roundabout,
created on Sunday, 26 May 2024, 14:42:07 (1716734527),
received on Sunday, 26 May 2024, 14:49:33 (1716734973)
Author identity: vlad <vlad.muntoiu@gmail.com>
2f1290bde604aeb56becd071c0e80593e2185da9
runs/detect/train24/args.yaml
@@ -0,0 +1,106 @@
task: detect
mode: train
model: yolov8n.pt
data: yolo.yaml
epochs: 12
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train24
exist_ok: false
pretrained: true
optimizer: auto
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
save_hybrid: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: false
opset: null
workspace: 4
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
label_smoothing: 0.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
auto_augment: randaugment
erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs/detect/train24
runs/detect/train24/labels.jpg
runs/detect/train24/labels_correlogram.jpg
runs/detect/train24/train_batch0.jpg
runs/detect/train24/train_batch1.jpg
runs/detect/train25/F1_curve.png
runs/detect/train25/PR_curve.png
runs/detect/train25/P_curve.png
runs/detect/train25/R_curve.png
runs/detect/train25/args.yaml
@@ -0,0 +1,106 @@
task: detect
mode: train
model: runs/detect/train23/weights/best.pt
data: yolo.yaml
epochs: 12
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train25
exist_ok: false
pretrained: true
optimizer: auto
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
save_hybrid: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: false
opset: null
workspace: 4
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
label_smoothing: 0.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
auto_augment: randaugment
erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs/detect/train25
runs/detect/train25/confusion_matrix.png
runs/detect/train25/confusion_matrix_normalized.png
runs/detect/train25/labels.jpg
runs/detect/train25/labels_correlogram.jpg
runs/detect/train25/results.csv
@@ -0,0 +1,13 @@
epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
1, 1.2454, 1.7569, 1.6817, 0.59824, 0.42948, 0.48665, 0.32315, 1.3649, 1.5701, 1.8807, 0.0002374, 0.0002374, 0.0002374
2, 1.2486, 1.7347, 1.6712, 0.62732, 0.44375, 0.4828, 0.30686, 1.4117, 1.6972, 1.9447, 0.00043618, 0.00043618, 0.00043618
3, 1.383, 1.7323, 1.9359, 0.688, 0.41098, 0.48747, 0.29905, 1.4607, 1.6992, 1.9699, 0.00059569, 0.00059569, 0.00059569
4, 1.3672, 1.6813, 1.9174, 0.55711, 0.45539, 0.47952, 0.29467, 1.4141, 1.6376, 1.9483, 0.00053728, 0.00053728, 0.00053728
5, 1.3457, 1.5926, 1.8889, 0.61795, 0.45828, 0.50845, 0.32317, 1.3911, 1.5817, 1.9197, 0.00047838, 0.00047838, 0.00047838
6, 1.2959, 1.5489, 1.8435, 0.68057, 0.44669, 0.5072, 0.3289, 1.3176, 1.5268, 1.8535, 0.00041947, 0.00041947, 0.00041947
7, 1.2501, 1.4741, 1.8115, 0.61363, 0.51986, 0.56462, 0.36514, 1.284, 1.4916, 1.8092, 0.00036057, 0.00036057, 0.00036057
8, 1.2166, 1.4044, 1.7806, 0.56984, 0.56159, 0.58051, 0.37996, 1.2141, 1.4034, 1.7481, 0.00030167, 0.00030167, 0.00030167
9, 1.1816, 1.3483, 1.7416, 0.74203, 0.51274, 0.5991, 0.40524, 1.1808, 1.3177, 1.7051, 0.00024276, 0.00024276, 0.00024276
