{"id":29902,"date":"2024-10-28T03:18:35","date_gmt":"2024-10-28T03:18:35","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29902"},"modified":"2024-11-26T06:50:54","modified_gmt":"2024-11-26T06:50:54","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-deeplabv3-deeplabv3","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29902\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DeepLabv3 DeepLabv3+"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc624\ub298\uc740 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubd84\ud560(segmentation)\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ubaa8\ub378\uc778 DeepLabv3<\/strong>\uc640 DeepLabv3+<\/strong>\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. DeepLab Architecture Overview<\/h2>\n

DeepLab\uc740 \uc774\ubbf8\uc9c0 \ubd84\ud560\uc744 \uc704\ud574 \uc124\uacc4\ub41c \ub525\ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. DeepLab\uc758 \ud575\uc2ec \uc544\uc774\ub514\uc5b4\ub294 \ucee8\ubcfc\ub8e8\uc158 \uc2e0\uacbd\ub9dd(CNN)\uc744 \uae30\ubc18\uc73c\ub85c \ud558\uc5ec \ub2e4\uc591\ud55c \uaddc\ubaa8\uc758 \ubb3c\uccb4\ub97c \uc778\uc2dd\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub525\ub7a9\uc740 \ub2e4\uc911 \uc2a4\ucf00\uc77c \ud2b9\uc9d5<\/strong>\uc744 \ucc98\ub9ac\ud558\ub294 \uc5ec\ub7ec \uac00\uc9c0 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n

1.1 DeepLabv3<\/h3>\n

DeepLabv3 \ubaa8\ub378\uc740 atrous convolution\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \ud574\uc0c1\ub3c4\ub85c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud569\ub2c8\ub2e4. \uc774 \ud569\uc131\uacf1 \ubc29\uc2dd\uc740 \ud544\ud130\uc758 \uc218\ub97c \uc904\uc774\uc9c0 \uc54a\uace0\ub3c4 \uc218\uc6a9 \uc601\uc5ed\uc744 \ub113\ud790 \uc218 \uc788\uac8c \ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc740 \ub354\uc6b1 \uc138\ubc00\ud55c \uc815\ubcf4\ub97c \uc720\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.2 DeepLabv3+<\/h3>\n

DeepLabv3+\ub294 DeepLabv3\uc5d0\uc11c \ubc1c\uc804\ub41c \ubc84\uc804\uc73c\ub85c, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \uad6c\uc870\ub97c \ucc44\ud0dd\ud558\uc5ec \ub354\uc6b1 \uc138\ubc00\ud55c \uacbd\uacc4 delineation\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \ud2b9\ud788, \ub514\ucf54\ub354 \ubd80\ubd84\uc5d0\uc11c \uc138\ubc00\ud55c \uc815\ubcf4 \ubcf5\uad6c\ub97c \ud1b5\ud574 \uacbd\uacc4\uac00 \ub69c\ub837\ud55c segmentation\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/p>\n

2. PyTorch \uc124\uce58\ud558\uae30<\/h2>\n

DeepLabv3\/DeepLabv3+ \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 PyTorch\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. PyTorch\ub294 \ub2e4\uc591\ud55c \ud50c\ub7ab\ud3fc\uc5d0\uc11c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud55c \uac15\ub825\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub85c PyTorch\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

