{"id":99294,"date":"2026-09-02T11:33:52","date_gmt":"2026-09-02T03:33:52","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/99294.html"},"modified":"2026-09-02T11:33:52","modified_gmt":"2026-09-02T03:33:52","slug":"%e5%9f%ba%e4%ba%8e%e6%94%b9%e8%bf%9byolov13-c3k2-convformer%e7%9a%84%e5%ae%a4%e5%86%85%e7%8c%ab%e8%af%86%e5%88%ab%e7%b3%bb%e7%bb%9f%e8%ae%be%e8%ae%a1%e4%b8%8e%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/99294.html","title":{"rendered":"\u57fa\u4e8e\u6539\u8fdbYOLOV13-C3k2-ConvFormer\u7684\u5ba4\u5185\u732b\u8bc6\u522b\u7cfb\u7edf\u8bbe\u8ba1\u4e0e\u5b9e\u73b0"},"content":{"rendered":"

\u6982\u8ff0<\/h2>\n

\u672c\u9879\u76ee\u8bbe\u8ba1\u5e76\u5b9e\u73b0\u4e86\u4e00\u4e2a\u57fa\u4e8e\u6539\u8fdbYOLOV13-C3k2-ConvFormer\u7684\u5ba4\u5185\u732b\u8bc6\u522b\u7cfb\u7edf，\u4e13\u6ce8\u4e8e\u5ba4\u5185\u73af\u5883\u4e0b\u7684\u732b\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u3002\u7cfb\u7edf\u91c7\u7528\u5355\u7c7b\u522b(nc:1)\u8bc6\u522b\u65b9\u6848，\u4e13\u653b\u2019cat\u2019\u7c7b\u522b\u7684\u68c0\u6d4b\u3002\u540e\u7aef\u7b97\u6cd5\u521b\u65b0\u6027\u5730\u878d\u5408\u4e86YOLOV13\u67b6\u6784\u4e0eC3k2-ConvFormer\u6a21\u5757，\u63d0\u5347\u4e86\u6a21\u578b\u5728\u590d\u6742\u5ba4\u5185\u73af\u5883\u4e0b\u7684\u68c0\u6d4b\u7cbe\u5ea6\u4e0e\u9c81\u68d2\u6027\u3002\u524d\u7aef\u91c7\u7528flask+vue+elementplus\u6280\u672f\u6808\u6784\u5efa，\u63d0\u4f9b\u4e86\u76f4\u89c2\u53cb\u597d\u7684\u7528\u6237\u754c\u9762，\u5b9e\u73b0\u4e86\u4ece\u56fe\u50cf\u8f93\u5165\u5230\u76ee\u6807\u68c0\u6d4b\u7ed3\u679c\u8f93\u51fa\u7684\u5b8c\u6574\u6d41\u7a0b\u3002\u8be5\u7cfb\u7edf\u901a\u8fc7\u7b97\u6cd5\u4f18\u5316\u548c\u6280\u672f\u521b\u65b0，\u4e3a\u5ba4\u5185\u732b\u7684\u81ea\u52a8\u5316\u8bc6\u522b\u63d0\u4f9b\u4e86\u9ad8\u6548\u53ef\u9760\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n

\u4efb\u52a1\u76ee\u6807<\/h2>\n

\u968f\u7740\u5bb6\u5ead\u5ba0\u7269\u6570\u91cf\u7684\u589e\u52a0，\u5ba4\u5185\u732b\u7684\u667a\u80fd\u8bc6\u522b\u4e0e\u7ba1\u7406\u9700\u6c42\u65e5\u76ca\u51f8\u663e\u3002\u672c\u9879\u76ee\u65e8\u5728\u8bbe\u8ba1\u5e76\u5b9e\u73b0\u4e00\u79cd\u57fa\u4e8e\u6539\u8fdbYOLOV13-C3k2-ConvFormer\u67b6\u6784\u7684\u5ba4\u5185\u732b\u8bc6\u522b\u7cfb\u7edf，\u901a\u8fc7\u878d\u5408C3k2\u6a21\u5757\u4e0eConvFormer\u6ce8\u610f\u529b\u673a\u5236，\u63d0\u5347\u6a21\u578b\u5728\u590d\u6742\u5ba4\u5185\u73af\u5883\u4e0b\u7684\u7279\u5f81\u63d0\u53d6\u80fd\u529b\u548c\u76ee\u6807\u68c0\u6d4b\u7cbe\u5ea6\u3002\u7814\u7a76\u610f\u4e49\u5728\u4e8e：\u4e00\u65b9\u9762，\u8be5\u7cfb\u7edf\u53ef\u4e3a\u5ba0\u7269\u4e3b\u4eba\u63d0\u4f9b\u4fbf\u6377\u7684\u732b\u54aa\u884c\u4e3a\u76d1\u6d4b\u4e0e\u5065\u5eb7\u7ba1\u7406\u5de5\u5177，\u4fc3\u8fdb\u4eba\u673a\u4ea4\u4e92\u7684\u667a\u80fd\u5316\u53d1\u5c55；\u53e6\u4e00\u65b9\u9762，\u6539\u8fdb\u7684YOLO\u67b6\u6784\u5728\u7279\u5b9a\u573a\u666f\u4e0b\u7684\u4f18\u5316\u65b9\u6cd5\u53ef\u4e3a\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u63d0\u4f9b\u65b0\u7684\u6280\u672f\u601d\u8def\u3002\u5177\u4f53\u76ee\u6807\u5305\u62ec：\u6784\u5efa\u9002\u5408\u5ba4\u5185\u73af\u5883\u7684\u732b\u7c7b\u56fe\u50cf\u6570\u636e\u96c6，\u8bbe\u8ba1\u878d\u5408\u5377\u79ef\u4e0eTransformer\u4f18\u52bf\u7684\u8f7b\u91cf\u5316\u68c0\u6d4b\u6a21\u578b，\u5b9e\u73b0\u9ad8\u7cbe\u5ea6\u5b9e\u65f6\u68c0\u6d4b，\u5e76\u901a\u8fc7\u5b9e\u9a8c\u9a8c\u8bc1\u6a21\u578b\u5728\u5149\u7167\u53d8\u5316\u3001\u80cc\u666f\u590d\u6742\u7b49\u6311\u6218\u6761\u4ef6\u4e0b\u7684\u9c81\u68d2\u6027\u3002<\/p>\n

