YOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning), Custom Dataset\uc744 \uc774\uc6a9\ud55c YOLO \ud559\uc2b5 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/38885\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"YOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning), Custom Dataset\uc744 \uc774\uc6a9\ud55c YOLO \ud559\uc2b5 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 YOLO(You Only Look Once) \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning) \uac1c\ub150\uacfc Custom Dataset\uc744 \uc0ac\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ucef4\ud4e8\ud130 \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uac1d\uccb4 \uc778\uc2dd\uacfc \ubd84\ub958 \uc791\uc5c5\uc5d0\uc11c YOLO \ubaa8\ub378\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. 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{"id":38885,"date":"2024-11-02T01:39:47","date_gmt":"2024-11-02T01:39:47","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=38885"},"modified":"2024-11-26T06:43:04","modified_gmt":"2024-11-26T06:43:04","slug":"yolo-%eb%aa%a8%eb%8d%b8%ec%9d%98-%ec%a0%84%ec%9d%b4-%ed%95%99%ec%8a%b5transfer-learning-custom-dataset%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-yolo-%ed%95%99%ec%8a%b5-%eb%b0%a9%eb%b2%95","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/38885\/","title":{"rendered":"YOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning), Custom Dataset\uc744 \uc774\uc6a9\ud55c YOLO \ud559\uc2b5 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 YOLO(You Only Look Once) \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning) \uac1c\ub150\uacfc Custom Dataset\uc744 \uc0ac\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ucef4\ud4e8\ud130 \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uac1d\uccb4 \uc778\uc2dd\uacfc \ubd84\ub958 \uc791\uc5c5\uc5d0\uc11c YOLO \ubaa8\ub378\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. YOLO \ubaa8\ub378\uc740 \ube60\ub978 \uc2e4\uc2dc\uac04 \uac80\ucd9c \uc131\ub2a5\uc73c\ub85c \uc720\uba85\ud558\uc9c0\ub9cc, \ud2b9\uc815 \ub370\uc774\ud130\uc14b\uc5d0 \ub9de\uac8c Fine-tuning\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n

\uc804\uc774 \ud559\uc2b5(Transfer Learning) \uac1c\uc694<\/h2>\n
\uc804\uc774 \ud559\uc2b5\uc774\ub780 \uc774\ubbf8 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \uc7ac\ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \ub370\uc774\ud130\uac00 \uc801\uc740 \uacbd\uc6b0 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4. YOLO\uc640 \uac19\uc740 \ub300\uaddc\ubaa8 \ubaa8\ub378\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c \ubbf8\ub9ac \ud559\uc2b5\ub418\uc5b4 \uc788\uc73c\uba70, \uc774\ub7ec\ud55c \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ud2b9\uc815 \uc791\uc5c5\uc5d0 \ub9de\uac8c \uc218\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc804\uc774 \ud559\uc2b5\uc758 \uc7a5\uc810<\/h3>\n

\ud559\uc2b5 \uc2dc\uac04 \ub2e8\ucd95: \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c \ud559\uc2b5\ud55c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ubbc0\ub85c \ube60\ub974\uac8c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\uc801\uc740 \ub370\uc774\ud130\ub7c9\uc73c\ub85c\ub3c4 \uc131\ub2a5 \ud5a5\uc0c1: \uc791\uc740 \ub370\uc774\ud130\uc14b\uc73c\ub85c\ub3c4 \uc88b\uc740 \uc131\ub2a5\uc744 \ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\ub354 \uc88b\uc740 \uc77c\ubc18\ud654 \uc131\ub2a5: \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc740 \uc774\ubbf8 \ub9ce\uc740 \ud328\ud134\uc744 \ud559\uc2b5\ud588\uae30 \ub54c\ubb38\uc5d0 \uc77c\ubc18\ud654 \ub2a5\ub825\uc774 \ub6f0\uc5b4\ub0a9\ub2c8\ub2e4.<\/li>\n<\/ul>\n
