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YOLOv8\u6838\u5fc3\u7279\u6027\u548c\u6539\u52a8\u5982\u4e0b： 1\u3001\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5168\u65b0\u7684SOTA\u6a21\u578b（state-of-the-art model），\u5305\u62ec P5 640 \u548c P6 1280 \u5206\u8fa8\u7387\u7684\u76ee\u6807\u68c0\u6d4b\u7f51\u7edc\u548c\u57fa\u4e8eYOLACT\u7684\u5b9e\u4f8b\u5206\u5272\u6a21\u578b\u3002\u548c YOLOv5 \u4e00\u6837，\u57fa\u4e8e\u7f29\u653e\u7cfb\u6570\u4e5f\u63d0\u4f9b\u4e86 N\/S\/M\/L\/X \u5c3a\u5ea6\u7684\u4e0d\u540c\u5927\u5c0f\u6a21\u578b，\u7528\u4e8e\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u9700\u6c42 2\u3001\u9aa8\u5e72\u7f51\u7edc\u548c Neck \u90e8\u5206\u53ef\u80fd\u53c2\u8003\u4e86 YOLOv7 ELAN \u8bbe\u8ba1\u601d\u60f3，\u5c06 YOLOv5 \u7684 C3 \u7ed3\u6784\u6362\u6210\u4e86\u68af\u5ea6\u6d41\u66f4\u4e30\u5bcc\u7684 C2f \u7ed3\u6784，\u5e76\u5bf9\u4e0d\u540c\u5c3a\u5ea6\u6a21\u578b\u8c03\u6574\u4e86\u4e0d\u540c\u7684\u901a\u9053\u6570，\u5c5e\u4e8e\u5bf9\u6a21\u578b\u7ed3\u6784\u7cbe\u5fc3\u5fae\u8c03，\u4e0d\u518d\u662f\u4e00\u5957\u53c2\u6570\u5e94\u7528\u6240\u6709\u6a21\u578b，\u5927\u5e45\u63d0\u5347\u4e86\u6a21\u578b\u6027\u80fd\u3002 3\u3001Head \u90e8\u5206\u76f8\u6bd4 YOLOv5 \u6539\u52a8\u8f83\u5927，\u6362\u6210\u4e86\u76ee\u524d\u4e3b\u6d41\u7684\u89e3\u8026\u5934\u7ed3\u6784，\u5c06\u5206\u7c7b\u548c\u68c0\u6d4b\u5934\u5206\u79bb，\u540c\u65f6\u4e5f\u4eceAnchor-Based \u6362\u6210\u4e86 Anchor-Free 4\u3001Loss \u8ba1\u7b97\u65b9\u9762\u91c7\u7528\u4e86TaskAlignedAssigner\u6b63\u6837\u672c\u5206\u914d\u7b56\u7565，\u5e76\u5f15\u5165\u4e86Distribution Focal Loss 5\u3001\u8bad\u7ec3\u7684\u6570\u636e\u589e\u5f3a\u90e8\u5206\u5f15\u5165\u4e86 YOLOX \u4e2d\u7684\u6700\u540e 10 epoch \u5173\u95ed Mosiac \u589e\u5f3a\u7684\u64cd\u4f5c，\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u5347\u7cbe\u5ea6<\/p>\n

\u5b98\u65b9\u9879\u76ee\u5730\u5740\u5728\u8fd9\u91cc，\u5982\u4e0b\u6240\u793a：<\/p>\n

\"\"<\/p>\n

\u00a0\u5b98\u65b9\u63d0\u4f9b\u7684\u57fa\u4e8eCOCO\u6570\u636e\u96c6\u7684\u9884\u8bad\u7ec3\u6a21\u578b\u5982\u4e0b\u6240\u793a：<\/p>\n

