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yolov8s-seg-solar-panels

Image Segmentation
PyTorch
ultralytics
ultralyticsplus
yolov8
yolo
vision
instance-segmentation
awesome-yolov8-models
solar-panels
pv-panels
Model card Files Community
1
Piotr Krawiec commited on
Commit
6d618a8
•
1 Parent(s): 26c7a12

Upload training results

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Files changed (29) hide show
  1. train/BoxF1_curve.png +0 -0
  2. train/BoxPR_curve.png +0 -0
  3. train/BoxP_curve.png +0 -0
  4. train/BoxR_curve.png +0 -0
  5. train/MaskF1_curve.png +0 -0
  6. train/MaskPR_curve.png +0 -0
  7. train/MaskP_curve.png +0 -0
  8. train/MaskR_curve.png +0 -0
  9. train/args.yaml +95 -0
  10. train/confusion_matrix.png +0 -0
  11. train/labels.jpg +0 -0
  12. train/labels_correlogram.jpg +0 -0
  13. train/model_artifacts.json +1 -0
  14. train/results.csv +201 -0
  15. train/results.png +0 -0
  16. train/train_batch0.jpg +0 -0
  17. train/train_batch1.jpg +0 -0
  18. train/train_batch101080.jpg +0 -0
  19. train/train_batch101081.jpg +0 -0
  20. train/train_batch101082.jpg +0 -0
  21. train/train_batch2.jpg +0 -0
  22. train/val_batch0_labels.jpg +0 -0
  23. train/val_batch0_pred.jpg +0 -0
  24. train/val_batch1_labels.jpg +0 -0
  25. train/val_batch1_pred.jpg +0 -0
  26. train/val_batch2_labels.jpg +0 -0
  27. train/val_batch2_pred.jpg +0 -0
  28. train/weights/best.pt +3 -0
  29. train/weights/last.pt +3 -0
train/BoxF1_curve.png ADDED
train/BoxPR_curve.png ADDED
train/BoxP_curve.png ADDED
train/BoxR_curve.png ADDED
train/MaskF1_curve.png ADDED
train/MaskPR_curve.png ADDED
train/MaskP_curve.png ADDED
train/MaskR_curve.png ADDED
train/args.yaml ADDED
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+ task: segment
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+ mode: train
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+ model: yolov8s-seg.pt
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+ data: data.yaml
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+ epochs: 200
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+ patience: 50
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+ batch: 16
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+ imgsz: 640
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+ save: true
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+ save_period: -1
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+ cache: false
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+ device: null
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+ workers: 8
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+ project: null
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+ name: null
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+ exist_ok: false
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+ pretrained: false
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+ optimizer: SGD
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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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+ image_weights: 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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+ 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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+ show: 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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+ hide_labels: false
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+ hide_conf: false
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+ vid_stride: 1
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+ line_thickness: 3
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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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+ boxes: true
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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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+ fl_gamma: 0.0
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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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+ cfg: null
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+ v5loader: false
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+ tracker: botsort.yaml
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+ save_dir: runs/segment/train
train/confusion_matrix.png ADDED
train/labels.jpg ADDED
train/labels_correlogram.jpg ADDED
train/model_artifacts.json ADDED
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+ {"names": ["solar-panel"], "yaml": {"nc": 1, "depth_multiple": 0.33, "width_multiple": 0.5, "backbone": [[-1, 1, "Conv", [64, 3, 2]], [-1, 1, "Conv", [128, 3, 2]], [-1, 3, "C2f", [128, true]], [-1, 1, "Conv", [256, 3, 2]], [-1, 6, "C2f", [256, true]], [-1, 1, "Conv", [512, 3, 2]], [-1, 6, "C2f", [512, true]], [-1, 1, "Conv", [1024, 3, 2]], [-1, 3, "C2f", [1024, true]], [-1, 1, "SPPF", [1024, 5]]], "head": [[-1, 1, "nn.Upsample", ["None", 2, "nearest"]], [[-1, 6], 1, "Concat", [1]], [-1, 3, "C2f", [512]], [-1, 1, "nn.Upsample", ["None", 2, "nearest"]], [[-1, 4], 1, "Concat", [1]], [-1, 3, "C2f", [256]], [-1, 1, "Conv", [256, 3, 2]], [[-1, 12], 1, "Concat", [1]], [-1, 3, "C2f", [512]], [-1, 1, "Conv", [512, 3, 2]], [[-1, 9], 1, "Concat", [1]], [-1, 3, "C2f", [1024]], [[15, 18, 21], 1, "Segment", ["nc", 32, 256]]], "yaml_file": "/usr/src/app/ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml", "ch": 3}, "nc": 1, "args": {"model": "yolov8s-seg.pt", "batch": 16, "imgsz": 640}, "ultralytics_version": "8.0.20", "model_type": "yolov8s-seg"}
