{"id":60559,"date":"2026-01-15T21:17:39","date_gmt":"2026-01-15T13:17:39","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/60559.html"},"modified":"2026-01-15T21:17:39","modified_gmt":"2026-01-15T13:17:39","slug":"yolo26%e4%b8%80%e6%96%87%e5%bc%84%e6%87%82-yolo26%e6%8a%80%e6%9c%af%e5%88%9b%e6%96%b0%e3%80%81%e7%bd%91%e7%bb%9c%e7%bb%93%e6%9e%84%e8%a7%a3%e8%af%bb-%e3%80%81yolo26-yaml%e9%85%8d%e7%bd%ae%e6%96%87","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/60559.html","title":{"rendered":"YOLO26\u4e00\u6587\u5f04\u61c2 | YOLO26\u6280\u672f\u521b\u65b0\u3001\u7f51\u7edc\u7ed3\u6784\u89e3\u8bfb \u3001yolo26.yaml\u914d\u7f6e\u6587\u4ef6\u8be6\u7ec6\u89e3\u8bfb\u4e0e\u8bf4\u660e\u3001YOLO26\u865a\u62df\u73af\u5883\u5b89\u88c5\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u9a8c\u8bc1\u3001\u63a8\u7406\u53c2\u6570\u8be6\u7ec6\u89e3\u6790 | \u901a\u4fd7\u6613\u61c2\uff01\u5165\u95e8\u5fc5\u770b\u7cfb\u5217\uff01"},"content":{"rendered":"<p><span style=\"color:#fe2c24\">\u770b\u8fd9\u4e00\u7bc7\u5c31\u591f\u4e86\u3002<\/span>\u672c\u6587\u5185\u542b<span style=\"color:#ff9900\">YOLO26\u6838\u5fc3\u521b\u65b0\u4ecb\u7ecd<\/span>&#043;<span style=\"color:#956fe7\">YOLO26\u7f51\u7edc\u7ed3\u6784\u56fe <\/span>&#043; <span style=\"color:#be191c\">yaml\u914d\u7f6e\u6587\u4ef6\u8be6\u7ec6\u89e3\u8bfb\u4e0e\u8bf4\u660e <\/span>&#043;<span style=\"color:#1c7331\"> YOLO\u865a\u62df\u73af\u5883\u5b89\u88c5&#043;<\/span><span style=\"color:#4da8ee\">\u8bad\u7ec3\u6559\u7a0b<\/span><span style=\"color:#1c7331\"> <\/span>&#043; <span style=\"color:#ed7976\">\u8bad\u7ec3\u53c2\u6570\u8bbe\u7f6e<\/span>&#043;<span style=\"color:#956fe7\">\u9a8c\u8bc1\u6559\u7a0b<\/span>&#043;<span style=\"color:#ff9900\">\u63a8\u7406\u6559\u7a0b<\/span>&#043;<span style=\"color:#fe2c24\">\u53c2\u6570\u89e3\u6790\u8bf4\u660e\u7b49\u4e00\u4e9b\u6709\u5173YOLO26\u7684\u5185\u5bb9&#xff01;<\/span><\/p>\n<p id=\"main-toc\">\u672c\u6587\u76ee\u5f55<\/p>\n<p id=\"%E4%B8%80%E3%80%81YOLO26%E7%AE%80%E4%BB%8B-toc\" style=\"margin-left:0px\">\u4e00\u3001YOLO26\u7b80\u4ecb<\/p>\n<p id=\"YOLO26%20%E7%9A%84%E6%9E%B6%E6%9E%84%E9%81%B5%E5%BE%AA%E5%9B%9B%E4%B8%AA%E6%A0%B8%E5%BF%83%E5%8E%9F%E5%88%99%EF%BC%9A-toc\" style=\"margin-left:40px\">YOLO26 \u7684\u67b6\u6784\u9075\u5faa\u56db\u4e2a\u6838\u5fc3\u539f\u5219&#xff1a;<\/p>\n<p id=\"YOLO26%20%E7%9A%84%E4%B8%BB%E8%A6%81%E6%8A%80%E6%9C%AF%E5%88%9B%E6%96%B0%EF%BC%9A-toc\" style=\"margin-left:40px\">YOLO26 \u7684\u4e3b\u8981\u6280\u672f\u521b\u65b0&#xff1a;<\/p>\n<p id=\"1.%20%E7%A7%BB%E9%99%A4DFL%E6%A8%A1%E5%9D%97%EF%BC%8C%E7%AE%80%E5%8C%96%E9%A2%84%E6%B5%8B%E6%B5%81%E7%A8%8B-toc\" style=\"margin-left:80px\">1. \u79fb\u9664DFL\u6a21\u5757&#xff0c;\u7b80\u5316\u9884\u6d4b\u6d41\u7a0b<\/p>\n<p id=\"2.%20%E7%AB%AF%E5%88%B0%E7%AB%AF%E6%97%A0NMS%E6%8E%A8%E7%90%86%EF%BC%8C%E9%99%8D%E4%BD%8E%20latency-toc\" style=\"margin-left:80px\">2. \u7aef\u5230\u7aef\u65e0NMS\u63a8\u7406&#xff0c;\u964d\u4f4e latency<\/p>\n<p id=\"3.%20ProgLoss%2BSTAL%EF%BC%8C%E6%8F%90%E5%8D%87%E6%A3%80%E6%B5%8B%E7%B2%BE%E5%BA%A6-toc\" style=\"margin-left:80px\">3. 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style=\"margin-left:0px\">\u516d\u3001\u672c\u6587\u603b\u7ed3<\/p>\n<hr id=\"hr-toc\" \/>\n<h2 id=\"%E4%B8%80%E3%80%81YOLO26%E7%AE%80%E4%BB%8B\">\u4e00\u3001YOLO26\u7b80\u4ecb<\/h2>\n<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"262\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131736-6968e8f0b3dda.png\" width=\"919\" \/><\/p>\n<h3 id=\"YOLO26%20%E7%9A%84%E6%9E%B6%E6%9E%84%E9%81%B5%E5%BE%AA%E5%9B%9B%E4%B8%AA%E6%A0%B8%E5%BF%83%E5%8E%9F%E5%88%99%EF%BC%9A\">YOLO26 \u7684\u67b6\u6784\u9075\u5faa\u56db\u4e2a\u6838\u5fc3\u539f\u5219&#xff1a;<\/h3>\n<ul>\n<li><span 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style=\"color:#1c7331\">\u90e8\u7f72\u6548\u7387&#xff1a;<\/span>\u00a0\u7aef\u5230\u7aef\u8bbe\u8ba1\u6d88\u9664\u4e86\u7ba1\u9053\u7684\u6574\u4e2a\u9636\u6bb5&#xff0c;\u4ece\u800c\u5927\u5927\u7b80\u5316\u4e86\u96c6\u6210&#xff0c;\u51cf\u5c11\u4e86\u5ef6\u8fdf&#xff0c;\u5e76\u4f7f\u90e8\u7f72\u5728\u5404\u79cd\u73af\u5883\u4e2d\u66f4\u52a0\u7a33\u5065\u3002<\/li>\n<li><span style=\"color:#956fe7\">\u8bad\u7ec3\u521b\u65b0&#xff1a;<\/span>YOLO26 \u5f15\u5165\u4e86MuSGD \u4f18\u5316\u5668&#xff0c;\u5b83\u662f\u00a0SGD\u548c\u00a0Muon\u7684\u6df7\u5408\u4f53\u2014\u2014\u7075\u611f\u6765\u6e90\u4e8e Moonshot AI \u5728 LLM \u8bad\u7ec3\u4e2d\u00a0Kimi K2\u7684\u7a81\u7834\u3002\u8be5\u4f18\u5316\u5668\u5e26\u6765\u4e86\u589e\u5f3a\u7684\u7a33\u5b9a\u6027\u548c\u66f4\u5feb\u7684\u6536\u655b&#xff0c;\u5c06\u8bed\u8a00\u6a21\u578b\u4e2d\u7684\u4f18\u5316\u8fdb\u5c55\u8f6c\u79fb\u5230\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u3002<\/li>\n<li><span style=\"color:#ff9900\">\u4efb\u52a1\u7279\u5b9a\u4f18\u5316&#xff1a;<\/span>YOLO26 \u9488\u5bf9\u4e13\u4e1a\u4efb\u52a1\u5f15\u5165\u4e86\u6709\u9488\u5bf9\u6027\u7684\u6539\u8fdb&#xff0c;\u5305\u62ec\u7528\u4e8e\u00a0Segmentation\u00a0\u7684\u8bed\u4e49\u5206\u5272\u635f\u5931\u548c\u591a\u5c3a\u5ea6\u539f\u578b\u6a21\u5757&#xff0c;\u7528\u4e8e\u9ad8\u7cbe\u5ea6\u00a0\u59ff\u52bf\u4f30\u8ba1\u00a0\u7684\u6b8b\u5dee\u5bf9\u6570\u4f3c\u7136\u4f30\u8ba1 (RLE)&#xff0c;\u4ee5\u53ca\u901a\u8fc7\u89d2\u5ea6\u635f\u5931\u4f18\u5316\u89e3\u7801\u4ee5\u89e3\u51b3\u00a0\u65cb\u8f6c\u6846\u68c0\u6d4b\u00a0\u4e2d\u7684\u8fb9\u754c\u95ee\u9898\u3002<\/li>\n<\/ul>\n<p>\u8fd9\u4e9b\u521b\u65b0\u5171\u540c\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6a21\u578b\u7cfb\u5217&#xff0c;\u8be5\u6a21\u578b\u7cfb\u5217\u5728\u5c0f\u5bf9\u8c61\u4e0a\u5b9e\u73b0\u4e86\u66f4\u9ad8\u7684\u7cbe\u5ea6&#xff0c;\u63d0\u4f9b\u4e86\u65e0\u7f1d\u90e8\u7f72&#xff0c;\u5e76\u4e14\u5728\u00a0CPU \u4e0a\u7684\u8fd0\u884c\u901f\u5ea6\u63d0\u9ad8\u4e86 43%\u00a0\u2014 \u4f7f YOLO26 \u6210\u4e3a\u8fc4\u4eca\u4e3a\u6b62\u8d44\u6e90\u53d7\u9650\u73af\u5883\u4e2d\u6700\u5b9e\u7528\u548c\u53ef\u90e8\u7f72\u7684 YOLO \u6a21\u578b\u4e4b\u4e00\u3002<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"508\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131736-6968e8f0e1b43.png\" width=\"1204\" \/><\/p>\n<h3 id=\"YOLO26%20%E7%9A%84%E4%B8%BB%E8%A6%81%E6%8A%80%E6%9C%AF%E5%88%9B%E6%96%B0%EF%BC%9A\">YOLO26 \u7684\u4e3b\u8981\u6280\u672f\u521b\u65b0&#xff1a;<\/h3>\n<h4 id=\"1.%20%E7%A7%BB%E9%99%A4DFL%E6%A8%A1%E5%9D%97%EF%BC%8C%E7%AE%80%E5%8C%96%E9%A2%84%E6%B5%8B%E6%B5%81%E7%A8%8B\">1.<span style=\"color:#fe2c24\"> \u79fb\u9664DFL\u6a21\u5757&#xff0c;\u7b80\u5316\u9884\u6d4b\u6d41\u7a0b<\/span><\/h4>\n<p>\u65e9\u671fYOLO\u6a21\u578b\u4f9d\u8d56\u5206\u5e03\u7126\u70b9\u635f\u5931&#xff08;DFL&#xff09;\u63d0\u5347\u8fb9\u754c\u6846\u7cbe\u5ea6&#xff0c;\u4f46\u8fd9\u4e00\u6a21\u5757\u589e\u52a0\u4e86\u90e8\u7f72\u590d\u6742\u5ea6&#xff0c;\u4e14\u5b58\u5728\u56fa\u5b9a\u56de\u5f52\u9650\u5236\u3002YOLO26\u5f7b\u5e95\u79fb\u9664DFL&#xff0c;\u4e0d\u4ec5\u7b80\u5316\u4e86\u8fb9\u754c\u6846\u9884\u6d4b\u903b\u8f91&#xff0c;\u63d0\u5347\u4e86\u786c\u4ef6\u517c\u5bb9\u6027&#xff0c;\u8fd8\u89e3\u51b3\u4e86\u8d85\u5927\u76ee\u6807\u68c0\u6d4b\u7684\u53ef\u9760\u6027\u95ee\u9898&#xff0c;\u8ba9\u6a21\u578b\u5728\u8fb9\u7f18\u8bbe\u5907\u4e0a\u7684\u5bfc\u51fa\u4e0e\u8fd0\u884c\u66f4\u987a\u7545\u3002<\/p>\n<h4 id=\"2.%20%E7%AB%AF%E5%88%B0%E7%AB%AF%E6%97%A0NMS%E6%8E%A8%E7%90%86%EF%BC%8C%E9%99%8D%E4%BD%8E%20latency\"><span style=\"color:#ff9900\">2. \u7aef\u5230\u7aef\u65e0NMS\u63a8\u7406&#xff0c;\u964d\u4f4e latency<\/span><\/h4>\n<p>\u4f20\u7edf\u76ee\u6807\u68c0\u6d4b\u9700\u901a\u8fc7\u975e\u6781\u5927\u503c\u6291\u5236&#xff08;NMS&#xff09;\u8fdb\u884c\u540e\u5904\u7406&#xff0c;\u8fc7\u6ee4\u91cd\u53e0\u9884\u6d4b\u6846\u2014\u2014\u8fd9\u4e00\u6b65\u9aa4\u4f1a\u589e\u52a0\u5ef6\u8fdf\u3001\u63d0\u5347\u90e8\u7f72\u96be\u5ea6\u3002YOLO26\u91c7\u7528\u539f\u751f\u7aef\u5230\u7aef\u67b6\u6784&#xff0c;\u76f4\u63a5\u8f93\u51fa\u6700\u7ec8\u9884\u6d4b\u7ed3\u679c&#xff0c;\u5185\u90e8\u81ea\u52a8\u5904\u7406\u91cd\u590d\u9884\u6d4b&#xff0c;\u5f7b\u5e95\u7701\u53bbNMS\u73af\u8282&#xff0c;\u5927\u5e45\u964d\u4f4e\u63a8\u7406\u5ef6\u8fdf\u4e0e\u96c6\u6210\u98ce\u9669&#xff0c;\u5b8c\u7f8e\u9002\u914d\u5b9e\u65f6\u8fb9\u7f18\u573a\u666f\u3002<\/p>\n<h4 id=\"3.%20ProgLoss%2BSTAL%EF%BC%8C%E6%8F%90%E5%8D%87%E6%A3%80%E6%B5%8B%E7%B2%BE%E5%BA%A6\"><span style=\"color:#be191c\">3. ProgLoss&#043;STAL&#xff0c;\u63d0\u5347\u68c0\u6d4b\u7cbe\u5ea6<\/span><\/h4>\n<ul>\n<li>\n<p><span style=\"color:#ff9900\">\u6e10\u8fdb\u5f0f\u635f\u5931\u5e73\u8861&#xff08;ProgLoss&#xff09;&#xff1a;<\/span>\u8ba9\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u66f4\u7a33\u5b9a&#xff0c;\u6536\u655b\u66f4\u5e73\u6ed1&#xff0c;\u51cf\u5c11\u8bad\u7ec3\u6ce2\u52a8<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#956fe7\">\u5c0f\u76ee\u6807\u611f\u77e5\u6807\u7b7e\u5206\u914d&#xff08;STAL&#xff09;&#xff1a;<\/span>\u9488\u5bf9\u5c0f\u76ee\u6807\u3001\u4f4e\u53ef\u89c1\u5ea6\u76ee\u6807\u4f18\u5316\u5b66\u4e60\u673a\u5236&#xff0c;\u89e3\u51b3\u8fb9\u7f18\u573a\u666f\u4e2d\u201c\u96be\u68c0\u6d4b\u201d\u95ee\u9898 \u4e24\u9879\u6280\u672f\u7ed3\u5408&#xff0c;\u8ba9YOLO26\u5728\u590d\u6742\u573a\u666f\u4e2d\u4ecd\u80fd\u4fdd\u6301\u9ad8\u53ef\u9760\u68c0\u6d4b&#xff0c;\u5c24\u5176\u9002\u7528\u4e8e\u7269\u8054\u7f51\u3001\u673a\u5668\u4eba\u3001\u822a\u62cd\u7b49\u5c0f\u76ee\u6807\u5bc6\u96c6\u573a\u666f\u3002<\/p>\n<\/li>\n<\/ul>\n<h4 id=\"4.