\uc644\uc804 \ud569\uc131\uacf1 \ub124\ud2b8\uc6cc\ud06c(Fully Convolutional Networks, FCN)\ub294 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc138\ubd84\ud654, \uc989 \uc774\ubbf8\uc9c0 \ub0b4\uc5d0\uc11c \ud2b9\uc815 \uac1d\uccb4\ub97c \ud53d\uc140 \ub2e8\uc704\ub85c \ubd84\ub9ac\ud558\ub294 \ubb38\uc81c\uc5d0 \uc801\ud569\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc870\uc785\ub2c8\ub2e4. \uae30\uc874\uc758 CNN(\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd)\uc740 \uc8fc\ub85c \ubd84\ub958 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub418\uba70, \ucd5c\uc885 \ucd9c\ub825\uc73c\ub85c \uace0\uc815\ub41c \ud06c\uae30\uc758 \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc FCN\uc740 \uc2dc\uac01\uc801\uc778 \uc815\ubcf4\ub97c \uc720\uc9c0\ud558\uba74\uc11c \ubcc0\ud658\ub41c \ucd9c\ub825\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\ub294 \uad6c\uc870\ub85c, \uc774\ubbf8\uc9c0\uc758 \ubaa8\ub4e0 \ud53d\uc140\uc5d0 \uc758\ubbf8\ub97c \ubd80\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
FCN\uc740 \uae30\ubcf8\uc801\uc73c\ub85c CNN\uc758 \uad6c\uc870\ub97c \uacc4\uc2b9\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc911\uc694\ud55c \uc810\uc740 CNN\uc758 \ub9c8\uc9c0\ub9c9 \ubd80\ubd84\uc5d0\uc11c \uc804\uacb0\ud569\uce35\uc744 \uc81c\uac70\ud558\uace0, \ub300\uc2e0\uc5d0 \ucee8\ubcfc\ub8e8\uc158 \uce35\uacfc \uc5c5\uc0d8\ud50c\ub9c1 \uce35\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc6d0\ud558\ub294 \ucd9c\ub825 \ud06c\uae30\ub97c \uac16\ub3c4\ub85d \ud55c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
FCN\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\uc774\uc81c FCN\uc744 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud574 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c FCN\uc758 \ud30c\uc774\uc36c \ucf54\ub4dc \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass FCN(nn.Module):\n def __init__(self, num_classes):\n super(FCN, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)\n self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n self.conv4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)\n \n self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n \n self.upconv1 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)\n self.upconv2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)\n \n self.final_conv = nn.Conv2d(64, num_classes, kernel_size=1)\n\n def forward(self, x):\n x1 = F.relu(self.conv1(x))\n x2 = self.pool(x1)\n x2 = F.relu(self.conv2(x2))\n x3 = self.pool(x2)\n x3 = F.relu(self.conv3(x3))\n x4 = self.pool(x3)\n x4 = F.relu(self.conv4(x4))\n\n x = self.upconv1(x4)\n x = self.upconv2(x)\n x = self.final_conv(x)\n \n return x<\/code><\/pre>\n<\/div>\n2.1 \ubaa8\ub378 \uc124\uba85<\/h3>\n
\uc704\uc758 \ucf54\ub4dc\uc5d0\uc11c \uc6b0\ub9ac FCN \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub97c \uac70\uce69\ub2c8\ub2e4:<\/p>\n
\n- 3\ucc44\ub110(\uc77c\ubc18\uc801\uc778 RGB \uc774\ubbf8\uc9c0) \uc785\ub825\uc744 \ubc1b\uc544 64\uac1c\uc758 \ud2b9\uc131 \ub9f5\uc744 \uc0dd\uc131\ud558\ub294 \uccab \ubc88\uc9f8 \ucee8\ubcfc\ub8e8\uc158 \uce35\uc744 \ud1b5\uacfc\ud569\ub2c8\ub2e4.<\/li>\n
- \ub2e4\uc74c \ub450 \uac1c\uc758 \ucee8\ubcfc\ub8e8\uc158 \uce35\uc744 \uc9c0\ub098\uba70 \uc810\ucc28\uc801\uc73c\ub85c \ub354 \ub9ce\uc740 \ud2b9\uc131 \ub9f5\uc744 \uc0dd\uc131\ud558\uace0, \ucd5c\ub300 \ud480\ub9c1\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \uc808\ubc18\uc73c\ub85c \uc904\uc785\ub2c8\ub2e4.<\/li>\n
