\ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\uc5d0 \uc624\uc2e0 \uac83\uc744 \ud658\uc601\ud569\ub2c8\ub2e4! \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 VGGNet\uc774\ub77c\ub294 \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. VGGNet\uc740 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc73c\ub85c \uc798 \uc54c\ub824\uc838 \uc788\uc73c\uba70, \ud2b9\ud788 \uc774\ubbf8\uc9c0 \ubd84\ub958 \uacfc\uc81c\uc5d0\uc11c \uadf8 \uc6b0\uc218\uc131\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \ub610\ud55c, \uc6b0\ub9ac\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec VGGNet\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c\ub3c4 \ud0d0\uad6c\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
1. VGGNet \uac1c\uc694<\/h2>\n
VGGNet\uc740 2014\ub144 ImageNet Large Scale Visual Recognition Challenge(ILSVRC)\uc5d0\uc11c \uc81c\uc548\ub41c \uc544\ud0a4\ud14d\ucc98\ub85c, \uc624\uc138\uc544\ub2c8\uc544 \ub300\ud559\uad50(Oxford University)\uc758 Visual Geometry Group(VGG)\uc5d0\uc11c \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uac15\ub825\ud55c \ucd94\uc0c1\ud654 \ub2a5\ub825\uc744 \uc81c\uacf5\ud558\uba70, \uae4a\uc774\uc5d0 \ub530\ub978 \uc131\ub2a5 \ud5a5\uc0c1\uc758 \uc88b\uc740 \uc608\uc2dc\ub85c \uc790\ub9ac \uc7a1\uc558\uc2b5\ub2c8\ub2e4. VGGNet\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \ub2e8\uc21c\ud788 \uae4a\uc774\ub97c \ub298\ub9bc\uc73c\ub85c\uc368 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
2. VGGNet \uc544\ud0a4\ud14d\ucc98<\/h2>\n
VGGNet\uc740 \uc5ec\ub7ec \uac1c\uc758 \ud569\uc131\uacf1(convolutional) \uce35\uacfc \ud480\ub9c1(pooling) \uce35\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. VGGNet\uc758 \uc8fc\uc694 \ud2b9\uc9d5 \uc911 \ud558\ub098\ub294 \ubaa8\ub4e0 \ud569\uc131\uacf1 \uce35\uc774 \ub3d9\uc77c\ud55c \ucee4\ub110 \ud06c\uae30\uc778 3×3\uc744 \uac16\uace0 \uc788\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc870\ub97c \uac16\uc2b5\ub2c8\ub2e4:<\/p>\n
\n - 2\uac1c\uc758 3x3 \ud569\uc131\uacf1 \uce35 + 2x2 max pooling\n - 2\uac1c\uc758 3x3 \ud569\uc131\uacf1 \uce35 + 2x2 max pooling (\uc774\ud558 \ubc18\ubcf5)\n - \ub9c8\uc9c0\ub9c9\uc73c\ub85c 4096, 4096, 1000 \ub274\ub7f0\uc744 \uac00\uc9c4 fully connected layer\n <\/pre>\n3. VGGNet\uc758 \uc7a5\uc810\uacfc \ub2e8\uc810<\/h2>\n
\uc7a5\uc810<\/h3>\n
\n
- \ub192\uc740 \uc815\ud655\ub3c4\ub97c \uc790\ub791\ud558\uba70, \uc774\ubbf8\uc9c0 \ubd84\ub958\ub97c \uc704\ud55c \ub9ce\uc740 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uc6b0\uc218\ud55c \uc131\ub2a5\uc744 \ubcf4\uc784.<\/li>\n
- \ub2e8\uc21c\ud55c \uc544\ud0a4\ud14d\ucc98 \uad6c\uc870\ub85c \uc778\ud574 \uc774\ud574\ud558\uace0 \uad6c\ud604\ud558\uae30 \uc26c\uc6c0.<\/li>\n
- \uc804\uc774 \ud559\uc2b5(Transfer Learning) \ubc0f \ubbf8\uc138 \uc870\uc815(Fine-tuning) \uc2dc \ub69c\ub837\ud55c \uc7a5\uc810 \uc81c\uacf5.<\/li>\n<\/ul>\n
\ub2e8\uc810<\/h3>\n
\n