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

\u672c\u6570\u636e\u96c6\u4e13\u6ce8\u4e8e\u5ba4\u5185\u73af\u5883\u4e0b\u7684\u732b\u7c7b\u76ee\u6807\u8bc6\u522b，\u5305\u542b\u5355\u4e00\u7c7b\u522b\u2019cat\u2019，\u5bf9\u5e94\u4e2d\u6587\u542b\u4e49\u4e3a\u2019\u732b\u2019\u3002\u8be5\u6570\u636e\u96c6\u9009\u62e9\u5177\u6709\u663e\u8457\u4f18\u52bf：\u9996\u5148，\u4e13\u6ce8\u4e8e\u5355\u4e00\u76ee\u6807\u7c7b\u522b\u4f7f\u6a21\u578b\u80fd\u591f\u6df1\u5ea6\u5b66\u4e60\u732b\u7c7b\u7279\u5f81，\u63d0\u9ad8\u68c0\u6d4b\u7cbe\u5ea6；\u5176\u6b21，\u5ba4\u5185\u573a\u666f\u4e0b\u7684\u56fe\u50cf\u6570\u636e\u80fd\u591f\u6a21\u62df\u771f\u5b9e\u5bb6\u5ead\u73af\u5883，\u589e\u5f3a\u6a21\u578b\u5728\u590d\u6742\u80cc\u666f\u4e0b\u7684\u9c81\u68d2\u6027；\u518d\u8005，\u6570\u636e\u96c6\u6db5\u76d6\u4e86\u4e0d\u540c\u5149\u7167\u6761\u4ef6\u3001\u59ff\u6001\u53d8\u5316\u548c\u906e\u6321\u60c5\u51b5\u4e0b\u7684\u732b\u7c7b\u56fe\u50cf，\u6709\u52a9\u4e8e\u63d0\u5347\u6a21\u578b\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u6cdb\u5316\u80fd\u529b\u3002\u6b64\u5916，\u8be5\u6570\u636e\u96c6\u4e3a\u6539\u8fdbYOLOV13-C3k2-ConvFormer\u67b6\u6784\u63d0\u4f9b\u4e86\u7406\u60f3\u7684\u9a8c\u8bc1\u5e73\u53f0，\u80fd\u591f\u6709\u6548\u8bc4\u4f30\u878d\u5408C3k2\u6a21\u5757\u4e0eConvFormer\u6ce8\u610f\u529b\u673a\u5236\u5728\u7279\u5b9a\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u4e2d\u7684\u6027\u80fd\u63d0\u5347\u3002<\/p>\n

\u7cfb\u7edf\u529f\u80fd\u56fe\u7247\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/h2>\n

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

\u7cfb\u7edf\u6e05\u5355\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/h2>\n

\u6a21\u578b\u8bad\u7ec3<\/h2>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" 15.\u6a21\u578b\u8bad\u7ec3\u6a21\u5757\u8be6\u89e3 15.1 \u6a21\u578b\u8bad\u7ec3\u6a21\u5757\u6982\u8ff0 \u6a21\u578b\u8bad\u7ec3\u6a21\u5757\u662f\u667a\u6167\u8bc6\u522b\u7cfb\u7edf\u7684\u6838\u5fc3\u529f\u80fd\u4e4b\u4e00，\u63d0\u4f9b\u4e86\u5b8c\u6574\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u89e3\u51b3\u65b9\u6848\u3002\u8be5\u6a21\u5757\u652f\u6301\u591a\u79cd\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u548c\u7b97\u6cd5，\u5305\u62ecYOLOv11\u3001ResNet\u3001EfficientNet\u7b49，\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u4ece\u6570\u636e\u9884\u5904\u7406\u5230\u6a21\u578b\u90e8\u7f72\u7684\u5168\u6d41\u7a0b\u8bad\u7ec3\u652f\u6301\u3002<\/p>\n

15.2 \u8bad\u7ec3\u6a21\u5757\u67b6\u6784\u8bbe\u8ba1 15.2.1 \u6574\u4f53\u67b6\u6784 \u6a21\u578b\u8bad\u7ec3\u6a21\u5757\u91c7\u7528\u6a21\u5757\u5316\u8bbe\u8ba1，\u5c06\u8bad\u7ec3\u6d41\u7a0b\u5206\u89e3\u4e3a\u591a\u4e2a\u72ec\u7acb\u7684\u7ec4\u4ef6：<\/p>\n

class<\/span> ModelTrainingWindow<\/span>(<\/span>QMainWindow)<\/span>:<\/span>
\n """\u6a21\u578b\u8bad\u7ec3\u7a97\u53e3"""<\/span><\/p>\n

def<\/span> __init__<\/span>(<\/span>self,<\/span> parent=<\/span>None<\/span>)<\/span>:<\/span>
\n super<\/span>(<\/span>)<\/span>.<\/span>__init__(<\/span>parent)<\/span>
\n self.<\/span>parent_window =<\/span> parent
\n self.<\/span>training_thread =<\/span> None<\/span>
\n self.<\/span>current_model =<\/span> None<\/span>
\n self.<\/span>training_config =<\/span> {<\/span>}<\/span>
\n self.<\/span>init_ui(<\/span>)<\/span>
\n self.<\/span>setup_training_components(<\/span>)<\/span>
\n self.<\/span>load_available_models(<\/span>)<\/span><\/p>\n

15.2.2 \u6838\u5fc3\u7ec4\u4ef6 \u6a21\u578b\u9009\u62e9\u5668: \u652f\u6301\u591a\u79cd\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u81ea\u5b9a\u4e49\u6a21\u578b \u6570\u636e\u96c6\u7ba1\u7406\u5668: \u5904\u7406\u8bad\u7ec3\u6570\u636e\u7684\u52a0\u8f7d\u548c\u9884\u5904\u7406 \u8bad\u7ec3\u914d\u7f6e\u9762\u677f: \u8bbe\u7f6e\u8bad\u7ec3\u53c2\u6570\u548c\u8d85\u53c2\u6570 \u8bad\u7ec3\u76d1\u63a7\u5668: \u5b9e\u65f6\u663e\u793a\u8bad\u7ec3\u8fdb\u5ea6\u548c\u6307\u6807 \u7ed3\u679c\u53ef\u89c6\u5316\u5668: \u5c55\u793a\u8bad\u7ec3\u7ed3\u679c\u548c\u6027\u80fd\u5206\u6790 15.3 \u652f\u6301\u7684\u6a21\u578b\u7c7b\u578b 15.3.1 \u76ee\u6807\u68c0\u6d4b\u6a21\u578b<\/p>\n

