\u4f7fCOCO\u5c0f\u76ee\u6807AP\u63d0\u5347\u4e862.3%&#xff0c;\u663e\u8457\u589e\u5f3a\u4e86\u6a21\u578b\u5bf9\u7ec6\u8282\u7684\u611f\u77e5\u80fd\u529b\u3002<\/p>\n<h3>3. \u57fa\u4e8e\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u7684\u5168\u6d41\u7a0b\u8f7b\u91cf\u5316<\/h3>\n<p>\u4e3a\u4e86\u5728\u5f15\u5165\u5f3a\u5927\u7684HyperACE\u548cFullPAD\u540e\u4f9d\u7136\u4fdd\u6301\u6a21\u578b\u7684\u8f7b\u91cf\u4e0e\u9ad8\u6548&#xff0c;YOLOv13\u8bbe\u8ba1\u4e86\u4e00\u7cfb\u5217\u57fa\u4e8eDSConv\u7684\u65b0\u578b\u8f7b\u91cf\u5316\u6a21\u5757&#xff1a;<\/p>\n<li>DS-Bottleneck&#xff1a;\u4f5c\u4e3a\u57fa\u7840\u5355\u5143&#xff0c;\u4f7f\u7528\u6df1\u5ea6\u5377\u79ef\u8fdb\u884c\u901a\u9053\u5185\u6ee4\u6ce2&#xff0c;\u518d\u7528\u70b9\u5377\u79ef\u8fdb\u884c\u8de8\u901a\u9053\u878d\u5408&#xff0c;\u5927\u5e45\u51cf\u5c11\u53c2\u6570\u3002<\/li>\n<li>DS-C3k \/DS-C3k2&#xff1a;\u5c06DS-Bottleneck\u5d4c\u5165\u5230CSP\u7ed3\u6784\u4e2d&#xff0c;\u66ff\u4ee3\u4e86\u4f20\u7edf\u7684C3k2\u6a21\u5757&#xff0c;\u6210\u4e3a\u9aa8\u5e72\u548c\u9888\u90e8\u7f51\u7edc\u7684\u57fa\u672c\u6784\u5efa\u5757\u3002\u8fd9\u4e9b\u6a21\u5757\u5728\u51e0\u4e4e\u4e0d\u635f\u5931\u7cbe\u5ea6\u7684\u524d\u63d0\u4e0b&#xff0c;\u663e\u8457\u964d\u4f4e\u4e86\u8ba1\u7b97\u590d\u6742\u5ea6\u3002<\/li>\n<li>\u8f7b\u91cf\u5316\u68c0\u6d4b\u5934&#xff1a;\u68c0\u6d4b\u5934\u90e8\u5206\u540c\u6837\u91c7\u7528\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u8fdb\u884c\u91cd\u6784&#xff0c;\u8fdb\u4e00\u6b65\u538b\u7f29\u4e86\u6a21\u578b\u4f53\u79ef\u3002<\/li>\n<h2>\u4e09\u3001YOLOv13\u6027\u80fd\u57fa\u51c6<\/h2>\n<p>\u4e86\u89e3\u67b6\u6784\u9769\u65b0\u540e&#xff0c;\u6211\u6574\u7406\u4e86\u5b98\u65b9YOLO13\u548cYOLOv12\u7684\u6d4b\u8bd5\u6570\u636e&#xff0c;\u505a\u6210\u4e0b\u9762\u8fd9\u4e2a\u5bf9\u6bd4\u8868\u3002\u4e0b\u8868\u57fa\u4e8eCOCO val2017\u6570\u636e\u96c6&#xff0c;\u5728\u76f8\u540c\u786c\u4ef6\u914d\u7f6e\u4e0b\u5bf9\u6bd4YOLOv13\u4e0eYOLOv12\u5404\u5c3a\u5bf8\u6a21\u578b\u7684\u6027\u80fd\u8868\u73b0&#xff1a;<\/p>\n<table>\n<tr>Modelsize(pixels)mAPval(50-95)Speed T4(ms)params(M)FLOPs(G)<\/tr>\n<tbody>\n<tr>\n<td>YOLOv12n<\/td>\n<td>640<\/td>\n<td>40.4<\/td>\n<td>1.60<\/td>\n<td>2.5<\/td>\n<td>6.0<\/td>\n<\/tr>\n<tr>\n<td>YOLOv13n<\/td>\n<td>640<\/td>\n<td>42.1<\/td>\n<td>1.97<\/td>\n<td>2.2<\/td>\n<td>4.8<\/td>\n<\/tr>\n<tr>\n<td>YOLOv12s<\/td>\n<td>640<\/td>\n<td>47.6<\/td>\n<td>2.42<\/td>\n<td>9.1<\/td>\n<td>19.4<\/td>\n<\/tr>\n<tr>\n<td>YOLOv13s<\/td>\n<td>640<\/td>\n<td>48.0<\/td>\n<td>3.20<\/td>\n<td>9.0<\/td>\n<td>20.8<\/td>\n<\/tr>\n<tr>\n<td>YOLOv12m<\/td>\n<td>640<\/td>\n<td>52.5<\/td>\n<td>4.27<\/td>\n<td>19.6<\/td>\n<