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YOLO\ub780 \ubb34\uc5c7\uc778\uac00? YOLO(You Only Look Once)\ub294 \uc774\ubbf8\uc9c0\uc5d0\uc11c \uac1d\uccb4\ub97c \uc2e4\uc2dc\uac04\uc73c\ub85c \ud0d0\uc9c0\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \ub2e8\uc77c \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c \ud1b5\uacfc\uc2dc\ud0a4\uba70 \ud574\ub2f9 \uc774\ubbf8\uc9c0 \ub0b4\uc758 \uc5ec\ub7ec \uac1d\uccb4\ub97c \ub3d9\uc2dc\uc5d0 \uc778\uc2dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 YOLO\ub294 \uc804\ud1b5\uc801\uc778 \uac1d\uccb4 \ud0d0\uc9c0 \uae30\ubc95\ub4e4\ubcf4\ub2e4 \ube60\ub974\uace0 \ud6a8\uacfc\uc801\uc778 \uc131\ub2a5\uc744 \ubcf4\uc774\uac8c \ub429\ub2c8\ub2e4. YOLO\ub294 \ucc98\uc74c \ubc1c\ud45c\ub41c \uc774\ud6c4\ub85c \uc9c0\uc18d\uc801\uc73c\ub85c\u2026","rel":"","context":""YOLO"\uc5d0\uc11c","block_context":{"text":"YOLO","link":"https:\/\/atmokpo.com\/w\/category\/yolo\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":30092,"url":"https:\/\/atmokpo.com\/w\/30092\/","url_meta":{"origin":38798,"position":4},"title":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub97c \uc704\ud55c \uc784\ubca0\ub529","author":"root","date":"2024\ub144 10\uc6d4 28\uc77c","format":false,"excerpt":"\uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \uc0ac\uc6a9\uc790\uac00 \uc758\ub3c4\ud55c \ubc14\ub97c \uc774\ud574\ud558\uace0, \ubb38\ub9e5\uc5d0 \ub9de\ub294 \uc751\ub2f5\uc744 \uc0dd\uc131\ud558\uba70, \uc5ec\ub7ec \uc5b8\uc5b4\uc801 \uc694\uc18c\ub97c \ubd84\uc11d\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uc911\uc694\ud55c \uae30\uc220 \uc911 \ud558\ub098\uac00 \uc784\ubca0\ub529(Embedding)\uc785\ub2c8\ub2e4. \uc784\ubca0\ub529\uc740 \ub2e8\uc5b4\ub97c \ubca1\ud130 \uacf5\uac04\uc5d0 \ub9e4\ud551\ud558\uc5ec \uc758\ubbf8\uc801 \uad00\uacc4\ub97c \ub354 \uc218\uce58\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4. \uc624\ub298\uc740 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub97c \uc704\ud55c \ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc784\ubca0\ub529\uc774\ub780?\u2026","rel":"","context":""\ud30c\uc774\ud1a0\uce58 \uac15\uc88c"\uc5d0\uc11c","block_context":{"text":"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c","link":"https:\/\/atmokpo.com\/w\/category\/%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":30064,"url":"https:\/\/atmokpo.com\/w\/30064\/","url_meta":{"origin":38798,"position":5},"title":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc2e4\uc2b5 \ud658\uacbd \uc124\uc815","author":"root","date":"2024\ub144 10\uc6d4 28\uc77c","format":false,"excerpt":"\ub525\ub7ec\ub2dd\uc740 \ud604\ub300 \uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc758 \ud575\uc2ec\uc73c\ub85c \uc790\ub9ac \uc7a1\uc558\uc73c\uba70, \uadf8\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ud504\ub808\uc784\uc6cc\ud06c\uac00 \ub4f1\uc7a5\ud588\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0\uc11c\ub3c4 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \ub3d9\uc801 \uc5f0\uc0b0 \ubc29\uc2dd\uacfc \uc9c1\uad00\uc801\uc778 API \ub355\ubd84\uc5d0 \ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc758 \uc0ac\ub791\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd \uc2e4\uc2b5\uc744 \uc704\ud55c \ud658\uacbd \uc124\uc815 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud30c\uc774\ud1a0\uce58 \uc18c\uac1c \ud30c\uc774\ud1a0\uce58\ub294 Facebook AI Research Group\uc5d0 \uc758\ud574 \uac1c\ubc1c\ub41c \uc624\ud508\u2026","rel":"","context":""\ud30c\uc774\ud1a0\uce58 \uac15\uc88c"\uc5d0\uc11c","block_context":{"text":"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c","link":"https:\/\/atmokpo.com\/w\/category\/%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts\/38798","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/comments?post=38798"}],"version-history":[{"count":2,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts\/38798\/revisions"}],"predecessor-version":[{"id":43081,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts\/38798\/revisions\/43081"}],"wp:attachment":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/media?parent=38798"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/categories?post=38798"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/tags?post=38798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