%20MuSGD%E4%BC%98%E5%8C%96%E5%99%A8%EF%BC%8C%E5%BC%BA%E5%8C%96%E8%AE%AD%E7%BB%83%E7%A8%B3%E5%AE%9A%E6%80%A7\"><span style=\"color:#1c7331\">4. MuSGD\u4f18\u5316\u5668&#xff0c;\u5f3a\u5316\u8bad\u7ec3\u7a33\u5b9a\u6027<\/span><\/h4>\n<p>YOLO26\u91c7\u7528\u5168\u65b0\u6df7\u5408\u4f18\u5316\u5668MuSGD&#xff0c;\u878d\u5408\u4f20\u7edf\u968f\u673a\u68af\u5ea6\u4e0b\u964d&#xff08;SGD&#xff09;\u7684\u5f3a\u6cdb\u5316\u80fd\u529b\u4e0e\u5927\u8bed\u8a00\u6a21\u578b&#xff08;LLM&#xff09;\u8bad\u7ec3\u4e2d\u7684\u5148\u8fdb\u4f18\u5316\u601d\u8def\u3002\u8be5\u4f18\u5316\u5668\u7075\u611f\u6e90\u81eaMoonshot AI\u7684Kimi K2&#xff0c;\u80fd\u8ba9\u6a21\u578b\u66f4\u5feb\u8fbe\u5230\u9ad8\u6027\u80fd\u6c34\u5e73&#xff0c;\u540c\u65f6\u964d\u4f4e\u8bad\u7ec3\u4e0d\u7a33\u5b9a\u6027&#xff0c;\u5c24\u5176\u5728\u5927\u578b\u590d\u6742\u6570\u636e\u96c6\u8bad\u7ec3\u4e2d\u8868\u73b0\u7a81\u51fa\u3002<\/p>\n<h4 id=\"5.%20CPU%E6%8E%A8%E7%90%86%E9%80%9F%E5%BA%A6%E6%8F%90%E5%8D%87%E9%AB%98%E8%BE%BE43%25\"><span style=\"color:#4da8ee\">5. CPU\u63a8\u7406\u901f\u5ea6\u63d0\u5347\u9ad8\u8fbe43%<\/span><\/h4>\n<p>YOLO26\u4e13\u4e3a\u8fb9\u7f18\u8ba1\u7b97\u4f18\u5316&#xff0c;\u63d0\u4f9b\u663e\u8457\u66f4\u5feb\u7684CPU\u63a8\u7406&#xff0c;\u786e\u4fdd\u5728\u6ca1\u6709GPU\u7684\u8bbe\u5907\u4e0a\u5b9e\u73b0\u5b9e\u65f6\u6027\u80fd\u3002\u8fd9\u4e00\u4f18\u5316\u8ba9\u65e0GPU\u8bbe\u5907&#xff08;\u5982\u6444\u50cf\u5934\u3001\u673a\u5668\u4eba\u3001\u5d4c\u5165\u5f0f\u7cfb\u7edf&#xff09;\u4e5f\u80fd\u5b9e\u73b0\u5b9e\u65f6\u89c6\u89c9\u5904\u7406\u3002<\/p>\n<h4 id=\"6.%20%E5%AE%9E%E4%BE%8B%E5%88%86%E5%89%B2%E5%A2%9E%E5%BC%BA%EF%BC%9A%E5%A4%9A%E5%B0%BA%E5%BA%A6%E4%BF%A1%E6%81%AF%E8%9E%8D%E5%90%88%EF%BC%8C%E6%8E%A9%E7%A0%81%E6%9B%B4%E7%B2%BE%E5%87%86\"><span style=\"color:#956fe7\">6. \u5b9e\u4f8b\u5206\u5272\u589e\u5f3a&#xff1a;\u591a\u5c3a\u5ea6\u4fe1\u606f\u878d\u5408&#xff0c;\u63a9\u7801\u66f4\u7cbe\u51c6<\/span><\/h4>\n<p>\u5f15\u5165\u8bed\u4e49\u5206\u5272\u635f\u5931\u4ee5\u6539\u5584\u6a21\u578b\u6536\u655b&#xff0c;\u4ee5\u53ca\u5347\u7ea7\u7684\u539f\u578b\u6a21\u5757&#xff0c;\u8be5\u6a21\u5757\u5229\u7528\u591a\u5c3a\u5ea6\u4fe1\u606f\u4ee5\u83b7\u5f97\u5353\u8d8a\u7684\u63a9\u819c\u8d28\u91cf\u3002<\/p>\n<h4 id=\"7.%20%E5%A7%BF%E6%80%81%E4%BC%B0%E8%AE%A1%E5%A2%9E%E5%BC%BA%EF%BC%9A%E9%9B%86%E6%88%90RLE%EF%BC%8C%20keypoint%E9%A2%84%E6%B5%8B%E6%9B%B4%E5%8F%AF%E9%9D%A0\"><span style=\"color:#ff9900\">7. \u59ff\u6001\u4f30\u8ba1\u589e\u5f3a&#xff1a;\u96c6\u6210RLE&#xff0c; keypoint\u9884\u6d4b\u66f4\u53ef\u9760<\/span><\/h4>\n<p>\u96c6\u6210\u6b8b\u5dee\u5bf9\u6570\u4f3c\u7136\u4f30\u8ba1&#xff08;RLE&#xff09;\u5efa\u6a21\u5173\u952e\u70b9\u9884\u6d4b\u4e0d\u786e\u5b9a\u6027&#xff0c;\u4f18\u5316\u89e3\u7801\u6d41\u7a0b&#xff0c;\u5728\u4fdd\u8bc1\u5b9e\u65f6\u6027\u7684\u540c\u65f6&#xff0c;\u663e\u8457\u63d0\u5347 keypoint \u68c0\u6d4b\u7cbe\u5ea6&#xff0c;\u9002\u7528\u4e8e\u4eba\u4f53\u52a8\u4f5c\u5206\u6790\u3001\u673a\u5668\u4eba\u4ea4\u4e92\u7b49\u573a\u666f\u3002<\/p>\n<h4 id=\"8.OBB%E6%97%8B%E8%BD%AC%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B%EF%BC%9A%E8%A7%92%E5%BA%A6%E6%8D%9F%E5%A4%B1%E4%BC%98%E5%8C%96%EF%BC%8C%E6%97%8B%E8%BD%AC%E9%A2%84%E6%B5%8B%E6%9B%B4%E7%A8%B3%E5%AE%9A\"><span style=\"color:#fe2c24\">8.OBB\u65cb\u8f6c\u76ee\u6807\u68c0\u6d4b&#xff1a;\u89d2\u5ea6\u635f\u5931\u4f18\u5316&#xff0c;\u65cb\u8f6c\u9884\u6d4b\u66f4\u7a33\u5b9a<\/span><\/h4>\n<p>\u5f15\u5165\u89d2\u5ea6\u635f\u5931\u51fd\u6570&#xff0c;\u89e3\u51b3\u65b9\u5f62\u76ee\u6807 orientation \u6a21\u7cca\u95ee\u9898&#xff1b;\u4f18\u5316OBB\u89e3\u7801\u903b\u8f91&#xff0c;\u907f\u514d\u65cb\u8f6c\u8fb9\u754c\u9644\u8fd1\u7684\u89d2\u5ea6\u7a81\u53d8&#xff0c;\u8ba9\u503e\u659c\u76ee\u6807\u7684\u65b9\u5411\u4f30\u8ba1\u66f4\u7a33\u5b9a&#xff0c;\u9002\u914d\u822a\u62cd\u6d4b\u7ed8\u3001\u5de5\u4e1a\u68c0\u6d4b\u7b49\u573a\u666f\u3002<\/p>\n<\/p>\n<h3 id=\"faq\">YOLO26 \u76f8\u6bd4\u8f83\u4e0a\u4e00\u4ee3YOLOv11\u533a\u522b&#xff1a;<\/h3>\n<h4 id=\"what-are-the-key-improvements-in-yolo26-compared-to-yolo11\"><span style=\"color:#fe2c24\">\u4e0e YOLO11 \u76f8\u6bd4&#xff0c;YOLO26 \u7684\u4e3b\u8981\u6539\u8fdb\u662f\u4ec0\u4e48&#xff1f;<\/span><\/h4>\n<ul>\n<li>DFL \u79fb\u9664&#xff1a;\u7b80\u5316\u5bfc\u51fa\u5e76\u6269\u5c55\u8fb9\u7f18\u517c\u5bb9\u6027<\/li>\n<li>\u7aef\u5230\u7aef\u65e0NMS\u63a8\u7406: \u6d88\u9664NMS&#xff0c;\u5b9e\u73b0\u66f4\u5feb\u3001\u66f4\u7b80\u5355\u7684\u90e8\u7f72<\/li>\n<li>ProgLoss &#043; STAL&#xff1a;\u63d0\u9ad8\u51c6\u786e\u6027&#xff0c;\u5c24\u5176\u662f\u5728\u5c0f\u7269\u4f53\u4e0a<\/li>\n<li>MuSGD Optimizer&#xff1a;\u7ed3\u5408 SGD \u548c Muon&#xff08;\u7075\u611f\u6765\u81ea Moonshot \u7684 Kimi K2&#xff09;&#xff0c;\u5b9e\u73b0\u66f4\u7a33\u5b9a\u3001\u9ad8\u6548\u7684\u8bad\u7ec3<\/li>\n<li>CPU \u63a8\u7406\u901f\u5ea6\u63d0\u9ad8\u9ad8\u8fbe 43%&#xff1a;CPU \u8bbe\u5907\u7684\u4e3b\u8981\u6027\u80fd\u63d0\u5347<\/li>\n<\/ul>\n<h3 id=\"supported-tasks-and-modes\">YOLO26\u00a0\u652f\u6301\u7684\u591a\u79cd\u4efb\u52a1\u548c\u591a\u5c3a\u5bf8\u6a21\u578b&#xff1a;<\/h3>\n<h4 id=\"1.%20%E6%94%AF%E6%8C%81%E5%A4%9A%E5%B0%BA%E5%AF%B8%E6%A8%A1%E5%9E%8B%EF%BC%8C%E9%80%82%E9%85%8D%E5%A4%9A%E5%9C%BA%E6%99%AF%E4%BB%BB%E5%8A%A1%E9%9C%80%E6%B1%82\"><span style=\"color:#fe2c24\">1. \u652f\u6301\u591a\u5c3a\u5bf8\u6a21\u578b&#xff0c;\u9002\u914d\u591a\u573a\u666f\u4efb\u52a1\u9700\u6c42<\/span><\/h4>\n<p>YOLO26\u63d0\u4f9b\u4e94\u79cd\u5c3a\u5bf8\u89c4\u683c&#xff0c;\u4eceNano&#xff08;n&#xff09;\u5230Extra Large&#xff08;x&#xff09;&#xff0c;\u5f00\u53d1\u8005\u53ef\u6839\u636e\u90e8\u7f72\u8bbe\u5907\u7684\u786c\u4ef6\u9650\u5236&#xff0c;\u7075\u6d3b\u5e73\u8861\u8fd0\u884c\u901f\u5ea6\u3001\u68c0\u6d4b\u7cbe\u5ea6\u4e0e\u6a21\u578b\u4f53\u79ef&#xff1a;<\/p>\n<ul>\n<li>\n<p>Nano&#xff08;n&#xff09;&#xff1a;\u6781\u81f4\u8f7b\u91cf\u5316&#xff0c;\u4e13\u4e3a\u4f4e\u529f\u8017\u8fb9\u7f18\u8bbe\u5907\u8bbe\u8ba1<\/p>\n<\/li>\n<li>\n<p>Small&#xff08;s&#xff09;\/Medium&#xff08;m&#xff09;&#xff1a;\u517c\u987e\u901f\u5ea6\u4e0e\u7cbe\u5ea6&#xff0c;\u9002\u7528\u4e8e\u591a\u6570\u7ec8\u7aef\u573a\u666f<\/p>\n<\/li>\n<li>\n<p>Large&#xff08;l&#xff09;\/Extra Large&#xff08;x&#xff09;&#xff1a;\u9ad8\u7cbe\u5ea6\u4f18\u5148&#xff0c;\u6ee1\u8db3\u590d\u6742\u573a\u666f\u68c0\u6d4b\u9700\u6c42<\/p>\n<\/li>\n<\/ul>\n<p><span style=\"color:#fe2c24\">\u6bcf\u4e2a\u5c3a\u5bf8\u53d8\u4f53&#xff08;n\u3001s\u3001m\u3001l\u3001x&#xff09;\u90fd\u652f\u6301\u6240\u6709\u4efb\u52a1\u3002<\/span><\/p>\n<h4 id=\"2.%20YOLO26%E6%94%AF%E6%8C%81%E5%A4%9A%E7%A7%8D%E8%A7%86%E8%A7%89%E4%BB%BB%E5%8A%A1\"><span style=\"color:#956fe7\">2. YOLO26\u652f\u6301\u591a\u79cd\u89c6\u89c9\u4efb\u52a1<\/span><\/h4>\n<p>YOLO26\u4ee5\u7edf\u4e00\u6a21\u578b\u67b6\u6784\u652f\u6491\u591a\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1&#xff0c;\u8bad\u7ec3\u3001\u9a8c\u8bc1\u3001\u63a8\u7406\u4e0e\u5bfc\u51fa\u6d41\u7a0b\u9ad8\u5ea6\u7edf\u4e00&#xff0c;\u964d\u4f4e\u6280\u672f\u843d\u5730\u95e8\u69db&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u56fe\u50cf\u5206\u7c7b&#xff1a;\u5206\u6790\u6574\u56fe\u573a\u666f&#xff0c;\u7cbe\u51c6\u5339\u914d\u7c7b\u522b\u6807\u7b7e<\/p>\n<\/li>\n<li>\n<p>\u76ee\u6807\u68c0\u6d4b&#xff1a;\u5b9a\u4f4d\u5e76\u8bc6\u522b\u56fe\u50cf\/\u89c6\u9891\u4e2d\u7684\u591a\u4e2a\u76ee\u6807<\/p>\n<\/li>\n<li>\n<p>\u5b9e\u4f8b\u5206\u5272&#xff1a;\u4ee5\u50cf\u7d20\u7ea7\u7cbe\u5ea6\u52fe\u52d2\u76ee\u6807\u8f6e\u5ed3<\/p>\n<\/li>\n<li>\n<p>\u59ff\u6001\u4f30\u8ba1&#xff1a;\u8bc6\u522b\u4eba\u4f53\u53ca\u7269\u4f53\u5173\u952e\u70b9&#xff0c;\u8fd8\u539f\u59ff\u6001\u4fe1\u606f<\/p>\n<\/li>\n<li>\n<p>\u65cb\u8f6c\u8fb9\u754c\u6846&#xff08;OBB&#xff09;\u68c0\u6d4b&#xff1a;\u7cbe\u51c6\u8bc6\u522b\u503e\u659c\u76ee\u6807&#xff0c;\u9002\u914d\u822a\u62cd\u3001\u536b\u661f\u5f71\u50cf\u7b49\u573a\u666f<\/p>\n<\/li>\n<\/ul>\n<h2 id=\"%E4%BA%8C%E3%80%81YOLO26%E7%9A%84%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84%E5%88%86%E6%9E%90%C2%A0\">\u4e8c\u3001YOLO26\u7684\u7f51\u7edc\u7ed3\u6784\u5206\u6790\u00a0<\/h2>\n<p><span style=\"color:#fe2c24\">YOLO26\u6574\u4f53\u7f51\u7edc\u7ed3\u6784\u56fe<\/span><\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"582\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131737-6968e8f14affc.png\" width=\"631\" \/><\/p>\n<h3 id=\"YOLO26%E6%95%B4%E4%BD%93%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84%E4%B8%BB%E8%A6%81%E7%94%B1%E4%BB%A5%E4%B8%8B%E4%B8%89%E4%B8%AA%E5%A4%A7%E9%83%A8%E5%88%86%E7%BB%84%E6%88%90%EF%BC%9A\">YOLO26\u6574\u4f53\u7f51\u7edc\u7ed3\u6784\u4e3b\u8981\u7531\u4ee5\u4e0b\u4e09\u4e2a\u5927\u90e8\u5206\u7ec4\u6210&#xff1a;<\/h3>\n<h4 id=\"Backbone%EF%BC%9A\">Backbone&#xff1a;<\/h4>\n<p>Backbone\u90e8\u5206\u8d1f\u8d23\u7279\u5f81\u63d0\u53d6&#xff0c;\u91c7\u7528\u4e86\u4e00\u7cfb\u5217\u5377\u79ef\u548c\u53cd\u5377\u79ef\u5c42&#xff0c;\u540c\u65f6\u4f7f\u7528\u4e86\u6b8b\u5dee\u8fde\u63a5\u548c\u74f6\u9888\u7ed3\u6784\u6765\u51cf\u5c0f\u7f51\u7edc\u7684\u5927\u5c0f\u5e76\u63d0\u9ad8\u6027\u80fd\u3002YOLO26\u4f7f\u7528<span