hdnh2006
commited on
Commit
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yolov8n added
Browse files- README.md +1 -1
- yolov8n/BoxF1_curve.png +0 -0
- yolov8n/BoxPR_curve.png +0 -0
- yolov8n/BoxP_curve.png +0 -0
- yolov8n/BoxR_curve.png +0 -0
- yolov8n/MaskF1_curve.png +0 -0
- yolov8n/MaskPR_curve.png +0 -0
- yolov8n/MaskP_curve.png +0 -0
- yolov8n/MaskR_curve.png +0 -0
- yolov8n/args.yaml +105 -0
- yolov8n/confusion_matrix.png +0 -0
- yolov8n/confusion_matrix_normalized.png +0 -0
- yolov8n/labels.jpg +0 -0
- yolov8n/labels_correlogram.jpg +0 -0
- yolov8n/results.csv +101 -0
- yolov8n/results.png +0 -0
- yolov8n/train_batch0.jpg +0 -0
- yolov8n/train_batch1.jpg +0 -0
- yolov8n/train_batch10530.jpg +0 -0
- yolov8n/train_batch10531.jpg +0 -0
- yolov8n/train_batch10532.jpg +0 -0
- yolov8n/train_batch2.jpg +0 -0
- yolov8n/val_batch0_labels.jpg +0 -0
- yolov8n/val_batch0_pred.jpg +0 -0
- yolov8n/val_batch1_labels.jpg +0 -0
- yolov8n/val_batch1_pred.jpg +0 -0
- yolov8n/val_batch2_labels.jpg +0 -0
- yolov8n/val_batch2_pred.jpg +0 -0
- yolov8n/weights/best.pt +3 -0
- yolov8n/weights/last.pt +3 -0
README.md
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@@ -10,7 +10,7 @@ license: agpl-3.0
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# YOLOv8 for crack segmentation 🚀
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This repository contains the Ultralytics YOLOv8 models trained on the Crack-seg dataset, a comprehensive resource designed for crack segmentation tasks in road and wall scenarios. The dataset includes 4029 static images divided into training (3717 images), testing (112 images), and validation (200 images) sets.
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<div align="center">
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# YOLOv8 for crack segmentation 🚀
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This repository contains the Ultralytics YOLOv8 models trained on the [Crack-seg dataset](https://docs.ultralytics.com/datasets/segment/crack-seg/) by [OpenSistemas](https://bit.ly/3NMFz8D), a comprehensive resource designed for crack segmentation tasks in road and wall scenarios. The dataset includes 4029 static images divided into training (3717 images), testing (112 images), and validation (200 images) sets.
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<div align="center">
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yolov8n/BoxF1_curve.png
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yolov8n/BoxPR_curve.png
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yolov8n/BoxP_curve.png
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yolov8n/BoxR_curve.png
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yolov8n/MaskF1_curve.png
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yolov8n/MaskPR_curve.png
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yolov8n/MaskP_curve.png
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yolov8n/MaskR_curve.png
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yolov8n/args.yaml
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task: segment
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mode: train
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model: yolov8n-seg.pt
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data: crack-seg.yaml
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epochs: 100
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time: null
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patience: 50
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batch: 32
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imgsz: 640
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save: true
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save_period: -1
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cache: ram
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device: null
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workers: 16
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project: crack-seg
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name: yolov8n
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exist_ok: false
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pretrained: true
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optimizer: auto
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verbose: true
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seed: 0
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deterministic: true
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single_cls: false
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rect: false
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cos_lr: false
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close_mosaic: 10
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resume: false
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amp: true
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fraction: 1.0
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profile: false
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freeze: null
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multi_scale: false