def<\/span> get_detection_models<\/span>(<\/span>self)<\/span>:<\/span>
\n """\u83b7\u53d6\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u5217\u8868"""<\/span>
\n return<\/span> {<\/span>
\n "YOLOv11n"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "detection"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "ultralytics"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u8f7b\u91cf\u7ea7\u76ee\u6807\u68c0\u6d4b\u6a21\u578b，\u9002\u5408\u5b9e\u65f6\u5e94\u7528"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 80<\/span>
\n }<\/span>,<\/span>
\n "YOLOv11s"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "detection"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "ultralytics"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u5c0f\u578b\u76ee\u6807\u68c0\u6d4b\u6a21\u578b，\u5e73\u8861\u7cbe\u5ea6\u548c\u901f\u5ea6"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 80<\/span>
\n }<\/span>,<\/span>
\n "YOLOv11m"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "detection"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "ultralytics"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u4e2d\u578b\u76ee\u6807\u68c0\u6d4b\u6a21\u578b，\u8f83\u9ad8\u7cbe\u5ea6"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 80<\/span>
\n }<\/span>,<\/span>
\n "YOLOv11l"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "detection"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "ultralytics"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u5927\u578b\u76ee\u6807\u68c0\u6d4b\u6a21\u578b，\u9ad8\u7cbe\u5ea6"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 80<\/span>
\n }<\/span>,<\/span>
\n "YOLOv11x"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "detection"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "ultralytics"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u8d85\u5927\u578b\u76ee\u6807\u68c0\u6d4b\u6a21\u578b，\u6700\u9ad8\u7cbe\u5ea6"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 80<\/span>
\n }<\/span>
\n }<\/span><\/p>\n

15.3.2 \u56fe\u50cf\u5206\u7c7b\u6a21\u578b<\/p>\n

def<\/span> get_classification_models<\/span>(<\/span>self)<\/span>:<\/span>
\n """\u83b7\u53d6\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u5217\u8868"""<\/span>
\n return<\/span> {<\/span>
\n "ResNet50"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "classification"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "torchvision"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u7ecf\u5178\u6b8b\u5dee\u7f51\u7edc，\u9002\u5408\u56fe\u50cf\u5206\u7c7b"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>224<\/span>,<\/span> 224<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 1000<\/span>
\n }<\/span>,<\/span>
\n "EfficientNet-B0"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "classification"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "timm"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u9ad8\u6548\u7f51\u7edc，\u53c2\u6570\u5c11\u7cbe\u5ea6\u9ad8"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>224<\/span>,<\/span> 224<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 1000<\/span>
\n }<\/span>,<\/span>
\n "Vision Transformer"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "classification"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "timm"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u89c6\u89c9Transformer，\u6ce8\u610f\u529b\u673a\u5236"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>224<\/span>,<\/span> 224<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 1000<\/span>
\n }<\/span>
\n }<\/span><\/p>\n

15.3.3 \u8bed\u4e49\u5206\u5272\u6a21\u578b<\/p>\n

def<\/span> get_segmentation_models<\/span>(<\/span>self)<\/span>:<\/span>
\n """\u83b7\u53d6\u8bed\u4e49\u5206\u5272\u6a21\u578b\u5217\u8868"""<\/span>
\n return<\/span> {<\/span>
\n "DeepLabV3+"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "segmentation"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "torchvision"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "\u8bed\u4e49\u5206\u5272\u6a21\u578b，\u652f\u6301\u591a\u5c3a\u5ea6\u7279\u5f81"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>512<\/span>,<\/span> 512<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 21<\/span>
\n }<\/span>,<\/span>
\n "U-Net"<\/span>:<\/span> {<\/span>
\n "type"<\/span>:<\/span> "segmentation"<\/span>,<\/span>
\n "framework"<\/span>:<\/span> "custom"<\/span>,<\/span>
\n "description"<\/span>:<\/span> "U\u578b\u7f51\u7edc，\u9002\u5408\u533b\u5b66\u56fe\u50cf\u5206\u5272"<\/span>,<\/span>
\n "input_size"<\/span>:<\/span> (<\/span>512<\/span>,<\/span> 512<\/span>)<\/span>,<\/span>
\n "classes"<\/span>:<\/span> 2<\/span>
\n }<\/span>
\n }<\/span><\/p>\n

15.4 \u6570\u636e\u96c6\u7ba1\u7406 15.4.1 \u6570\u636e\u96c6\u52a0\u8f7d<\/p>\n

def<\/span> load_dataset<\/span>(<\/span>self,<\/span> dataset_path,<\/span> dataset_type)<\/span>:<\/span>
\n """\u52a0\u8f7d\u6570\u636e\u96c6"""<\/span>
\n try<\/span>:<\/span>
\n if<\/span> dataset_type ==<\/span> "detection"<\/span>:<\/span>
\n return<\/span> self.<\/span>load_detection_dataset(<\/span>dataset_path)<\/span>
\n elif<\/span> dataset_type ==<\/span> "classification"<\/span>:<\/span>
\n return<\/span> self.<\/span>load_classification_dataset(<\/span>dataset_path)<\/span>
\n elif<\/span> dataset_type ==<\/span> "segmentation"<\/span>:<\/span>
\n return<\/span> self.<\/span>load_segmentation_dataset(<\/span>dataset_path)<\/span>
\n else<\/span>:<\/span>
\n raise<\/span> ValueError(<\/span>f"\u4e0d\u652f\u6301\u7684\u6570\u636e\u96c6\u7c7b\u578b: <\/span>{<\/span>dataset_type}<\/span><\/span>"<\/span><\/span>)<\/span>
\n except<\/span> Exception as<\/span> e:<\/span>
\n QMessageBox.<\/span>critical(<\/span>self,<\/span> "\u6570\u636e\u96c6\u52a0\u8f7d\u9519\u8bef"<\/span>,<\/span> f"\u65e0\u6cd5\u52a0\u8f7d\u6570\u636e\u96c6: <\/span>{<\/span>str<\/span>(<\/span>e)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span>
\n return<\/span> None<\/span><\/p>\n

def<\/span> load_detection_dataset<\/span>(<\/span>self,<\/span> dataset_path)<\/span>:<\/span>
\n """\u52a0\u8f7d\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6"""<\/span>
\n # \u68c0\u67e5\u6570\u636e\u96c6\u683c\u5f0f<\/span>
\n if<\/span> not<\/span> os.<\/span>path.<\/span>exists(<\/span>os.<\/span>path.<\/span>join(<\/span>dataset_path,<\/span> "images"<\/span>)<\/span>)<\/span>:<\/span>
\n raise<\/span> FileNotFoundError(<\/span>"\u6570\u636e\u96c6\u7f3a\u5c11images\u6587\u4ef6\u5939"<\/span>)<\/span><\/p>\n

if<\/span> not<\/span> os.<\/span>path.<\/span>exists(<\/span>os.<\/span>path.<\/span>join(<\/span>dataset_path,<\/span> "labels"<\/span>)<\/span>)<\/span>:<\/span>
\n raise<\/span> FileNotFoundError(<\/span>"\u6570\u636e\u96c6\u7f3a\u5c11labels\u6587\u4ef6\u5939"<\/span>)<\/span><\/p>\n