\"\"<\/p>\n

\u9664\u4e86\u5e38\u89c4\u7684\u76ee\u6807\u68c0\u6d4b\u4efb\u52a1\u4e4b\u5916，\u8fd8\u6709\u65cb\u8f6c\u76ee\u6807\u68c0\u6d4b，\u5982\u4e0b：<\/p>\n

\"\"<\/p>\n

\u662f\u57fa\u4e8eDOTAv1\u6570\u636e\u96c6\u8bad\u7ec3\u5f97\u5230\u7684\u3002<\/p>\n

YOLOv8\u7684\u5b9a\u4f4d\u4e0d\u4ec5\u4ec5\u662f\u76ee\u6807\u68c0\u6d4b，\u800c\u662f\u6027\u80fd\u5f3a\u5927\u5168\u9762\u7684\u5de5\u5177\u5e93，\u6545\u800c\u5728\u4efb\u52a1\u7c7b\u578b\u4e0a\u540c\u65f6\u652f\u6301：\u59ff\u6001\u4f30\u8ba1\u3001\u68c0\u6d4b\u3001\u5206\u7c7b\u3001\u5206\u5272\u3001\u8ddf\u8e2a\u591a\u79cd\u7c7b\u578b，\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u8981\u8fdb\u884c\u9009\u62e9\u4f7f\u7528，\u8fd9\u91cc\u5c31\u4e0d\u518d\u8be6\u7ec6\u5c55\u5f00\u4e86\u3002<\/p>\n

\u7b80\u5355\u7684\u5b9e\u4f8b\u5b9e\u73b0\u5982\u4e0b\u6240\u793a：<\/p>\n

from ultralytics import YOLO<\/p>\n

# yolov8n
\nmodel = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
\nmodel.train(data='data\/self.yaml', epochs=100, imgsz=640)<\/p>\n

# yolov8s
\nmodel = YOLO('yolov8s.yaml').load('yolov8s.pt') # build from YAML and transfer weights
\nmodel.train(data='data\/self.yaml', epochs=100, imgsz=640)<\/p>\n

# yolov8m
\nmodel = YOLO('yolov8m.yaml').load('yolov8m.pt') # build from YAML and transfer weights
\nmodel.train(data='data\/self.yaml', epochs=100, imgsz=640)<\/p>\n

# yolov8l
\nmodel = YOLO('yolov8l.yaml').load('yolov8l.pt') # build from YAML and transfer weights
\nmodel.train(data='data\/self.yaml', epochs=100, imgsz=640)<\/p>\n

# yolov8x
\nmodel = YOLO('yolov8x.yaml').load('yolov8x.pt') # build from YAML and transfer weights
\nmodel.train(data='data\/self.yaml', epochs=100, imgsz=640) <\/p>\n

\u8fd9\u91cc\u6211\u4eec\u4f9d\u6b21\u9009\u62e9n\u3001s\u3001m\u3001l\u548cx\u4e94\u6b3e\u4e0d\u540c\u53c2\u6570\u91cf\u7ea7\u7684\u6a21\u578b\u6765\u8fdb\u884c\u5f00\u53d1\u3002<\/p>\n

\u8fd9\u91cc\u7ed9\u51fayolov8\u7684\u6a21\u578b\u6587\u4ef6\u5982\u4e0b：<\/p>\n

# Parameters
\nnc: 1 # number of classes
\nscales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
\n # [depth, width, max_channels]
\n n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
\n s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
\n m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
\n l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
\n x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs<\/p>\n

# YOLOv8.0n backbone
\nbackbone:
\n # [from, repeats, module, args]
\n – [-1, 1, Conv, [64, 3, 2]] # 0-P1\/2
\n – [-1, 1, Conv, [128, 3, 2]] # 1-P2\/4
\n – [-1, 3, C2f, [128, True]]
\n – [-1, 1, Conv, [256, 3, 2]] # 3-P3\/8
\n – [-1, 6, C2f, [256, True]]
\n – [-1, 1, Conv, [512, 3, 2]] # 5-P4\/16
\n – [-1, 6, C2f, [512, True]]
\n – [-1, 1, Conv, [1024, 3, 2]] # 7-P5\/32
\n – [-1, 3, C2f, [1024, True]]
\n – [-1, 1, SPPF, [1024, 5]] # 9<\/p>\n

