{"id":38846,"date":"2024-11-02T01:39:32","date_gmt":"2024-11-02T01:39:32","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=38846"},"modified":"2024-11-26T06:43:12","modified_gmt":"2024-11-26T06:43:12","slug":"yolo-%eb%aa%a8%eb%8d%b8%ec%9d%98-%ed%8c%8c%eb%9d%bc%eb%af%b8%ed%84%b0-%ed%8a%9c%eb%8b%9d-%eb%b0%b0%ec%b9%98-%ed%81%ac%ea%b8%b0-%eb%b0%8f-%ec%9e%85%eb%a0%a5-%ec%9d%b4%eb%af%b8%ec%a7%80-%ed%81%ac","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/38846\/","title":{"rendered":"YOLO \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \ubc30\uce58 \ud06c\uae30 \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815\uc5d0 \ub530\ub978 \uc131\ub2a5 \ubcc0\ud654"},"content":{"rendered":"<p><body><\/p>\n<p>YOLO(You Only Look Once) \ubaa8\ub378\uc740 \uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uac00\uc7a5 \uc778\uae30\uac00 \ub192\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. YOLO \ubaa8\ub378\uc740 \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c \ud55c\ubc88\uc5d0 \ucc98\ub9ac\ud558\uc5ec \ube60\ub974\uace0 \uc815\ud655\ud55c \ud0d0\uc9c0\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ucd5c\uc801\uc758 \uc131\ub2a5\uc744 \ub04c\uc5b4\ub0b4\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \ubc30\uce58 \ud06c\uae30(batch size) \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30(input image size) \uc870\uc815\uc774 \uc131\ub2a5\uc5d0 \ubbf8\uce58\ub294 \ubcc0\ud654\uc5d0 \ub300\ud558\uc5ec \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. YOLO \ubaa8\ub378 \uc18c\uac1c<\/h2>\n<p>YOLO\ub294 \uac1d\uccb4 \ud0d0\uc9c0\ub97c \uc704\ud55c CNN(Convolutional Neural Network) \ubaa8\ub378\ub85c, \uc774\ubbf8\uc9c0 \uc804\uccb4\ub97c \ud55c \ubc88\uc5d0 \ucc98\ub9ac\ud558\uc5ec \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ub3d9\uc791\ud569\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \uc811\uadfc \ubc29\uc2dd\uc740 \uc774\ubbf8\uc9c0\ub97c \uc5ec\ub7ec \uac1c\uc758 \ud328\uce58\ub85c \ub098\ub204\uc5b4 \uac01 \ud328\uce58\ub97c \uac1c\ubcc4\uc801\uc73c\ub85c \ubd84\uc11d\ud558\ub294 \ubc18\uba74, YOLO\ub294 \uc804\uccb4 \uc774\ubbf8\uc9c0\ub97c \ub2e8\uc77c \ub124\ud2b8\uc6cc\ud06c\ub97c \ud1b5\ud574 \ucc98\ub9ac\ud568\uc73c\ub85c\uc368 \uc18d\ub3c4\ub97c \ud68d\uae30\uc801\uc73c\ub85c \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4. YOLO\ub294 \ud1b5\uc0c1\uc801\uc73c\ub85c \uba87 \uac00\uc9c0 \uc8fc\uc694 \ub2e8\uacc4\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n<h3>1.1 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c<\/h3>\n<ul>\n<li><strong>\ub124\ud2b8\uc6cc\ud06c \uc544\ud0a4\ud14d\ucc98:<\/strong> YOLO\ub294 \ub2e8\uc77c CNN\uc73c\ub85c \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c Darknet\uc774\ub77c\ub294 \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ub429\ub2c8\ub2e4.