# \u52a0\u8f7d\u6570\u636e\u96c6\u4fe1\u606f<\/span>
\n dataset_info =<\/span> {<\/span>
\n "path"<\/span>:<\/span> dataset_path,<\/span>
\n "type"<\/span>:<\/span> "detection"<\/span>,<\/span>
\n "images"<\/span>:<\/span> [<\/span>]<\/span>,<\/span>
\n "labels"<\/span>:<\/span> [<\/span>]<\/span>,<\/span>
\n "classes"<\/span>:<\/span> [<\/span>]<\/span>
\n }<\/span><\/p>\n

# \u626b\u63cf\u56fe\u50cf\u6587\u4ef6<\/span>
\n image_extensions =<\/span> [<\/span>'.jpg'<\/span>,<\/span> '.jpeg'<\/span>,<\/span> '.png'<\/span>,<\/span> '.bmp'<\/span>]<\/span>
\n for<\/span> file<\/span> in<\/span> os.<\/span>listdir(<\/span>os.<\/span>path.<\/span>join(<\/span>dataset_path,<\/span> "images"<\/span>)<\/span>)<\/span>:<\/span>
\n if<\/span> any<\/span>(<\/span>file<\/span>.<\/span>lower(<\/span>)<\/span>.<\/span>endswith(<\/span>ext)<\/span> for<\/span> ext in<\/span> image_extensions)<\/span>:<\/span>
\n dataset_info[<\/span>"images"<\/span>]<\/span>.<\/span>append(<\/span>file<\/span>)<\/span><\/p>\n

# \u626b\u63cf\u6807\u7b7e\u6587\u4ef6<\/span>
\n for<\/span> file<\/span> in<\/span> os.<\/span>listdir(<\/span>os.<\/span>path.<\/span>join(<\/span>dataset_path,<\/span> "labels"<\/span>)<\/span>)<\/span>:<\/span>
\n if<\/span> file<\/span>.<\/span>endswith(<\/span>'.txt'<\/span>)<\/span>:<\/span>
\n dataset_info[<\/span>"labels"<\/span>]<\/span>.<\/span>append(<\/span>file<\/span>)<\/span><\/p>\n

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

15.4.2 \u6570\u636e\u9884\u5904\u7406<\/p>\n

def<\/span> preprocess_dataset<\/span>(<\/span>self,<\/span> dataset_info,<\/span> preprocessing_config)<\/span>:<\/span>
\n """\u6570\u636e\u9884\u5904\u7406"""<\/span>
\n preprocessing_pipeline =<\/span> [<\/span>]<\/span><\/p>\n

# \u56fe\u50cf\u589e\u5f3a<\/span>
\n if<\/span> preprocessing_config.<\/span>get(<\/span>"augmentation"<\/span>,<\/span> False<\/span>)<\/span>:<\/span>
\n augmentation_transforms =<\/span> [<\/span>
\n "RandomHorizontalFlip"<\/span>,<\/span>
\n "RandomVerticalFlip"<\/span>,<\/span>
\n "RandomRotation"<\/span>,<\/span>
\n "ColorJitter"<\/span>,<\/span>
\n "RandomResizedCrop"<\/span>
\n ]<\/span>
\n preprocessing_pipeline.<\/span>extend(<\/span>augmentation_transforms)<\/span><\/p>\n

# \u6570\u636e\u6807\u51c6\u5316<\/span>
\n if<\/span> preprocessing_config.<\/span>get(<\/span>"normalization"<\/span>,<\/span> True<\/span>)<\/span>:<\/span>
\n preprocessing_pipeline.<\/span>append(<\/span>"Normalize"<\/span>)<\/span><\/p>\n

# \u5c3a\u5bf8\u8c03\u6574<\/span>
\n if<\/span> preprocessing_config.<\/span>get(<\/span>"resize"<\/span>,<\/span> True<\/span>)<\/span>:<\/span>
\n target_size =<\/span> preprocessing_config.<\/span>get(<\/span>"target_size"<\/span>,<\/span> (<\/span>640<\/span>,<\/span> 640<\/span>)<\/span>)<\/span>
\n preprocessing_pipeline.<\/span>append(<\/span>f"Resize_<\/span>{<\/span>target_size}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

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

15.5 \u8bad\u7ec3\u914d\u7f6e\u7cfb\u7edf 15.5.1 \u8bad\u7ec3\u53c2\u6570\u914d\u7f6e<\/p>\n

def<\/span> create_training_config_panel<\/span>(<\/span>self,<\/span> parent_layout)<\/span>:<\/span>
\n """\u521b\u5efa\u8bad\u7ec3\u914d\u7f6e\u9762\u677f"""<\/span>
\n config_frame =<\/span> QGroupBox(<\/span>"\u8bad\u7ec3\u914d\u7f6e"<\/span>)<\/span>
\n config_layout =<\/span> QFormLayout(<\/span>config_frame)<\/span><\/p>\n

# \u57fa\u7840\u53c2\u6570<\/span>
\n self.<\/span>epochs_input =<\/span> QSpinBox(<\/span>)<\/span>
\n self.<\/span>epochs_input.<\/span>setRange(<\/span>1<\/span>,<\/span> 1000<\/span>)<\/span>
\n self.<\/span>epochs_input.<\/span>setValue(<\/span>100<\/span>)<\/span>
\n config_layout.<\/span>addRow(<\/span>"\u8bad\u7ec3\u8f6e\u6570:"<\/span>,<\/span> self.<\/span>epochs_input)<\/span><\/p>\n

self.<\/span>batch_size_input =<\/span> QSpinBox(<\/span>)<\/span>
\n self.<\/span>batch_size_input.<\/span>setRange(<\/span>1<\/span>,<\/span> 128<\/span>)<\/span>
\n self.<\/span>batch_size_input.<\/span>setValue(<\/span>16<\/span>)<\/span>
\n config_layout.<\/span>addRow(<\/span>"\u6279\u6b21\u5927\u5c0f:"<\/span>,<\/span> self.<\/span>batch_size_input)<\/span><\/p>\n

self.<\/span>learning_rate_input =<\/span> QDoubleSpinBox(<\/span>)<\/span>
\n self.<\/span>learning_rate_input.<\/span>setRange(<\/span>0.0001<\/span>,<\/span> 1.0<\/span>)<\/span>
\n self.<\/span>learning_rate_input.<\/span>setValue(<\/span>0.001<\/span>)<\/span>
\n self.<\/span>learning_rate_input.<\/span>setDecimals(<\/span>4<\/span>)<\/span>
\n config_layout.<\/span>addRow(<\/span>"\u5b66\u4e60\u7387:"<\/span>,<\/span> self.<\/span>learning_rate_input)<\/span><\/p>\n