- \uc5c5\uc0d8\ud50c\ub9c1\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \uc6d0\ub798 \ud06c\uae30\ub85c \ub298\ub9bd\ub2c8\ub2e4.<\/li>\n
- \ucd5c\uc885 \ucd9c\ub825\uc740 \ud074\ub798\uc2a4 \uac1c\uc218\ub9cc\ud07c\uc758 \ucc44\ub110\uc744 \uac00\uc9c4 \ucee8\ubcfc\ub8e8\uc158 \uce35\uc744 \uac70\uce69\ub2c8\ub2e4.<\/li>\n<\/ol>\n
3. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
FCN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc120 \uc801\uc808\ud55c \ub370\uc774\ud130\uc14b\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0 \uc138\ubd84\ud654\uc5d0 \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\uc14b\uc73c\ub85c\ub294 Pascal VOC, COCO \ub4f1\uc774 \uc788\uc73c\uba70, \uc5ec\uae30\uc11c\ub294 \uac04\ub2e8\ud55c \uc774\ubbf8\uc9c0\uc640 \ub9c8\uc2a4\ud06c\uc758 \uc608\uc81c\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
3.1 \uc608\uc81c \ub370\uc774\ud130\uc14b \uc0dd\uc131<\/h3>\n\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\ndef generate_example_data():\n h, w = 128, 128\n image = np.random.randint(0, 255, (h, w, 3), dtype=np.uint8)\n mask = np.zeros((h, w), dtype=np.uint8)\n mask[30:70, 30:70] = 1 # \uc0ac\uac01\ud615 \uac1d\uccb4\n \n return image, mask\n\nimage, mask = generate_example_data()\n\n# \uc774\ubbf8\uc9c0\uc640 \ub9c8\uc2a4\ud06c \uc2dc\uac01\ud654\nplt.subplot(1, 2, 1)\nplt.title('Image')\nplt.imshow(image)\nplt.subplot(1, 2, 2)\nplt.title('Mask')\nplt.imshow(mask, cmap='gray')\nplt.show()<\/code><\/pre>\n<\/div>\n4. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uc5c8\ub2e4\uba74, \uc774\uc81c FCN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0ac \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c PyTorch\uc758 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \ucd08\uae30\ud654\nnum_classes = 2 # \uac1d\uccb4\uc640 \ubc30\uacbd\nmodel = FCN(num_classes)\ncriterion = nn.CrossEntropyLoss() # \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4 \ud568\uc218\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# Dummy training loop\nfor epoch in range(5): # 5\ubc88\uc758 epoch \ub3d9\uc548 \ud559\uc2b5\n model.train()\n optimizer.zero_grad()\n \n # Forward pass\n inputs = torch.Tensor(image).permute(2, 0, 1).unsqueeze(0) # (1, 3, 128, 128)\n targets = torch.Tensor(mask).long().unsqueeze(0) # (1, 128, 128)\n \n outputs = model(inputs)\n loss = criterion(outputs, targets)\n \n # Backward pass\n loss.backward()\n optimizer.step()\n \n print(f'Epoch [{epoch+1}\/5], Loss: {loss.item():.4f}')<\/code><\/pre>\n<\/div>\n5. \uacb0\ub860<\/h2>\n
\uc774 \uae00\uc5d0\uc11c\ub294 \uc644\uc804 \ud569\uc131\uacf1 \ub124\ud2b8\uc6cc\ud06c(FCN)\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130, \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uac04\ub2e8\ud55c FCN \ubaa8\ub378 \uad6c\ud604 \uacfc\uc815, \ub370\uc774\ud130\uc14b \uc900\ube44, \ud559\uc2b5 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. FCN\uc740 \uc774\ubbf8\uc9c0 \uc138\ubd84\ud654\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub354\uc6b1 \ubc1c\uc804\ub41c FCN \ubaa8\ub378\uc5d0 \ub300\ud55c \uc5f0\uad6c\uc640 \ucd94\uac00\uc801\uc778 \ub370\uc774\ud130\uc14b\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \ubaa9\ud45c\ub85c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. FCN\uc758 \uc751\uc6a9\uc774 \uad81\uae08\ud558\uc2dc\ub2e4\uba74, \ub354 \ub9ce\uc740 \ub0b4\uc6a9\uc744 \ud0d0\uc0c9\ud574\ubcf4\uc2dc\uae38 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"