- \ud30c\ub77c\ubbf8\ud130 \uc218\uac00 \ub9ce\uc544 \ubaa8\ub378\uc774 \ucee4\uc9c0\uace0, \uacc4\uc0b0 \ub9ac\uc18c\uc2a4\ub97c \ub9ce\uc774 \uc18c\ubaa8\ud568.<\/li>\n
- \ud559\uc2b5 \uc18d\ub3c4\uac00 \ub290\ub9ac\uba70, \uacfc\uc801\ud569(overfitting) \uc704\ud5d8\uc774 \uc874\uc7ac\ud568.<\/li>\n<\/ul>\n
4. PyTorch\ub97c \uc774\uc6a9\ud55c VGGNet \uad6c\ud604<\/h2>\n
\uc774\uc81c VGGNet\uc744 PyTorch\ub85c \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\ub294 Python\uc5d0\uc11c \uad6c\ud604\ub41c \uc624\ud508\uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ud2b9\ud788 \ub3d9\uc801 \uc2e0\uacbd\ub9dd \uad6c\ucd95\uacfc \ucc98\ub9ac\uc5d0 \uc720\uc6a9\ud569\ub2c8\ub2e4. VGGNet\uc758 \uad6c\ud604\uc744 \ud1b5\ud558\uc5ec torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 \uc77c\ubd80\ub85c \uc81c\uacf5\ub418\ub294 \uc0ac\uc804 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ud658\uacbd \uc124\uc815<\/h3>\n
\uc6b0\uc120, \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub85c PyTorch\uc640 torchvision\uc744 \uc124\uce58\ud574\uc8fc\uc138\uc694.<\/p>\n
!pip install torch torchvision<\/code><\/pre>\n4.2 VGGNet \ubaa8\ub378 \ub85c\ub529<\/h3>\n
\uc774\uc81c PyTorch\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 VGG \ubaa8\ub378\uc744 \ub85c\ub529\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 VGG11 \ubaa8\ub378\uc744 \ub85c\ub529\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\nimport torch\nimport torchvision.models as models\nvgg11 = models.vgg11(pretrained=True)\n <\/code><\/pre>\n4.3 \ub370\uc774\ud130 \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
VGGNet\uc5d0 \uc785\ub825\ub420 \uc774\ubbf8\uc9c0\ub97c \ub85c\ub4dc\ud558\uace0 \uc804\ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. torchvision.transforms\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubcc0\ud658\ud569\ub2c8\ub2e4:<\/p>\n
\nfrom torchvision import transforms\nfrom PIL import Image\n\ntransform = transforms.Compose([\n transforms.Resize((224, 224)), # \uc774\ubbf8\uc9c0 \ud06c\uae30 \uc870\uc815\n transforms.ToTensor(), # \ud150\uc11c \ubcc0\ud658\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # \uc815\uaddc\ud654\n])\n \n# \uc774\ubbf8\uc9c0 \ub85c\ub4dc\nimage = Image.open('image.jpg')\nimage = transform(image).unsqueeze(0) # \ubc30\uce58 \ucc28\uc6d0 \ucd94\uac00\n <\/code><\/pre>\n4.4 \uc774\ubbf8\uc9c0 \ucd94\ub860<\/h3>\n
\ub85c\ub4dc\ud55c \uc774\ubbf8\uc9c0\ub97c VGGNet \ubaa8\ub378\uc5d0 \ud1b5\uacfc\uc2dc\ucf1c \uc608\uce21\uc744 \uc218\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
\nvgg11.eval() # \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc804\ud658\n\nwith torch.no_grad(): # \uae30\uc6b8\uae30 \uacc4\uc0b0 \ube44\ud65c\uc131\ud654\n output = vgg11(image)\n\n# \uacb0\uacfc \ud655\uc778\n_, predicted = torch.max(output, 1)\nprint(\"Predicted class:\", predicted.item())\n <\/code><\/pre>\n5. VGGNet\uc758 \uc2dc\uac01\ud654<\/h2>\n