\uc800\uc790: \uc870\uad11\ud615<\/p>\n

\uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c<\/p>\n<\/header>\n

1. \uc11c\ub860<\/h2>\n

\n OpenCV(Open Source Computer Vision Library)\ub294 \ucef4\ud4e8\ud130 \ube44\uc804 \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc704\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c,
\n \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubc0f \ube44\ub514\uc624 \ucc98\ub9ac \uae30\uc220\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. PyTorch\ub294 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ud2b9\ud788 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758
\n \uac1c\ubc1c\uacfc \ud559\uc2b5\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub85c \ud559\uc2b5\ud55c \ubaa8\ub378\uc744 OpenCV\ub97c \ud1b5\ud574 \ub85c\ub4dc\ud558\uc5ec
\n \uc2e4\uc2dc\uac04\uc73c\ub85c \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n<\/section>\n

2. \uc694\uad6c \uc0ac\ud56d<\/h2>\n

\uc774 \uac15\uc88c\ub97c \uc9c4\ud589\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \uc694\uad6c \uc0ac\ud56d\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

Python 3.x \ubc84\uc804<\/li>\n
OpenCV \ub77c\uc774\ube0c\ub7ec\ub9ac<\/li>\n
PyTorch \ub77c\uc774\ube0c\ub7ec\ub9ac<\/li>\n

NumPy<\/li>\n<\/ul>\n

Python\uacfc \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\ub824\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