# \u4f18\u5316\u5668\u9009\u62e9<\/span>
\n self.<\/span>optimizer_combo =<\/span> QComboBox(<\/span>)<\/span>
\n self.<\/span>optimizer_combo.<\/span>addItems(<\/span>[<\/span>"Adam"<\/span>,<\/span> "SGD"<\/span>,<\/span> "AdamW"<\/span>,<\/span> "RMSprop"<\/span>]<\/span>)<\/span>
\n config_layout.<\/span>addRow(<\/span>"\u4f18\u5316\u5668:"<\/span>,<\/span> self.<\/span>optimizer_combo)<\/span><\/p>\n

# \u635f\u5931\u51fd\u6570\u9009\u62e9<\/span>
\n self.<\/span>loss_function_combo =<\/span> QComboBox(<\/span>)<\/span>
\n self.<\/span>loss_function_combo.<\/span>addItems(<\/span>[<\/span>"CrossEntropyLoss"<\/span>,<\/span> "MSELoss"<\/span>,<\/span> "BCELoss"<\/span>]<\/span>)<\/span>
\n config_layout.<\/span>addRow(<\/span>"\u635f\u5931\u51fd\u6570:"<\/span>,<\/span> self.<\/span>loss_function_combo)<\/span><\/p>\n

parent_layout.<\/span>addWidget(<\/span>config_frame)<\/span><\/p>\n

15.5.2 \u9ad8\u7ea7\u914d\u7f6e\u9009\u9879<\/p>\n

def<\/span> create_advanced_config_panel<\/span>(<\/span>self,<\/span> parent_layout)<\/span>:<\/span>
\n """\u521b\u5efa\u9ad8\u7ea7\u914d\u7f6e\u9762\u677f"""<\/span>
\n advanced_frame =<\/span> QGroupBox(<\/span>"\u9ad8\u7ea7\u914d\u7f6e"<\/span>)<\/span>
\n advanced_layout =<\/span> QFormLayout(<\/span>advanced_frame)<\/span><\/p>\n

# \u5b66\u4e60\u7387\u8c03\u5ea6\u5668<\/span>
\n self.<\/span>scheduler_combo =<\/span> QComboBox(<\/span>)<\/span>
\n self.<\/span>scheduler_combo.<\/span>addItems(<\/span>[<\/span>"StepLR"<\/span>,<\/span> "CosineAnnealingLR"<\/span>,<\/span> "ReduceLROnPlateau"<\/span>]<\/span>)<\/span>
\n advanced_layout.<\/span>addRow(<\/span>"\u5b66\u4e60\u7387\u8c03\u5ea6\u5668:"<\/span>,<\/span> self.<\/span>scheduler_combo)<\/span><\/p>\n

# \u65e9\u505c\u673a\u5236<\/span>
\n self.<\/span>early_stopping_check =<\/span> QCheckBox(<\/span>"\u542f\u7528\u65e9\u505c"<\/span>)<\/span>
\n self.<\/span>early_stopping_check.<\/span>setChecked(<\/span>True<\/span>)<\/span>
\n advanced_layout.<\/span>addRow(<\/span>"\u65e9\u505c\u673a\u5236:"<\/span>,<\/span> self.<\/span>early_stopping_check)<\/span><\/p>\n

self.<\/span>patience_input =<\/span> QSpinBox(<\/span>)<\/span>
\n self.<\/span>patience_input.<\/span>setRange(<\/span>1<\/span>,<\/span> 50<\/span>)<\/span>
\n self.<\/span>patience_input.<\/span>setValue(<\/span>10<\/span>)<\/span>
\n advanced_layout.<\/span>addRow(<\/span>"\u65e9\u505c\u8010\u5fc3\u503c:"<\/span>,<\/span> self.<\/span>patience_input)<\/span><\/p>\n

# \u6a21\u578b\u4fdd\u5b58\u7b56\u7565<\/span>
\n self.<\/span>save_best_check =<\/span> QCheckBox(<\/span>"\u4fdd\u5b58\u6700\u4f73\u6a21\u578b"<\/span>)<\/span>
\n self.<\/span>save_best_check.<\/span>setChecked(<\/span>True<\/span>)<\/span>
\n advanced_layout.<\/span>addRow(<\/span>"\u6a21\u578b\u4fdd\u5b58:"<\/span>,<\/span> self.<\/span>save_best_check)<\/span><\/p>\n

# \u9a8c\u8bc1\u9891\u7387<\/span>
\n self.<\/span>val_frequency_input =<\/span> QSpinBox(<\/span>)<\/span>
\n self.<\/span>val_frequency_input.<\/span>setRange(<\/span>1<\/span>,<\/span> 10<\/span>)<\/span>
\n self.<\/span>val_frequency_input.<\/span>setValue(<\/span>1<\/span>)<\/span>
\n advanced_layout.<\/span>addRow(<\/span>"\u9a8c\u8bc1\u9891\u7387:"<\/span>,<\/span> self.<\/span>val_frequency_input)<\/span><\/p>\n

parent_layout.<\/span>addWidget(<\/span>advanced_frame)<\/span><\/p>\n

15.6 \u8bad\u7ec3\u76d1\u63a7\u7cfb\u7edf 15.6.1 \u5b9e\u65f6\u8fdb\u5ea6\u663e\u793a def create_training_monitor(self, parent_layout): \u201c\u201d\u201c\u521b\u5efa\u8bad\u7ec3\u76d1\u63a7\u9762\u677f\u201d\u201c\u201d monitor_frame = QGroupBox(\u201c\u8bad\u7ec3\u76d1\u63a7\u201d) monitor_layout = QVBoxLayout(monitor_frame)<\/p>\n

# \u8fdb\u5ea6\u6761
\nself.progress_bar = QProgressBar()
\nself.progress_bar.setRange(0, 100)
\nmonitor_layout.addWidget(self.progress_bar)<\/p>\n

# \u8bad\u7ec3\u72b6\u6001
\nself.status_label = QLabel("\u51c6\u5907\u5f00\u59cb\u8bad\u7ec3…")
\nself.status_label.setObjectName("statusLabel")
\nmonitor_layout.addWidget(self.status_label)<\/p>\n