<\/li>\n<li><strong>\ucd9c\ub825:<\/strong> YOLO\ub294 \ubc14\uc6b4\ub529 \ubc15\uc2a4\uc640 \ud074\ub798\uc2a4 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. \ucd9c\ub825\uc740 \ubc14\uc6b4\ub529 \ubc15\uc2a4 \uc88c\ud45c, \uc2e0\ub8b0\ub3c4 \uc810\uc218 \ubc0f \uac1d\uccb4 \ud074\ub798\uc2a4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc190\uc2e4 \ud568\uc218:<\/strong> YOLO\ub294 \ubc14\uc6b4\ub529 \ubc15\uc2a4 \ud68c\uadc0 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ud2b9\uc815 \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h2>\n<p>YOLO \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc139\uc158\uc5d0\uc11c\ub294 \uc8fc\uc694 \ud30c\ub77c\ubbf8\ud130\uc640 \uadf8 \uc870\uc815 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ud559\uc2b5\ub960 (Learning Rate)<\/h3>\n<p>\ud559\uc2b5\ub960\uc740 \ubaa8\ub378\uc774 \ub9e4 \ub2e8\uacc4\uc5d0\uc11c \uc5bc\ub9c8\ub098 \ud070 \ubcc0\ud654\ub97c \ud5c8\uc6a9\ud560\uc9c0\ub97c \uacb0\uc815\ud558\ub294 \uc911\uc694\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc785\ub2c8\ub2e4. \ud559\uc2b5\ub960\uc774 \ub108\ubb34 \ud074 \uacbd\uc6b0 \ubaa8\ub378\uc774 \ucd5c\uc801\uc758 Solution\uc744 \ub193\uce60 \uc218 \uc788\uc73c\uba70, \ub108\ubb34 \uc791\uc744 \uacbd\uc6b0 \ud559\uc2b5 \uc18d\ub3c4\uac00 \ub290\ub824\uc9c0\uac8c \ub429\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ud559\uc2b5\ub960\uc744 \ucd08\uae30\uc5d0\ub294 \ud06c\uac8c \uc124\uc815 \ud6c4 \uc810\uc9c4\uc801\uc73c\ub85c \uc904\uc5ec\uac00\ub294 \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>import torch.optim as optim\n\n# Optimizer \uc124\uc815\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ud559\uc2b5\ub960 \uc870\uc815\nfor epoch in range(num_epochs):\n    # learning_rate schedule\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = initial_lr * (0.1 ** (epoch \/\/ 30))<\/code><\/pre>\n<h3>2.2 \ubc30\uce58 \ud06c\uae30 (Batch Size)<\/h3>\n<p>\ubc30\uce58 \ud06c\uae30\ub294 \ud55c \ubc88\uc758 \uc5c5\ub370\uc774\ud2b8\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \uc0d8\ud50c\uc758 \uc218\uc785\ub2c8\ub2e4. \uc791\uc740 \ubc30\uce58 \ud06c\uae30\ub294 \uc790\uc8fc \uc5c5\ub370\uc774\ud2b8\ud558\uc5ec \ub354 \uc138\ubc00\ud55c \uc870\uc815\uc744 \uac00\ub2a5\ud558\uac8c \ud558\uc9c0\ub9cc \uc2dc\uac04\uc774 \ub354 \uac78\ub9b0\ub2e4\ub294 \ub2e8\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ubc18\uba74, \ud070 \ubc30\uce58 \ud06c\uae30\ub294 GPU \uba54\ubaa8\ub9ac \uc0ac\uc6a9\ub7c9\uc744 \uc99d\uac00\uc2dc\ud0a4\uba70 \uacc4\uc0b0 \uc18d\ub3c4\ub97c \ub192\uc774\ub294 \ubc18\uba74, \ucd5c\uc801\ud654\uc5d0 \ub300\ud55c \uc815\ubc00\ub3c4\uac00 \ub0ae\uc544\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\ubc30\uce58 \ud06c\uae30 \uc124\uc815\uc5d0 \ub530\ub974\ub294 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \uc704\ud574\uc11c\ub294 \ud14c\uc2a4\ud2b8\ub97c \ud1b5\ud574 \ucd5c\uc801\uc758 \ubc30\uce58 \ud06c\uae30\ub97c \ucc3e\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<h2>3. \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815<\/h2>\n<p>\uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30\ub294 YOLO \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \uc694\uc18c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c YOLO\ub294 416&#215;416, 608&#215;608\uc640 \uac19\uc740 \uc815\uc0ac\uac01\ud615 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc2b5\ub2c8\ub2e4. \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30\uc5d0 \ub530\ub77c \ud0d0\uc9c0 \uc131\ub2a5\uacfc \uc18d\ub3c4\uac00 \ub2ec\ub77c\uc9c0\uac8c \ub429\ub2c8\ub2e4.