pip install opencv-python torchvision torch numpy<\/code><\/pre>\n<\/section>\n\n3. PyTorch \ubaa8\ub378 \ud559\uc2b5\ud558\uae30<\/h2>\n\n                \uc6b0\uc120 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c CNN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n            <\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n\n# \ubaa8\ub378 \uc815\uc758\nclass SimpleCNN(nn.Module):\n    def __init__(self):\n        super(SimpleCNN, self).__init__()\n        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)\n        self.pool = nn.MaxPool2d(kernel_size=2)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)\n        self.fc1 = nn.Linear(64 * 6 * 6, 128)\n        self.fc2 = nn.Linear(128, 10)\n    \n    def forward(self, x):\n        x = self.pool(torch.relu(self.conv1(x)))\n        x = self.pool(torch.relu(self.conv2(x)))\n        x = x.view(-1, 64 * 6 * 6)\n        x = torch.relu(self.fc1(x))\n        return self.fc2(x)\n\n# \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4 \uc0dd\uc131 \ubc0f \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc815\uc758\nmodel = SimpleCNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(5):  # 5 \uc5d0\ud3ec\ud06c \ub3d9\uc548 \ud559\uc2b5\n    for images, labels in train_loader:\n        optimizer.zero_grad()\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n    \n    print(f'Epoch [{epoch+1}\/5], Loss: {loss.item():.4f}')\n\n# \ubaa8\ub378 \uc800\uc7a5\ntorch.save(model.state_dict(), 'mnist_cnn.pth')\n            <\/code><\/pre>\n\n                \uc704 \ucf54\ub4dc\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uc5ec \uac04\ub2e8\ud55c CNN \ubaa8\ub378\uc744 \uc815\uc758\ud558\uace0 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.
\n                \ubaa8\ub378\uc774 \ud559\uc2b5\ud55c \ud6c4, ‘mnist_cnn.pth’ \ud30c\uc77c\ub85c \ubaa8\ub378\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4.\n            <\/p>\n<\/section>\n
\n4. OpenCV\uc5d0\uc11c PyTorch \ubaa8\ub378 \ub85c\ub4dc\ud558\uae30<\/h2>\n\n                \uc800\uc7a5\ud55c PyTorch \ubaa8\ub378\uc744 OpenCV\uc5d0\uc11c \uc811\uadfc\ud558\ub824\uba74, \uba3c\uc800 \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 OpenCV \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4.
\n                OpenCV\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub824\uba74 ONNX(Open Neural Network Exchange) \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub97c
\n                \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 ONNX \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.\n            <\/p>\n
\ndummy_input = torch.randn(1, 1, 28, 28)  # MNIST\uc758 \uc774\ubbf8\uc9c0 \ud06c\uae30\ntorch.onnx.export(model, dummy_input, 'mnist_cnn.onnx')\n            <\/code><\/pre>\n\n                \uc704 \ucf54\ub4dc\ub294 \ubaa8\ub378\uc744 ONNX \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\uc81c OpenCV\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 ONNX \ubaa8\ub378\uc744 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n            <\/p>\n<\/section>\n
\n5. OpenCV\ub97c \uc0ac\uc6a9\ud558\uc5ec ONNX \ubaa8\ub378 \uc2e4\ud589\ud558\uae30<\/h2>\n\n                OpenCV\uc5d0\uc11c ONNX \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. OpenCV\uc758 dnn \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud558\uc5ec
\n                \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0 \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n            <\/p>\n
\nimport cv2\nimport numpy as np\n\n# \ubaa8\ub378 \ub85c\ub4dc\nnet = cv2.dnn.readNetFromONNX('mnist_cnn.onnx')\n\n# \uc774\ubbf8\uc9c0 \uc804\ucc98\ub9ac\nimage = cv2.imread('test_image.png', cv2.IMREAD_GRAYSCALE)\nimage = cv2.resize(image, (28, 28))\nimage = image.astype(np.float32) \/ 255.0\nimage = np.expand_dims(image, axis=0)\nimage = np.expand_dims(image, axis=0)\nimage = np.array(image, dtype=np.float32)\n\n# \ubaa8\ub378\uc5d0 \uc785\ub825\nblob = cv2.dnn.blobFromImage(image)\nnet.setInput(blob)\noutput = net.forward()\n\n# \uacb0\uacfc \ucd9c\ub825\npredicted_class = np.argmax(output, axis=1)\nprint(f'Predicted class: {predicted_class[0]}')\n            <\/code><\/pre>\n\n                \uc704 \ucf54\ub4dc\ub294 OpenCV\ub97c \uc0ac\uc6a9\ud558\uc5ec ONNX \ubaa8\ub378\uc744 \ub85c\ub4dc\ud558\uace0, \uc8fc\uc5b4\uc9c4 \uc774\ubbf8\uc9c0\ub97c \uc804\ucc98\ub9ac\ud55c \ud6c4 \ubaa8\ub378\uc744 \ud1b5\ud574
\n                \uc608\uce21 \uacb0\uacfc\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4. \uc774\ubbf8\uc9c0\uac00 MNIST \ub370\uc774\ud130\uc14b\uc758 \ud06c\uae30\uc5d0 \ub9de\uac8c \uc804\ucc98\ub9ac\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud558\uc138\uc694.\n            <\/p>\n<\/section>\n
\n6. \uc2e4\uc2dc\uac04 \uc774\ubbf8\uc9c0 \ucc98\ub9ac<\/h2>\n\n                OpenCV\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc6f9\ucea0\uc5d0\uc11c \uc2e4\uc2dc\uac04\uc73c\ub85c \uc774\ubbf8\uc9c0\ub97c \ucc98\ub9ac\ud558\uace0 \uc608\uce21\ud558\ub294 \ubc29\ubc95\ub3c4 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n                \uc544\ub798 \ucf54\ub4dc\ub294 \uc6f9\ucea0\uc5d0\uc11c \uc774\ubbf8\uc9c0\ub97c \ucea1\ucc98\ud558\uace0, \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790\ub97c \uc778\uc2dd\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.\n            <\/p>\n