train/results.csv ADDED
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
+ 80, 0.65933, 0.96515, 0.43089, 0.87851, 0.88429, 0.76587, 0.81545, 0.66516, 0.86632, 0.73893, 0.79579, 0.59989, 0.67933, nan, 0.45942, 0.90243, 0.0060895, 0.0060895, 0.0060895
83
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84
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85
+ 83, 0.65412, 0.95496, 0.42595, 0.87675, 0.89524, 0.76515, 0.81792, 0.66704, 0.88667, 0.73214, 0.79432, 0.60071, 0.67131, nan, 0.45588, 0.89954, 0.005941, 0.005941, 0.005941
86
+ 84, 0.65647, 0.95563, 0.42629, 0.87667, 0.90368, 0.75619, 0.81739, 0.66807, 0.89385, 0.73252, 0.7942, 0.60208, 0.6687, nan, 0.45535, 0.89901, 0.0058915, 0.0058915, 0.0058915
87
+ 85, 0.65657, 0.9539, 0.42462, 0.87574, 0.89595, 0.76569, 0.81731, 0.66996, 0.88657, 0.73701, 0.79414, 0.60558, 0.667, nan, 0.45535, 0.90018, 0.005842, 0.005842, 0.005842
88
+ 86, 0.65207, 0.95326, 0.4262, 0.87565, 0.88755, 0.76974, 0.81984, 0.67214, 0.88542, 0.74148, 0.80021, 0.60747, 0.66819, nan, 0.45045, 0.90203, 0.0057925, 0.0057925, 0.0057925
89
+ 87, 0.65112, 0.95222, 0.4231, 0.87568, 0.89118, 0.76637, 0.81929, 0.67036, 0.88793, 0.74148, 0.80072, 0.6071, 0.66841, nan, 0.45054, 0.90319, 0.005743, 0.005743, 0.005743
90
+ 88, 0.65382, 0.94723, 0.42498, 0.87615, 0.89297, 0.76286, 0.81981, 0.67156, 0.88209, 0.74292, 0.80322, 0.60779, 0.66651, nan, 0.45331, 0.90171, 0.0056935, 0.0056935, 0.0056935
91
+ 89, 0.64885, 0.93118, 0.42011, 0.87313, 0.87952, 0.77304, 0.82045, 0.67194, 0.8717, 0.74758, 0.80212, 0.60733, 0.66718, nan, 0.45283, 0.90054, 0.005644, 0.005644, 0.005644
92
+ 90, 0.64739, 0.93802, 0.42083, 0.87293, 0.8797, 0.77698, 0.82019, 0.67328, 0.87646, 0.74789, 0.80476, 0.61042, 0.66474, nan, 0.45115, 0.89914, 0.0055945, 0.0055945, 0.0055945
93
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94
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95
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96
+ 94, 0.64102, 0.92745, 0.41542, 0.87071, 0.88495, 0.76945, 0.82096, 0.67662, 0.88679, 0.73897, 0.80563, 0.61031, 0.65774, nan, 0.44525, 0.89402, 0.0053965, 0.0053965, 0.0053965
97
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98
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99
+ 97, 0.6319, 0.92537, 0.40966, 0.87023, 0.88459, 0.77411, 0.82017, 0.67694, 0.87782, 0.75009, 0.80612, 0.60822, 0.6548, nan, 0.44327, 0.8915, 0.005248, 0.005248, 0.005248
100
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101
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102
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103
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104
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105
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106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
+ 120, 0.60241, 0.86838, 0.38241, 0.85847, 0.8899, 0.77555, 0.82136, 0.68302, 0.88769, 0.75475, 0.80931, 0.61845, 0.64944, nan, 0.43152, 0.89368, 0.0041095, 0.0041095, 0.0041095
123
+ 121, 0.59974, 0.86675, 0.38132, 0.85926, 0.88769, 0.77626, 0.82141, 0.68287, 0.88648, 0.75403, 0.80956, 0.61828, 0.64891, nan, 0.43123, 0.89327, 0.00406, 0.00406, 0.00406
124
+ 122, 0.59283, 0.8517, 0.37744, 0.85782, 0.8849, 0.77734, 0.82119, 0.68282, 0.89313, 0.74616, 0.80909, 0.61889, 0.64863, nan, 0.43153, 0.89311, 0.0040105, 0.0040105, 0.0040105
125
+ 123, 0.59687, 0.86234, 0.37978, 0.85838, 0.88399, 0.77734, 0.8216, 0.6833, 0.89597, 0.74579, 0.80889, 0.61913, 0.64793, nan, 0.43101, 0.89263, 0.003961, 0.003961, 0.003961
126
+ 124, 0.59327, 0.8577, 0.37513, 0.85681, 0.88613, 0.7777, 0.8215, 0.68353, 0.8961, 0.74686, 0.80814, 0.61959, 0.6467, nan, 0.42998, 0.89192, 0.0039115, 0.0039115, 0.0039115
127
+ 125, 0.5989, 0.85899, 0.37965, 0.85982, 0.88428, 0.77806, 0.82246, 0.68426, 0.89799, 0.74486, 0.80788, 0.61964, 0.64572, nan, 0.42988, 0.89145, 0.003862, 0.003862, 0.003862
128
+ 126, 0.59199, 0.85412, 0.37588, 0.85835, 0.88811, 0.7777, 0.82213, 0.68471, 0.89339, 0.74866, 0.8082, 0.62069, 0.64563, nan, 0.42907, 0.8911, 0.0038125, 0.0038125, 0.0038125
129
+ 127, 0.5922, 0.85448, 0.37703, 0.85724, 0.89096, 0.77591, 0.82212, 0.68497, 0.89047, 0.75209, 0.80877, 0.6214, 0.64523, nan, 0.42813, 0.8911, 0.003763, 0.003763, 0.003763
130
+ 128, 0.59092, 0.85292, 0.37544, 0.85641, 0.89565, 0.77248, 0.82265, 0.68531, 0.89321, 0.75045, 0.80888, 0.62158, 0.64494, nan, 0.42779, 0.89106, 0.0037135, 0.0037135, 0.0037135
131
+ 129, 0.59151, 0.8496, 0.37468, 0.85716, 0.89257, 0.77453, 0.82324, 0.68495, 0.89775, 0.7492, 0.80878, 0.62214, 0.64485, nan, 0.42745, 0.89098, 0.003664, 0.003664, 0.003664
132
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