<\/p>\n<h3>3.1 \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30\uc640 \uc131\ub2a5 \uad00\uacc4<\/h3>\n<p>\uc785\ub825 \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\uac00 \ucee4\uc9c8\uc218\ub85d \uc5ec\ub7ec \uc791\uc740 \uac1d\uccb4\ub4e4\uc744 \ub354 \uc798 \ud0d0\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774 \uacbd\uc6b0 \ubaa8\ub378\uc758 \uacc4\uc0b0 \ube44\uc6a9\uc774 \uc99d\uac00\ud558\uac8c \ub418\uc5b4 \ucc98\ub9ac \uc18d\ub3c4\uac00 \uac10\uc18c\ud558\ub294 \ub2e8\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ubc18\ub300\ub85c, \uc791\uc740 \uc785\ub825 \ud06c\uae30\ub294 \uc18d\ub3c4\ub97c \ub192\uc774\uc9c0\ub9cc \uac1d\uccb4 \ud0d0\uc9c0\uc758 \uc815\ubc00\ub3c4\uac00 \ub5a8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>from models import Model\n\n# YOLO \ubaa8\ub378 \uc0dd\uc131\nmodel = Model(\"cfg\/yolov3.cfg\", \"weights\/yolov3.weights\")\nmodel.eval()\n\n# \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc124\uc815\ninput_size = (416, 416)\nimage = preprocess_image(image_path, input_size)\noutput = model(image.unsqueeze(0))<\/code><\/pre>\n<h3>3.2 \uc2e4\ud5d8 \uc608\uc81c<\/h3>\n<p>\uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30\uc640 \ubc30\uce58 \ud06c\uae30 \uc870\uc815\uc5d0 \ub530\ub978 \uc131\ub2a5 \ubcc0\ud654\ub97c \uc2e4\ud5d8\ud558\uae30 \uc704\ud574 \ub2e4\uc74c\uacfc \uac19\uc740 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>import numpy as np\nimport time\n\n# \uc131\ub2a5 \ubcc0\ud654\ub97c \uae30\ub85d\ud558\uae30 \uc704\ud55c \ub9ac\uc2a4\ud2b8\nresults = []\n\nfor batch_size in [1, 8, 16]:\n    for input_size in [416, 608]:\n        model.eval()\n        \n        # \ud14c\uc2a4\ud2b8 \uc774\ubbf8\uc9c0 \uc900\ube44\n        test_images = [preprocess_image(image_path, (input_size, input_size)) for _ in range(batch_size)]\n        input_tensor = torch.stack(test_images)\n\n        # \uc2dc\uac04 \uce21\uc815 \uc2dc\uc791\n        start_time = time.time()\n        with torch.no_grad():\n            output = model(input_tensor)\n        elapsed_time = time.time() - start_time\n\n        # \uae30\ub85d\n        results.append((batch_size, input_size, elapsed_time))\n\n# \uacb0\uacfc \ucd9c\ub825\nfor batch_size, input_size, elapsed_time in results:\n    print(f\"Batch Size: {batch_size}, Input Size: {input_size}, Time: {elapsed_time:.2f}s\")<\/code><\/pre>\n<h2>4. \uc131\ub2a5 \ubd84\uc11d<\/h2>\n<p>\uc704\uc758 \ucf54\ub4dc\ub97c \ud1b5\ud574 \ubc30\uce58 \ud06c\uae30\uc640 \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \ubcc0\ud654\uc2dc\ud0a4\uba70 \uc131\ub2a5\uc744 \ubd84\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uacb0\uacfc\uc801\uc73c\ub85c \ub2e4\uc591\ud55c \uc870\ud569\uc744 \ud1b5\ud574 YOLO \ubaa8\ub378\uc774 \uc5b4\ub5bb\uac8c \ub3d9\uc791\ud558\ub294\uc9c0\ub97c \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4. \uc131\ub2a5 \ubd84\uc11d \uc2dc \uace0\ub824\ud560 \uc0ac\ud56d\uc740 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uad50\ucc28 \uac80\uc99d\uc744 \ud1b5\ud574 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud569\ub2c8\ub2e4.<\/li>\n<li>\uac01 \ubc30\uce58 \ud06c\uae30\uc640 \uc785\ub825 \ud06c\uae30\uc5d0\uc11c\uc758 \uc815\ud655\ub3c4 \ubc0f \ucc98\ub9ac \uc18d\ub3c4\ub97c \ube44\uad50\ud569\ub2c8\ub2e4.