YOLO \ubaa8\ub378 \uac1c\uc694<\/h2>\n
YOLO\ub294 \ud55c \ubc88\uc758 \uc2e0\uacbd\ub9dd \ud3c9\uac00\ub85c \uc804\uccb4 \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4\ub97c \ub3d9\uc2dc\uc5d0 \ud0d0\uc9c0\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. YOLO\uc758 \uad6c\uc870\ub294 \ub2e4\uc18c \ubcf5\uc7a1\ud558\uc9c0\ub9cc, \uae30\ubcf8\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0\ub97c \uaca9\uc790(grid)\ub85c \ub098\ub204\uace0 \uac01 \uaca9\uc790\uc5d0\uc11c \ud2b9\uc815 \uac1d\uccb4\uac00 \uc788\uc744 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. YOLOv5 \ubaa8\ub378\uc774 \ud604\uc7ac \uac00\uc7a5 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \ud30c\uc774\uc36c\uc5d0\uc11c YOLOv5\ub85c \uc804\uc774 \ud559\uc2b5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
YOLO\uc758 \uae30\ubcf8 \uac1c\ub150<\/h3>\n
- \uaca9\uc790(cell): \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c SxS\uc758 \uadf8\ub9ac\ub4dc\ub85c \ub098\ub215\ub2c8\ub2e4.<\/li>\n
- \ubc14\uc6b4\ub529 \ubc15\uc2a4 \ubc0f \uc810\uc218: \uac01 \uaca9\uc790\uc5d0\uc11c \ucd5c\ub300 B\uac1c\uc758 \ubc14\uc6b4\ub529 \ubc15\uc2a4\ub97c \uc608\uce21\ud558\uace0, \uac01 \ubc15\uc2a4\uc5d0 \ub300\ud574 \uc2e0\ub8b0\ub3c4 \uc810\uc218\ub97c \ubd80\uc5ec\ud569\ub2c8\ub2e4.<\/li>\n
- \ud074\ub798\uc2a4 \ud655\ub960: \uac01 \uaca9\uc790\uc5d0 \ub300\ud574 K\uac1c\uc758 \ud074\ub798\uc2a4 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
  Custom Dataset \uc900\ube44\ud558\uae30<\/h2>\n
  YOLO \ubaa8\ub378\uc744 \uc7ac\ud559\uc2b5\ud558\uae30 \uc704\ud574\uc11c\ub294 Custom Dataset\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 \uc774\ubbf8\uc9c0\uc640 \uac01 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \uc8fc\uc11d(annotation) \ud30c\uc77c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc8fc\uc11d \ud30c\uc77c\uc740 \uc77c\ubc18\uc801\uc73c\ub85c YOLO \ud615\uc2dd\uc73c\ub85c \uc900\ube44\ub429\ub2c8\ub2e4.<\/p>\n
  YOLO \ub370\uc774\ud130\uc14b \ud3ec\ub9f7<\/h3>\n
  YOLO\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\uc14b \ud3ec\ub9f7\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
  - \uc774\ubbf8\uc9c0 \ud30c\uc77c<\/strong>: \ud559\uc2b5\uc5d0 \uc0ac\uc6a9\ud560 \uc774\ubbf8\uc9c0 \ud30c\uc77c\ub4e4.<\/li>\n
  - \ub77c\ubca8 \ud30c\uc77c<\/strong>: \uac01 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ubc14\uc6b4\ub529 \ubc15\uc2a4\uc758 \uc815\ubcf4\ub97c \ud3ec\ud568\ud558\ub294 \ud14d\uc2a4\ud2b8 \ud30c\uc77c. \uac01 \uc904\uc740 \ud615\uc2dd\uc774 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4:\n
    \ud074\ub798\uc2a4_id x_center y_center width height<\/code><\/pre>\n \uc5ec\uae30\uc11c (x_center, y_center)\ub294 \ud574\ub2f9 \ubc14\uc6b4\ub529 \ubc15\uc2a4\uc758 \uc911\uc2ec \uc88c\ud45c, width\uc640 height\ub294 \ubc14\uc6b4\ub529 \ubc15\uc2a4\uc758 \ub108\ube44\uc640 \ub192\uc774\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n <\/li>\n<\/ul>\n \uc608\uc81c \ub370\uc774\ud130\uc14b<\/h3>\n\uc608\ub97c \ub4e4\uc5b4, \uc790\ub3d9\ucc28\uc640 \uc0ac\ub78c\uc744 \ud0d0\uc9c0\ud558\ub294 Custom Dataset\uc744 \uc900\ube44\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc0d8\ud50c \ub370\uc774\ud130\uc14b\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/a><\/p>\n YOLOv5 \uc124\uce58 \ubc0f \ud658\uacbd \uc124\uc815<\/h2>\nYOLOv5\ub97c \uc124\uce58\ud558\uae30 \uc704\ud574\uc11c\ub294 PyTorch\uc640 \uae30\ud0c0 \uc885\uc18d\uc131 \ub77c\uc774\ube0c\ub7ec\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \ub2e8\uacc4\ub97c \ud1b5\ud574 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n1. \ud658\uacbd \uc124\uc815<\/h3>\ngit clone https:\/\/github.com\/ultralytics\/yolov5.git\ncd yolov5\npip install -r requirements.txt<\/code><\/pre>\n2. \ub370\uc774\ud130\uc14b \uad6c\uc131<\/h3>\nCustom Dataset \ub514\ub809\ud1a0\ub9ac \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc124\uc815\ud569\ub2c8\ub2e4:<\/p>\nyolov5\/\n\u2502\n\u251c\u2500\u2500 datasets\/\n\u2502 \u251c\u2500\u2500 my_custom_dataset\/\n\u2502 \u2502 \u251c\u2500\u2500 images\/\n\u2502 \u2502 \u251c\u2500\u2500 labels\/\n\u2502 \u2502 \u2514\u2500\u2500 data.yaml\n<\/code><\/pre>\n3. data.yaml \ud30c\uc77c \uc608\uc2dc<\/h3>\ndata.yaml \ud30c\uc77c\uc740 YOLO \ubaa8\ub378\uc5d0 \ub370\uc774\ud130\uc14b \uc815\ubcf4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud30c\uc77c\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\uc2dd\uc744 \uac00\uc838\uc57c \ud569\ub2c8\ub2e4:<\/p>\ntrain: datasets\/my_custom_dataset\/images\/train\nval: datasets\/my_custom_dataset\/images\/val\n\nnc: 2\nnames: ['car', 'person']<\/code><\/pre>\nYOLOv5 \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h2>\n\uc704\uc758 \uc900\ube44\uac00 \ub05d\ub0ac\ub2e4\uba74, YOLOv5 \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\uc744 \uc2dc\uc791\ud569\ub2c8\ub2e4.<\/p>\n\ud6c8\ub828 \uba85\ub839\uc5b4<\/h3>\npython train.py --img 640 --batch 16 --epochs 50 --data datasets\/my_custom_dataset\/data.yaml --weights yolov5s.pt<\/code><\/pre>\n\uc5ec\uae30\uc11c \uac01 \uc778\uc790\uc758 \uc758\ubbf8\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n \n--img<\/code>: \uc785\ub825 \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30. \uc77c\ubc18\uc801\uc73c\ub85c 640 \ub610\ub294 416\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n --batch<\/code>: \ubc30\uce58 \uc0ac\uc774\uc988\uc785\ub2c8\ub2e4.<\/li>\n --epochs<\/code>: \uc804\uccb4 \ud6c8\ub828 \ubc18\ubcf5 \uc218\uc785\ub2c8\ub2e4.<\/li>\n --data<\/code>: \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c yaml \ud30c\uc77c \uacbd\ub85c\uc785\ub2c8\ub2e4.<\/li>\n --weights<\/code>: \ucd08\uae30 \uac00\uc911\uce58 \ud30c\uc77c\uc785\ub2c8\ub2e4. \ucd5c\uc804\uc758 pretrained weights\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n\ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n\ud6c8\ub828\uc774 \uc644\ub8cc\ub418\uba74, \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n python val.py --weights runs\/train\/exp\/weights\/best.pt --data datasets\/my_custom_dataset\/data.yaml --img 640<\/code><\/pre>\n\ud3c9\uac00 \uacb0\uacfc \ud655\uc778<\/h3>\n\uc131\ub2a5 \ud3c9\uac00\ub294 mAP(Mean Average Precision) \ubc0f IOU(Intersection Over Union) \ub4f1\uc758 \uc9c0\ud45c\ub97c \ud1b5\ud574 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5d0\ud3ed \ubc18\ubcf5 \ud6c4 \ud2b8\ub808\uc774\ub2dd \ub85c\uadf8\uc5d0 \uc774\ub7ec\ud55c \uc9c0\ud45c\uac00 \uae30\ub85d\ub429\ub2c8\ub2e4.