\ud3c9\uac00 \uc9c0\ud45c \uacc4\uc0b0: \uc704\uc5d0\uc11c \uc124\uba85\ud55c \uc131\ub2a5 \uc9c0\ud45c\ub4e4\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n1.3. mAP \uacc4\uc0b0 \uc608\uc81c<\/h3>\n
\uc5ec\uae30\uc11c\ub294 COCO \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec YOLO \ubaa8\ub378\uc758 mAP\ub97c \uacc4\uc0b0\ud558\ub294 \uac04\ub2e8\ud55c \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport numpy as np\n\n# \uc2e4\uc81c \ub808\uc774\ube14\uacfc \uc608\uce21 \uacb0\uacfc\ntrue_boxes = np.array([[50, 50, 100, 100, 0], [30, 30, 40, 40, 1]])\npred_boxes = np.array([[70, 70, 150, 150, 0.9], [25, 25, 45, 45, 0.8]])\niou_threshold = 0.5\n\ndef calculate_iou(box1, box2):\n x1, y1, x2, y2 = max(box1[0], box2[0]), max(box1[1], box2[1]), min(box1[2], box2[2]), min(box1[3], box2[3])\n inter_area = max(0, x2 - x1) * max(0, y2 - y1)\n box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])\n box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])\n return inter_area \/ float(box1_area + box2_area - inter_area)\n\ndef compute_map(true_boxes, pred_boxes, iou_threshold):\n matches = 0\n total_true = len(true_boxes)\n \n for p_box in pred_boxes:\n for t_box in true_boxes:\n iou = calculate_iou(t_box, p_box[:-1])\n if iou >= iou_threshold:\n matches += 1\n break\n\n return matches \/ total_true if total_true > 0 else 0\n\nmAP = compute_map(true_boxes, pred_boxes, iou_threshold)\nprint(f\"mAP: {mAP:.2f}\")\n<\/code><\/pre>\n2. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95<\/h2>\n2.1. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub780?<\/h3>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\uc131\ud558\ub294 \uc678\ubd80 \uc124\uc815\uac12\uc73c\ub85c, \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \uc9c1\uc811\uc801\uc778 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \uc8fc\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \ud559\uc2b5\ub960(Learning Rate)<\/strong>: \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud560 \ub54c\uc758 \ud06c\uae30\uc785\ub2c8\ub2e4.<\/li>\n
- \ubc30\uce58 \ud06c\uae30(Batch Size)<\/strong>: \ud55c \ubc88\uc758 \uc5c5\ub370\uc774\ud2b8\uc5d0 \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130 \uc0d8\ud50c \uc218\uc785\ub2c8\ub2e4.<\/li>\n
- \uc5d0\ud3ed\uc2a4(Epochs)<\/strong>: \uc804\uccb4 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \ud559\uc2b5\uc744 \ubc18\ubcf5\ud558\ub294 \ud69f\uc218\uc785\ub2c8\ub2e4.<\/li>\n
- \ubaa8\ub378 \uc544\ud0a4\ud14d\ucc98<\/strong>: \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870(\uc608: \uce35\uc758 \uc218, \ub274\ub7f0\uc758 \uc218 \ub4f1)\uc785\ub2c8\ub2e4.<\/li>\n
- \uc190\uc2e4 \ud568\uc218(Loss Function)<\/strong>: \ubaa8\ub378 \uc131\ub2a5\uc744 \uce21\uc815\ud558\ub294 \uae30\uc900\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n
2.2. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \uae30\ubc95<\/h3>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- Grid Search<\/strong>: \uc608\uce21 \uac00\ub2a5\ud55c \ubaa8\ub4e0 \uc870\ud569\uc744 \uac80\uc0c9\ud558\uc5ec \ucd5c\uc0c1\uc758 \uc870\ud569\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/li>\n
- Random Search<\/strong>: \ubb34\uc791\uc704\ub85c \uc870\ud569\uc744 \uc120\ud0dd\ud558\uc5ec \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/li>\n
- \ubca0\uc774\uc9c0\uc548 \ucd5c\uc801\ud654(Bayesian Optimization)<\/strong>: \ucee8\ud2b8\ub864 \ubcc0\uc218\ub97c \ubaa8\ub378\ub9c1\ud558\uc5ec \ud6a8\uc728\uc801\uc73c\ub85c \ucd5c\uc801 \uc870\ud569\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uc870\uae30 \uc885\ub8cc(Early Stopping)<\/strong>: \uac80\uc99d \ub370\uc774\ud130\uc5d0\uc11c \uc131\ub2a5\uc774 \uac10\uc18c\ud558\uae30 \uc2dc\uc791\ud560 \ub54c \ud559\uc2b5\uc744 \uc911\ub2e8\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
2.3. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \uc608\uc81c<\/h3>\n
