git --version<\/code>\uc744 \uc785\ub825\ud558\uc5ec \ud655\uc778\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n2.2 YOLOv5 \ud074\ub860\ud558\uae30<\/h3>\n
YOLOv5\ub294 GitHub\uc5d0\uc11c \uad00\ub9ac\ub418\uace0 \uc788\uc73c\uba70, \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 YOLOv5 \ub9ac\ud3ec\uc9c0\ud1a0\ub9ac\ub97c \ud074\ub860\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
git clone https:\/\/github.com\/ultralytics\/yolov5.git\ncd yolov5<\/code><\/pre>\n2.3 \ud544\uc218 \ud328\ud0a4\uc9c0 \uc124\uce58\ud558\uae30<\/h3>\n
YOLOv5\ub294 \uc5ec\ub7ec Python \ud328\ud0a4\uc9c0\ub97c \ud544\uc694\ub85c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud544\uc218 \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\n
pip install -U -r requirements.txt<\/code><\/pre>\n2.4 CUDA \uc124\uce58 (\uc120\ud0dd \uc0ac\ud56d)<\/h3>\n
GPU \uac00\uc18d\uc744 \uc0ac\uc6a9\ud558\ub824\uba74 CUDA\uc640 cuDNN\uc744 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. NVIDIA\uc758 \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8\uc5d0\uc11c \uc790\uc2e0\uc758 GPU\uc5d0 \ub9de\ub294 \ubc84\uc804\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \uc124\uce58\ud558\uc138\uc694. CUDA\ub97c \uc124\uce58\ud55c \uacbd\uc6b0, PyTorch\ub97c CUDA \uc9c0\uc6d0 \ubc84\uc804\uc73c\ub85c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub85c PyTorch\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install torch torchvision torchaudio --extra-index-url https:\/\/download.pytorch.org\/whl\/cu117<\/code><\/pre>\n\uadf8 \uc678\uc5d0\ub3c4 CPU \ubc84\uc804\uc758 PyTorch\ub97c \uc124\uce58\ud558\uace0\uc790 \ud55c\ub2e4\uba74 \ub2e8\uc21c\ud788 pip install torch torchvision torchaudio<\/code>\ub97c \uc0ac\uc6a9\ud558\uba74 \ub429\ub2c8\ub2e4.<\/p>\n3. YOLO \ubaa8\ub378 \ub85c\ub4dc \ubc0f \uc124\uc815\ud558\uae30<\/h2>\n
YOLOv5\uac00 \uc131\uacf5\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub2e4\uba74, \ub2e4\uc74c \ub2e8\uacc4\ub294 \uc0ac\uc804 \ud559\uc2b5\ub41c YOLO \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \uc124\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
3.1 \ubaa8\ub378 \ub85c\ub4dc\ud558\uae30<\/h3>\n
YOLOv5\uc5d0\uc11c \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc740 \uae30\ubcf8\uc801\uc73c\ub85c yolov5s.pt<\/code>, yolov5m.pt<\/code>, yolov5l.pt<\/code>, yolov5x.pt<\/code>\uc758 \ub124 \uac00\uc9c0 \ubc84\uc804\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c s<\/code>, m<\/code>, l<\/code>, x<\/code>\ub294 \uac01\uac01 small, medium, large, extra large\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \ud1b5\ud574 YOLOv5 \ubaa8\ub378\uc744 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\nimport torch\n\n# \ubaa8\ub378 \ub85c\ub4dc\nmodel = torch.hub.load('ultralytics\/yolov5', 'yolov5s', pretrained=True)<\/code><\/pre>\n3.2 \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4 \ud0d0\uc9c0\ud558\uae30<\/h3>\n
\ub85c\ub4dc\ud55c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \ubc29\ubc95\uc740 \uac04\ub2e8\ud569\ub2c8\ub2e4. \uba3c\uc800 \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544\uc11c \ubaa8\ub378\uc5d0 \uc804\ub2ec\ud558\uba74 \ub429\ub2c8\ub2e4. \ud0d0\uc9c0\ub41c \uac1d\uccb4\ub294 \ubc14\uc6b4\ub529 \ubc15\uc2a4\uc640 \ub808\uc774\ube14\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/p>\n
# \uc774\ubbf8\uc9c0 \uacbd\ub85c\nimg_path = 'path\/to\/your\/image.jpg'\n\n# \uc774\ubbf8\uc9c0 \ub85c\ub4dc \ubc0f \uac1d\uccb4 \ud0d0\uc9c0\nresults = model(img_path)\n\n# \uacb0\uacfc \ucd9c\ub825\nresults.print() # \ud0d0\uc9c0\ub41c \uac1d\uccb4 \uc815\ubcf4 \ucd9c\ub825\nresults.show() # \ud0d0\uc9c0\ub41c \uac1d\uccb4\uac00 \ud45c\uc2dc\ub41c \uc774\ubbf8\uc9c0 \ucd9c\ub825<\/code><\/pre>\n\ud0d0\uc9c0\ub41c \uac1d\uccb4\ub294 \uc544\ub798\ucc98\ub7fc \ud45c\uc2dc\ub429\ub2c8\ub2e4. \uac01 \ubc14\uc6b4\ub529 \ubc15\uc2a4\uc5d0\ub294 \ud074\ub798\uc2a4ID\uc640 \uc2e0\ub8b0\ub3c4\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/p>\n
3.3 \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc5d0\uc11c \uac1d\uccb4 \ud0d0\uc9c0\ud558\uae30<\/h3>\n