# YOLOv8.0n head
\nhead:
\n – [-1, 1, nn.Upsample, [None, 2, 'nearest']]
\n – [[-1, 6], 1, Concat, [1]] # cat backbone P4
\n – [-1, 3, C2f, [512]] # 12<\/p>\n

– [-1, 1, nn.Upsample, [None, 2, 'nearest']]
\n – [[-1, 4], 1, Concat, [1]] # cat backbone P3
\n – [-1, 3, C2f, [256]] # 15 (P3\/8-small)<\/p>\n

– [-1, 1, Conv, [256, 3, 2]]
\n – [[-1, 12], 1, Concat, [1]] # cat head P4
\n – [-1, 3, C2f, [512]] # 18 (P4\/16-medium)<\/p>\n

– [-1, 1, Conv, [512, 3, 2]]
\n – [[-1, 9], 1, Concat, [1]] # cat head P5
\n – [-1, 3, C2f, [1024]] # 21 (P5\/32-large)<\/p>\n

– [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5) <\/p>\n

\u56ca\u62ec\u4e86\u4e94\u6b3e\u4e0d\u540c\u53c2\u6570\u91cf\u7ea7\u7684\u6a21\u578b\u3002\u5728\u8bad\u7ec3\u7ed3\u7b97\u4fdd\u6301\u76f8\u540c\u7684\u53c2\u6570\u8bbe\u7f6e，\u7b49\u5f85\u8bad\u7ec3\u5b8c\u6210\u540e\u6211\u4eec\u6a2a\u5411\u5bf9\u6bd4\u53ef\u89c6\u5316\u6765\u6574\u4f53\u5bf9\u6bd4\u5206\u6790\u3002<\/p>\n