# \u6307\u6807\u663e\u793a
\nmetrics_frame = QFrame()
\nmetrics_layout = QGridLayout(metrics_frame)<\/p>\n

# \u635f\u5931\u503c
\nself.loss_label = QLabel("\u635f\u5931: –")
\nself.loss_label.setObjectName("metricLabel")
\nmetrics_layout.addWidget(self.loss_label, 0, 0)<\/p>\n

# \u51c6\u786e\u7387
\nself.accuracy_label = QLabel("\u51c6\u786e\u7387: –")
\nself.accuracy_label.setObjectName("metricLabel")
\nmetrics_layout.addWidget(self.accuracy_label, 0, 1)<\/p>\n

# \u5b66\u4e60\u7387
\nself.lr_label = QLabel("\u5b66\u4e60\u7387: –")
\nself.lr_label.setObjectName("metricLabel")
\nmetrics_layout.addWidget(self.lr_label, 1, 0)<\/p>\n

# \u8bad\u7ec3\u65f6\u95f4
\nself.time_label = QLabel("\u8bad\u7ec3\u65f6\u95f4: –")
\nself.time_label.setObjectName("metricLabel")
\nmetrics_layout.addWidget(self.time_label, 1, 1)<\/p>\n

monitor_layout.addWidget(metrics_frame)
\nparent_layout.addWidget(monitor_frame)<\/p>\n

15.6.2 \u8bad\u7ec3\u6307\u6807\u53ef\u89c6\u5316 def create_metrics_plot(self, parent_layout): \u201c\u201d\u201c\u521b\u5efa\u8bad\u7ec3\u6307\u6807\u56fe\u8868\u201d\u201c\u201d plot_frame = QGroupBox(\u201c\u8bad\u7ec3\u6307\u6807\u201d) plot_layout = QVBoxLayout(plot_frame)<\/p>\n

# \u521b\u5efamatplotlib\u56fe\u8868
\nself.figure = Figure(figsize=(12, 8))
\nself.canvas = FigureCanvas(self.figure)<\/p>\n

# \u521b\u5efa\u5b50\u56fe
\nself.ax1 = self.figure.add_subplot(221) # \u635f\u5931\u66f2\u7ebf
\nself.ax2 = self.figure.add_subplot(222) # \u51c6\u786e\u7387\u66f2\u7ebf
\nself.ax3 = self.figure.add_subplot(223) # \u5b66\u4e60\u7387\u66f2\u7ebf
\nself.ax4 = self.figure.add_subplot(224) # \u9a8c\u8bc1\u6307\u6807<\/p>\n

# \u521d\u59cb\u5316\u56fe\u8868
\nself.init_plots()<\/p>\n

plot_layout.addWidget(self.canvas)
\nparent_layout.addWidget(plot_frame)<\/p>\n

def init_plots(self): \u201c\u201d\u201c\u521d\u59cb\u5316\u56fe\u8868\u201d\u201c\u201d # \u635f\u5931\u66f2\u7ebf self.ax1.set_title(\u201c\u8bad\u7ec3\u635f\u5931\u201d) self.ax1.set_xlabel(\u201cEpoch\u201d) self.ax1.set_ylabel(\u201cLoss\u201d) self.ax1.grid(True)<\/p>\n

# \u51c6\u786e\u7387\u66f2\u7ebf
\nself.ax2.set_title("\u8bad\u7ec3\u51c6\u786e\u7387")
\nself.ax2.set_xlabel("Epoch")
\nself.ax2.set_ylabel("Accuracy")
\nself.ax2.grid(True)<\/p>\n

# \u5b66\u4e60\u7387\u66f2\u7ebf
\nself.ax3.set_title("\u5b66\u4e60\u7387\u53d8\u5316")
\nself.ax3.set_xlabel("Epoch")
\nself.ax3.set_ylabel("Learning Rate")
\nself.ax3.grid(True)<\/p>\n

# \u9a8c\u8bc1\u6307\u6807
\nself.ax4.set_title("\u9a8c\u8bc1\u6307\u6807")
\nself.ax4.set_xlabel("Epoch")
\nself.ax4.set_ylabel("Metrics")
\nself.ax4.grid(True)<\/p>\n

self.figure.tight_layout()
\nself.canvas.draw()<\/p>\n

15.7 \u8bad\u7ec3\u6267\u884c\u5f15\u64ce 15.7.1 \u8bad\u7ec3\u7ebf\u7a0b class TrainingThread(QThread): \u201c\u201d\u201c\u8bad\u7ec3\u7ebf\u7a0b\u201d\u201c\u201d<\/p>\n

progress_updated = Signal(int, dict) # \u8fdb\u5ea6\u66f4\u65b0\u4fe1\u53f7
\ntraining_finished = Signal(dict) # \u8bad\u7ec3\u5b8c\u6210\u4fe1\u53f7
\ntraining_error = Signal(str) # \u8bad\u7ec3\u9519\u8bef\u4fe1\u53f7<\/p>\n

def __init__(self, model_config, dataset_config, training_config):
\n super().__init__()
\n self.model_config = model_config
\n self.dataset_config = dataset_config
\n self.training_config = training_config
\n self.is_running = False<\/p>\n

def run(self):
\n """\u6267\u884c\u8bad\u7ec3"""
\n try:
\n self.is_running = True
\n self.start_training()
\n except Exception as e:
\n self.training_error.emit(str(e))
\n finally:
\n self.is_running = False<\/p>\n

def start_training(self):
\n """\u5f00\u59cb\u8bad\u7ec3"""
\n # \u521d\u59cb\u5316\u6a21\u578b
\n model = self.initialize_model()<\/p>\n

# \u52a0\u8f7d\u6570\u636e\u96c6
\n train_loader, val_loader = self.load_data()<\/p>\n

# \u8bbe\u7f6e\u4f18\u5316\u5668\u548c\u635f\u5931\u51fd\u6570
\n optimizer = self.setup_optimizer(model)
\n criterion = self.setup_criterion()<\/p>\n

# \u8bad\u7ec3\u5faa\u73af
\n for epoch in range(self.training_config['epochs']):
\n if not self.is_running:
\n break<\/p>\n

# \u8bad\u7ec3\u4e00\u4e2aepoch
\n train_metrics = self.train_epoch(model, train_loader, optimizer, criterion)<\/p>\n

# \u9a8c\u8bc1
\n val_metrics = self.validate_epoch(model, val_loader, criterion)<\/p>\n

# \u66f4\u65b0\u8fdb\u5ea6
\n progress = int((epoch + 1) \/ self.training_config['epochs'] * 100)
\n metrics = {**train_metrics, **val_metrics}
\n self.progress_updated.emit(progress, metrics)<\/p>\n

# \u8bad\u7ec3\u5b8c\u6210
\n final_metrics = self.get_final_metrics(model)
\n self.training_finished.emit(final_metrics)<\/p>\n