style=\"color:#fe2c24\">C3k2<\/span>\u5757\u6765\u5904\u7406\u4e3b\u5e72\u4e0d\u540c\u9636\u6bb5\u7684\u7279\u5f81\u63d0\u53d6\u3002\u8f83\u5c0f\u76843&#215;3\u5185\u6838\u5141\u8bb8\u66f4\u6709\u6548\u7684\u8ba1\u7b97&#xff0c;\u540c\u65f6\u4fdd\u7559\u6a21\u578b\u6355\u83b7\u56fe\u50cf\u4e2d\u57fa\u672c\u7279\u5f81\u7684\u80fd\u529b\u3002YOLO26\u4e3b\u5e72\u7684\u6838\u5fc3\u662fC3k2\u5757&#xff0c;\u5b83\u662f\u65e9\u671f\u7248\u672c\u4e2d\u5f15\u5165\u7684CSP&#xff08;\u8de8\u9636\u6bb5\u90e8\u5206&#xff09;\u74f6\u9888\u7684\u6f14\u53d8\u3002C3k2\u6a21\u5757\u901a\u8fc7\u5206\u5272\u7279\u5f81\u56fe\u5e76\u5e94\u7528\u4e00\u7cfb\u5217\u8f83\u5c0f\u7684\u5185\u6838\u5377\u79ef&#xff08;3&#215;3&#xff09;\u6765\u4f18\u5316\u7f51\u7edc\u4e2d\u7684\u4fe1\u606f\u6d41&#xff0c;\u8fd9\u6bd4\u8f83\u5927\u7684\u5185\u6838\u5377\u79ef\u66f4\u5feb&#xff0c;\u8ba1\u7b97\u6210\u672c\u66f4\u4f4e\u3002\u901a\u8fc7\u5904\u7406\u8f83\u5c0f\u7684\u72ec\u7acb\u7279\u5f81\u56fe\u5e76\u5728\u51e0\u6b21\u5377\u79ef\u540e\u5408\u5e76\u5b83\u4eec&#xff0c;C3k2\u6a21\u5757\u4e0eYOLOv8\u7684C2f\u6a21\u5757\u76f8\u6bd4&#xff0c;\u4f7f\u7528\u66f4\u5c11\u7684\u53c2\u6570\u6765\u6539\u8fdb\u7279\u5f81\u8868\u793a\u3002<\/p>\n<p><span style=\"color:#fe2c24\">C2PSA<\/span>\u5757\u4f7f\u7528\u4e24\u4e2aPSA&#xff08;\u90e8\u5206\u7a7a\u95f4\u6ce8\u610f\u529b&#xff09;\u6a21\u5757&#xff0c;\u5b83\u4eec\u5728\u7279\u5f81\u56fe\u7684\u4e0d\u540c\u5206\u652f\u4e0a\u64cd\u4f5c&#xff0c;\u7136\u540e\u8fde\u63a5\u8d77\u6765&#xff0c;\u7c7b\u4f3c\u4e8eC2F\u5757\u7ed3\u6784\u3002\u8fd9\u79cd\u8bbe\u7f6e\u786e\u4fdd\u6a21\u578b\u4e13\u6ce8\u4e8e\u7a7a\u95f4\u4fe1\u606f&#xff0c;\u540c\u65f6\u4fdd\u6301\u8ba1\u7b97\u6210\u672c\u548c\u68c0\u6d4b\u7cbe\u5ea6\u4e4b\u95f4\u7684\u5e73\u8861\u3002C2PSA\u6a21\u5757\u901a\u8fc7\u5728\u63d0\u53d6\u7684\u7279\u5f81\u4e0a\u5e94\u7528\u7a7a\u95f4\u6ce8\u610f\u529b\u6765\u7ec6\u5316\u6a21\u578b\u9009\u62e9\u6027\u5730\u5173\u6ce8\u611f\u5174\u8da3\u533a\u57df\u7684\u80fd\u529b\u3002\u8fd9\u4f7f\u5f97YOLO26\u5728\u7cbe\u786e\u68c0\u6d4b\u9700\u8981\u7cbe\u7ec6\u5bf9\u8c61\u7ec6\u8282\u7684\u573a\u666f\u4e2d\u4f18\u4e8eYOLOv8\u7b49\u4ee5\u524d\u7684\u7248\u672c\u3002<\/p>\n<p><span style=\"color:#ff9900\">\u6b64\u5916&#xff0c;Backbone\u90e8\u5206\u8fd8\u5305\u62ec\u4e00\u4e9b\u5e38\u89c1\u7684\u6539\u8fdb\u6280\u672f&#xff0c;\u5982\u7f1d\u5408\u4e00\u4e9b\u5377\u79ef\u6a21\u5757&#xff0c;\u6ce8\u610f\u529b\u6a21\u5757&#xff0c;\u66ff\u6362\u4e3b\u5e72\u7f51\u7edc\u7b49\u6539\u8fdb&#xff0c;\u4ee5\u8fdb\u4e00\u6b65\u589e\u5f3a\u7279\u5f81\u63d0\u53d6\u7684\u80fd\u529b\u3002<\/span><span style=\"color:#fe2c24\">\u540e\u7eed\u4f1a\u66f4\u65b0\u5927\u91cf\u76f8\u5173\u90e8\u5206\u6539\u8fdb\u70b9\u3002<\/span><\/p>\n<h4 id=\"Neck%EF%BC%9A\">Neck&#xff1a;<\/h4>\n<p>Neck\u9888\u90e8\u7f51\u7edc\u4f4d\u4e8e\u4e3b\u5e72\u7f51\u7edc\u548c\u5934\u90e8\u7f51\u7edc\u4e4b\u95f4&#xff0c;\u5b83\u7684\u4f5c\u7528\u662f\u8fdb\u884c\u7279\u5f81\u878d\u5408\u548c\u589e\u5f3a\u3002<span style=\"color:#fe2c24\">\u540e\u7eed\u4f1a\u66f4\u65b0\u5927\u91cf\u76f8\u5173\u90e8\u5206\u6539\u8fdb\u70b9\u3002<\/span><\/p>\n<h4 id=\"Head%20%EF%BC%9A\">Head &#xff1a;<\/h4>\n<p>Head\u5934\u90e8\u7f51\u7edc\u662f\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u7684\u51b3\u7b56\u90e8\u5206&#xff0c;\u8d1f\u8d23\u4ea7\u751f\u6700\u7ec8\u7684\u68c0\u6d4b\u7ed3\u679c\u3002<span style=\"color:#fe2c24\">\u540e\u7eed\u4f1a\u66f4\u65b0\u5927\u91cf\u76f8\u5173\u90e8\u5206\u6539\u8fdb\u70b9\u3002<\/span><\/p>\n<h2 id=\"%E4%B8%89%E3%80%81yolo26.yaml%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E8%BF%9B%E8%A1%8C%E8%AF%A6%E7%BB%86%E8%AE%B2%E8%A7%A3\">\u4e09\u3001yolo26.yaml\u914d\u7f6e\u6587\u4ef6\u8fdb\u884c\u8be6\u7ec6\u8bb2\u89e3<\/h2>\n<p>\u914d\u7f6e\u6587\u4ef6\u4e3b\u8981\u5206\u4e3a\u4e09\u4e2a\u90e8\u5206:<span style=\"color:#fe2c24\">\u00a0<\/span><span style=\"color:#fe2c24\">\u53c2\u6570\u90e8\u5206\u3010Parameters\u3011<\/span>&#xff0c;<span style=\"color:#ff9900\">\u4e3b\u5e72\u90e8\u5206\u3010backone\u3011<\/span>&#xff0c;<span style=\"color:#956fe7\">\u5934\u90e8\u90e8\u5206\u3010head\u3011<\/span>\u3002<span style=\"color:#fe2c24\">\u4e0b\u9762\u5206\u522b\u5bf9\u8fd9\u51e0\u4e2a\u90e8\u5206\u8fdb\u884c\u8be6\u7ec6\u8bf4\u660e\u3002<\/span><\/p>\n<p><span style=\"color:#956fe7\">\u5173\u4e8eYOLO26\u7f51\u7edc\u7684\u914d\u7f6e\u6587\u4ef6yolo26.yaml\u7684\u8be6\u7ec6\u5185\u5bb9\u5982\u4e0b&#xff1a;<\/span><\/p>\n<p># Ultralytics &#x1f680; AGPL-3.0 License &#8211; https:\/\/ultralytics.com\/license<\/p>\n<p># Ultralytics YOLO26 object detection model with P3\/8 &#8211; P5\/32 outputs<br \/>\n# Model docs: https:\/\/docs.ultralytics.com\/models\/yolo26<br \/>\n# Task docs: https:\/\/docs.ultralytics.com\/tasks\/detect<\/p>\n<p># Parameters<br \/>\nnc: 80 # number of classes<br \/>\nend2end: True # whether to use end-to-end mode<br \/>\nreg_max: 1 # DFL bins<br \/>\nscales: # model compound scaling constants, i.e. &#039;model&#061;YOLO26n.yaml&#039; will call YOLO26.yaml with scale &#039;n&#039;<br \/>\n  # [depth, width, max_channels]<br \/>\n  n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs<br \/>\n  s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs<br \/>\n  m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs<br \/>\n  l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs<br \/>\n  x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs<\/p>\n<p># YOLO26n backbone<br \/>\nbackbone:<br \/>\n  # [from, repeats, module, args]<br \/>\n  &#8211; [-1, 1, Conv, [64, 3, 2]] # 0-P1\/2<br \/>\n  &#8211; [-1, 1, Conv, [128, 3, 2]] # 1-P2\/4<br \/>\n  &#8211; [-1, 2, C3k2, [256, False, 0.25]]<br \/>\n  &#8211; [-1, 1, Conv, [256, 3, 2]] # 3-P3\/8<br \/>\n  &#8211; [-1, 2, C3k2, [512, False, 0.25]]<br \/>\n  &#8211; [-1, 1, Conv, [512, 3, 2]] # 5-P4\/16<br \/>\n  &#8211; [-1, 2, C3k2, [512, True]]<br \/>\n  &#8211; [-1, 1, Conv, [1024, 3, 2]] # 7-P5\/32<br \/>\n  &#8211; [-1, 2, C3k2, [1024, True]]<br \/>\n  &#8211; [-1, 1, SPPF, [1024, 5, 3, True]] # 9<br \/>\n  &#8211; [-1, 2, C2PSA, [1024]] # 10<\/p>\n<p># YOLO26n head<br \/>\nhead:<br \/>\n  &#8211; [-1, 1, nn.Upsample, [None, 2, &#034;nearest&#034;]]<br \/>\n  &#8211; [[-1, 6], 1, Concat, [1]] # cat backbone P4<br \/>\n  &#8211; [-1, 2, C3k2, [512, True]] # 13<\/p>\n<p>  &#8211; [-1, 1, nn.Upsample, [None, 2, &#034;nearest&#034;]]<br \/>\n  &#8211; [[-1, 4], 1, Concat, [1]] # cat backbone P3<br \/>\n  &#8211; [-1, 2, C3k2, [256, True]] # 16 (P3\/8-small)<\/p>\n<p>  &#8211; [-1, 1, Conv, [256, 3, 2]]<br \/>\n  &#8211; [[-1, 13], 1, Concat, [1]] # cat head P4<br \/>\n  &#8211; [-1, 2, C3k2, [512, True]] # 19 (P4\/16-medium)<\/p>\n<p>  &#8211; [-1, 1, Conv, [512, 3, 2]]<br \/>\n  &#8211; [[-1, 10], 1, Concat, [1]] # cat head P5<br \/>\n  &#8211; [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5\/32-large)<\/p>\n<p>  &#8211; [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)<\/p>\n<h3 id=\"3.1%C2%A0%E5%8F%82%E6%95%B0%E9%83%A8%E5%88%86%E3%80%90Parameters%E3%80%91\">3.1\u00a0\u53c2\u6570\u90e8\u5206\u3010Parameters\u3011<\/h3>\n<p># Parameters<br \/>\nnc: 80 # number of classes<br \/>\nend2end: True # whether to use end-to-end mode<br \/>\nreg_max: 1 # DFL bins<br \/>\nscales: # model compound scaling constants, i.e. &#039;model&#061;YOLO26n.yaml&#039; will call YOLO26.yaml with scale &#039;n&#039;<br \/>\n  # [depth, width, max_channels]<br \/>\n  n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs<br \/>\n  s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs<br \/>\n  m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs<br \/>\n  l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs<br \/>\n  x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs <\/p>\n<ul>\n<li>\n<p><span style=\"color:#fe2c24\">nc:80<\/span>\u00a0\u00a0\u662f COCO \u6570\u636e\u96c6\u7684\u9ed8\u8ba4\u7c7b\u522b\u6570&#xff08;\u5305\u542b\u4eba\u3001\u8f66\u3001\u52a8\u7269\u3001\u65e5\u5e38\u7269\u54c1\u7b49 80 \u7c7b&#xff09;&#xff0c;\u82e5\u7528\u4e8e\u81ea\u5b9a\u4e49\u6570\u636e\u96c6&#xff0c;\u9700\u4fee\u6539\u4e3a\u5bf9\u5e94\u7c7b\u522b\u6570&#xff08;\u5982\u4ec5\u68c0\u6d4b\u884c\u4eba\u5219\u8bbe\u4e3a 1&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">end2end: True \u3002<\/span>end2end\u00a0\u63a7\u5236\u6a21\u578b\u662f\u5426\u542f\u7528 \u201c\u7aef\u5230\u7aef\u201d \u63a8\u7406\u6a21\u5f0f\u3002True&#xff1a;\u6a21\u578b\u8f93\u51fa\u76f4\u63a5\u662f\u6700\u7ec8\u7684\u68c0\u6d4b\u7ed3\u679c&#xff08;\u5750\u6807 &#043; \u7f6e\u4fe1\u5ea6 &#043; \u7c7b\u522b&#xff09;&#xff0c;\u65e0\u9700\u989d\u5916\u7684\u975e\u6781\u5927\u503c\u6291\u5236&#xff08;NMS&#xff09;\u540e\u5904\u7406&#xff1b;False&#xff1a;\u6a21\u578b\u8f93\u51fa\u539f\u59cb\u9884\u6d4b\u5f20\u91cf&#xff0c;\u9700\u5728\u63a8\u7406\u9636\u6bb5\u624b\u52a8\u6267\u884c NMS \u8fc7\u6ee4\u91cd\u590d\u6846\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">reg_max: 1\u3002<\/span>DFL \u662f YOLO \u7cfb\u5217\u7528\u4e8e\u63d0\u5347\u6846\u56de\u5f52\u7cbe\u5ea6\u7684\u65b9\u6cd5&#xff0c;\u901a\u8fc7\u5c06\u5750\u6807\u56de\u5f52\u8f6c\u5316\u4e3a \u201c\u5206\u7bb1\u5206\u7c7b\u201d \u95ee\u9898&#xff1b;YOLO26 \u6b64\u5904\u8bbe\u4e3a 1&#xff0c;\u8868\u793a\u7981\u7528 DFL&#xff08;\u4ec5\u7528\u666e\u901a\u56de\u5f52&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">scales:\u00a0<\/span>\u4ee3\u8868\u6a21\u578b\u5c3a\u5bf8&#xff0c;\u5206\u4e86n,s,m,l,x\u8fd95\u4e2a\u4e0d\u540c\u5927\u5c0f\u7684\u5c3a\u5bf8&#xff0c;\u53c2\u6570\u91cf\u4f9d\u6b21\u4ece\u5c0f\u5230\u5927\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">[depth, width, max_channels]:<\/span>\u00a0\u5206\u522b\u8868\u793a\u7f51\u7edc\u6a21\u578b\u7684\u6df1\u5ea6\u56e0\u5b50\u3001\u7f51\u7edc\u6a21\u578b\u7684\u5bbd\u5ea6\u56e0\u5b50\u3001\u6700\u5927\u901a\u9053\u6570\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">depth\u6df1\u5ea6\u56e0\u5b50\u7684\u4f5c\u7528&#xff1a;<\/span><span style=\"color:#0d0016\">\u8868\u793a\u6a21\u578b\u4e2d\u91cd\u590d\u6a21\u5757\u7684\u6570\u91cf\u6216\u5c42\u6570\u7684\u7f29\u653e\u6bd4\u4f8b\u3002\u8fd9\u91cc\u4e3b\u8981\u7528\u6765\u8c03\u6574C3k2\u6a21\u5757\u4e2d\u7684\u5b50\u6a21\u5757<\/span><span style=\"color:#0d0016\">Bottleneck\u91cd\u590d\u6b21\u6570\u3002\u6bd4\u5982\u4e3b\u5e72\u4e2d\u7b2c\u4e00\u4e2aC3k2\u6a21\u5757\u7684repeats\u7cfb\u6570\u662f2&#xff0c;\u6211\u4eec\u4f7f\u75280.5&#215;2\u5e76\u4e14\u5411\u4e0a\u53d6\u6574\u5c31\u7b49\u4e8e1\u4e86&#xff0c;\u8fd9\u5c31\u4ee3\u8868\u7b2c\u4e00\u4e2aC3k2\u6a21\u5757\u4e2dBottleneck\u53ea\u91cd\u590d\u4e00\u6b21\u3002<\/span><\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">width\u5bbd\u5ea6\u56e0\u5b50\u7684\u4f5c\u7528&#xff1a;<\/span>\u8868\u793a\u6a21\u578b\u4e2d\u901a\u9053\u6570&#xff08;\u5373\u7279\u5f81\u56fe\u7684\u6df1\u5ea6&#xff09;\u7684\u7f29\u653e\u6bd4\u4f8b,\u5982\u679c\u67d0\u4e2a\u5c42\u539f\u672c\u670964\u4e2a\u901a\u9053&#xff0c;\u800cwidth\u8bbe\u7f6e\u4e3a0.5&#xff0c;\u5219\u8be5\u5c42\u7684\u901a\u9053\u6570\u53d8\u4e3a32\u3002\u4e3e\u4f8b&#xff1a;\u6bd4\u5982\u4f7f\u7528yolo26n.yaml\u6587\u4ef6&#xff0c;\u53c2\u6570\u4e3a[0.50, 0.25, 1024]\u3002\u7b2c\u4e00\u4e2aConv\u6a21\u5757\u7684\u8f93\u51fa\u901a\u9053\u6570\u5199\u7684\u662f64&#xff0c;\u4f46\u662f\u5b9e\u9645\u4e0a\u8fd9\u4e2a\u901a\u9053\u6570\u5e76\u4e0d\u662f64&#xff0c;\u800c\u662f\u4f7f\u7528\u5bbd\u5ea6\u56e0\u5b50\u00a00.25&#215;64\u5f97\u5230\u7684\u6700\u7ec8\u7ed3\u679c16&#xff1b;\u540c\u7406&#xff0c;C3k2\u6a21\u5757\u7684\u8f93\u51fa\u901a\u9053\u867d\u7136\u5728yaml\u6587\u4ef6\u4e0a\u5199\u7684\u662f256&#xff0c;\u4f46\u662f\u5728\u5b9e\u9645\u4f7f\u7528\u65f6\u4f9d\u7136\u8981\u4e58\u4e0a\u5bbd\u5ea6\u56e0\u5b500.25&#xff0c;\u90a3\u4e48\u7b2c\u4e00\u4e2aC3k2\u6a21\u5757\u6700\u7ec8\u7684\u5230\u5b9e\u9645\u901a\u9053\u6570\u5c31\u662f0.25&#215;256 &#061; 64\u3002\u5982\u4e0b\u56fe\u6240\u793a&#xff0c;\u5176\u4ed6\u7684\u4f9d\u6b21\u7c7b\u63a8\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">max-channels:\u00a0<\/span>\u8868\u793a\u6bcf\u5c42\u6700\u5927\u901a\u9053\u6570\u3002\u6bcf\u5c42\u7684\u901a\u9053\u6570\u4f1a\u4e0e\u8fd9\u4e2a\u53c2\u6570\u8fdb\u884c\u4e00\u4e2a\u5bf9\u6bd4&#xff0c;\u5982\u679c\u7279\u5f81\u56fe\u901a\u9053\u6570\u5927\u4e8e\u8fd9\u4e2a\u6570&#xff0c;\u90a3\u5c31\u53d6\u00a0max_channels\u7684\u503c\u3002<\/p>\n<\/li>\n<\/ul>\n<h3 id=\"3.2%20%E4%B8%BB%E5%B9%B2%E9%83%A8%E5%88%86%E3%80%90backbone%E3%80%91\">3.2 \u4e3b\u5e72\u90e8\u5206\u3010backbone\u3011<\/h3>\n<p># YOLO26n backbone<br \/>\nbackbone:<br \/>\n  # [from, repeats, module, args]<br \/>\n  &#8211; [-1, 1, Conv, [64, 3, 2]] # 0-P1\/2<br \/>\n  &#8211; [-1, 1, Conv, [128, 3, 2]] # 1-P2\/4<br \/>\n  &#8211; [-1, 2, C3k2, [256, False, 0.25]]<br \/>\n  &#8211; [-1, 1, Conv, [256, 3, 2]] # 3-P3\/8<br \/>\n  &#8211; [-1, 2, C3k2, [512, False, 0.25]]<br \/>\n  &#8211; [-1, 1, Conv, [512, 3, 2]] # 5-P4\/16<br \/>\n  &#8211; [-1, 2, C3k2, [512, True]]<br \/>\n  &#8211; [-1, 1, Conv, [1024, 3, 2]] # 7-P5\/32<br \/>\n  &#8211; [-1, 2, C3k2, [1024, True]]<br \/>\n  &#8211; [-1, 1, SPPF, [1024, 5, 3, True]] # 9<br \/>\n  &#8211; [-1, 2, C2PSA, [1024]] # 10 <\/p>\n<p><span style=\"color:#956fe7\">\u4e3b\u5e72\u90e8\u5206\u6709\u56db\u4e2a\u53c2\u6570[from, number, module, args]\u00a0&#xff0c;\u89e3\u91ca\u5982\u4e0b&#xff1a;<\/span><\/p>\n<ul>\n<li>\n<p><span style=\"color:#ff9900\">from&#xff1a;<\/span>\u8fd9\u4e2a\u53c2\u6570\u4ee3\u8868\u4ece\u54ea\u4e00\u5c42\u83b7\u5f97\u8f93\u5165&#xff0c;-1\u5c31\u8868\u793a\u4ece\u4e0a\u4e00\u5c42\u83b7\u5f97\u8f93\u5165&#xff0c;[-1, 6]\u5c31\u8868\u793a\u4ece\u4e0a\u4e00\u5c42\u548c\u7b2c6\u5c42\u8fd9\u4e24\u5c42\u83b7\u5f97\u8f93\u5165\u3002\u7b2c\u4e00\u5c42\u6bd4\u8f83\u7279\u6b8a&#xff0c;\u8fd9\u91cc\u7b2c\u4e00\u5c42\u4e0a\u4e00\u5c42 \u6ca1\u6709\u8f93\u5165&#xff0c;from\u9ed8\u8ba4-1\u5c31\u597d\u4e86\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#ff9900\">repeats&#xff1a;<\/span>\u8fd9\u4e2a\u53c2\u6570\u8868\u793a\u6a21\u5757\u91cd\u590d\u7684\u6b21\u6570&#xff0c;\u5982\u679c\u4e3a2\u5219\u8868\u793a\u8be5\u6a21\u5757\u91cd\u590d2\u6b21&#xff0c;\u8fd9\u91cc\u5e76\u4e0d\u4e00\u5b9a\u662f\u8fd9\u4e2a\u6a21\u5757\u7684\u91cd\u590d\u6b21\u6570&#xff0c;\u4e5f\u6709\u53ef\u80fd\u662f\u8fd9\u4e2a\u6a21\u5757\u4e2d\u7684\u5b50\u6a21\u5757\u91cd\u590d\u7684\u6b21\u6570\u3002\u5bf9\u4e8eC3k2\u6a21\u5757\u6765\u8bf4&#xff0c;\u8fd9\u4e2arepeats\u5c31\u4ee3\u8868C3k2\u4e2dBottelneck\u6216\u662fC3k\u6a21\u5757\u91cd\u590d\u7684\u6b21\u6570\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#ff9900\">module&#xff1a;<\/span>\u8fd9\u4e2a\u5c31\u4ee3\u8868\u4f60\u8fd9\u5c42\u4f7f\u7528\u7684\u6a21\u5757\u7684\u540d\u79f0&#xff0c;\u6bd4\u5982\u4f60\u7b2c\u4e00\u5c42\u4f7f\u7528\u4e86Conv\u6a21\u5757&#xff0c;\u7b2c\u4e09\u5c42\u4f7f\u7528\u4e86C3k2\u6a21\u5757\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#ff9900\">args&#xff1a;<\/span>\u8868\u793a\u8fd9\u4e2a\u6a21\u5757\u9700\u8981\u4f20\u5165\u7684\u53c2\u6570&#xff0c;\u7b2c\u4e00\u4e2a\u53c2\u6570\u5747\u8868\u793a\u8be5\u5c42\u7684\u8f93\u51fa\u901a\u9053\u6570\u3002\u5bf9\u4e8e\u7b2c\u4e00\u5c42conv\u53c2\u6570\u301064,3&#xff0c;2\u3011&#xff1a;64\u4ee3\u8868\u8f93\u51fa\u901a\u9053\u6570&#xff0c;3\u4ee3\u8868\u5377\u79ef\u6838\u5927\u5c0fk&#xff0c;2\u4ee3\u8868stride\u6b65\u957f\u3002\u6bcf\u5c42\u8f93\u5165\u901a\u9053\u6570&#xff0c;\u9ed8\u8ba4\u662f\u4e0a\u4e00\u5c42\u7684\u8f93\u51fa\u901a\u9053\u6570\u3002<\/p>\n<\/li>\n<\/ul>\n<p><span style=\"color:#fe2c24\">\u5176\u4ed6\u8bf4\u660e&#xff1a;<\/span>\u5404\u5c42\u6ce8\u91ca\u4e2d\u7684P1\/2\u8868\u793a\u8be5\u5c42\u7279\u5f81\u56fe\u7f29\u653e\u4e3a\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\u76841\/2&#xff0c;\u662f\u7b2c1\u7279\u5f81\u5c42&#xff1b;P2\/4\u8868\u793a\u8be5\u5c42\u7279\u5f81\u56fe\u7f29\u653e\u4e3a\u8f93\u5165\u56fe\u50cf\u5c3a\u5bf8\u76841\/4&#xff0c;\u662f\u7b2c2\u7279\u5f81\u5c42&#xff1b;\u5176\u4ed6\u7684\u4f9d\u6b21\u7c7b\u63a8\u3002\u00a0<\/p>\n<\/p>\n<h3 id=\"3.3%20%E5%A4%B4%E9%83%A8%E3%80%90head%E3%80%91\">3.3 \u5934\u90e8\u3010head\u3011<\/h3>\n<p># YOLO26n head<br \/>\nhead:<br \/>\n  &#8211; [-1, 1, nn.Upsample, [None, 2, &#034;nearest&#034;]]<br \/>\n  &#8211; [[-1, 6], 1, Concat, [1]] # cat backbone P4<br \/>\n  &#8211; [-1, 2, C3k2, [512, True]] # 13<\/p>\n<p>  &#8211; [-1, 1, nn.Upsample, [None, 2, &#034;nearest&#034;]]<br \/>\n  &#8211; [[-1, 4], 1, Concat, [1]] # cat backbone P3<br \/>\n  &#8211; [-1, 2, C3k2, [256, True]] # 16 (P3\/8-small)<\/p>\n<p>  &#8211; [-1, 1, Conv, [256, 3, 2]]<br \/>\n  &#8211; [[-1, 13], 1, Concat, [1]] # cat head P4<br \/>\n  &#8211; [-1, 2, C3k2, [512, True]] # 19 (P4\/16-medium)<\/p>\n<p>  &#8211; [-1, 1, Conv, [512, 3, 2]]<br \/>\n  &#8211; [[-1, 10], 1, Concat, [1]] # cat head P5<br \/>\n  &#8211; [-1, 1, C3k2, [1024, True, 0.5, True]] # 22 (P5\/32-large)<\/p>\n<p>  &#8211; [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)<\/p>\n<p><span style=\"color:#ff9900\">\u5934\u90e8\u5206\u6709\u56db\u4e2a\u53c2\u6570[from, number, module, args]\u00a0&#xff0c;\u89e3\u91ca\u5982\u4e0b&#xff1a;<\/span><\/p>\n<ul>\n<li>\n<p><span style=\"color:#fe2c24\">from&#xff1a;<\/span>\u8fd9\u4e2a\u53c2\u6570\u4ee3\u8868\u4ece\u54ea\u4e00\u5c42\u83b7\u5f97\u8f93\u5165&#xff0c;-1\u5c31\u8868\u793a\u4ece\u4e0a\u4e00\u5c42\u83b7\u5f97\u8f93\u5165&#xff0c;[-1, 6]\u5c31\u8868\u793a\u4ece\u4e0a\u4e00\u5c42\u548c\u7b2c6\u5c42\u8fd9\u4e24\u5c42\u83b7\u5f97\u8f93\u5165\u3002\u7b2c\u4e00\u5c42\u6bd4\u8f83\u7279\u6b8a&#xff0c;\u8fd9\u91cc\u7b2c\u4e00\u5c42\u4e0a\u4e00\u5c42 \u6ca1\u6709\u8f93\u5165&#xff0c;from\u9ed8\u8ba4-1\u5c31\u597d\u4e86\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">repeats&#xff1a;<\/span>\u8fd9\u4e2a\u53c2\u6570\u8868\u793a\u6a21\u5757\u91cd\u590d\u7684\u6b21\u6570&#xff0c;\u5982\u679c\u4e3a2\u5219\u8868\u793a\u8be5\u6a21\u5757\u91cd\u590d2\u6b21&#xff0c;\u8fd9\u91cc\u5e76\u4e0d\u4e00\u5b9a\u662f\u8fd9\u4e2a\u6a21\u5757\u7684\u91cd\u590d\u6b21\u6570&#xff0c;\u4e5f\u6709\u53ef\u80fd\u662f\u8fd9\u4e2a\u6a21\u5757\u4e2d\u7684\u5b50\u6a21\u5757\u91cd\u590d\u7684\u6b21\u6570\u3002\u5bf9\u4e8eC3k2\u6a21\u5757\u6765\u8bf4&#xff0c;\u8fd9\u4e2arepeats\u5c31\u4ee3\u8868C3k2\u4e2dBottelneck\u6216\u662fC3k\u6a21\u5757\u91cd\u590d\u7684\u6b21\u6570\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">module&#xff1a;<\/span>\u8fd9\u4e2a\u5c31\u4ee3\u8868\u4f60\u8fd9\u5c42\u4f7f\u7528\u7684\u6a21\u5757\u7684\u540d\u79f0&#xff0c;\u6bd4\u5982\u4f60\u7b2c\u4e00\u5c42\u4f7f\u7528\u4e86Conv\u6a21\u5757&#xff0c;\u7b2c\u4e09\u5c42\u4f7f\u7528\u4e86C3k2\u6a21\u5757\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">args&#xff1a;<\/span>\u8868\u793a\u8fd9\u4e2a\u6a21\u5757\u9700\u8981\u4f20\u5165\u7684\u53c2\u6570&#xff0c;\u7b2c\u4e00\u4e2a\u53c2\u6570\u5747\u8868\u793a\u8be5\u5c42\u7684\u8f93\u51fa\u901a\u9053\u6570\u3002\u5bf9\u4e8e\u7b2c\u4e00\u5c42Conv\u53c2\u6570[64,3&#xff0c;2]&#xff1a;64\u4ee3\u8868\u8f93\u51fa\u901a\u9053\u6570&#xff0c;3\u4ee3\u8868\u5377\u79ef\u6838\u5927\u5c0fk&#xff0c;2\u4ee3\u8868stride\u6b65\u957f\u3002\u6bcf\u5c42\u8f93\u5165\u901a\u9053\u6570&#xff0c;\u9ed8\u8ba4\u662f\u4e0a\u4e00\u5c42\u7684\u8f93\u51fa\u901a\u9053\u6570\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">nn.Upsample: <\/span>\u8868\u793a\u4e0a\u91c7\u6837&#xff0c;\u5c06\u7279\u5f81\u56fe\u5927\u5c0f\u8fdb\u884c\u7ffb\u500d\u64cd\u4f5c\u3002\u6bd4\u5982\u5c06\u5927\u5c0f\u4e3a20X20\u7684\u7279\u5f81\u56fe&#xff0c;\u53d8\u4e3a40X40\u7684\u7279\u5f81\u56fe\u5927\u5c0f\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">Concat&#xff1a;<\/span>\u4ee3\u8868\u62fc\u63a5\u64cd\u4f5c&#xff0c;\u5c06\u76f8\u540c\u5927\u5c0f\u7684\u7279\u5f81\u56fe&#xff0c;\u901a\u9053\u8fdb\u884c\u62fc\u63a5&#xff0c;\u8981\u6c42\u662f\u7279\u5f81\u56fe\u5927\u5c0f\u4e00\u81f4&#xff0c;\u901a\u9053\u6570\u53ef\u4ee5\u4e0d\u76f8\u540c\u3002\u4f8b\u5982[-1, 6]&#xff1a;-1\u4ee3\u8868\u4e0a\u4e00\u5c42&#xff0c;6\u4ee3\u8868\u7b2c\u516d\u5c42&#xff08;\u4ece\u7b2c0\u5c42\u5f00\u59cb\u6570&#xff09;&#xff0c;\u5c06\u4e0a\u4e00\u5c42\u4e0e\u7b2c6\u5c42\u8fdb\u884cconcat\u62fc\u63a5\u64cd\u4f5c\u3002<\/p>\n<\/li>\n<li>\n<p><span style=\"color:#fe2c24\">Detect\u7684from\u6709\u4e09\u4e2a\u6570&#xff1a;<\/span> [16&#xff0c;19&#xff0c;22]&#xff0c;\u8fd9\u4e09\u4e2a\u5c31\u662f\u6700\u7ec8\u7f51\u7edc\u7684\u8f93\u51fa\u7279\u5f81\u56fe&#xff0c;\u5206\u522b\u5bf9\u5e94P3&#xff0c;P4&#xff0c;P5\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u00a0\u6a21\u578b\u8bad\u7ec3\u65f6\u6253\u5370\u51fa\u7684\u7ed3\u6784\u53c2\u6570\u5982\u4e0b&#xff0c;\u4e0b\u56fe\u4e3ayolo26.yaml\u6253\u5370\u4fe1\u606f&#xff1a;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"594\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131737-6968e8f17edfa.png\" width=\"831\" \/><\/p>\n<h2 id=\"%E5%9B%9B%E3%80%81YOLO26%E8%99%9A%E6%8B%9F%E7%8E%AF%E5%A2%83%E9%85%8D%E7%BD%AE\">\u56db\u3001YOLO26\u865a\u62df\u73af\u5883\u914d\u7f6e<\/h2>\n<h3 id=\"%E5%88%9B%E5%81%A5YOLO26%E7%9A%84%E8%99%9A%E6%8B%9F%E7%8E%AF%E5%A2%83%EF%BC%9A\">\u521b\u5065YOLO26\u7684\u865a\u62df\u73af\u5883&#xff1a;<\/h3>\n<p>\u9996\u5148\u4e0b\u8f7dYOLO26\u9879\u76ee\u6e90\u7801&#xff0c;\u70b9\u51fb\u94fe\u63a5\u5373\u53ef\u8df3\u8f6c<\/p>\n<p>YOLO26\u5b98\u65b9\u6e90\u7801\u94fe\u63a5&#xff0c;\u70b9\u51fb\u5373\u53ef\u8df3\u8f6c&#xff01;<\/p>\n<p># \u5728\u7ec8\u7aef\u4f7f\u7528\u5982\u4e0b\u547d\u4ee4\u5b89\u88c5YOLO26\u7684\u865a\u62df\u73af\u5883<br \/>\n# \u7b2c\u4e00\u6b65\u521b\u5efa\u4e00\u4e2a\u81ea\u5df1\u7684\u865a\u62df\u73af\u5883&#xff1a;<br \/>\nconda create -n yolo26 python&#061;3.10<\/p>\n<p># \u7b2c\u4e8c\u6b65\u8fdb\u5165\u5230\u81ea\u5df1\u7684\u865a\u62df\u73af\u5883&#xff1a;<br \/>\nconda activate yolo26<\/p>\n<p># \u7b2c\u4e09\u6b65\u5b89\u88c5\u9879\u76ee\u9700\u8981\u7684torch\u5e93&#xff1a;\u6211\u5b89\u88c5\u7684\u662f\u8fd9\u4e2a\u7248\u672c&#xff0c;\u4e0d\u5efa\u8bae\u5b89\u88c5\u9ad8\u7248\u672c\u6216\u662f\u6700\u65b0\u7248\u672c<br \/>\npip install torch&#061;&#061;2.5.1 torchvision&#061;&#061;0.20.1 torchaudio&#061;&#061;2.5.1 &#8211;index-url https:\/\/download.pytorch.org\/whl\/cu121<\/p>\n<p># \u7b2c\u56db\u6b65\u5b89\u88c5\u9879\u76ee\u9700\u8981\u7684\u5305&#xff1a;<br \/>\npip install ultralytics <\/p>\n<p>YOLO26\u5b98\u65b9\u6e90\u7801\u94fe\u63a5&#xff0c;\u70b9\u51fb\u5373\u53ef\u8df3\u8f6c&#xff01;<\/p>\n<p><span style=\"color:#fe2c24\">\u63d0\u793a&#xff01;<\/span><span style=\"color:#1c7331\">\u5982\u679c\u4e4b\u524d\u8ba2\u9605\u8fc7\u6211\u9879\u76ee\u7684\u5c0f\u4f19\u4f34&#xff0c;\u4e4b\u524d\u7684\u73af\u5883\u662f\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884cyolo26\u3002\u5982\u679c\u8ba2\u9605\u4e13\u680f\u7684\u5c0f\u4f19\u4f34&#xff0c;\u4e0d\u4f1a\u521b\u5efa\u865a\u62df\u73af\u5883&#xff0c;\u53ef\u4ee5\u54a8\u8be2\u535a\u4e3b&#xff0c;\u63d0\u4f9bWindows\u7cfb\u7edf\u4e0b\u7684\u73af\u5883\u5305\u3002\u5982\u679c\u4e0d\u4f1a\u914d\u7f6e\u73af\u5883&#xff0c;\u4e5f\u53ef\u4ee5\u8fdc\u7a0b\u63a7\u5236\u5e2e\u5fd9\u914d\u7f6e\u73af\u5883\u51e0\u5206\u949f\u5c31\u53ef\u4ee5\u914d\u597d\u53bb\u8dd1\u5b9e\u9a8c\u3002<\/span><\/p>\n<\/p>\n<h2 id=\"%E4%BA%94%E3%80%81YOLO26%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83%E5%8F%82%E6%95%B0%E8%AF%A6%E7%BB%86%E8%A7%A3%E6%9E%90%C2%A0\">\u4e94\u3001YOLO26\u6a21\u578b\u8bad\u7ec3\u53c2\u6570\u8be6\u7ec6\u89e3\u6790\u00a0<\/h2>\n<p><span style=\"color:#fe2c24\">\u5173\u4e8eyolo26\u7684\u8bad\u7ec3\u53c2\u6570\u8be5\u5982\u4f55\u8bbe\u7f6e\u3002<\/span>\u63a5\u4e0b\u6765\u5bf9yolo26\u7684\u76f8\u5173\u8bad\u7ec3\u53c2\u6570\u548c\u4f7f\u7528\u65b9\u6cd5\u8fdb\u884c\u4e86\u8be6\u7ec6\u8bf4\u660e\u3002<span style=\"color:#956fe7\">\u5e0c\u671b\u5bf9\u5927\u5bb6\u6709\u6240\u5e2e\u52a9&#xff01;<\/span><\/p>\n<h3 id=\"5.1%20YOLO26%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83%E4%BB%A3%E7%A0%81\">5.1 YOLO26\u6a21\u578b\u8bad\u7ec3\u4ee3\u7801<\/h3>\n<p>YOLO26\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u8bad\u7ec3\u65f6\u4f7f\u7528\u7684\u4ee3\u7801\u5982\u4e0b&#xff1a;<\/p>\n<p>from ultralytics import YOLO<br \/>\n# \u52a0\u8f7d\u5b98\u65b9\u9884\u8bad\u7ec3\u6a21\u578b<br \/>\nmodel &#061; YOLO(&#034;yolo26n.pt&#034;&#xff0c;task&#061;&#034;detect&#034;)<br \/>\n# \u6a21\u578b\u8bad\u7ec3<br \/>\nresults &#061; model.train(data&#061;&#034;data.yaml&#034;, epochs&#061;100, batch&#061;4) <\/p>\n<p>YOLO26\u56fe\u50cf\u5206\u5272\u6a21\u578b\u8bad\u7ec3\u65f6\u4f7f\u7528\u7684\u4ee3\u7801\u5982\u4e0b&#xff1a;<\/p>\n<p>from ultralytics import YOLO<br \/>\n# \u52a0\u8f7d\u5b98\u65b9\u9884\u8bad\u7ec3\u6a21\u578b<br \/>\nmodel &#061; YOLO(&#034;yolo26n.pt&#034;&#xff0c;task&#061;&#034;segment&#034;)<br \/>\n# \u6a21\u578b\u8bad\u7ec3<br \/>\nresults &#061; model.train(data&#061;&#034;data.yaml&#034;, epochs&#061;100, batch&#061;4) <\/p>\n<p><span style=\"color:#fe2c24\">YOLO26\u6a21\u578b\u8bad\u7ec3\u811a\u672ctrain.py\u4ee3\u7801\u5982\u4e0b&#xff1a;<\/span>\u00a0<\/p>\n<\/p>\n<p>import warnings<br \/>\nwarnings.filterwarnings(&#039;ignore&#039;)<br \/>\nfrom ultralytics import YOLO<br \/>\n# \u6a21\u578b\u914d\u7f6e\u6587\u4ef6<br \/>\nmodel_yaml_path &#061; r&#034;.\/ultralytics\/cfg\/models\/26\/yolo26.yaml&#034; # \u521b\u5efa\u4e00\u4e2a YOLO \u6a21\u578b\u5b9e\u4f8b&#xff0c;\u52a0\u8f7d yolo26.yaml \u914d\u7f6e\u6587\u4ef6&#xff08;Nano \u7248\u672c&#xff09;<\/p>\n<p>#\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6<br \/>\ndata_yaml_path &#061; r&#039;.\/datasets\/data.yaml&#039; # \u6307\u5b9a\u6570\u636e\u96c6\u7684\u914d\u7f6e\u6587\u4ef6&#xff0c;\u8fd9\u91cc\u4f7f\u7528\u7684\u662f data.yaml \u914d\u7f6e\u6587\u4ef6<br \/>\nif __name__ &#061;&#061; &#039;__main__&#039;:<br \/>\n    model &#061; YOLO(model_yaml_path&#xff0c;task&#061;&#034;detect&#034;)<br \/>\n    model &#061; model<br \/>\n    #\u8bad\u7ec3\u6a21\u578b<br \/>\n    results &#061; model.train(data&#061;data_yaml_path, # \u6307\u5b9a\u6570\u636e\u96c6\u7684\u914d\u7f6e\u6587\u4ef6<br \/>\n                          imgsz&#061;640,           # \u8f93\u5165\u56fe\u50cf\u7684\u5c3a\u5bf8&#xff0c;\u6240\u6709\u8f93\u5165\u56fe\u50cf\u5c06\u88ab\u7f29\u653e\u5230 640&#215;640 \u50cf\u7d20<br \/>\n                          epochs&#061;500,          # \u8bad\u7ec3\u7684\u8f6e\u6570&#xff0c;\u6a21\u578b\u5c06\u8bad\u7ec3 500 \u4e2a\u5468\u671f<br \/>\n                          batch&#061;8,             # \u6bcf\u6b21\u8bad\u7ec3\u7684\u6279\u6b21\u5927\u5c0f&#xff0c;\u6bcf\u6b21\u8bad\u7ec3\u4f1a\u7528\u5230 8 \u5f20\u56fe\u50cf<br \/>\n                          # amp&#061;False,         # \u5982\u679c\u51fa\u73b0\u8bad\u7ec3\u635f\u5931\u4e3aNan\u53ef\u4ee5\u5173\u95edamp<br \/>\n                          device&#061;&#034;0&#034;,          # device&#061;&#034;0,1,2,3&#034;\u8bad\u7ec3\u65f6\u4f7f\u7528\u7684 GPU \u8bbe\u5907\u7f16\u53f7&#xff0c;\u8fd9\u91cc\u4f7f\u7528\u7f16\u53f7\u4e3a 0\u30011\u30012 \u548c 3 \u7684\u56db\u4e2a GPU \u8fdb\u884c\u8bad\u7ec3\u3002\u901a\u5e38\u8bbe\u7f6e\u4e3a0<br \/>\n                          project&#061;&#039;runs\/26train&#039;, # \u4fdd\u5b58\u8bad\u7ec3\u7ed3\u679c\u6587\u4ef6\u5939<br \/>\n                          name&#061;&#039;exp&#039;,<br \/>\n                          )<\/p>\n<h3 id=\"5.2%20YOLO26%E6%A8%A1%E5%9E%8B%E9%AA%8C%E8%AF%81%E4%BB%A3%E7%A0%81\">5.2 YOLO26\u6a21\u578b\u9a8c\u8bc1\u4ee3\u7801<\/h3>\n<p><span style=\"color:#fe2c24\">\u81ea\u5df1\u521b\u5efa\u4e00\u4e2aval.py\u811a\u672c\u6587\u4ef6&#xff0c;\u628a\u4ee3\u7801\u7c98\u8d34\u8fdb\u53bb\u5c31\u597d\u4e86&#xff1a;<\/span><\/p>\n<p>from ultralytics import YOLO<br \/>\nimport warnings<br \/>\nwarnings.filterwarnings(&#039;ignore&#039;)<br \/>\n# \u6a21\u578b\u914d\u7f6e\u6587\u4ef6<br \/>\nmodel_yaml_path &#061; r&#034;.