10, 1.1515, 1.2874, 1.7135, 0.76834, 0.53124, 0.63673, 0.4347, 1.1526, 1.2774, 1.6729, 0.00018385, 0.00018385, 0.00018385
11, 1.1105, 1.227, 1.6783, 0.7746, 0.53733, 0.65685, 0.45276, 1.1192, 1.2198, 1.643, 0.00012495, 0.00012495, 0.00012495
12, 1.0766, 1.2029, 1.6479, 0.75353, 0.57269, 0.66865, 0.46779, 1.0896, 1.2053, 1.6157, 6.6045e-05, 6.6045e-05, 6.6045e-05
runs/detect/train25/results.png
runs/detect/train25/train_batch0.jpg
runs/detect/train25/train_batch1.jpg
runs/detect/train25/train_batch2.jpg
runs/detect/train25/train_batch788.jpg
runs/detect/train25/train_batch789.jpg
runs/detect/train25/train_batch790.jpg
runs/detect/train25/val_batch0_labels.jpg
runs/detect/train25/val_batch0_pred.jpg
runs/detect/train25/val_batch1_labels.jpg
runs/detect/train25/val_batch1_pred.jpg
runs/detect/train25/val_batch2_labels.jpg
runs/detect/train25/val_batch2_pred.jpg
runs/detect/train25/weights/best.pt
runs/detect/train25/weights/last.pt
runs/detect/train26/F1_curve.png
runs/detect/train26/PR_curve.png
runs/detect/train26/P_curve.png
runs/detect/train26/R_curve.png
runs/detect/train26/args.yaml
@@ -0,0 +1,106 @@
task: detect
mode: train
model: runs/detect/train25/weights/best.pt
data: yolo.yaml
epochs: 72
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train26
exist_ok: false
pretrained: true
optimizer: auto
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
save_hybrid: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: false
opset: null
workspace: 4
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
label_smoothing: 0.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
auto_augment: randaugment
erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs/detect/train26
runs/detect/train26/confusion_matrix.png
runs/detect/train26/confusion_matrix_normalized.png
runs/detect/train26/labels.jpg
runs/detect/train26/labels_correlogram.jpg
runs/detect/train26/results.csv
@@ -0,0 +1,73 @@
epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
1, 1.1988, 1.6343, 1.6252, 0.71552, 0.53564, 0.60596, 0.41121, 1.2027, 1.3155, 1.7287, 0.0002374, 0.0002374, 0.0002374
2, 1.1651, 1.5462, 1.5943, 0.70339, 0.5223, 0.58526, 0.3756, 1.3047, 1.4371, 1.8161, 0.00046886, 0.00046886, 0.00046886
3, 1.2839, 1.7545, 1.6838, 0.65521, 0.40005, 0.45801, 0.28967, 1.4184, 1.6151, 1.9186, 0.00069378, 0.00069378, 0.00069378
4, 1.3031, 1.7757, 1.6982, 0.60067, 0.44306, 0.48296, 0.2816, 1.4753, 1.6554, 1.9585, 0.00068455, 0.00068455, 0.00068455
5, 1.2994, 1.7803, 1.6969, 0.59964, 0.4589, 0.48976, 0.30314, 1.4488, 1.6687, 1.9472, 0.00067473, 0.00067473, 0.00067473
6, 1.2903, 1.7813, 1.6937, 0.64599, 0.43202, 0.48886, 0.30768, 1.4419, 1.6271, 1.9539, 0.00066491, 0.00066491, 0.00066491
7, 1.2962, 1.7672, 1.693, 0.61577, 0.46334, 0.49865, 0.31306, 1.4249, 1.6394, 1.9344, 0.00065509, 0.00065509, 0.00065509
8, 1.2747, 1.7301, 1.6728, 0.59719, 0.4333, 0.47928, 0.30085, 1.3884, 1.6793, 1.9025, 0.00064528, 0.00064528, 0.00064528
9, 1.2662, 1.731, 1.6731, 0.62698, 0.4398, 0.48754, 0.30301, 1.4329, 1.6015, 1.9364, 0.00063546, 0.00063546, 0.00063546
10, 1.2412, 1.685, 1.6534, 0.68824, 0.4391, 0.52525, 0.32901, 1.4079, 1.5527, 1.9155, 0.00062564, 0.00062564, 0.00062564