VGGNet\uc758 \ud559\uc2b5 \uacfc\uc815\uacfc \uc911\uc694\ud55c feature map\uc744 \uc2dc\uac01\ud654\ud558\uc5ec \uc774\ud574\ub97c \ub3d5\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c\ub3c4 \ud0d0\uad6c\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. Grad-CAM\uacfc \uac19\uc740 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1 Grad-CAM<\/h3>\n
Grad-CAM(Gradient-weighted Class Activation Mapping)\uc740 \uc774\ubbf8\uc9c0\uc758 \ud2b9\uc815 \ud074\ub798\uc2a4\uc5d0 \ub300\ud558\uc5ec \ubaa8\ub378\uc774 \uc5b4\ub5a4 \ubd80\ubd84\uc5d0 \uc8fc\ubaa9\ud588\ub294\uc9c0\ub97c \uc2dc\uac01\ud654\ud574 \uc8fc\ub294 \uac15\ub825\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. PyTorch\uc5d0\uc11c Grad-CAM\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\nimport numpy as np\nimport cv2\n\n# \ud568\uc218 \uc815\uc758\ndef generate_gradcam(image, model, layer_name):\n # ... implement Grad-CAM algorithm using hooks ...\n return heatmap\n\n# Grad-CAM \uc0dd\uc131 \ubc0f \uc2dc\uac01\ud654\nheatmap = generate_gradcam(image, vgg11, 'conv5_3')\nheatmap = cv2.resize(heatmap, (image.size(2), image.size(3)))\nheatmap = np.maximum(heatmap, 0)\nheatmap = heatmap \/ heatmap.max()\n <\/code><\/pre>\n6. VGGNet\uc758 \ubc1c\uc804 \ubc29\ud5a5<\/h2>\n
VGGNet\uc740 \uadf8 \uc790\uccb4\ub85c\ub3c4 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc600\uc9c0\ub9cc, \uc774\ud6c4 \uc5ec\ub7ec \uc544\ud0a4\ud14d\ucc98\uac00 \ub4f1\uc7a5\ud558\uba74\uc11c \uc131\ub2a5 \uba74\uc5d0\uc11c \uc810\ucc28 \uae34\uc7a5\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. ResNet, Inception, EfficientNet \ub4f1 \ub2e4\uc591\ud55c \ubcc0\ud615\ub4e4\uc774 VGGNet\uc758 \ub2e8\uc810\ub4e4\uc744 \ubcf4\uc644\ud558\uace0 \ubcf4\ub2e4 \ud6a8\uc728\uc801\uc778 \ud559\uc2b5 \ubc0f \uc608\uce21\uc774 \uac00\ub2a5\ud558\ub3c4\ub85d \ubc1c\uc804\ud574 \uc654\uc2b5\ub2c8\ub2e4.<\/p>\n
7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \ube14\ub85c\uadf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 VGGNet\uc758 \uac1c\uc694\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, PyTorch\ub97c \ud1b5\ud55c \uad6c\ud604, \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ubaa8\ub378 \ucd94\ub860, Grad-CAM\uc744 \ud1b5\ud55c \uc2dc\uac01\ud654\uae4c\uc9c0 \ud3ed\ub113\uc740 \ub0b4\uc6a9\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. VGGNet\uc740 \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc5d0 \uc911\uc694\ud55c \uae30\uc5ec\ub97c \ud55c \ubaa8\ub378\uc774\uba70, \uc9c0\uae08\ub3c4 \ub9ce\uc740 \uc5f0\uad6c\uc640 \uc2e4\uc81c \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \uc9c0\uc2dd \ud655\uc7a5\uc744 \uc704\ud574 \ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\ub4e4\uc744 \ud0d0\uad6c\ud558\ub294 \uac83\ub3c4 \uc88b\uc740 \uc2dc\ub3c4\uac00 \ub420 \uac83\uc785\ub2c8\ub2e4. \ub3c5\uc790 \uc5ec\ub7ec\ubd84\uc758 \uacc4\uc18d\ub41c \ud559\uc2b5\uacfc \uc5f0\uad6c\uc5d0 \ud070 \uc751\uc6d0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
\ucc38\uace0 \ubb38\ud5cc<\/h2>\n