\u3010Precision\u66f2\u7ebf\u3011 \u7cbe\u786e\u7387\u66f2\u7ebf（Precision Curve）\u662f\u4e00\u79cd\u7528\u4e8e\u8bc4\u4f30\u4e8c\u5206\u7c7b\u6a21\u578b\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u6027\u80fd\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3002\u5b83\u901a\u8fc7\u7ed8\u5236\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u4e4b\u95f4\u7684\u5173\u7cfb\u56fe\u6765\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6a21\u578b\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u8868\u73b0\u3002 \u7cbe\u786e\u7387（Precision）\u662f\u6307\u88ab\u6b63\u786e\u9884\u6d4b\u4e3a\u6b63\u4f8b\u7684\u6837\u672c\u6570\u5360\u6240\u6709\u9884\u6d4b\u4e3a\u6b63\u4f8b\u7684\u6837\u672c\u6570\u7684\u6bd4\u4f8b\u3002\u53ec\u56de\u7387（Recall）\u662f\u6307\u88ab\u6b63\u786e\u9884\u6d4b\u4e3a\u6b63\u4f8b\u7684\u6837\u672c\u6570\u5360\u6240\u6709\u5b9e\u9645\u4e3a\u6b63\u4f8b\u7684\u6837\u672c\u6570\u7684\u6bd4\u4f8b\u3002 \u7ed8\u5236\u7cbe\u786e\u7387\u66f2\u7ebf\u7684\u6b65\u9aa4\u5982\u4e0b： \u4f7f\u7528\u4e0d\u540c\u7684\u9608\u503c\u5c06\u9884\u6d4b\u6982\u7387\u8f6c\u6362\u4e3a\u4e8c\u8fdb\u5236\u7c7b\u522b\u6807\u7b7e\u3002\u901a\u5e38，\u5f53\u9884\u6d4b\u6982\u7387\u5927\u4e8e\u9608\u503c\u65f6，\u6837\u672c\u88ab\u5206\u7c7b\u4e3a\u6b63\u4f8b，\u5426\u5219\u5206\u7c7b\u4e3a\u8d1f\u4f8b\u3002 \u5bf9\u4e8e\u6bcf\u4e2a\u9608\u503c，\u8ba1\u7b97\u76f8\u5e94\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3002 \u5c06\u6bcf\u4e2a\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u7ed8\u5236\u5728\u540c\u4e00\u4e2a\u56fe\u8868\u4e0a，\u5f62\u6210\u7cbe\u786e\u7387\u66f2\u7ebf\u3002 \u6839\u636e\u7cbe\u786e\u7387\u66f2\u7ebf\u7684\u5f62\u72b6\u548c\u53d8\u5316\u8d8b\u52bf，\u53ef\u4ee5\u9009\u62e9\u9002\u5f53\u7684\u9608\u503c\u4ee5\u8fbe\u5230\u6240\u9700\u7684\u6027\u80fd\u8981\u6c42\u3002 \u901a\u8fc7\u89c2\u5bdf\u7cbe\u786e\u7387\u66f2\u7ebf，\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u786e\u5b9a\u6700\u4f73\u7684\u9608\u503c，\u4ee5\u5e73\u8861\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3002\u8f83\u9ad8\u7684\u7cbe\u786e\u7387\u610f\u5473\u7740\u8f83\u5c11\u7684\u8bef\u62a5，\u800c\u8f83\u9ad8\u7684\u53ec\u56de\u7387\u5219\u8868\u793a\u8f83\u5c11\u7684\u6f0f\u62a5\u3002\u6839\u636e\u5177\u4f53\u7684\u4e1a\u52a1\u9700\u6c42\u548c\u6210\u672c\u6743\u8861，\u53ef\u4ee5\u5728\u66f2\u7ebf\u4e0a\u9009\u62e9\u5408\u9002\u7684\u64cd\u4f5c\u70b9\u6216\u9608\u503c\u3002 \u7cbe\u786e\u7387\u66f2\u7ebf\u901a\u5e38\u4e0e\u53ec\u56de\u7387\u66f2\u7ebf（Recall Curve）\u4e00\u8d77\u4f7f\u7528，\u4ee5\u63d0\u4f9b\u66f4\u5168\u9762\u7684\u5206\u7c7b\u5668\u6027\u80fd\u5206\u6790，\u5e76\u5e2e\u52a9\u8bc4\u4f30\u548c\u6bd4\u8f83\u4e0d\u540c\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n