td>59.8<\/td>\n<\/tr>\n<tr>\n<td>YOLOv13m<\/td>\n<td>640<\/td>\n<td>53.5<\/td>\n<td>5.10<\/td>\n<td>18.5<\/td>\n<td>60.0<\/td>\n<\/tr>\n<tr>\n<td>YOLOv12l<\/td>\n<td>640<\/td>\n<td>53.8<\/td>\n<td>5.83<\/td>\n<td>26.5<\/td>\n<td>82.4<\/td>\n<\/tr>\n<tr>\n<td>YOLOv13l<\/td>\n<td>640<\/td>\n<td>55.2<\/td>\n<td>7.50<\/td>\n<td>24.5<\/td>\n<td>68.3<\/td>\n<\/tr>\n<tr>\n<td>YOLOv12x<\/td>\n<td>640<\/td>\n<td>55.4<\/td>\n<td>10.38<\/td>\n<td>59.3<\/td>\n<td>184.6<\/td>\n<\/tr>\n<tr>\n<td>YOLOv13x<\/td>\n<td>640<\/td>\n<td>56.8<\/td>\n<td>13.80<\/td>\n<td>55.0<\/td>\n<td>152.6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u51e0\u4e2a\u5173\u952e\u53d1\u73b0&#xff1a; YOLOv13-N\u6bd4YOLOv12-N\u9ad81.5% mAP&#xff0c;\u540c\u65f6\u53c2\u6570\u91cf\u548cFLOPs\u66f4\u4f4e\u3002\u8fd9\u8bc1\u660e\u4e86\u8d85\u56fe\u9a71\u52a8\u7684\u9ad8\u9636\u5efa\u6a21\u5728\u63d0\u5347\u7cbe\u5ea6\u65b9\u9762\u7684\u5de8\u5927\u6f5c\u529b\u3002\u4e0e\u7ade\u54c1\u7684\u5bf9\u6bd4 \u4e0e\u540c\u7c7b\u6a21\u578b\u76f8\u6bd4&#xff0c;YOLOv13\u5c55\u73b0\u51fa\u5168\u9762\u4f18\u52bf\u3002\u4e0eYOLO\u7cfb\u5217\u524d\u4ee3\u6bd4\u8f83&#xff0c;\u6240\u6709\u5c3a\u5bf8\u6a21\u578b\u5747\u53d6\u5f97SOTA\u7cbe\u5ea6&#xff0c;\u4e14\u6a21\u578b\u66f4\u8f7b\u91cf\u3002\u5b9a\u6027\u5206\u6790\u663e\u793aYOLOv13\u5728\u590d\u6742\u573a\u666f&#xff08;\u5982\u5bc6\u96c6\u3001\u906e\u6321&#xff09;\u548c\u5c0f\u76ee\u6807\u68c0\u6d4b\u4e0a\u5177\u6709\u663e\u8457\u4f18\u52bf&#xff0c;\u80fd\u6709\u6548\u533a\u5206\u6613\u6df7\u6dc6\u76ee\u6807\u3002 \u76ee\u524d&#xff0c;YOLOv13\u4e3b\u8981\u4e13\u6ce8\u4e8e\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u3002\u867d\u7136\u5176\u67b6\u6784\u8bbe\u8ba1\u4e3a\u672a\u6765\u6269\u5c55&#xff08;\u5982\u5206\u5272\u3001\u59ff\u6001\u4f30\u8ba1&#xff09;\u5960\u5b9a\u4e86\u826f\u597d\u57fa\u7840&#xff0c;\u4f46\u5b98\u65b9\u5c1a\u672a\u53d1\u5e03\u76f8\u5173\u6a21\u578b\u3002\u5bf9\u4e8e\u9700\u8981\u591a\u529f\u80fd\u4e00\u4f53\u7684\u7528\u6237&#xff0c;YOLOv11\u6216YOLOv12\u4ecd\u662f\u66f4\u5168\u9762\u7684\u9009\u62e9\u3002<\/p>\n<h2>\u56db\u3001\u73af\u5883\u642d\u5efa&#xff08;windows\u7248\u672c&#xff09;<\/h2>\n<p>\u6ce8&#xff1a;\u5229\u7528Anaconda\u6765\u642d\u5efawindows\u57fa\u7840\u73af\u5883&#xff0c;YOLOv13\u63a8\u8350python3.11\u3001pyTorch 2.4.1\u3001torchvision 0.19.1\u3001torchaudio 2.4.1 ultralytics 8.3.63 \u4e00\u5b9a\u8981\u5728cuda12\u4ee5\u4e0a\u7684\u73af\u5883&#xff0c;\u9002\u914d\u540e\u9762\u7684flash-attention\u5305<\/p>\n<h3>1\u3001\u57fa\u7840\u73af\u5883\u642d\u5efa<\/h3>\n<p><span class=\"token comment\">#1. \u521b\u5efa\u865a\u62df\u73af\u5883<\/span><br \/>\nconda create <span