15.7.2 \u6a21\u578b\u521d\u59cb\u5316 def initialize_model(self): \u201c\u201d\u201c\u521d\u59cb\u5316\u6a21\u578b\u201d\u201c\u201d model_type = self.model_config[\u2018type\u2019] model_name = self.model_config[\u2018name\u2019]<\/p>\n

if model_type == 'detection':
\n return self.init_detection_model(model_name)
\nelif model_type == 'classification':
\n return self.init_classification_model(model_name)
\nelif model_type == 'segmentation':
\n return self.init_segmentation_model(model_name)
\nelse:
\n raise ValueError(f"\u4e0d\u652f\u6301\u7684\u6a21\u578b\u7c7b\u578b: {model_type}")<\/p>\n

def init_detection_model(self, model_name): \u201c\u201d\u201c\u521d\u59cb\u5316\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u201d\u201c\u201d from ultralytics import YOLO<\/p>\n

# \u6839\u636e\u6a21\u578b\u540d\u79f0\u9009\u62e9\u9884\u8bad\u7ec3\u6743\u91cd
\nmodel_weights = {
\n 'YOLOv11n': 'yolo11n.pt',
\n 'YOLOv11s': 'yolo11s.pt',
\n 'YOLOv11m': 'yolo11m.pt',
\n 'YOLOv11l': 'yolo11l.pt',
\n 'YOLOv11x': 'yolo11x.pt'
\n}<\/p>\n

if model_name in model_weights:
\n model = YOLO(model_weights[model_name])
\nelse:
\n # \u4f7f\u7528\u81ea\u5b9a\u4e49\u6a21\u578b
\n model = YOLO(model_name)<\/p>\n

return model<\/p>\n

15.8 \u7ed3\u679c\u5206\u6790\u548c\u5bfc\u51fa 15.8.1 \u8bad\u7ec3\u7ed3\u679c\u5206\u6790 def analyze_training_results(self, results): \u201c\u201d\u201c\u5206\u6790\u8bad\u7ec3\u7ed3\u679c\u201d\u201c\u201d analysis = { \u201cbest_epoch\u201d: results.get(\u201cbest_epoch\u201d, 0), \u201cbest_accuracy\u201d: results.get(\u201cbest_accuracy\u201d, 0.0), \u201cbest_loss\u201d: results.get(\u201cbest_loss\u201d, float(\u2018inf\u2019)), \u201ctraining_time\u201d: results.get(\u201ctraining_time\u201d, 0), \u201cconvergence_analysis\u201d: self.analyze_convergence(results), \u201coverfitting_analysis\u201d: self.analyze_overfitting(results) }<\/p>\n

return analysis<\/p>\n

def analyze_convergence(self, results): \u201c\u201d\u201c\u5206\u6790\u6536\u655b\u6027\u201d\u201c\u201d train_losses = results.get(\u201ctrain_losses\u201d, []) val_losses = results.get(\u201cval_losses\u201d, [])<\/p>\n

if len(train_losses) < 10:
\n return "\u6570\u636e\u4e0d\u8db3，\u65e0\u6cd5\u5206\u6790\u6536\u655b\u6027"<\/p>\n

# \u8ba1\u7b97\u6700\u540e10\u4e2aepoch\u7684\u635f\u5931\u53d8\u5316
\nrecent_train_loss = train_losses[-10:]
\nrecent_val_loss = val_losses[-10:]<\/p>\n

train_trend = self.calculate_trend(recent_train_loss)
\nval_trend = self.calculate_trend(recent_val_loss)<\/p>\n

if abs(train_trend) < 0.001 and abs(val_trend) < 0.001:
\n return "\u6a21\u578b\u5df2\u6536\u655b"
\nelif train_trend > 0.01:
\n return "\u8bad\u7ec3\u635f\u5931\u4ecd\u5728\u4e0a\u5347，\u53ef\u80fd\u9700\u8981\u8c03\u6574\u5b66\u4e60\u7387"
\nelse:
\n return "\u6a21\u578b\u6b63\u5728\u6536\u655b\u4e2d"<\/p>\n

15.8.2 \u6a21\u578b\u5bfc\u51fa def export_model(self, model, export_format=\u201connx\u201d): \u201c\u201d\u201c\u5bfc\u51fa\u6a21\u578b\u201d\u201c\u201d export_path = QFileDialog.getSaveFileName( self, \u201c\u4fdd\u5b58\u6a21\u578b\u201d, f"model.{export_format}\u201c, f\u201d{export_format.upper()} files (*.{export_format})" )[0]<\/p>\n

if not export_path:
\n return<\/p>\n

try:
\n if export_format == "onnx":
\n model.export(format="onnx", dynamic=True, simplify=True)
\n elif export_format == "torchscript":
\n model.export(format="torchscript")
\n elif export_format == "tflite":
\n model.export(format="tflite")
\n else:
\n raise ValueError(f"\u4e0d\u652f\u6301\u7684\u5bfc\u51fa\u683c\u5f0f: {export_format}")<\/p>\n

QMessageBox.information(self, "\u5bfc\u51fa\u6210\u529f", f"\u6a21\u578b\u5df2\u6210\u529f\u5bfc\u51fa\u5230: {export_path}")
\nexcept Exception as e:
\n QMessageBox.critical(self, "\u5bfc\u51fa\u5931\u8d25", f"\u6a21\u578b\u5bfc\u51fa\u5931\u8d25: {str(e)}")<\/p>\n

15.9 \u6027\u80fd\u4f18\u5316 15.9.1 \u5185\u5b58\u4f18\u5316 def optimize_memory_usage(self): \u201c\u201d\u201c\u4f18\u5316\u5185\u5b58\u4f7f\u7528\u201d\u201c\u201d # \u6e05\u7406GPU\u7f13\u5b58 if torch.cuda.is_available(): torch.cuda.empty_cache()<\/p>\n

# \u8bbe\u7f6e\u5185\u5b58\u5206\u914d\u7b56\u7565
\nos.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'<\/p>\n

# \u542f\u7528\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3
\nif self.training_config.get("mixed_precision", False):
\n self.scaler = torch.cuda.amp.GradScaler()<\/p>\n

15.9.2 \u8bad\u7ec3\u52a0\u901f def setup_training_acceleration(self): \u201c\u201d\u201c\u8bbe\u7f6e\u8bad\u7ec3\u52a0\u901f\u201d\u201c\u201d # \u6570\u636e\u52a0\u8f7d\u4f18\u5316 num_workers = min(8, os.cpu_count()) pin_memory = torch.cuda.is_available()<\/p>\n