\"\"<\/p>\n

\u3010Recall\u66f2\u7ebf\u3011 \u53ec\u56de\u7387\u66f2\u7ebf（Recall Curve）\u662f\u4e00\u79cd\u7528\u4e8e\u8bc4\u4f30\u4e8c\u5206\u7c7b\u6a21\u578b\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u53ec\u56de\u7387\u6027\u80fd\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3002\u5b83\u901a\u8fc7\u7ed8\u5236\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u53ec\u56de\u7387\u548c\u5bf9\u5e94\u7684\u7cbe\u786e\u7387\u4e4b\u95f4\u7684\u5173\u7cfb\u56fe\u6765\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6a21\u578b\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u8868\u73b0\u3002 \u53ec\u56de\u7387（Recall）\u662f\u6307\u88ab\u6b63\u786e\u9884\u6d4b\u4e3a\u6b63\u4f8b\u7684\u6837\u672c\u6570\u5360\u6240\u6709\u5b9e\u9645\u4e3a\u6b63\u4f8b\u7684\u6837\u672c\u6570\u7684\u6bd4\u4f8b\u3002\u53ec\u56de\u7387\u4e5f\u88ab\u79f0\u4e3a\u7075\u654f\u5ea6（Sensitivity）\u6216\u771f\u6b63\u4f8b\u7387（True Positive Rate）\u3002 \u7ed8\u5236\u53ec\u56de\u7387\u66f2\u7ebf\u7684\u6b65\u9aa4\u5982\u4e0b： \u4f7f\u7528\u4e0d\u540c\u7684\u9608\u503c\u5c06\u9884\u6d4b\u6982\u7387\u8f6c\u6362\u4e3a\u4e8c\u8fdb\u5236\u7c7b\u522b\u6807\u7b7e\u3002\u901a\u5e38，\u5f53\u9884\u6d4b\u6982\u7387\u5927\u4e8e\u9608\u503c\u65f6，\u6837\u672c\u88ab\u5206\u7c7b\u4e3a\u6b63\u4f8b，\u5426\u5219\u5206\u7c7b\u4e3a\u8d1f\u4f8b\u3002 \u5bf9\u4e8e\u6bcf\u4e2a\u9608\u503c，\u8ba1\u7b97\u76f8\u5e94\u7684\u53ec\u56de\u7387\u548c\u5bf9\u5e94\u7684\u7cbe\u786e\u7387\u3002 \u5c06\u6bcf\u4e2a\u9608\u503c\u4e0b\u7684\u53ec\u56de\u7387\u548c\u7cbe\u786e\u7387\u7ed8\u5236\u5728\u540c\u4e00\u4e2a\u56fe\u8868\u4e0a，\u5f62\u6210\u53ec\u56de\u7387\u66f2\u7ebf\u3002 \u6839\u636e\u53ec\u56de\u7387\u66f2\u7ebf\u7684\u5f62\u72b6\u548c\u53d8\u5316\u8d8b\u52bf，\u53ef\u4ee5\u9009\u62e9\u9002\u5f53\u7684\u9608\u503c\u4ee5\u8fbe\u5230\u6240\u9700\u7684\u6027\u80fd\u8981\u6c42\u3002 \u901a\u8fc7\u89c2\u5bdf\u53ec\u56de\u7387\u66f2\u7ebf，\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u786e\u5b9a\u6700\u4f73\u7684\u9608\u503c，\u4ee5\u5e73\u8861\u53ec\u56de\u7387\u548c\u7cbe\u786e\u7387\u3002\u8f83\u9ad8\u7684\u53ec\u56de\u7387\u8868\u793a\u8f83\u5c11\u7684\u6f0f\u62a5，\u800c\u8f83\u9ad8\u7684\u7cbe\u786e\u7387\u610f\u5473\u7740\u8f83\u5c11\u7684\u8bef\u62a5\u3002\u6839\u636e\u5177\u4f53\u7684\u4e1a\u52a1\u9700\u6c42\u548c\u6210\u672c\u6743\u8861，\u53ef\u4ee5\u5728\u66f2\u7ebf\u4e0a\u9009\u62e9\u5408\u9002\u7684\u64cd\u4f5c\u70b9\u6216\u9608\u503c\u3002 \u53ec\u56de\u7387\u66f2\u7ebf\u901a\u5e38\u4e0e\u7cbe\u786e\u7387\u66f2\u7ebf（Precision Curve）\u4e00\u8d77\u4f7f\u7528，\u4ee5\u63d0\u4f9b\u66f4\u5168\u9762\u7684\u5206\u7c7b\u5668\u6027\u80fd\u5206\u6790，\u5e76\u5e2e\u52a9\u8bc4\u4f30\u548c\u6bd4\u8f83\u4e0d\u540c\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n