class=\"token parameter variable\">-n<\/span> yolov13 <span class=\"token assign-left variable\">python<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">3.11<\/span><\/p>\n<p><span class=\"token comment\">#2. \u8fdb\u5165\u865a\u62df\u73af\u5883<\/span><br \/>\nconda activate yolov13<\/p>\n<p><span class=\"token comment\">#3. \u4e0b\u8f7dYOLOv13\u6e90\u7801<\/span><br \/>\n<span class=\"token function\">git<\/span> clone https:\/\/github.com\/iMoonLab\/yolov13.git<\/p>\n<p><span class=\"token comment\">#4. \u4e0b\u8f7d\u9884\u8bad\u7ec3\u6743\u91cd<\/span><br \/>\n<span class=\"token function\">wget<\/span> https:\/\/github.com\/iMoonLab\/yolov13\/releases\/download\/yolov13\/yolov13n.pt<br \/>\n<span class=\"token function\">wget<\/span> https:\/\/github.com\/iMoonLab\/yolov13\/releases\/download\/yolov13\/yolov13s.pt<\/p>\n<p><span class=\"token comment\">#5. \u5b89\u88c5pytorch&#xff08;GPU&#xff09;,\u5982\u679c\u7f51\u901f\u6bd4\u8f83\u6162&#xff0c;\u6211\u4eec\u4e5f\u53ef\u4ee5\u79d1\u5b66\u4e0a\u7f51\u4e0b\u8f7d\u540e&#xff0c;\u8fdb\u884c\u79bb\u7ebf\u5b89\u88c5<\/span><br \/>\npip <span class=\"token function\">install<\/span> <span class=\"token assign-left variable\">torch<\/span><span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">2.4<\/span>.1 <span class=\"token assign-left variable\">torchvision<\/span><span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">0.19<\/span>.1 <span class=\"token assign-left variable\">torchaudio<\/span><span class=\"token operator\">&#061;&#061;<\/span><span class=\"token number\">2.4<\/span>.1 &#8211;index-url https:\/\/download.pytorch.org\/whl\/cu124<\/p>\n<p>\u79bb\u7ebf\u5b89\u88c5\u5305\u4e0b\u8f7d\u5730\u5740&#xff1a;http:\/\/download.pytorch.org\/whl\/torch_stable.html \u4e5f\u53ef\u4ee5\u901a\u8fc7\u79bb\u7ebf\u5b89\u88c5\u65b9\u5f0f&#xff0c;\u4f46\u53ef\u80fd\u5b58\u5728\u6700\u65b0\u7684\u5305\u540d\u627e\u4e0d\u5230\u7684\u95ee\u9898 \u6253\u5f00\u94fe\u63a5\u540e&#xff0c;\u6211\u4eec\u627e\u5230\u4ee5\u4e0b\u5bf9\u5e94\u7248\u672c&#xff0c;torch2.4.1, torchvision0.19.1&#xff0c;torchaudio&#061;&#061;2.4.1&#xff0c; \u4f9d\u6b21\u4e0b\u8f7d\u540e&#xff0c;\u5728\u73af\u5883\u4e2d\u8fdb\u884cpip install \u5b89\u88c5 \u987a\u5e8f\u4ee5\u6b64\u662f&#xff1a;torch &#8211;&gt;torchvision\u2013&gt;trochaudio \u6309\u7167\u4ee5\u4e0a\u65b9\u5f0f&#xff0c;\u5728\u73af\u5883\u4e2d\u6210\u529f\u5b89\u88c5torch\u76f8\u5173\u5de5\u5177 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005656-698147d8bf486.