\/runs\/26train\/exp\/weights\/best.pt&#034;<br \/>\n#\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6<br \/>\ndata_yaml_path &#061; r&#039;.\/datasets\/data.yaml&#039;<\/p>\n<p>if __name__ &#061;&#061; &#039;__main__&#039;:<br \/>\n    model &#061; YOLO(model_yaml_path)<br \/>\n    model.val(data&#061;data_yaml_path,<br \/>\n              split&#061;&#039;val&#039;,<br \/>\n              imgsz&#061;640,<br \/>\n              batch&#061;8,<br \/>\n              project&#061;&#039;runs\/val&#039;,<br \/>\n              name&#061;&#039;exp&#039;,<br \/>\n              save_json &#061; True<br \/>\n              ) <\/p>\n<h3 id=\"5.3%C2%A0YOLO26%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E4%BB%A3%E7%A0%81\">5.3\u00a0YOLO26\u6a21\u578b\u63a8\u7406\u4ee3\u7801<\/h3>\n<p><span style=\"color:#fe2c24\">\u81ea\u5df1\u521b\u5efa\u4e00\u4e2apredict.py\u811a\u672c\u6587\u4ef6&#xff0c;\u628a\u4ee3\u7801\u7c98\u8d34\u8fdb\u53bb\u5c31\u597d\u4e86&#xff1a;<\/span><\/p>\n<p>import warnings<br \/>\nwarnings.filterwarnings(&#039;ignore&#039;)<br \/>\nfrom ultralytics import YOLO<\/p>\n<p>if __name__ &#061;&#061; &#039;__main__&#039;:<br \/>\n    model &#061; YOLO(r&#039;.\/yolo26n.pt&#039;) # \u4f7f\u7528\u81ea\u5df1\u8bad\u7ec3\u597d\u7684\u6743\u91cd\u6587\u4ef6<br \/>\n    model.predict(<br \/>\n        source&#061;r&#039;.\/ultralytics\/assets\/bus.jpg&#039;,  # \u9884\u6d4b\u7684\u6570\u636e\u6e90&#xff0c;\u53ef\u4ee5\u662f\u56fe\u7247\u3001\u6587\u4ef6\u5939\u3001\u89c6\u9891\u8def\u5f84\u7b49<br \/>\n        imgsz&#061;640,                     # \u56fe\u7247\u7684\u8f93\u5165\u5c3a\u5bf8<br \/>\n        project&#061;&#039;runs\/detect&#039;,         # \u9884\u6d4b\u7ed3\u679c\u4fdd\u5b58\u7684\u9879\u76ee\u76ee\u5f55<br \/>\n        name&#061;&#039;exp&#039;,                    # \u5b50\u76ee\u5f55\u540d&#xff08;\u6700\u7ec8\u4fdd\u5b58\u8def\u5f84\u4e3a runs\/detect\/exp&#xff09;<br \/>\n        save&#061;True,                     # \u662f\u5426\u4fdd\u5b58\u9884\u6d4b\u540e\u7684\u56fe\u7247<br \/>\n        # visualize&#061;True,              # \u662f\u5426\u53ef\u89c6\u5316\u4e2d\u95f4\u7279\u5f81\u56fe&#xff08;\u9ed8\u8ba4\u6ce8\u91ca\u6389&#xff0c;\u5373\u4e0d\u542f\u7528&#xff09;<br \/>\n        # show_conf&#061;False,             # \u662f\u5426\u663e\u793a\u7f6e\u4fe1\u5ea6&#xff08;\u5173\u95ed\u663e\u793a&#xff09;<br \/>\n        # show_labels&#061;False,           # \u662f\u5426\u663e\u793a\u6807\u7b7e&#xff08;\u5173\u95ed\u663e\u793a&#xff09;<br \/>\n    ) <\/p>\n<h3 id=\"5.4%E6%A8%A1%E5%9E%8B%E5%A4%A7%E5%B0%8F%E9%80%89%E6%8B%A9\">5.4\u6a21\u578b\u5927\u5c0f\u9009\u62e9<\/h3>\n<p>model &#061; YOLO(&#034;yolo26n.pt&#034;)\u00a0\u8868\u793a\u4f7f\u7528\u7684\u662fyolov26n\u6a21\u578b\u6765\u8bad\u7ec3, <span style=\"color:#fe2c24\">\u56e0\u4e3a\u6211\u4eec\u81ea\u5df1\u9700\u8981\u6539\u8fdb\u521b\u65b0\u53d1\u8bba\u6587&#xff0c;\u901a\u5e38\u90fd\u4e0d\u4f1a\u4f7f\u7528\u8fd9\u79cd\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u7ee7\u7eed\u8bad\u7ec3&#xff0c;\u53ef\u4ee5\u9009\u62e9\u6027\u7684\u52a0\u8f7d\u5b98\u65b9\u6a21\u578b\u7684\u9884\u8bad\u7ec3\u6743\u91cd&#xff0c;\u5982\u679c\u6539\u52a8\u6bd4\u8f83\u5927\u5efa\u8bae\u5927\u5bb6\u81ea\u5df1\u91cd\u65b0\u8bad\u7ec3\u3002<\/span>\u5982\u679c\u60f3\u4f7f\u7528\u5176\u4ed6\u5927\u5c0f\u7684\u6a21\u578b&#xff0c;\u53ea\u9700\u8981\u628an\u6539\u4e3a\u5176\u4ed6\u5927\u5c0f\u7684\u5bf9\u5e94\u5b57\u6bcd\u5373\u53ef\u3002\u4f8b\u5982&#xff1a;<\/p>\n<p>model &#061; YOLO(&#034;yolo26n.pt&#034;)<br \/>\nmodel &#061; YOLO(&#034;yolo26s.pt&#034;)<br \/>\nmodel &#061; YOLO(&#034;yolo26m.pt&#034;)<br \/>\nmodel &#061; YOLO(&#034;yolo26l.pt&#034;)<br \/>\nmodel &#061; YOLO(&#034;yolo26x.pt&#034;) <\/p>\n<p><span style=\"color:#956fe7\">\u52a0\u8f7d\u5b98\u65b9\u9884\u8bad\u7ec3\u6743\u91cd\u53bb\u8bad\u7ec3\u65b9\u6cd5&#xff1a;<\/span><\/p>\n<p>import warnings<br \/>\nwarnings.filterwarnings(&#039;ignore&#039;)<br \/>\nfrom ultralytics import YOLO<br \/>\n# \u6a21\u578b\u914d\u7f6e\u6587\u4ef6<br \/>\nmodel_yaml_path &#061; r&#034;.\/ultralytics\/cfg\/models\/26\/yolo26.yaml&#034; # \u521b\u5efa\u4e00\u4e2a YOLO \u6a21\u578b\u5b9e\u4f8b&#xff0c;\u52a0\u8f7d yolo26.yaml \u914d\u7f6e\u6587\u4ef6&#xff08;Nano \u7248\u672c&#xff09;<\/p>\n<p>#\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6<br \/>\ndata_yaml_path &#061; r&#039;.\/datasets\/data.yaml&#039; # \u6307\u5b9a\u6570\u636e\u96c6\u7684\u914d\u7f6e\u6587\u4ef6&#xff0c;\u8fd9\u91cc\u4f7f\u7528\u7684\u662f data.yaml \u914d\u7f6e\u6587\u4ef6<br \/>\nif __name__ &#061;&#061; &#039;__main__&#039;:<br \/>\n    model &#061; YOLO(model_yaml_path)<br \/>\n    model.load(&#034;yolo26n.pt&#034;)  # \u52a0\u8f7d\u9884\u8bad\u7ec3\u6743\u91cd<br \/>\n    #\u8bad\u7ec3\u6a21\u578b<br \/>\n    results &#061; model.train(data&#061;data_yaml_path, # \u6307\u5b9a\u6570\u636e\u96c6\u7684\u914d\u7f6e\u6587\u4ef6<br \/>\n                          imgsz&#061;640,           # \u8f93\u5165\u56fe\u50cf\u7684\u5c3a\u5bf8&#xff0c;\u6240\u6709\u8f93\u5165\u56fe\u50cf\u5c06\u88ab\u7f29\u653e\u5230 640&#215;640 \u50cf\u7d20<br \/>\n                          epochs&#061;500,          # \u8bad\u7ec3\u7684\u8f6e\u6570&#xff0c;\u6a21\u578b\u5c06\u8bad\u7ec3 500 \u4e2a\u5468\u671f<br \/>\n                          batch&#061;8,             # \u6bcf\u6b21\u8bad\u7ec3\u7684\u6279\u6b21\u5927\u5c0f&#xff0c;\u6bcf\u6b21\u8bad\u7ec3\u4f1a\u7528\u5230 8 \u5f20\u56fe\u50cf<br \/>\n                          # amp&#061;False,         # \u5982\u679c\u51fa\u73b0\u8bad\u7ec3\u635f\u5931\u4e3aNan\u53ef\u4ee5\u5173\u95edamp<br \/>\n                          device&#061;&#034;0&#034;,          # device&#061;&#034;0,1,2,3&#034;\u8bad\u7ec3\u65f6\u4f7f\u7528\u7684 GPU \u8bbe\u5907\u7f16\u53f7&#xff0c;\u8fd9\u91cc\u4f7f\u7528\u7f16\u53f7\u4e3a 0\u30011\u30012 \u548c 3 \u7684\u56db\u4e2a GPU \u8fdb\u884c\u8bad\u7ec3\u3002\u901a\u5e38\u8bbe\u7f6e\u4e3a0<br \/>\n                          project&#061;&#039;runs\/26train&#039;, # \u4fdd\u5b58\u8bad\u7ec3\u7ed3\u679c\u6587\u4ef6\u5939<br \/>\n                          name&#061;&#039;exp&#039;,<br \/>\n                          )<\/p>\n<p><span style=\"color:#fe2c24\">YOLO26\u4e0d\u540c\u6a21\u578b\u53c2\u6570\u5927\u5c0f\u5982\u4e0b&#xff1a;<\/span><\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"321\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131737-6968e8f1b1b6b.png\" width=\"773\" \/><\/p>\n<p><span style=\"color:#956fe7\">n\u662f\u53c2\u6570\u91cf\u6700\u5c0f\u7684\u6a21\u578b\u3002<\/span>\u4e00\u822c\u60c5\u51b5\u4e0b&#xff0c;\u6a21\u578b\u8d8a\u5927&#xff0c;\u6700\u7ec8\u6a21\u578b\u7684\u6027\u80fd\u6548\u679c\u4e5f\u4f1a\u8d8a\u597d\u3002\u53ef\u6839\u636e\u81ea\u5df1\u5b9e\u9645\u9700\u6c42\u9009\u62e9\u76f8\u5e94\u7684\u6a21\u578b\u5927\u5c0f\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"432\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131737-6968e8f1e1cac.png\" width=\"1024\" \/><\/p>\n<h3 id=\"5.5%20%E8%AE%AD%E7%BB%83%E5%8F%82%E6%95%B0%E8%AE%BE%E7%BD%AE\">5.5 \u8bad\u7ec3\u53c2\u6570\u8bbe\u7f6e<\/h3>\n<p>\u901a\u8fc7\u8fd0\u884cmodel.train(data&#061;&#034;data.yaml&#034;, epochs&#061;100, batch&#061;4)\u8bad\u7ec3YOLO26\u6a21\u578b&#xff0c;\u5176\u4e2d(data&#061;&#034;data.yaml&#034;, epochs&#061;100, batch&#061;4)\u662f\u8bad\u7ec3\u8bbe\u7f6e\u7684\u53c2\u6570,\u6ca1\u6709\u6dfb\u52a0\u7684\u8bad\u7ec3\u53c2\u6570\u90fd\u662f\u4f7f\u7528\u7684\u9ed8\u8ba4\u503c\u3002\u5b98\u65b9\u5176\u5b9e\u7ed9\u51fa\u4e86\u5f88\u591a\u5176\u4ed6\u76f8\u5173\u53c2\u6570&#xff0c;\u8be6\u7ec6\u8bf4\u660e\u89c1\u4e0b\u6587\u3002<\/p>\n<p>\u5982\u679c\u6211\u4eec\u9700\u8981\u81ea\u5df1\u4fee\u6539\u5176\u4ed6\u8bad\u7ec3\u53c2\u6570&#xff0c;\u53ea\u9700\u8981\u5728train\u540e\u9762\u7684\u62ec\u53f7\u4e2d\u52a0\u5165\u76f8\u5e94\u7684\u53c2\u6570\u548c\u5177\u4f53\u503c\u5373\u53ef\u3002<\/p>\n<p>\u4f8b\u5982\u52a0\u4e0a\u6a21\u578b\u8bad\u7ec3\u4f18\u5316\u5668\u53c2\u6570optimizer,\u5176\u9ed8\u8ba4\u503c\u662fauto\u3002<\/p>\n<p>\u53ef\u8bbe\u7f6e\u7684\u503c\u4e3a&#xff1a;SGD, Adam, AdamW, NAdam, RAdam, RMSProp\u3002\u5e38\u7528SGD\u6216\u8005AdamW\u3002<\/p>\n<p>\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u5c06\u5176\u8bbe\u7f6e\u4e3aSGD&#xff0c;\u5199\u6cd5\u5982\u4e0b&#xff1a;<\/p>\n<p># \u6a21\u578b\u8bad\u7ec3&#xff0c;\u6dfb\u52a0\u6a21\u578b\u4f18\u5316\u5668\u8bbe\u7f6e<br \/>\nresults &#061; model.train(data&#061;&#034;data.yaml&#034;, epochs&#061;100, batch&#061;4, optimizer&#061;&#039;SGD&#039;) <\/p>\n<h3 id=\"5.6%20%E8%AE%AD%E7%BB%83%E5%8F%82%E6%95%B0%E8%AF%B4%E6%98%8E\">5.6 \u8bad\u7ec3\u53c2\u6570\u8bf4\u660e<\/h3>\n<p>YOLO26 \u6a21\u578b\u7684\u8bad\u7ec3\u8bbe\u7f6e\u5305\u62ec\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u7684\u5404\u79cd\u8d85\u53c2\u6570\u548c\u914d\u7f6e\u3002\u8fd9\u4e9b\u8bbe\u7f6e\u4f1a\u5f71\u54cd\u6a21\u578b\u7684\u6027\u80fd\u3001\u901f\u5ea6\u548c\u51c6\u786e\u6027\u3002\u5173\u952e\u7684\u8bad\u7ec3\u8bbe\u7f6e\u5305\u62ec\u6279\u91cf\u5927\u5c0f\u3001\u5b66\u4e60\u7387\u3001\u52a8\u91cf\u548c\u6743\u91cd\u8870\u51cf\u3002\u6b64\u5916&#xff0c;\u4f18\u5316\u5668\u3001\u635f\u5931\u51fd\u6570\u548c\u8bad\u7ec3\u6570\u636e\u96c6\u7ec4\u6210\u7684\u9009\u62e9\u4e5f\u4f1a\u5f71\u54cd\u8bad\u7ec3\u8fc7\u7a0b\u3002\u5bf9\u8fd9\u4e9b\u8bbe\u7f6e\u8fdb\u884c\u4ed4\u7ec6\u7684\u8c03\u6574\u548c\u5b9e\u9a8c\u5bf9\u4e8e\u4f18\u5316\u6027\u80fd\u81f3\u5173\u91cd\u8981\u3002\u4ee5\u4e0b\u662f\u5b98\u65b9\u7ed9\u51fa\u4e86\u8bad\u7ec3\u53ef\u8bbe\u7f6e\u53c2\u6570\u548c\u8bf4\u660e:<\/p>\n<table border=\"1\" cellpadding=\"1\" cellspacing=\"1\">\n<tr>\u53c2\u6570\u9ed8\u8ba4\u503c\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>model<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u6307\u5b9a\u7528\u4e8e\u8bad\u7ec3\u7684\u6a21\u578b\u6587\u4ef6\u3002\u63a5\u53d7\u6307\u5411\u00a0.pt\u00a0\u9884\u8bad\u7ec3\u6a21\u578b\u6216\u00a0.yaml\u00a0\u914d\u7f6e\u6587\u4ef6\u3002\u5bf9\u4e8e\u5b9a\u4e49\u6a21\u578b\u7ed3\u6784\u6216\u521d\u59cb\u5316\u6743\u91cd\u81f3\u5173\u91cd\u8981\u3002<\/td>\n<\/tr>\n<tr>\n<td>data<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6\u7684\u8def\u5f84&#xff08;\u4f8b\u5982\u00a0coco8.yaml).