YOLOv5\ub294 \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc5d0\uc11c\ub3c4 \uac1d\uccb4 \ud0d0\uc9c0\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \ud65c\uc6a9\ud558\uc5ec \ub3d9\uc601\uc0c1\uc5d0\uc11c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
video_path = 'path\/to\/your\/video.mp4'\n\n# \ub3d9\uc601\uc0c1 \ud30c\uc77c\uc5d0\uc11c \uac1d\uccb4 \ud0d0\uc9c0\nresults = model(video_path) \n\n# \uacb0\uacfc\ub97c \ucc3d\uc5d0\uc11c \ubcf4\uc5ec\uc8fc\uae30\nresults.show() # \ud0d0\uc9c0 \uacb0\uacfc \ucd9c\ub825<\/code><\/pre>\n3.4 \uc2e4\uc2dc\uac04 \uac1d\uccb4 \ud0d0\uc9c0<\/h3>\n
\uc6f9\ucea0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc2dc\uac04\uc73c\ub85c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \uac83\ub3c4 \uac00\ub2a5\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 OpenCV \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc2e4\uc2dc\uac04 \uac1d\uccb4 \ud0d0\uc9c0\ub97c \uad6c\ud604\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
import cv2\n\n# \uc6f9\ucea0 \uc5f4\uae30\ncap = cv2.VideoCapture(0)\n\nwhile cap.isOpened():\n ret, frame = cap.read() # \uc6f9\ucea0\uc73c\ub85c\ubd80\ud130 \ud504\ub808\uc784 \uc77d\uae30\n if not ret:\n break\n \n # \ud0d0\uc9c0\ud558\uae30\n results = model(frame)\n \n # \uacb0\uacfc\ub97c \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud658\n annotated_frame = results.render()[0]\n \n # \uc774\ubbf8\uc9c0 \ubcf4\uc5ec\uc8fc\uae30\n cv2.imshow('YOLOv5 Webcam Detection', annotated_frame)\n \n if cv2.waitKey(1) & 0xFF == ord('q'): # 'q' \ud0a4\ub97c \ub20c\ub7ec \uc885\ub8cc\n break\n\ncap.release()\ncv2.destroyAllWindows()<\/code><\/pre>\n4. YOLOv5\uc758 \ub2e4\uc591\ud55c \uc124\uc815<\/h2>\n
YOLOv5\ub294 \ub2e4\uc591\ud55c \uc635\uc158\uc744 \uc81c\uacf5\ud558\uc5ec \uc0ac\uc6a9\uc790 \ub9de\ucda4\ud615 \uac1d\uccb4 \ud0d0\uc9c0\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc124\uc815\ud560 \uc218 \uc788\ub294 \uc8fc\uc694 \uc778\uc790\ub4e4\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- conf_thres<\/strong>: \uc2e0\ub8b0\ub3c4 \uc784\uacc4\uac12. \ud0d0\uc9c0 \uacb0\uacfc\uc758 \uc2e0\ub8b0\ub3c4\uac00 \uc774 \uac12 \uc774\uc0c1\uc77c \uacbd\uc6b0\ub9cc \uacb0\uacfc\ub85c \ub098\uc635\ub2c8\ub2e4.<\/li>\n
- iou_thres<\/strong>: IoU(Intersection over Union) \uc784\uacc4\uac12. \ud0d0\uc9c0\ub41c \uac1d\uccb4 \uac04 \uc911\ubcf5\uc744 \uc904\uc774\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/li>\n
- device<\/strong>: \ubaa8\ub378\uc744 \uc2e4\ud589\ud560 \uc7a5\uce58(GPU\/CPU)\ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.<\/li>\n
- half<\/strong>: \ubc18 \uc815\ubc00\ub3c4(half precision) \uc2e4\ud589 \uc5ec\ubd80. GPU\uc5d0\uc11c \uba54\ubaa8\ub9ac\ub97c \uc808\uc57d\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc774\ub97c \ud1b5\ud574 \ud0d0\uc9c0\uc758 \uc131\ub2a5\uc744 \uc870\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc774\ub7ec\ud55c \uc778\uc790\ub97c \ud65c\uc6a9\ud558\uc5ec \uac1d\uccb4 \ud0d0\uc9c0\ub97c \uc218\ud589\ud558\ub294 \uc608\uc785\ub2c8\ub2e4.<\/p>\n
results = model(img_path, conf_thres=0.5, iou_thres=0.4, device='cuda:0') # GPU \uc0ac\uc6a9 \uc2dc<\/code><\/pre>\n4.1 \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378 \ubcc0\ud615<\/h3>\n
YOLOv5\ub294 \ud6c8\ub828\ub41c \ubaa8\ub378 \uc678\uc5d0\ub3c4 \uc6d0\ud558\ub294 \ub370\uc774\ud130\uc14b\uc73c\ub85c Fine-tuning\uc744 \ud558\uc5ec \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\ub3c4 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ud2b9\uc815 \uac1d\uccb4 \ud0d0\uc9c0 \uc131\ub2a5\uc744 \ub354\uc6b1 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5. \uacb0\ub860<\/h2>\n