\"\"<\/p>\n

\u3010mAP0.5\u3011 mAP0.5，\u4e5f\u88ab\u79f0\u4e3amAP@0.5\u6216AP50，\u6307\u7684\u662f\u5f53Intersection over Union（IoU）\u9608\u503c\u4e3a0.5\u65f6\u7684\u5e73\u5747\u7cbe\u5ea6（mean Average Precision）\u3002IoU\u662f\u4e00\u4e2a\u7528\u4e8e\u8861\u91cf\u9884\u6d4b\u8fb9\u754c\u6846\u4e0e\u771f\u5b9e\u8fb9\u754c\u6846\u4e4b\u95f4\u91cd\u53e0\u7a0b\u5ea6\u7684\u6307\u6807，\u5176\u503c\u8303\u56f4\u57280\u52301\u4e4b\u95f4\u3002\u5f53IoU\u503c\u4e3a0.5\u65f6，\u610f\u5473\u7740\u9884\u6d4b\u6846\u4e0e\u771f\u5b9e\u6846\u81f3\u5c11\u670950%\u7684\u91cd\u53e0\u90e8\u5206\u3002 \u5728\u8ba1\u7b97mAP0.5\u65f6，\u9996\u5148\u4f1a\u4e3a\u6bcf\u4e2a\u7c7b\u522b\u8ba1\u7b97\u6240\u6709\u56fe\u7247\u7684AP（Average Precision），\u7136\u540e\u5c06\u6240\u6709\u7c7b\u522b\u7684AP\u503c\u6c42\u5e73\u5747，\u5f97\u5230mAP0.5\u3002AP\u662fPrecision-Recall Curve\u66f2\u7ebf\u4e0b\u9762\u7684\u9762\u79ef，\u8fd9\u4e2a\u9762\u79ef\u8d8a\u5927，\u8bf4\u660eAP\u7684\u503c\u8d8a\u5927，\u7c7b\u522b\u7684\u68c0\u6d4b\u7cbe\u5ea6\u5c31\u8d8a\u9ad8\u3002 mAP0.5\u4e3b\u8981\u5173\u6ce8\u6a21\u578b\u5728IoU\u9608\u503c\u4e3a0.5\u65f6\u7684\u6027\u80fd，\u5f53mAP0.5\u7684\u503c\u5f88\u9ad8\u65f6，\u8bf4\u660e\u7b97\u6cd5\u80fd\u591f\u51c6\u786e\u68c0\u6d4b\u5230\u7269\u4f53\u7684\u4f4d\u7f6e，\u5e76\u4e14\u5c06\u5176\u4e0e\u771f\u5b9e\u6807\u6ce8\u6846\u7684IoU\u503c\u8d85\u8fc7\u4e86\u9608\u503c0.5\u3002<\/p>\n