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h3>2. requirements.txt\u5b89\u88c5<\/h3>\n<p>\u8fdb\u5165yolov13-main \u8fd9\u4e2a\u6587\u4ef6\u5939&#xff0c;\u5c06\u524d\u4e09\u884c\u7ed9\u6ce8\u91ca\u6389&#xff0c;\u56e0\u4e3atorch\u76f8\u5173\u7684\u6211\u4eec\u5df2\u7ecf\u624b\u52a8\u5b8c\u6210\u5b89\u88c5&#xff0c;\u4ee5\u53caflash_attn\u7ec4\u4ef6\u662flinux\u7248\u672c&#xff0c;\u6211\u4eecwindows\u4e0a\u7684\u9700\u8981\u624b\u52a8\u5b89\u88c5\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005657-698147d95cdf3.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u4e4b\u540e\u8fdb\u884c\u5b89\u88c5<\/p>\n<p><span class=\"token comment\">#\u4e0b\u8f7d\u6bd4\u8f83\u6162\u7684\u8bdd&#xff0c;\u53ef\u4ee5\u6dfb\u52a0\u955c\u50cf\u6e90<\/span><br \/>\npip <span class=\"token function\">install<\/span> <span class=\"token parameter variable\">-r<\/span> requirements.txt<br \/>\npip <span class=\"token function\">install<\/span> <span class=\"token parameter variable\">-r<\/span> requirements.txt <span class=\"token parameter variable\">-i<\/span> https:\/\/pypi.tuna.tsinghua.edu.cn\/simple<br \/>\npip <span class=\"token function\">install<\/span> <span class=\"token parameter variable\">-e<\/span> <span class=\"token builtin class-name\">.<\/span><\/p>\n<h3>3. \u5b89\u88c5flash-attention<\/h3>\n<p>flashAttention\u662f\u9ad8\u6548\u3001\u5185\u5b58\u4f18\u5316\u7684\u6ce8\u610f\u529b\u673a\u5236\u7684\u5b9e\u73b0\u65b9\u5f0f&#xff0c;\u80fd\u663e\u8457\u52a0\u901f Transformer \u6216\u6ce8\u610f\u529b\u6a21\u5757\u7684\u8ba1\u7b97&#xff0c;\u5e76\u964d\u4f4e GPU \u663e\u5b58\u5360\u7528\u3002 \u8fdb\u5165flash-attention&#xff0c;\u9009\u4e2d\u76f8\u5bf9\u5e94\u7248\u672c&#xff0c;\u4e0b\u8f7d\u5b8c\u6210\u4e4b\u540e\u9700\u8981\u672c\u5730\u5b89\u88c5&#xff0c;\u590d\u5236\u5230yolov13-main\u6587\u4ef6\u5939\u4e2d&#xff0c;cd\u5230yolov13-main\u6587\u4ef6\u5939\u540e&#xff0c;\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n<p>pip <span class=\"token function\">install<\/span> flash_attn-2.7.0.post2&#043;cu124torch2.4.1cxx11abiFALSE-cp311-cp311-win_amd64.whl<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005657-698147d9d0fcc.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005659-698147dbd7fb8.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u5b89\u88c5\u5b8c\u6210\u4e4b\u540e&#xff0c;\u6211\u4eec\u901a\u8fc7\u4e0b\u9762\u4ee3\u7801\u6765\u6821\u9a8c\u73af\u5883&#xff0c;\u770b\u662f\u5426\u5b58\u5728\u95ee\u9898&#xff0c;\u5982\u679c\u90fd\u53ef\u4ee5\u6b63\u5e38\u8f93\u51fa&#xff0c;\u5219\u6ca1\u6709\u95ee\u9898<\/p>\n<p><span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;PyTorch version:&#034;<\/span><span class=\"token punctuation\">,<\/span> torch<span class=\"token punctuation\">.