\ub2e4\uc74c\uc740 PyTorch\ub97c \uc0ac\uc6a9\ud55c YOLO \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \uc608\uc2dc\uc785\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 Grid Search \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ub960\uacfc \ubc30\uce58 \ud06c\uae30\ub97c \uc870\uc815\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch\nfrom torch.utils.data import DataLoader\n\n# \uae30\ubcf8 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\nlearning_rates = [0.001, 0.01, 0.1]\nbatch_sizes = [16, 32, 64]\nbest_mAP = 0\n\n# \ub370\uc774\ud130\uc14b \ubc0f \ubaa8\ub378 \ucd08\uae30\ud654\ndataset = ... # \ub370\uc774\ud130\uc14b\ub85c\ub4dc\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nfor lr in learning_rates:\n for batch_size in batch_sizes:\n loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n model = ... # YOLO \ubaa8\ub378 \ucd08\uae30\ud654\n optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n\n # \ubaa8\ub378 \ud559\uc2b5\n for epoch in range(10): # \uac04\ub2e8\ud55c \ubc18\ubcf5 \uc608\uc2dc\n for images, targets in loader:\n # Forward pass\n outputs = model(images.to(device))\n\n # \uc190\uc2e4 \uacc4\uc0b0 \ubc0f \uc5ed\uc804\ud30c\n loss = ... # \uc190\uc2e4 \ud568\uc218\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n # mAP \uacc4\uc0b0 \ud6c4 \ube44\uad50\n mAP = ... # mAP \uacc4\uc0b0 \ud568\uc218\n if mAP > best_mAP:\n best_mAP = mAP\n best_params = (lr, batch_size)\n\nprint(f\"\ucd5c\uc0c1\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130: \ud559\uc2b5\ub960={best_params[0]}, \ubc30\uce58 \ud06c\uae30={best_params[1]}\")\n<\/code><\/pre>\n3. \uacb0\ub860<\/h2>\n
YOLO\ub294 \ub9ce\uc740 \uac1d\uccb4 \ud0d0\uc9c0 \ubb38\uc81c\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uc9c0\ub9cc, \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uacfc \uc131\ub2a5 \ud3c9\uac00\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 YOLO \ubaa8\ub378\uc758 \uc131\ub2a5 \ud3c9\uac00 \ubc29\ubc95\uacfc \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud55c \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774 \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uc2e4\uc2dc\uac04 \uac1d\uccb4 \ud0d0\uc9c0 \uae30\uc220\uc744 \ub354\uc6b1 \ubc1c\uc804\uc2dc\ud0ac \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
YOLO(You Only Look Once)\ub294 \uc2e4\uc2dc\uac04 \uac1d\uccb4 \ud0d0\uc9c0 \uc2dc\uc2a4\ud15c\uc73c\ub85c, \ud55c \ubc88\uc758 \uc2e0\uacbd\ub9dd \ud3c9\uac00\ub85c \uc774\ubbf8\uc9c0 \uc804\uccb4\uc5d0\uc11c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \ud601\uc2e0\uc801\uc778 \uae30\uc220\uc785\ub2c8\ub2e4. YOLO\ub294 \uc774\ubbf8\uc9c0 \ucc98\ub9ac \uc18d\ub3c4\uac00 \ube60\ub974\uba70, \ub2e4\uc591\ud55c \uac1d\uccb4 \ud0d0\uc9c0 \ubb38\uc81c\uc5d0\uc11c \ub192\uc740 \uc815\ud655\ub3c4\ub97c \uc790\ub791\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574\uc11c\ub294 \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uacfc \uc131\ub2a5 \ud3c9\uac00\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 YOLO\uc758 \uc131\ub2a5 \ud3c9\uac00 \ubc29\ubc95, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 … \ub354 \ubcf4\uae30 “YOLO\uc758 \uc131\ub2a5 \ud3c9\uac00\uc640 \uac1c\uc120 \ubc29\ubc95, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95”<\/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-38838","post","type-post","status-publish","format-standard","hentry","category-yolo"],"yoast_head":"\nYOLO\uc758 \uc131\ub2a5 \ud3c9\uac00\uc640 \uac1c\uc120 \ubc29\ubc95, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95 - \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\/38838\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"YOLO\uc758 \uc131\ub2a5 \ud3c9\uac00\uc640 \uac1c\uc120 \ubc29\ubc95, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc744 \ud1b5\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 \ubc29\ubc95 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"YOLO(You Only Look Once)\ub294 \uc2e4\uc2dc\uac04 \uac1d\uccb4 \ud0d0\uc9c0 \uc2dc\uc2a4\ud15c\uc73c\ub85c, \ud55c \ubc88\uc758 \uc2e0\uacbd\ub9dd \ud3c9\uac00\ub85c \uc774\ubbf8\uc9c0 \uc804\uccb4\uc5d0\uc11c \uac1d\uccb4\ub97c \ud0d0\uc9c0\ud558\ub294 \ud601\uc2e0\uc801\uc778 \uae30\uc220\uc785\ub2c8\ub2e4. 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