\u8be5\u6587\u4ef6\u5305\u542b\u7279\u5b9a\u4e8e\u6570\u636e\u96c6\u7684\u53c2\u6570&#xff0c;\u5305\u62ec\u8bad\u7ec3\u6570\u636e\u548c\u9a8c\u8bc1\u6570\u636e\u7684\u8def\u5f84\u3001\u7c7b\u540d\u548c\u7c7b\u6570\u3002<\/td>\n<\/tr>\n<tr>\n<td>epochs<\/td>\n<td style=\"width:61px\">100<\/td>\n<td style=\"width:540px\">\u8bad\u7ec3\u603b\u8f6e\u6570\u3002\u6bcf\u4e2aepoch\u4ee3\u8868\u5bf9\u6574\u4e2a\u6570\u636e\u96c6\u8fdb\u884c\u4e00\u6b21\u5b8c\u6574\u7684\u8bad\u7ec3\u3002\u8c03\u6574\u8be5\u503c\u4f1a\u5f71\u54cd\u8bad\u7ec3\u65f6\u95f4\u548c\u6a21\u578b\u6027\u80fd\u3002<\/td>\n<\/tr>\n<tr>\n<td>time<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u6700\u957f\u8bad\u7ec3\u65f6\u95f4&#xff08;\u5c0f\u65f6&#xff09;\u3002\u5982\u679c\u8bbe\u7f6e\u4e86\u8be5\u503c&#xff0c;\u5219\u4f1a\u8986\u76d6\u00a0epochs\u00a0\u53c2\u6570&#xff0c;\u5141\u8bb8\u8bad\u7ec3\u5728\u6307\u5b9a\u7684\u6301\u7eed\u65f6\u95f4\u540e\u81ea\u52a8\u505c\u6b62\u3002\u5bf9\u4e8e\u65f6\u95f4\u6709\u9650\u7684\u8bad\u7ec3\u573a\u666f\u975e\u5e38\u6709\u7528\u3002<\/td>\n<\/tr>\n<tr>\n<td>patience<\/td>\n<td style=\"width:61px\">100<\/td>\n<td style=\"width:540px\">\u5728\u9a8c\u8bc1\u6307\u6807\u6ca1\u6709\u6539\u5584\u7684\u60c5\u51b5\u4e0b&#xff0c;\u63d0\u524d\u505c\u6b62\u8bad\u7ec3\u6240\u9700\u7684epoch\u6570\u3002\u5f53\u6027\u80fd\u8d8b\u4e8e\u5e73\u7a33\u65f6\u505c\u6b62\u8bad\u7ec3&#xff0c;\u6709\u52a9\u4e8e\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u3002<\/td>\n<\/tr>\n<tr>\n<td>batch<\/td>\n<td style=\"width:61px\">16<\/td>\n<td style=\"width:540px\">\u6279\u91cf\u5927\u5c0f&#xff0c;\u6709\u4e09\u79cd\u6a21\u5f0f:\u8bbe\u7f6e\u4e3a\u6574\u6570(\u4f8b\u5982&#xff0c;&#039; Batch &#061;16 &#039;)&#xff0c; 60% GPU\u5185\u5b58\u5229\u7528\u7387\u7684\u81ea\u52a8\u6a21\u5f0f(&#039; Batch &#061;-1 &#039;)&#xff0c;\u6216\u6307\u5b9a\u5229\u7528\u7387\u5206\u6570\u7684\u81ea\u52a8\u6a21\u5f0f(&#039; Batch &#061;0.70 &#039;)\u3002<\/td>\n<\/tr>\n<tr>\n<td>imgsz<\/td>\n<td style=\"width:61px\">640<\/td>\n<td style=\"width:540px\">\u7528\u4e8e\u8bad\u7ec3\u7684\u76ee\u6807\u56fe\u50cf\u5c3a\u5bf8\u3002\u6240\u6709\u56fe\u50cf\u5728\u8f93\u5165\u6a21\u578b\u524d\u90fd\u4f1a\u88ab\u8c03\u6574\u5230\u8fd9\u4e00\u5c3a\u5bf8\u3002\u5f71\u54cd\u6a21\u578b\u7cbe\u5ea6\u548c\u8ba1\u7b97\u590d\u6742\u5ea6\u3002<\/td>\n<\/tr>\n<tr>\n<td>save<\/td>\n<td style=\"width:61px\">True<\/td>\n<td style=\"width:540px\">\u53ef\u4fdd\u5b58\u8bad\u7ec3\u68c0\u67e5\u70b9\u548c\u6700\u7ec8\u6a21\u578b\u6743\u91cd\u3002\u8fd9\u5bf9\u6062\u590d\u8bad\u7ec3\u6216\u6a21\u578b\u90e8\u7f72\u975e\u5e38\u6709\u7528\u3002<\/td>\n<\/tr>\n<tr>\n<td>save_period<\/td>\n<td style=\"width:61px\">-1<\/td>\n<td style=\"width:540px\">\u4fdd\u5b58\u6a21\u578b\u68c0\u67e5\u70b9\u7684\u9891\u7387&#xff0c;\u4ee5 epochs \u4e3a\u5355\u4f4d\u3002\u503c\u4e3a-1 \u65f6\u5c06\u7981\u7528\u6b64\u529f\u80fd\u3002\u8be5\u529f\u80fd\u9002\u7528\u4e8e\u5728\u957f\u65f6\u95f4\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4fdd\u5b58\u4e34\u65f6\u6a21\u578b\u3002<\/td>\n<\/tr>\n<tr>\n<td>cache<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u5728\u5185\u5b58\u4e2d\u7f13\u5b58\u6570\u636e\u96c6\u56fe\u50cf (True\/ram&#xff09;\u3001\u78c1\u76d8 (disk&#xff09;&#xff0c;\u6216\u7981\u7528\u5b83 (False).\u901a\u8fc7\u51cf\u5c11\u78c1\u76d8 I\/O \u63d0\u9ad8\u8bad\u7ec3\u901f\u5ea6&#xff0c;\u4f46\u4ee3\u4ef7\u662f\u589e\u52a0\u5185\u5b58\u4f7f\u7528\u91cf\u3002<\/td>\n<\/tr>\n<tr>\n<td>device<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u6307\u5b9a\u7528\u4e8e\u8bad\u7ec3\u7684\u8ba1\u7b97\u8bbe\u5907&#xff1a;\u5355\u4e2a GPU (device&#061;0&#xff09;\u3001\u591a\u4e2a GPU (device&#061;0,1)\u3001CPU (device&#061;cpu)&#xff0c;\u6216\u82f9\u679c\u82af\u7247\u7684 MPS (device&#061;mps).<\/td>\n<\/tr>\n<tr>\n<td>workers<\/td>\n<td style=\"width:61px\">8<\/td>\n<td style=\"width:540px\">\u52a0\u8f7d\u6570\u636e\u7684\u5de5\u4f5c\u7ebf\u7a0b\u6570&#xff08;\u6bcf\u00a0RANK\u00a0\u591a GPU \u8bad\u7ec3&#xff09;\u3002\u5f71\u54cd\u6570\u636e\u9884\u5904\u7406\u548c\u8f93\u5165\u6a21\u578b\u7684\u901f\u5ea6&#xff0c;\u5c24\u5176\u9002\u7528\u4e8e\u591a GPU \u8bbe\u7f6e\u3002<\/td>\n<\/tr>\n<tr>\n<td>project<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u4fdd\u5b58\u8bad\u7ec3\u7ed3\u679c\u7684\u9879\u76ee\u76ee\u5f55\u540d\u79f0\u3002\u5141\u8bb8\u6709\u7ec4\u7ec7\u5730\u5b58\u50a8\u4e0d\u540c\u7684\u5b9e\u9a8c\u3002<\/td>\n<\/tr>\n<tr>\n<td>name<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u8bad\u7ec3\u8fd0\u884c\u7684\u540d\u79f0\u3002\u7528\u4e8e\u5728\u9879\u76ee\u6587\u4ef6\u5939\u5185\u521b\u5efa\u4e00\u4e2a\u5b50\u76ee\u5f55&#xff0c;\u7528\u4e8e\u5b58\u50a8\u8bad\u7ec3\u65e5\u5fd7\u548c\u8f93\u51fa\u7ed3\u679c\u3002<\/td>\n<\/tr>\n<tr>\n<td>exist_ok<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u5982\u679c\u4e3a True&#xff0c;\u5219\u5141\u8bb8\u8986\u76d6\u73b0\u6709\u7684\u9879\u76ee\/\u540d\u79f0\u76ee\u5f55\u3002\u8fd9\u5bf9\u8fed\u4ee3\u5b9e\u9a8c\u975e\u5e38\u6709\u7528&#xff0c;\u65e0\u9700\u624b\u52a8\u6e05\u9664\u4e4b\u524d\u7684\u8f93\u51fa\u3002<\/td>\n<\/tr>\n<tr>\n<td>pretrained<\/td>\n<td style=\"width:61px\">True<\/td>\n<td style=\"width:540px\">\u51b3\u5b9a\u662f\u5426\u4ece\u9884\u5904\u7406\u6a21\u578b\u5f00\u59cb\u8bad\u7ec3\u3002\u53ef\u4ee5\u662f\u5e03\u5c14\u503c&#xff0c;\u4e5f\u53ef\u4ee5\u662f\u52a0\u8f7d\u6743\u91cd\u7684\u7279\u5b9a\u6a21\u578b\u7684\u5b57\u7b26\u4e32\u8def\u5f84\u3002\u63d0\u9ad8\u8bad\u7ec3\u6548\u7387\u548c\u6a21\u578b\u6027\u80fd\u3002<\/td>\n<\/tr>\n<tr>\n<td>optimizer<\/td>\n<td style=\"width:61px\">&#039;auto&#039;<\/td>\n<td style=\"width:540px\">\u4e3a\u8bad\u7ec3\u6a21\u578b\u9009\u62e9\u4f18\u5316\u5668\u3002\u9009\u9879\u5305\u62ec\u00a0SGD,\u00a0Adam,\u00a0AdamW,\u00a0NAdam,\u00a0RAdam,\u00a0RMSProp\u00a0\u7b49&#xff0c;\u6216\u00a0auto\u00a0\u7528\u4e8e\u6839\u636e\u6a21\u578b\u914d\u7f6e\u8fdb\u884c\u81ea\u52a8\u9009\u62e9\u3002\u5f71\u54cd\u6536\u655b\u901f\u5ea6\u548c\u7a33\u5b9a\u6027<\/td>\n<\/tr>\n<tr>\n<td>verbose<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u542f\u7528\u5197\u957f\u8f93\u51fa&#xff0c;\u63d0\u4f9b\u8be6\u7ec6\u65e5\u5fd7\u548c\u8fdb\u5ea6\u66f4\u65b0\u3002\u6709\u52a9\u4e8e\u8c03\u8bd5\u548c\u5bc6\u5207\u76d1\u63a7\u57f9\u8bad\u8fc7\u7a0b\u3002<\/td>\n<\/tr>\n<tr>\n<td>seed<\/td>\n<td style=\"width:61px\">0<\/td>\n<td style=\"width:540px\">\u4e3a\u8bad\u7ec3\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50&#xff0c;\u786e\u4fdd\u5728\u76f8\u540c\u914d\u7f6e\u4e0b\u8fd0\u884c\u7684\u7ed3\u679c\u5177\u6709\u53ef\u91cd\u590d\u6027\u3002<\/td>\n<\/tr>\n<tr>\n<td>deterministic<\/td>\n<td style=\"width:61px\">True<\/td>\n<td style=\"width:540px\">\u5f3a\u5236\u4f7f\u7528\u786e\u5b9a\u6027\u7b97\u6cd5&#xff0c;\u786e\u4fdd\u53ef\u91cd\u590d\u6027&#xff0c;\u4f46\u7531\u4e8e\u5bf9\u975e\u786e\u5b9a\u6027\u7b97\u6cd5\u7684\u9650\u5236&#xff0c;\u53ef\u80fd\u4f1a\u5f71\u54cd\u6027\u80fd\u548c\u901f\u5ea6\u3002<\/td>\n<\/tr>\n<tr>\n<td>single_cls<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5c06\u591a\u7c7b\u6570\u636e\u96c6\u4e2d\u7684\u6240\u6709\u7c7b\u522b\u89c6\u4e3a\u5355\u4e00\u7c7b\u522b\u3002\u9002\u7528\u4e8e\u4e8c\u5143\u5206\u7c7b\u4efb\u52a1&#xff0c;\u6216\u4fa7\u91cd\u4e8e\u5bf9\u8c61\u7684\u5b58\u5728\u800c\u975e\u5206\u7c7b\u3002<\/td>\n<\/tr>\n<tr>\n<td>rect<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u53ef\u8fdb\u884c\u77e9\u5f62\u8bad\u7ec3&#xff0c;\u4f18\u5316\u6279\u6b21\u7ec4\u6210\u4ee5\u51cf\u5c11\u586b\u5145\u3002\u8fd9\u53ef\u4ee5\u63d0\u9ad8\u6548\u7387\u548c\u901f\u5ea6&#xff0c;\u4f46\u53ef\u80fd\u4f1a\u5f71\u54cd\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/td>\n<\/tr>\n<tr>\n<td>cos_lr<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u5229\u7528\u4f59\u5f26\u5b66\u4e60\u7387\u8c03\u5ea6\u5668&#xff0c;\u6839\u636e\u5386\u65f6\u7684\u4f59\u5f26\u66f2\u7ebf\u8c03\u6574\u5b66\u4e60\u7387\u3002\u8fd9\u6709\u52a9\u4e8e\u7ba1\u7406\u5b66\u4e60\u7387&#xff0c;\u5b9e\u73b0\u66f4\u597d\u7684\u6536\u655b\u3002<\/td>\n<\/tr>\n<tr>\n<td>close_mosaic<\/td>\n<td