\ncap = cv2.VideoCapture(0)  # \uc6f9\ucea0 \ucea1\ucc98 \uc2dc\uc791\n\nwhile True:\n    ret, frame = cap.read()\n    if not ret:\n        break\n    \n    # \uc774\ubbf8\uc9c0 \uc804\ucc98\ub9ac\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    resized = cv2.resize(gray, (28, 28))\n    normed = resized.astype(np.float32) \/ 255.0\n    blob = cv2.dnn.blobFromImage(normed)\n    \n    # \ubaa8\ub378\uc5d0 \uc785\ub825\n    net.setInput(blob)\n    output = net.forward()\n    predicted_class = np.argmax(output, axis=1)[0]\n    \n    # \uc608\uce21 \uacb0\uacfc \ud45c\uc2dc\n    cv2.putText(frame, f'Predicted: {predicted_class}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, \n                1, (0, 255, 0), 2, cv2.LINE_AA)\n    \n    cv2.imshow('Webcam', frame)\n    \n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n            <\/code><\/pre>\n\n                \uc704 \ucf54\ub4dc\ub294 \uc6f9\ucea0\uc73c\ub85c\ubd80\ud130 \ud504\ub808\uc784\uc744 \uac00\uc838\uc624\uace0 \uac01 \ud504\ub808\uc784\uc5d0 \ub300\ud574 \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud558\uc5ec \ubaa8\ub378\uc744 \ud1b5\ud574
\n                \uc608\uce21\uc744 \uc218\ud589\ud558\uace0, \uc608\uce21\ub41c \uc22b\uc790\ub97c \ud654\uba74\uc5d0 \ud45c\uc2dc\ud569\ub2c8\ub2e4.
\n                ‘q’ \ud0a4\ub97c \ub20c\ub7ec\uc11c \uc885\ub8cc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n            <\/p>\n<\/section>\n
\n7. \uacb0\ub860<\/h2>\n\n                \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub85c \ud559\uc2b5\ud55c CNN \ubaa8\ub378\uc744 OpenCV\uc5d0\uc11c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4.
\n                ONNX \ud615\uc2dd\uc73c\ub85c \ubaa8\ub378\uc744 \ubcc0\ud658\ud558\uace0, OpenCV\uc758 DNN \ubaa8\ub4c8\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2e4\uc2dc\uac04 \uc774\ubbf8\uc9c0 \ucc98\ub9ac\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc74c\uc744
\n                \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc73c\ub85c \uae30\uc874\uc758 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 OpenCV\uc640 \ud1b5\ud569\ud558\uc5ec \uc774\ubbf8\uc9c0\ub098 \ube44\ub514\uc624 \ucc98\ub9ac
\n                \uc791\uc5c5\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378 \ubc0f \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uc73c\ub85c\uc758 \ud655\uc7a5\uc744 \uace0\ub824\ud574 \ubcf4\uc138\uc694.\n            <\/p>\n<\/section>\n
\n8. \ucc38\uace0 \uc790\ub8cc<\/h2>\n\nPyTorch \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
OpenCV \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
ONNX \uacf5\uc2dd \uc0ac\uc774\ud2b8<\/a><\/li>\n<\/ul>\n<\/footer>\n<\/article>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\uc800\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 OpenCV(Open Source Computer Vision Library)\ub294 \ucef4\ud4e8\ud130 \ube44\uc804 \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc704\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubc0f \ube44\ub514\uc624 \ucc98\ub9ac \uae30\uc220\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. PyTorch\ub294 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ud2b9\ud788 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uac1c\ubc1c\uacfc \ud559\uc2b5\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub85c \ud559\uc2b5\ud55c \ubaa8\ub378\uc744 OpenCV\ub97c \ud1b5\ud574 \ub85c\ub4dc\ud558\uc5ec \uc2e4\uc2dc\uac04\uc73c\ub85c \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 2. \uc694\uad6c \uc0ac\ud56d \uc774 \uac15\uc88c\ub97c \uc9c4\ud589\ud558\uae30 … \ub354 \ubcf4\uae30 “OpenCV \uac15\uc88c, PyTorch \ubaa8\ub378\uc744 OpenCV\ub85c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\uae30”<\/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":[187],"tags":[],"class_list":["post-38798","post","type-post","status-publish","format-standard","hentry","category-opencv"],"yoast_head":"\nOpenCV \uac15\uc88c, PyTorch \ubaa8\ub378\uc744 OpenCV\ub85c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\uae30 - \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\/38798\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OpenCV \uac15\uc88c, PyTorch \ubaa8\ub378\uc744 OpenCV\ub85c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc800\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 OpenCV(Open Source Computer Vision Library)\ub294 \ucef4\ud4e8\ud130 \ube44\uc804 \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc704\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \ubc0f \ube44\ub514\uc624 \ucc98\ub9ac \uae30\uc220\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. PyTorch\ub294 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ud2b9\ud788 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uac1c\ubc1c\uacfc \ud559\uc2b5\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub85c \ud559\uc2b5\ud55c \ubaa8\ub378\uc744 OpenCV\ub97c \ud1b5\ud574 \ub85c\ub4dc\ud558\uc5ec \uc2e4\uc2dc\uac04\uc73c\ub85c \uc2e4\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 2. \uc694\uad6c \uc0ac\ud56d \uc774 \uac15\uc88c\ub97c \uc9c4\ud589\ud558\uae30 … \ub354 \ubcf4\uae30 "OpenCV \uac15\uc88c, PyTorch \ubaa8\ub378\uc744 OpenCV\ub85c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\uae30"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/38798\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-11-02T00:57:10+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:43:19+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/38798\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/38798\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"OpenCV \uac15\uc88c, PyTorch \ubaa8\ub378\uc744 OpenCV\ub85c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\uae30\",\"datePublished\":\"2024-11-02T00:57:10+00:00\",\"dateModified\":\"2024-11-26T06:43:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/38798\/\"},\"wordCount\":63,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"OpenCV\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/38798\/\",\"url\":\"https:\/\/atmokpo.com\/w\/38798\/\",\"name\":\"OpenCV \uac15\uc88c, PyTorch \ubaa8\ub378\uc744 OpenCV\ub85c \ub85c\ub4dc\ud558\uc5ec \uc2e4\ud589\ud558\uae30 - 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