\"\"<\/p>\n

\u3010mAP0.5:0.95\u3011 mAP0.5:0.95，\u4e5f\u88ab\u79f0\u4e3amAP@[0.5:0.95]\u6216AP@[0.5:0.95]，\u8868\u793a\u5728IoU\u9608\u503c\u4ece0.5\u52300.95\u53d8\u5316\u65f6，\u53d6\u5404\u4e2a\u9608\u503c\u5bf9\u5e94\u7684mAP\u7684\u5e73\u5747\u503c\u3002\u5177\u4f53\u6765\u8bf4，\u5b83\u4f1a\u5728IoU\u9608\u503c\u4ece0.5\u5f00\u59cb，\u4ee50.05\u4e3a\u6b65\u957f，\u9010\u6b65\u589e\u52a0\u52300.95，\u5e76\u5728\u6bcf\u4e2a\u9608\u503c\u4e0b\u8ba1\u7b97mAP，\u7136\u540e\u5c06\u8fd9\u4e9bmAP\u503c\u6c42\u5e73\u5747\u3002 \u8fd9\u4e2a\u6307\u6807\u8003\u8651\u4e86\u591a\u4e2aIoU\u9608\u503c\u4e0b\u7684\u5e73\u5747\u7cbe\u5ea6，\u4ece\u800c\u66f4\u5168\u9762\u3001\u66f4\u51c6\u786e\u5730\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002\u5f53mAP0.5:0.95\u7684\u503c\u5f88\u9ad8\u65f6，\u8bf4\u660e\u7b97\u6cd5\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u68c0\u6d4b\u7ed3\u679c\u5747\u975e\u5e38\u51c6\u786e，\u8986\u76d6\u9762\u5e7f，\u53ef\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u573a\u666f\u548c\u5e94\u7528\u9700\u6c42\u3002 \u5bf9\u4e8e\u4e00\u4e9b\u9700\u6c42\u6bd4\u8f83\u9ad8\u7684\u573a\u5408，\u6bd4\u5982\u5b89\u5168\u76d1\u63a7\u7b49\u9886\u57df，\u9700\u8981\u4fdd\u8bc1\u9ad8\u7684\u51c6\u786e\u7387\u548c\u53ec\u56de\u7387，\u8fd9\u65f6mAP0.5:0.95\u53ef\u80fd\u66f4\u9002\u5408\u4f5c\u4e3a\u6a21\u578b\u7684\u8bc4\u4ef7\u6807\u51c6\u3002 \u7efc\u4e0a\u6240\u8ff0，mAP0.5\u548cmAP0.5:0.95\u90fd\u662f\u7528\u4e8e\u8bc4\u4f30\u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6307\u6807，\u4f46\u5b83\u4eec\u7684\u5173\u6ce8\u70b9\u6709\u6240\u4e0d\u540c\u3002mAP0.5\u4e3b\u8981\u5173\u6ce8\u6a21\u578b\u5728IoU\u9608\u503c\u4e3a0.5\u65f6\u7684\u6027\u80fd，\u800cmAP0.5:0.95\u5219\u8003\u8651\u4e86\u591a\u4e2aIoU\u9608\u503c\u4e0b\u7684\u5e73\u5747\u7cbe\u5ea6，\u4ece\u800c\u66f4\u5168\u9762\u3001\u66f4\u51c6\u786e\u5730\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n

\"\"<\/p>\n

\u3010loss\u66f2\u7ebf\u3011 \u5728\u6df1\u5ea6\u5b66\u4e60\u7684\u8bad\u7ec3\u8fc7\u7a0b\u4e2d，loss\u51fd\u6570\u7528\u4e8e\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u7ed3\u679c\u4e0e\u5b9e\u9645\u6807\u7b7e\u4e4b\u95f4\u7684\u5dee\u5f02\u3002loss\u66f2\u7ebf\u5219\u662f\u901a\u8fc7\u8bb0\u5f55\u6bcf\u4e2aepoch（\u6216\u8005\u8fed\u4ee3\u6b65\u6570）\u7684loss\u503c，\u5e76\u5c06\u5176\u4ee5\u56fe\u5f62\u5316\u7684\u65b9\u5f0f\u5c55\u73b0\u51fa\u6765，\u4ee5\u4fbf\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u6a21\u578b\u7684\u8bad\u7ec3\u8fc7\u7a0b\u3002<\/p>\n

\"\"<\/p>\n

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\"\"<\/p>\n

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\u3010\u79bb\u7ebf\u63a8\u7406\u5b9e\u4f8b\u3011<\/p>\n

\"\"<\/p>\n

\u3010Batch\u5b9e\u4f8b\u3011<\/p>\n

\"\"<\/p>\n

\u3010\u6df7\u6dc6\u77e9\u9635\u3011<\/p>\n

\"\"<\/p>\n

\u3010F1\u503c\u66f2\u7ebf\u3011<\/p>\n

\"\"<\/p>\n

\u3010Precision\u66f2\u7ebf\u3011<\/p>\n

\"\"<\/p>\n

\u3010PR\u66f2\u7ebf\u3011<\/p>\n

\"\"<\/p>\n

\u3010Recall\u66f2\u7ebf\u3011<\/p>\n

\"\"<\/p>\n

\u3010\u8bad\u7ec3\u53ef\u89c6\u5316\u3011<\/p>\n

\"\"<\/p>\n

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