<\/li>\n<li>\ucd5c\uc801\uc758 \uc870\ud569\uc744 \ucc3e\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc2dc\ud5d8\ud574\ubcf4\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>5. \uacb0\ub860<\/h2>\n<p>YOLO \ubaa8\ub378\uc740 \uac15\ub825\ud55c \uac1d\uccb4 \ud0d0\uc9c0 \uc131\ub2a5\uc744 \uc81c\uacf5\ud558\uc9c0\ub9cc, \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \ub2e4\uc591\ud55c \ud30c\ub77c\ubbf8\ud130\ub97c \ud29c\ub2dd\ud558\uace0 \ubc30\uce58 \ud06c\uae30 \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \uc870\uc815\ud588\uc744 \ub54c \uc131\ub2a5 \ubcc0\ud654\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 \uc124\uc815\uc744 \ucc3e\uc544\uc57c \ud558\uba70, \uc774\ub97c \ud1b5\ud574 YOLO\uc758 \uc7a0\uc7ac\ub825\uc744 \ucd5c\ub300\ud55c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>6. \ucc38\uace0 \uc790\ub8cc<\/h2>\n<ul>\n<li>Redmon, J., &amp; Farhadi, A. (2018). YOLOv3: An Incremental Improvement.<\/li>\n<li>Darknet: <a href=\"https:\/\/pjreddie.com\/darknet\/\">https:\/\/pjreddie.com\/darknet\/<\/a><\/li>\n<li>PyTorch: <a href=\"https:\/\/pytorch.org\/\">https:\/\/pytorch.org\/<\/a><\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>YOLO(You Only Look Once) \ubaa8\ub378\uc740 \uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uac00\uc7a5 \uc778\uae30\uac00 \ub192\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. YOLO \ubaa8\ub378\uc740 \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c \ud55c\ubc88\uc5d0 \ucc98\ub9ac\ud558\uc5ec \ube60\ub974\uace0 \uc815\ud655\ud55c \ud0d0\uc9c0\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ucd5c\uc801\uc758 \uc131\ub2a5\uc744 \ub04c\uc5b4\ub0b4\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \ubc30\uce58 \ud06c\uae30(batch size) \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30(input image size) \uc870\uc815\uc774 \uc131\ub2a5\uc5d0 \ubbf8\uce58\ub294 \ubcc0\ud654\uc5d0 \ub300\ud558\uc5ec &hellip; <a href=\"https:\/\/atmokpo.com\/w\/38846\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;YOLO \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \ubc30\uce58 \ud06c\uae30 \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815\uc5d0 \ub530\ub978 \uc131\ub2a5 \ubcc0\ud654&#8221;<\/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-38846","post","type-post","status-publish","format-standard","hentry","category-yolo"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>YOLO \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \ubc30\uce58 \ud06c\uae30 \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815\uc5d0 \ub530\ub978 \uc131\ub2a5 \ubcc0\ud654 - \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\/38846\/\" \/>\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 \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd, \ubc30\uce58 \ud06c\uae30 \ubc0f \uc785\ub825 \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815\uc5d0 \ub530\ub978 \uc131\ub2a5 \ubcc0\ud654 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"YOLO(You Only Look Once) \ubaa8\ub378\uc740 \uac1d\uccb4 \ud0d0\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uac00\uc7a5 \uc778\uae30\uac00 \ub192\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. 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