# \u7f16\u8bd1\u6a21\u578b（PyTorch 2.0+）
\nif hasattr(torch, 'compile'):
\n self.model = torch.compile(self.model)<\/p>\n

# \u542f\u7528\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6
\nif self.training_config.get("amp", True):
\n self.use_amp = True<\/p>\n

15.10 \u9519\u8bef\u5904\u7406\u548c\u65e5\u5fd7 15.10.1 \u9519\u8bef\u5904\u7406 def handle_training_error(self, error_message): \u201c\u201d\u201c\u5904\u7406\u8bad\u7ec3\u9519\u8bef\u201d\u201c\u201d self.status_label.setText(f"\u8bad\u7ec3\u9519\u8bef: {error_message}") self.progress_bar.setValue(0)<\/p>\n

# \u8bb0\u5f55\u9519\u8bef\u65e5\u5fd7
\nself.log_error(error_message)<\/p>\n

# \u663e\u793a\u9519\u8bef\u5bf9\u8bdd\u6846
\nQMessageBox.critical(self, "\u8bad\u7ec3\u9519\u8bef", f"\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u53d1\u751f\u9519\u8bef:\\\\n{error_message}")<\/p>\n

def log_error(self, error_message): \u201c\u201d\u201c\u8bb0\u5f55\u9519\u8bef\u65e5\u5fd7\u201d\u201c\u201d timestamp = datetime.now().strftime(\u201c%Y-%m-%d %H:%M:%S\u201d) log_entry = f"[{timestamp}] ERROR: {error_message}\\\\n"<\/p>\n

with open("training_errors.log", "a", encoding="utf-8") as f:
\n f.write(log_entry)<\/p>\n

15.10.2 \u8bad\u7ec3\u65e5\u5fd7 def setup_training_logger(self): \u201c\u201d\u201c\u8bbe\u7f6e\u8bad\u7ec3\u65e5\u5fd7\u201d\u201c\u201d import logging<\/p>\n

# \u521b\u5efa\u65e5\u5fd7\u8bb0\u5f55\u5668
\nlogger = logging.getLogger("training")
\nlogger.setLevel(logging.INFO)<\/p>\n

# \u521b\u5efa\u6587\u4ef6\u5904\u7406\u5668
\nfile_handler = logging.FileHandler("training.log", encoding="utf-8")
\nfile_handler.setLevel(logging.INFO)<\/p>\n

# \u521b\u5efa\u683c\u5f0f\u5668
\nformatter = logging.Formatter(
\n '%(asctime)s – %(name)s – %(levelname)s – %(message)s'
\n)
\nfile_handler.setFormatter(formatter)<\/p>\n

# \u6dfb\u52a0\u5904\u7406\u5668
\nlogger.addHandler(file_handler)<\/p>\n

return logger<\/p>\n

15.11 \u603b\u7ed3 \u6a21\u578b\u8bad\u7ec3\u6a21\u5757\u4f5c\u4e3a\u667a\u6167\u8bc6\u522b\u7cfb\u7edf\u7684\u6838\u5fc3\u7ec4\u4ef6，\u63d0\u4f9b\u4e86\u5b8c\u6574\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u89e3\u51b3\u65b9\u6848\u3002\u901a\u8fc7\u6a21\u5757\u5316\u8bbe\u8ba1\u548c\u4e30\u5bcc\u7684\u529f\u80fd\u7279\u6027，\u8be5\u6a21\u5757\u652f\u6301\u591a\u79cd\u6a21\u578b\u7c7b\u578b\u548c\u8bad\u7ec3\u573a\u666f，\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u4ece\u6570\u636e\u51c6\u5907\u5230\u6a21\u578b\u90e8\u7f72\u7684\u5168\u6d41\u7a0b\u652f\u6301\u3002\u901a\u8fc7\u5b9e\u65f6\u76d1\u63a7\u3001\u6027\u80fd\u4f18\u5316\u548c\u9519\u8bef\u5904\u7406\u673a\u5236，\u786e\u4fdd\u4e86\u8bad\u7ec3\u8fc7\u7a0b\u7684\u7a33\u5b9a\u6027\u548c\u53ef\u9760\u6027，\u4e3a\u6784\u5efa\u9ad8\u8d28\u91cf\u7684AI\u6a21\u578b\u5960\u5b9a\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/p>\n

\u6a21\u578b\u8bc6\u522b<\/h2>\n

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

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

\u53ef\u4ee5\u6253\u5f00👉https:\/\/flypeppa.blog.csdn.net\/article\/details\/159383415，\u6eda\u52a8\u6d4f\u89c8\u5230\u6587\u7ae0\u672b\u5c3e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6982\u8ff0
\n\u672c\u9879\u76ee\u8bbe\u8ba1\u5e76\u5b9e\u73b0\u4e86\u4e00\u4e2a\u57fa\u4e8e\u6539\u8fdbYOLOV13-C3k2-ConvFormer\u7684\u5ba4\u5185\u732b\u8bc6\u522b\u7cfb\u7edf，\u4e13\u6ce8\u4e8e\u5ba4\u5185\u73af\u5883\u4e0b\u7684\u732b\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u3002\u7cfb\u7edf\u91c7\u7528\u5355\u7c7b\u522b(nc:1)\u8bc6\u522b\u65b9\u6848，\u4e13\u653b\u2019cat\u2019\u7c7b\u522b\u7684\u68c0\u6d4b\u3002\u540e\u7aef\u7b97\u6cd5\u521b\u65b0\u6027\u5730\u878d\u5408\u4e86YOLOV13\u67b6\u6784\u4e0eC3k2-ConvFormer\u6a21\u5757，\u63d0\u5347\u4e86\u6a21\u578b\u5728\u590d\u6742\u5ba4\u5185\u73af\u5883\u4e0b\u7684\u68c0\u6d4b\u7cbe\u5ea6\u4e0e\u9c81\u68d2\u6027\u3002\u524d\u7aef\u91c7\u7528flaskvueelementplus\u6280\u672f\u6808\u6784\u5efa，\u63d0\u4f9b\u4e86\u76f4\u89c2\u53cb\u597d\u7684\u7528\u6237\u754c\u9762，\u5b9e\u73b0<\/p>\n","protected":false},"author":2,"featured_media":99281,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[11359,156,1665,427,523],"topic":[],"class_list":["post-99294","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-11359","tag-yolo","tag-1665","tag-427","tag-523"],"yoast_head":"\n\u57fa\u4e8e\u6539\u8fdbYOLOV13-C3k2-ConvFormer\u7684\u5ba4\u5185\u732b\u8bc6\u522b\u7cfb\u7edf\u8bbe\u8ba1\u4e0e\u5b9e\u73b0 - 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