style=\"width:61px\">10<\/td>\n<td style=\"width:540px\">\u5728\u8bad\u7ec3\u5b8c\u6210\u524d\u7981\u7528\u6700\u540e N \u4e2aepoch\u7684\u9a6c\u8d5b\u514b\u6570\u636e\u589e\u5f3a\u4ee5\u7a33\u5b9a\u8bad\u7ec3\u3002\u8bbe\u7f6e\u4e3a 0 \u5219\u7981\u7528\u6b64\u529f\u80fd\u3002<\/td>\n<\/tr>\n<tr>\n<td>resume<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u4ece\u4e0a\u6b21\u4fdd\u5b58\u7684\u68c0\u67e5\u70b9\u6062\u590d\u8bad\u7ec3\u3002\u81ea\u52a8\u52a0\u8f7d\u6a21\u578b\u6743\u91cd\u3001\u4f18\u5316\u5668\u72b6\u6001\u548c\u5386\u65f6\u8ba1\u6570&#xff0c;\u65e0\u7f1d\u7ee7\u7eed\u8bad\u7ec3\u3002<\/td>\n<\/tr>\n<tr>\n<td>amp<\/td>\n<td style=\"width:61px\">True<\/td>\n<td style=\"width:540px\">\u542f\u7528\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6 (AMP) \u8bad\u7ec3&#xff0c;\u53ef\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\u91cf\u5e76\u52a0\u5feb\u8bad\u7ec3\u901f\u5ea6&#xff0c;\u540c\u65f6\u5c06\u5bf9\u7cbe\u5ea6\u7684\u5f71\u54cd\u964d\u81f3\u6700\u4f4e\u3002<\/td>\n<\/tr>\n<tr>\n<td>fraction<\/td>\n<td style=\"width:61px\">1.0<\/td>\n<td style=\"width:540px\">\u6307\u5b9a\u7528\u4e8e\u8bad\u7ec3\u7684\u6570\u636e\u96c6\u7684\u90e8\u5206\u3002\u5141\u8bb8\u5728\u5b8c\u6574\u6570\u636e\u96c6\u7684\u5b50\u96c6\u4e0a\u8fdb\u884c\u8bad\u7ec3&#xff0c;\u8fd9\u5bf9\u5b9e\u9a8c\u6216\u8d44\u6e90\u6709\u9650\u7684\u60c5\u51b5\u975e\u5e38\u6709\u7528\u3002<\/td>\n<\/tr>\n<tr>\n<td>profile<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d&#xff0c;\u53ef\u5bf9ONNX \u548cTensorRT \u901f\u5ea6\u8fdb\u884c\u5256\u6790&#xff0c;\u6709\u52a9\u4e8e\u4f18\u5316\u6a21\u578b\u90e8\u7f72\u3002<\/td>\n<\/tr>\n<tr>\n<td>freeze<\/td>\n<td style=\"width:61px\">None<\/td>\n<td style=\"width:540px\">\u51bb\u7ed3\u6a21\u578b\u7684\u524d N \u5c42\u6216\u6309\u7d22\u5f15\u6307\u5b9a\u7684\u5c42&#xff0c;\u4ece\u800c\u51cf\u5c11\u53ef\u8bad\u7ec3\u53c2\u6570\u7684\u6570\u91cf\u3002\u8fd9\u5bf9\u5fae\u8c03\u6216\u8fc1\u79fb\u5b66\u4e60\u975e\u5e38\u6709\u7528\u3002<\/td>\n<\/tr>\n<tr>\n<td>lr0<\/td>\n<td style=\"width:61px\">0.01<\/td>\n<td style=\"width:540px\">\u521d\u59cb\u5b66\u4e60\u7387&#xff08;\u5373\u00a0SGD&#061;1E-2,\u00a0Adam&#061;1E-3) .\u8c03\u6574\u8fd9\u4e2a\u503c\u5bf9\u4f18\u5316\u8fc7\u7a0b\u81f3\u5173\u91cd\u8981&#xff0c;\u4f1a\u5f71\u54cd\u6a21\u578b\u6743\u91cd\u7684\u66f4\u65b0\u901f\u5ea6\u3002<\/td>\n<\/tr>\n<tr>\n<td>lrf<\/td>\n<td style=\"width:61px\">0.01<\/td>\n<td style=\"width:540px\">\u6700\u7ec8\u5b66\u4e60\u7387\u5360\u521d\u59cb\u5b66\u4e60\u7387\u7684\u767e\u5206\u6bd4 &#061; (lr0 * lrf)&#xff0c;\u4e0e\u8c03\u5ea6\u7a0b\u5e8f\u7ed3\u5408\u4f7f\u7528&#xff0c;\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u8c03\u6574\u5b66\u4e60\u7387\u3002<\/td>\n<\/tr>\n<tr>\n<td>momentum<\/td>\n<td style=\"width:61px\">0.937<\/td>\n<td style=\"width:540px\">\u7528\u4e8e SGD \u7684\u52a8\u91cf\u56e0\u5b50&#xff0c;\u6216\u7528\u4e8e Adam \u4f18\u5316\u5668\u7684 beta1&#xff0c;\u7528\u4e8e\u5c06\u8fc7\u53bb\u7684\u68af\u5ea6\u7eb3\u5165\u5f53\u524d\u66f4\u65b0\u3002<\/td>\n<\/tr>\n<tr>\n<td>weight_decay<\/td>\n<td style=\"width:61px\">0.0005<\/td>\n<td style=\"width:540px\">L2 \u6b63\u5219\u5316\u9879&#xff0c;\u5bf9\u5927\u6743\u91cd\u8fdb\u884c\u60e9\u7f5a&#xff0c;\u4ee5\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u3002<\/td>\n<\/tr>\n<tr>\n<td>warmup_epochs<\/td>\n<td style=\"width:61px\">3.0<\/td>\n<td style=\"width:540px\">\u5b66\u4e60\u7387\u9884\u70ed\u7684\u5386\u5143\u6570&#xff0c;\u5b66\u4e60\u7387\u4ece\u4f4e\u503c\u9010\u6e10\u589e\u52a0\u5230\u521d\u59cb\u5b66\u4e60\u7387&#xff0c;\u4ee5\u5728\u65e9\u671f\u7a33\u5b9a\u8bad\u7ec3\u3002<\/td>\n<\/tr>\n<tr>\n<td>warmup_momentum<\/td>\n<td style=\"width:61px\">0.8<\/td>\n<td style=\"width:540px\">\u70ed\u8eab\u9636\u6bb5\u7684\u521d\u59cb\u52a8\u529b&#xff0c;\u5728\u70ed\u8eab\u671f\u95f4\u9010\u6e10\u8c03\u6574\u5230\u8bbe\u5b9a\u52a8\u529b\u3002<\/td>\n<\/tr>\n<tr>\n<td>warmup_bias_lr<\/td>\n<td style=\"width:61px\">0.1<\/td>\n<td style=\"width:540px\">\u70ed\u8eab\u9636\u6bb5\u7684\u504f\u7f6e\u53c2\u6570\u5b66\u4e60\u7387&#xff0c;\u6709\u52a9\u4e8e\u7a33\u5b9a\u521d\u59cb\u5386\u5143\u7684\u6a21\u578b\u8bad\u7ec3\u3002<\/td>\n<\/tr>\n<tr>\n<td>box<\/td>\n<td style=\"width:61px\">7.5<\/td>\n<td style=\"width:540px\">\u635f\u5931\u51fd\u6570\u4e2d\u8fb9\u6846\u635f\u5931\u90e8\u5206\u7684\u6743\u91cd&#xff0c;\u5f71\u54cd\u5bf9\u51c6\u786e\u9884\u6d4b\u8fb9\u6846\u5750\u6807\u7684\u91cd\u89c6\u7a0b\u5ea6\u3002<\/td>\n<\/tr>\n<tr>\n<td>cls<\/td>\n<td style=\"width:61px\">0.5<\/td>\n<td style=\"width:540px\">\u5206\u7c7b\u635f\u5931\u5728\u603b\u635f\u5931\u51fd\u6570\u4e2d\u7684\u6743\u91cd&#xff0c;\u5f71\u54cd\u6b63\u786e\u5206\u7c7b\u9884\u6d4b\u76f8\u5bf9\u4e8e\u5176\u4ed6\u90e8\u5206\u7684\u91cd\u8981\u6027\u3002<\/td>\n<\/tr>\n<tr>\n<td>dfl<\/td>\n<td style=\"width:61px\">1.5<\/td>\n<td style=\"width:540px\">\u5206\u5e03\u7126\u70b9\u635f\u5931\u6743\u91cd&#xff0c;\u5728\u67d0\u4e9bYOLO \u7248\u672c\u4e2d\u7528\u4e8e\u7cbe\u7ec6\u5206\u7c7b\u3002<\/td>\n<\/tr>\n<tr>\n<td>pose<\/td>\n<td style=\"width:61px\">12.0<\/td>\n<td style=\"width:540px\">\u59ff\u6001\u635f\u5931\u5728\u59ff\u6001\u4f30\u8ba1\u6a21\u578b\u4e2d\u7684\u6743\u91cd&#xff0c;\u5f71\u54cd\u7740\u51c6\u786e\u9884\u6d4b\u59ff\u6001\u5173\u952e\u70b9\u7684\u91cd\u70b9\u3002<\/td>\n<\/tr>\n<tr>\n<td>kobj<\/td>\n<td style=\"width:61px\">2.0<\/td>\n<td style=\"width:540px\">\u59ff\u6001\u4f30\u8ba1\u6a21\u578b\u4e2d\u5173\u952e\u70b9\u5bf9\u8c61\u6027\u635f\u5931\u7684\u6743\u91cd&#xff0c;\u5e73\u8861\u68c0\u6d4b\u53ef\u4fe1\u5ea6\u4e0e\u59ff\u6001\u7cbe\u5ea6\u3002<\/td>\n<\/tr>\n<tr>\n<td>label_smoothing<\/td>\n<td style=\"width:61px\">0.0<\/td>\n<td style=\"width:540px\">\u5e94\u7528\u6807\u7b7e\u5e73\u6ed1&#xff0c;\u5c06\u786c\u6807\u7b7e\u8f6f\u5316\u4e3a\u76ee\u6807\u6807\u7b7e\u548c\u6807\u7b7e\u5747\u5300\u5206\u5e03\u7684\u6df7\u5408\u6807\u7b7e&#xff0c;\u53ef\u4ee5\u63d0\u9ad8\u6cdb\u5316\u6548\u679c\u3002<\/td>\n<\/tr>\n<tr>\n<td>nbs<\/td>\n<td style=\"width:61px\">64<\/td>\n<td style=\"width:540px\">\u7528\u4e8e\u635f\u8017\u6b63\u5e38\u5316\u7684\u6807\u79f0\u6279\u91cf\u5927\u5c0f\u3002<\/td>\n<\/tr>\n<tr>\n<td>overlap_mask<\/td>\n<td style=\"width:61px\">True<\/td>\n<td style=\"width:540px\">\u51b3\u5b9a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5206\u5272\u63a9\u7801\u662f\u5426\u5e94\u8be5\u91cd\u53e0&#xff0c;\u9002\u7528\u4e8e\u5b9e\u4f8b\u5206\u5272\u4efb\u52a1\u3002<\/td>\n<\/tr>\n<tr>\n<td>mask_ratio<\/td>\n<td style=\"width:61px\">4<\/td>\n<td style=\"width:540px\">\u5206\u5272\u63a9\u7801\u7684\u4e0b\u91c7\u6837\u7387&#xff0c;\u5f71\u54cd\u8bad\u7ec3\u65f6\u4f7f\u7528\u7684\u63a9\u7801\u5206\u8fa8\u7387\u3002<\/td>\n<\/tr>\n<tr>\n<td>dropout<\/td>\n<td style=\"width:61px\">0.0<\/td>\n<td style=\"width:540px\">\u5206\u7c7b\u4efb\u52a1\u4e2d\u6b63\u5219\u5316\u7684\u4e22\u5f03\u7387&#xff0c;\u901a\u8fc7\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u968f\u673a\u7701\u7565\u5355\u5143\u6765\u9632\u6b62\u8fc7\u62df\u5408\u3002<\/td>\n<\/tr>\n<tr>\n<td>val<\/td>\n<td style=\"width:61px\">True<\/td>\n<td style=\"width:540px\">\u53ef\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u9a8c\u8bc1&#xff0c;\u4ee5\u4fbf\u5728\u5355\u72ec\u7684\u6570\u636e\u96c6\u4e0a\u5bf9\u6a21\u578b\u6027\u80fd\u8fdb\u884c\u5b9a\u671f\u8bc4\u4f30\u3002<\/td>\n<\/tr>\n<tr>\n<td>plots<\/td>\n<td style=\"width:61px\">False<\/td>\n<td style=\"width:540px\">\u751f\u6210\u5e76\u4fdd\u5b58\u8bad\u7ec3\u548c\u9a8c\u8bc1\u6307\u6807\u56fe\u4ee5\u53ca\u9884\u6d4b\u793a\u4f8b\u56fe&#xff0c;\u4ee5\u4fbf\u76f4\u89c2\u5730\u4e86\u89e3\u6a21\u578b\u6027\u80fd\u548c\u5b66\u4e60\u8fdb\u5ea6\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5e38\u7528\u7684\u51e0\u4e2a\u8bad\u7ec3\u53c2\u6570\u662f:\u00a0\u6570\u636e\u96c6\u914d\u7f6e\u6587\u4ef6data\u3001\u8bad\u7ec3\u8f6e\u6570epochs\u3001\u8bad\u7ec3\u6279\u6b21\u5927\u5c0fbatch\u3001\u8bad\u7ec3\u4f7f\u7528\u7684\u8bbe\u5907device,\u6a21\u578b\u4f18\u5316\u5668optimizer\u3001\u521d\u59cb\u5b66\u4e60\u7387lr0\u3002<\/p>\n<p><span style=\"color:#956fe7\">\u4ee5\u4e0a\u4fbf\u662f\u5173\u4e8eYOLO26\u6a21\u578b\u8be6\u7ec6\u8bf4\u660e&#xff0c;\u770b\u5230\u8fd9\u91cc\u7684\u5c0f\u4f19\u4f34&#xff0c;\u76f8\u4fe1\u4f60\u4e00\u5b9a\u5bf9yolo26\u6a21\u578b\u6709\u4e86\u4e00\u5b9a\u7684\u8ba4\u8bc6\u5566\u3002<\/span><\/p>\n<p><span style=\"color:#fe2c24\">\u5728Visdrone2019\u6570\u636e\u96c6\u4e0a\u63a8\u7406\u9884\u6d4b\u793a\u4f8b&#xff1a;<\/span><\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"506\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131738-6968e8f20e880.png\" width=\"902\" \/><\/p>\n<p><span style=\"color:#fe2c24\">\u751f\u6210YOLO26\u53ef\u89c6\u5316\u70ed\u529b\u56fe\u793a\u4f8b&#xff1a;<\/span><\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"337\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115131738-6968e8f2780b9.png\" width=\"601\" \/><\/p>\n<h2 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