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overlap_mask: true
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mask_ratio: 4
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dropout: 0.0
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val: true
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split: val
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save_json: false
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save_hybrid: false
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conf: null
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iou: 0.7
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max_det: 300
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half: false
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dnn: false
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plots: true
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source: null
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vid_stride: 1
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stream_buffer: false
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visualize: false
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augment: false
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agnostic_nms: false
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classes: null
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retina_masks: false
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embed: null
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show: false
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save_frames: false
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save_txt: false
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save_conf: false
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save_crop: false
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show_labels: true
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show_conf: true
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show_boxes: true
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line_width: null
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format: torchscript
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keras: false
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optimize: false
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int8: false
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dynamic: false
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simplify: false
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opset: null
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workspace: 4
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nms: false
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lr0: 0.01
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lrf: 0.01
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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warmup_momentum: 0.8
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warmup_bias_lr: 0.1
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box: 7.5
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cls: 0.5
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dfl: 1.5
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pose: 12.0
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kobj: 1.0
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label_smoothing: 0.0
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nbs: 64
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 0.0
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translate: 0.1
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scale: 0.5
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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mosaic: 1.0
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mixup: 0.0
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copy_paste: 0.0
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auto_augment: randaugment
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erasing: 0.4
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crop_fraction: 1.0
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cfg: null
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tracker: botsort.yaml
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save_dir: crack-seg/yolov8n
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yolov8n/confusion_matrix.png
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yolov8n/confusion_matrix_normalized.png
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yolov8n/labels.jpg
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yolov8n/labels_correlogram.jpg
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yolov8n/results.csv
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epoch, train/box_loss, train/seg_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), metrics/precision(M), metrics/recall(M), metrics/mAP50(M), metrics/mAP50-95(M), val/box_loss, val/seg_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
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1, 1.4215, 2.1162, 2.0227, 1.3462, 0.21215, 0.26104, 0.14708, 0.0494, 0.35628, 0.27341, 0.20119, 0.05147, 2.3727, 1.5706, 3.7141, 2.4328, 0.00066097, 0.00066097, 0.00066097