YOLOv5\ub294 \uc124\uce58\uc640 \uc0ac\uc6a9\uc774 \uac04\ud3b8\ud558\uba74\uc11c\ub3c4 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 demonstrably \uc81c\uacf5\ud569\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 YOLOv5 \uc124\uce58 \ubc29\ubc95\uacfc \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378 \ub85c\ub4dc, \uac1d\uccb4 \ud0d0\uc9c0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uc600\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc124\uc815\uc744 \ud1b5\ud574 \uc131\ub2a5\uc744 \uc870\uc815\ud558\uc5ec \ucd5c\uc801\uc758 \uacb0\uacfc\ub97c \uc774\ub04c\uc5b4\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
YOLO\ub97c \uc774\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc5f0\uad6c\ub098 \ud504\ub85c\uc81d\ud2b8\uc5d0 \ud65c\uc6a9\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4. \ucd94\ud6c4 YOLO\ub97c \uc774\uc6a9\ud55c \uc2e4\uc804 \ud504\ub85c\uc81d\ud2b8\ub098 \ub370\uc774\ud130\uc14b \uc900\ube44 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c\ub3c4 \ub2e4\ub8e8\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n
\ucc38\uace0:<\/strong> YOLO \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\ub294 \ub370 \uc788\uc5b4 \ub77c\uc774\uc13c\uc2a4 \ubc0f \uc800\uc791\uad8c \ubb38\uc81c\uc5d0 \uc720\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. YOLOv5\ub294 Ultralytics\uc758 \uc800\uc791\ubb3c\uc774\uba70, \uc0ac\uc6a9 \uc2dc \uc870\uac74\uc744 \uc900\uc218\ud574\uc57c \ud569\ub2c8\ub2e4.\n<\/div>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 YOLO(You Only Look Once) \uac1d\uccb4 \ud0d0\uc9c0 \ubaa8\ub378\uc744 \uc124\uce58\ud558\uace0 \uc124\uc815\ud558\ub294 \ubc29\ubc95, \uadf8\ub9ac\uace0 \uc0ac\uc804 \ud559\uc2b5\ub41c YOLO \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uc5ec \uc0ac\uc6a9\ud574\ubcf4\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. YOLO\ub294 \uadf8 \uc131\ub2a5\uacfc \ud6a8\uc728\uc131 \ub355\ubd84\uc5d0 \uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc778\uae30 \uc788\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub098 \ub3d9\uc601\uc0c1\uc5d0\uc11c \uc2e4\uc2dc\uac04\uc73c\ub85c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. YOLO\ub780 \ubb34\uc5c7\uc778\uac00? YOLO\ub294 \ud55c \ubc88\uc758 \uc2e0\uacbd\ub9dd \ud1b5\uacfc\ub85c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c, … \ub354 \ubcf4\uae30 “YOLO \uc124\uce58 \ubc0f \uc124\uc815, \uc0ac\uc804 \ud559\uc2b5\ub41c YOLO \ubaa8\ub378 \ub85c\ub4dc \ubc0f \uc124\uc815 \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-38907","post","type-post","status-publish","format-standard","hentry","category-yolo"],"yoast_head":"\nYOLO \uc124\uce58 \ubc0f \uc124\uc815, \uc0ac\uc804 \ud559\uc2b5\ub41c YOLO \ubaa8\ub378 \ub85c\ub4dc \ubc0f \uc124\uc815 \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\/38907\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"YOLO \uc124\uce58 \ubc0f \uc124\uc815, \uc0ac\uc804 \ud559\uc2b5\ub41c YOLO \ubaa8\ub378 \ub85c\ub4dc \ubc0f \uc124\uc815 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 YOLO(You Only Look Once) \uac1d\uccb4 \ud0d0\uc9c0 \ubaa8\ub378\uc744 \uc124\uce58\ud558\uace0 \uc124\uc815\ud558\ub294 \ubc29\ubc95, \uadf8\ub9ac\uace0 \uc0ac\uc804 \ud559\uc2b5\ub41c YOLO \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uc5ec \uc0ac\uc6a9\ud574\ubcf4\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. YOLO\ub294 \uadf8 \uc131\ub2a5\uacfc \ud6a8\uc728\uc131 \ub355\ubd84\uc5d0 \uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc778\uae30 \uc788\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub098 \ub3d9\uc601\uc0c1\uc5d0\uc11c \uc2e4\uc2dc\uac04\uc73c\ub85c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. YOLO\ub780 \ubb34\uc5c7\uc778\uac00? 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