pip install torch torchvision<\/code><\/pre>\n
3. DeepLabv3\/DeepLabv3+ \uad6c\ud604\ud558\uae30<\/h2>\n
\uc774\uc81c DeepLabv3\uc640 DeepLabv3+ \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc784\ud3ec\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nfrom torchvision.models.segmentation import deeplabv3_resnet50<\/code><\/pre>\n
\ub2e4\uc74c\uc73c\ub85c\ub294 DeepLabv3 \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud558\uace0, \uc785\ub825 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 DeepLabv3 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h3>\n
# DeepLabv3 \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = deeplabv3_resnet50(pretrained=True)\nmodel.eval()  # \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815<\/code><\/pre>\n
3.2 \uc774\ubbf8\uc9c0 \uc804\ucc98\ub9ac<\/h3>\n
\ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc704\ud55c \uc774\ubbf8\uc9c0\ub97c \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \uc870\uc815\ud558\uace0, \ud150\uc11c\ub85c \ubcc0\ud658\ud558\uba70, \uc815\uaddc\ud654\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n
# \uc774\ubbf8\uc9c0 \ubd88\ub7ec\uc624\uae30\nfrom PIL import Image\n\ninput_image = Image.open('path_to_your_image.jpg')\n\n# \uc804\ucc98\ub9ac\npreprocess = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\n\ninput_tensor = preprocess(input_image)\ninput_batch = input_tensor.unsqueeze(0)  # \ubc30\uce58 \ucc28\uc6d0\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.<\/code><\/pre>\n
3.3 \uc608\uce21 \uc218\ud589<\/h3>\n
# \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\ud558\uae30\nwith torch.no_grad():  # \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0 \ubc29\uc9c0\ub97c \uc704\ud574\n    output = model(input_batch)['out'][0]  # \uc608\uce21 \uacb0\uacfc\uc5d0\uc11c \uccab \ubc88\uc9f8 \ucd9c\ub825 \uc5bb\uae30\n\n# \uc608\uce21 \uacb0\uacfc\ub97c \ud074\ub798\uc2a4 \uc778\ub371\uc2a4\ub85c \ubcc0\ud658\noutput_predictions = output.argmax(0)  # \ud074\ub798\uc2a4 \uc608\uce21\n<\/code><\/pre>\n
3.4 \uc2dc\uac01\ud654<\/h3>\n
\uc608\uce21\ub41c segmentation \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\n\n# \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654\nplt.imshow(output_predictions.numpy())\nplt.title('Predicted Segmentation')\nplt.axis('off')  # \ucd95 \uc228\uae30\uae30\nplt.show()<\/code><\/pre>\n
4. DeepLabv3+ \uad6c\ud604\ud558\uae30<\/h2>\n
DeepLabv3+\ub294 DeepLabv3 \ubaa8\ub378\uc744 \ud655\uc7a5\ud55c \uac83\uc73c\ub85c, \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc5d0\uc11c \ub354 \ucd94\uac00\uc801\uc778 \ucef4\ud3ec\ub10c\ud2b8\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. PyTorch\uc5d0\uc11c\ub294 torchvision<\/a> \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. DeepLabv3+\uc758 \uc608\uce21\ub3c4 \ube44\uc2b7\ud55c \ubc29\uc2dd\uc73c\ub85c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30<\/h3>\n
from torchvision.models.segmentation import deeplabv3_resnet101\n\n# DeepLabv3+ \ubaa8\ub378 \ucd08\uae30\ud654\nmodel_plus = deeplabv3_resnet101(pretrained=True)\nmodel_plus.eval()<\/code><\/pre>\n
4.2 \uc608\uce21 \uc218\ud589<\/h3>\n
# \uc608\uce21 \uc218\ud589\nwith torch.no_grad():\n    output_plus = model_plus(input_batch)['out'][0]\n\n# \ud074\ub798\uc2a4 \uc778\ub371\uc2a4 \ubcc0\ud658\noutput_predictions_plus = output_plus.argmax(0)<\/code><\/pre>\n
4.3 \uc2dc\uac01\ud654<\/h3>\n
# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.imshow(output_predictions_plus.numpy())\nplt.title('Predicted Segmentation with DeepLabv3+')\nplt.axis('off')\nplt.show()<\/code><\/pre>\n
5. \uc2ec\uce35 \ud559\uc2b5\uc758 \uc911\uc694\uc131<\/h2>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c\ubd80\ud130 \uc9c0\uc2dd\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ud2b9\ud788, \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc740 \uace0\uce35 \ud2b9\uc9d5\uc744 \uc790\ub3d9\uc73c\ub85c \ucd94\ucd9c\ud568\uc73c\ub85c\uc368 \uc608\uce21\uc758 \uc815\ud655\uc131\uc744 \ub192\uc774\uace0 \uc788\uc2b5\ub2c8\ub2e4. DeepLabv3 \ubc0f DeepLabv3+\ub294 \uc774\ub7ec\ud55c \ud2b9\uc9d5\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ud65c\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ubd84\ud560 \ubb38\uc81c\uc5d0 \ud601\uc2e0\uc801\uc778 \uc194\ub8e8\uc158\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860<\/h2>\n
\uc774 \uae00\uc5d0\uc11c\ub294 DeepLabv3\uc640 DeepLabv3+\uc758 \uae30\ubcf8\uc801\uc778 \uac1c\ub150\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ub4e4\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uac15\ub825\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560 \ubaa8\ub378\ub4e4\uc740 \ub2e4\uc591\ud55c \ucef4\ud4e8\ud130 \ube44\uc804 \uc751\uc6a9 \ubd84\uc57c\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc790\uc728\uc8fc\ud589\ucc28\uc758 \uc2dc\uac01 \uc778\uc2dd \uc2dc\uc2a4\ud15c, \uc758\ub8cc \uc774\ubbf8\uc9c0 \ubd84\uc11d \ubc0f \ub2e4\uc591\ud55c \ub3d9\uc601\uc0c1 \ucc98\ub9ac \uc791\uc5c5\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud558\uac8c \ud65c\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
\ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud29c\ub2dd\uc758 \ub2e4\uc74c \ub2e8\uacc4\ub294 \ucd94\uac00\uc801\uc778 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ubbf8\uc138 \uc870\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ud2b9\uc815 \uc751\uc6a9 \ubd84\uc57c\uc5d0 \ub9de\ub294 \ucd5c\uc801\uc758 \uc131\ub2a5\uc744 \ub2ec\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc624\ub298\uc740 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubd84\ud560(segmentation)\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ubaa8\ub378\uc778 DeepLabv3\uc640 DeepLabv3+\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. DeepLab Architecture Overview DeepLab\uc740 \uc774\ubbf8\uc9c0 \ubd84\ud560\uc744 \uc704\ud574 \uc124\uacc4\ub41c \ub525\ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. DeepLab\uc758 \ud575\uc2ec \uc544\uc774\ub514\uc5b4\ub294 \ucee8\ubcfc\ub8e8\uc158 \uc2e0\uacbd\ub9dd(CNN)\uc744 \uae30\ubc18\uc73c\ub85c \ud558\uc5ec \ub2e4\uc591\ud55c \uaddc\ubaa8\uc758 \ubb3c\uccb4\ub97c \uc778\uc2dd\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub525\ub7a9\uc740 \ub2e4\uc911 \uc2a4\ucf00\uc77c … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DeepLabv3 DeepLabv3+”<\/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":[33],"tags":[],"class_list":["post-29902","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DeepLabv3 DeepLabv3+ - \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\/29902\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DeepLabv3 DeepLabv3+ - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc624\ub298\uc740 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubd84\ud560(segmentation)\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ubaa8\ub378\uc778 DeepLabv3\uc640 DeepLabv3+\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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