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2, 1.3604, 1.6691, 1.5045, 1.2913, 0.54522, 0.47791, 0.44629, 0.17504, 0.52887, 0.38956, 0.32759, 0.09306, 2.0162, 1.4068, 2.9415, 2.0302, 0.0013145, 0.0013145, 0.0013145
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3, 1.3304, 1.6352, 1.3483, 1.2781, 0.40678, 0.49398, 0.29781, 0.10512, 0.38788, 0.46061, 0.30576, 0.07611, 2.2461, 1.378, 2.2787, 1.9873, 0.0019548, 0.0019548, 0.0019548
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4, 1.2986, 1.6638, 1.2646, 1.2541, 0.49629, 0.59353, 0.47817, 0.22973, 0.40326, 0.51807, 0.31822, 0.08533, 1.8045, 1.3485, 1.7746, 1.6785, 0.0019406, 0.0019406, 0.0019406
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5, 1.2295, 1.6228, 1.1945, 1.2299, 0.65961, 0.59924, 0.54689, 0.26858, 0.54161, 0.46977, 0.34837, 0.09579, 1.6908, 1.407, 1.6586, 1.5769, 0.0019406, 0.0019406, 0.0019406
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6, 1.1965, 1.6065, 1.1405, 1.2241, 0.44743, 0.55422, 0.38188, 0.16786, 0.32903, 0.4739, 0.23394, 0.05615, 1.8158, 1.3222, 2.0589, 1.7699, 0.0019208, 0.0019208, 0.0019208
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7, 1.1536, 1.5711, 1.097, 1.1912, 0.7091, 0.64612, 0.66919, 0.3483, 0.59485, 0.55422, 0.49547, 0.14569, 1.5551, 1.2506, 1.4384, 1.391, 0.001901, 0.001901, 0.001901
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8, 1.1254, 1.569, 1.0548, 1.1796, 0.70713, 0.65936, 0.63815, 0.39316, 0.59024, 0.53012, 0.43279, 0.12199, 1.4237, 1.3442, 1.4993, 1.3534, 0.0018812, 0.0018812, 0.0018812
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9, 1.1028, 1.554, 1.046, 1.1772, 0.66467, 0.62888, 0.66343, 0.36417, 0.48539, 0.57831, 0.46756, 0.1311, 1.4044, 1.23, 1.5193, 1.346, 0.0018614, 0.0018614, 0.0018614
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10, 1.085, 1.5388, 1.0042, 1.1595, 0.64374, 0.65462, 0.60482, 0.31533, 0.51015, 0.49398, 0.391, 0.1082, 1.6036, 1.2481, 1.5655, 1.5104, 0.0018416, 0.0018416, 0.0018416
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11, 1.0708, 1.547, 1.0194, 1.1606, 0.74693, 0.68675, 0.71499, 0.45273, 0.69128, 0.60643, 0.55742, 0.17542, 1.4129, 1.19, 1.3117, 1.3378, 0.0018218, 0.0018218, 0.0018218
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12, 1.0663, 1.5584, 0.99878, 1.1528, 0.77969, 0.61446, 0.69412, 0.42635, 0.67777, 0.53414, 0.51236, 0.16316, 1.3591, 1.1968, 1.3206, 1.2983, 0.001802, 0.001802, 0.001802
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13, 1.0324, 1.5302, 0.97604, 1.1461, 0.76375, 0.69478, 0.7232, 0.44486, 0.68199, 0.57707, 0.5462, 0.17274, 1.3814, 1.2332, 1.276, 1.2974, 0.0017822, 0.0017822, 0.0017822
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14, 1.0265, 1.5065, 0.95635, 1.1375, 0.74337, 0.66265, 0.67047, 0.43493, 0.62448, 0.5743, 0.45878, 0.15248, 1.258, 1.2828, 1.3367, 1.2539, 0.0017624, 0.0017624, 0.0017624
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25, 0.92318, 1.4441, 0.83466, 1.0871, 0.76848, 0.71486, 0.75394, 0.54226, 0.72933, 0.62764, 0.60902, 0.19747, 1.147, 1.2039, 1.113, 1.1512, 0.0015446, 0.0015446, 0.0015446
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26, 0.93179, 1.4744, 0.8445, 1.0946, 0.75054, 0.71084, 0.72348, 0.52246, 0.65038, 0.61446, 0.54402, 0.17394, 1.0815, 1.2175, 1.1119, 1.1124, 0.0015248, 0.0015248, 0.0015248
|
28 |
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27, 0.918, 1.4555, 0.8238, 1.0938, 0.78876, 0.7198, 0.78369, 0.53492, 0.70074, 0.63948, 0.62518, 0.19676, 1.1715, 1.1774, 1.0766, 1.1551, 0.001505, 0.001505, 0.001505
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29 |
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28, 0.92719, 1.465, 0.82293, 1.0926, 0.77424, 0.71888, 0.75937, 0.53169, 0.68205, 0.64257, 0.61697, 0.20315, 1.1155, 1.1819, 1.0793, 1.1267, 0.0014852, 0.0014852, 0.0014852
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29, 0.91256, 1.4473, 0.80261, 1.0828, 0.84284, 0.70683, 0.77823, 0.53964, 0.74352, 0.60241, 0.60278, 0.19807, 1.1111, 1.2165, 1.0732, 1.1333, 0.0014654, 0.0014654, 0.0014654
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30, 0.9074, 1.4463, 0.8266, 1.0868, 0.76259, 0.7353, 0.77474, 0.54252, 0.68313, 0.63205, 0.62009, 0.2076, 1.1087, 1.226, 1.0426, 1.101, 0.0014456, 0.0014456, 0.0014456
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31, 0.90073, 1.4353, 0.80767, 1.0763, 0.79321, 0.70861, 0.76638, 0.54722, 0.68517, 0.6506, 0.60815, 0.20459, 1.0109, 1.2374, 1.0754, 1.0693, 0.0014258, 0.0014258, 0.0014258
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34 |
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33, 0.89268, 1.4301, 0.79419, 1.0776, 0.79858, 0.69076, 0.75654, 0.51713, 0.70454, 0.59438, 0.58393, 0.18669, 1.1357, 1.1965, 1.0853, 1.1595, 0.0013862, 0.0013862, 0.0013862
|
35 |
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34, 0.88709, 1.4385, 0.77524, 1.0692, 0.83159, 0.73376, 0.8136, 0.58427, 0.74241, 0.62505, 0.61839, 0.183, 1.0874, 1.2378, 0.98556, 1.1056, 0.0013664, 0.0013664, 0.0013664
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