<\/span>__version__<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;CUDA available:&#034;<\/span><span class=\"token punctuation\">,<\/span> torch<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">.<\/span>is_available<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\">&#034;CUDA version used to build PyTorch:&#034;<\/span><span class=\"token punctuation\">,<\/span> torch<span class=\"token punctuation\">.<\/span>version<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">from<\/span> flash_attn <span class=\"token keyword\">import<\/span> flash_attn_func<br \/>\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;\u2705 flash_attn imported successfully!&#034;<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h3>4. \u73af\u5883\u9a8c\u8bc1<\/h3>\n<p>\u5728yolov13-main\u6587\u4ef6\u5939\u4e2d\u521b\u5efa\u4e00\u4e2ainfer.py\u811a\u672c&#xff0c;\u52a0\u8f7d\u68c0\u6d4b\u9884\u8bad\u7ec3\u6743\u91cd&#xff0c;\u786e\u4fdd\u73af\u5883\u6ca1\u6709\u95ee\u9898<\/p>\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;yolov13{n\/s}.pt&#039;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>source<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#039;bus.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><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005700-698147dc59350.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> ![<img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260203005700-698147dc92db8.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<h2>\u4e94\u3001\u91cf\u5316\u6d41\u7a0b<\/h2>\n<h3>1. \u5728\u4e0a\u8ff0\u73af\u5883\u4e2d\u5b89\u88c5\u91cf\u5316\u4f9d\u8d56\u5e93<\/h3>\n<p>pip <span class=\"token function\">install<\/span> <span class=\"token parameter variable\">-U<\/span> pnnx ncnn<\/p>\n<h3>2. \u5bfc\u51fa yolov13 torchscript<\/h3>\n<p>yolo <span class=\"token builtin class-name\">export<\/span> <span class=\"token assign-left variable\">model<\/span><span class=\"token operator\">&#061;<\/span>yolov13n.pt <span class=\"token assign-left variable\">format<\/span><span class=\"token operator\">&#061;<\/span>torchscript<\/p>\n<p>\u4e00\u5b9a\u8981\u5c06\u751f\u6210\u7684.torchscript\u540e\u7f00\u7684\u6a21\u578b\u5355\u72ec\u653e\u7f6e\u6587\u4ef6\u5939&#xff0c;\u540e\u9762\u6b65\u9aa4\u4f1a\u751f\u6210\u6bd4\u8f83\u591a\u7684\u6587\u4ef6<\/p>\n<h3>3. \u8f6c\u6362\u9759\u6001\u5c3a\u5bf8\u8f93\u5165\u7684 torchscript<\/h3>\n<p>pnnx yolov13n.torchscript<\/p>\n<p>\u5728\u8fdb\u884c\u8f6c\u6362\u65f6&#xff0c;\u4f1a\u51fa\u73b0\u4e00\u4e9b\u8b66\u544a&#xff0c;\u63d0\u793a\u5f3a\u5236\u5c06\u6240\u67091\u7ef4tensor&#xff08;shape.size() &#061;&#061; 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