<\/p>\n \uacb0\ub860<\/h2>\nYOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5\uc744 \ud1b5\ud574 Custom Dataset\uc744 \uc774\uc6a9\ud558\uc5ec \uc6d0\ud558\ub294 \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. YOLOv5\uc758 \uae30\ub2a5\uc744 \ud65c\uc6a9\ud558\uc5ec \ub9ce\uc740 \uacbd\uc6b0\uc5d0 \ud6cc\ub96d\ud55c \uc131\ub2a5\uc744 \uc5bb\uc744 \uc218 \uc788\uc74c\uc744 \uc54c\uc558\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uae30\ubcf8\uc801\uc778 \uacfc\uc815\ub9cc\uc744 \ub2e4\ub8e8\uc5c8\uc9c0\ub9cc, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc774\ub098 \ub370\uc774\ud130 \uc99d\uac15 \ub4f1\uc758 \ucd94\uac00\uc801\uc778 \uae30\uc220\uc744 \ud1b5\ud574 \uc131\ub2a5\uc744 \ub354\uc6b1 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ucc38\uace0 \uc790\ub8cc<\/h2>\n\nYOLOv5 GitHub Repository<\/a><\/li>\n PyTorch Official Site<\/a><\/li>\n YOLO in Python(Analytics Vidhya)<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 YOLO(You Only Look Once) \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning) \uac1c\ub150\uacfc Custom Dataset\uc744 \uc0ac\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ucef4\ud4e8\ud130 \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uac1d\uccb4 \uc778\uc2dd\uacfc \ubd84\ub958 \uc791\uc5c5\uc5d0\uc11c YOLO \ubaa8\ub378\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. YOLO \ubaa8\ub378\uc740 \ube60\ub978 \uc2e4\uc2dc\uac04 \uac80\ucd9c \uc131\ub2a5\uc73c\ub85c \uc720\uba85\ud558\uc9c0\ub9cc, \ud2b9\uc815 \ub370\uc774\ud130\uc14b\uc5d0 \ub9de\uac8c Fine-tuning\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc804\uc774 \ud559\uc2b5(Transfer Learning) \uac1c\uc694 \uc804\uc774 \ud559\uc2b5\uc774\ub780 \uc774\ubbf8 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 … \ub354 \ubcf4\uae30 “YOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning), Custom Dataset\uc744 \uc774\uc6a9\ud55c YOLO \ud559\uc2b5 \ubc29\ubc95”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[189],"tags":[],"class_list":["post-38885","post","type-post","status-publish","format-standard","hentry","category-yolo"],"yoast_head":"\nYOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning), Custom Dataset\uc744 \uc774\uc6a9\ud55c YOLO \ud559\uc2b5 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/38885\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"YOLO \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning), Custom Dataset\uc744 \uc774\uc6a9\ud55c YOLO \ud559\uc2b5 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 YOLO(You Only Look Once) \ubaa8\ub378\uc758 \uc804\uc774 \ud559\uc2b5(Transfer Learning) \uac1c\ub150\uacfc Custom Dataset\uc744 \uc0ac\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ucef4\ud4e8\ud130 \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c\ub294 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uac1d\uccb4 \uc778\uc2dd\uacfc \ubd84\ub958 \uc791\uc5c5\uc5d0\uc11c YOLO \ubaa8\ub378\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. 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2. \ub370\uc774\ud130\uc14b \uad6c\uc131<\/h3>\nCustom Dataset \ub514\ub809\ud1a0\ub9ac \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc124\uc815\ud569\ub2c8\ub2e4:<\/p>\n

3. data.yaml \ud30c\uc77c \uc608\uc2dc<\/h3>\ndata.yaml \ud30c\uc77c\uc740 YOLO \ubaa8\ub378\uc5d0 \ub370\uc774\ud130\uc14b \uc815\ubcf4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud30c\uc77c\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\uc2dd\uc744 \uac00\uc838\uc57c \ud569\ub2c8\ub2e4:<\/p>\n