11, 1.2544, 1.6839, 1.6608, 0.67309, 0.47558, 0.54859, 0.34177, 1.3836, 1.4634, 1.8812, 0.00061583, 0.00061583, 0.00061583
12, 1.2367, 1.6518, 1.6391, 0.67504, 0.46868, 0.54732, 0.33433, 1.3916, 1.5137, 1.8844, 0.00060601, 0.00060601, 0.00060601
13, 1.236, 1.657, 1.6464, 0.65773, 0.49529, 0.55864, 0.35637, 1.3269, 1.4954, 1.8521, 0.00059619, 0.00059619, 0.00059619
14, 1.2395, 1.6678, 1.6429, 0.62937, 0.46428, 0.54987, 0.34408, 1.3366, 1.554, 1.8618, 0.00058637, 0.00058637, 0.00058637
15, 1.2318, 1.6301, 1.6374, 0.66738, 0.46978, 0.54819, 0.34204, 1.3434, 1.5095, 1.8593, 0.00057655, 0.00057655, 0.00057655
16, 1.2268, 1.6226, 1.631, 0.61689, 0.51115, 0.56745, 0.35887, 1.3401, 1.4698, 1.8483, 0.00056674, 0.00056674, 0.00056674
17, 1.2161, 1.6069, 1.6264, 0.61482, 0.54849, 0.60744, 0.39442, 1.3208, 1.4486, 1.8275, 0.00055692, 0.00055692, 0.00055692
18, 1.2016, 1.5787, 1.6217, 0.73594, 0.48847, 0.62929, 0.39978, 1.3127, 1.4333, 1.8206, 0.0005471, 0.0005471, 0.0005471
19, 1.2077, 1.5838, 1.6133, 0.66005, 0.51849, 0.59395, 0.37833, 1.2941, 1.355, 1.8025, 0.00053728, 0.00053728, 0.00053728
20, 1.1862, 1.5562, 1.6012, 0.64754, 0.53676, 0.60403, 0.39232, 1.276, 1.3342, 1.7858, 0.00052747, 0.00052747, 0.00052747
21, 1.2036, 1.5828, 1.6176, 0.67544, 0.56507, 0.63688, 0.41299, 1.2662, 1.3772, 1.7796, 0.00051765, 0.00051765, 0.00051765
22, 1.1881, 1.537, 1.6013, 0.71446, 0.54732, 0.63539, 0.40828, 1.2728, 1.3253, 1.7828, 0.00050783, 0.00050783, 0.00050783
23, 1.1732, 1.5116, 1.5945, 0.64473, 0.58321, 0.64178, 0.41881, 1.2397, 1.3173, 1.7552, 0.00049801, 0.00049801, 0.00049801
24, 1.175, 1.5267, 1.5913, 0.68893, 0.56641, 0.64711, 0.42174, 1.2415, 1.308, 1.7649, 0.0004882, 0.0004882, 0.0004882
25, 1.1677, 1.4978, 1.5837, 0.70344, 0.56616, 0.65972, 0.4241, 1.2343, 1.2892, 1.7446, 0.00047838, 0.00047838, 0.00047838
26, 1.1673, 1.4994, 1.5926, 0.68324, 0.54891, 0.63361, 0.42194, 1.2118, 1.3245, 1.7272, 0.00046856, 0.00046856, 0.00046856
27, 1.1527, 1.4707, 1.5728, 0.69431, 0.55991, 0.65642, 0.44587, 1.1794, 1.2387, 1.7064, 0.00045874, 0.00045874, 0.00045874
28, 1.1499, 1.4699, 1.5701, 0.71564, 0.57844, 0.67109, 0.44929, 1.1749, 1.2388, 1.7016, 0.00044893, 0.00044893, 0.00044893
29, 1.1407, 1.4579, 1.5605, 0.72666, 0.59582, 0.69061, 0.46453, 1.1735, 1.1986, 1.7092, 0.00043911, 0.00043911, 0.00043911
30, 1.1432, 1.4417, 1.5634, 0.76234, 0.62325, 0.71536, 0.47305, 1.1553, 1.186, 1.6785, 0.00042929, 0.00042929, 0.00042929
31, 1.1423, 1.4299, 1.5571, 0.80203, 0.60151, 0.72524, 0.47755, 1.1551, 1.1835, 1.6732, 0.00041947, 0.00041947, 0.00041947
32, 1.1319, 1.424, 1.545, 0.73093, 0.60379, 0.70759, 0.47579, 1.1385, 1.1944, 1.6709, 0.00040966, 0.00040966, 0.00040966
33, 1.1225, 1.4137, 1.5547, 0.73209, 0.61232, 0.70041, 0.47874, 1.1325, 1.1727, 1.6482, 0.00039984, 0.00039984, 0.00039984
34, 1.1195, 1.397, 1.5423, 0.74336, 0.62722, 0.71766, 0.48394, 1.1349, 1.1657, 1.6535, 0.00039002, 0.00039002, 0.00039002
35, 1.1026, 1.3755, 1.5312, 0.79773, 0.60304, 0.72471, 0.494, 1.1215, 1.1327, 1.643, 0.0003802, 0.0003802, 0.0003802
36, 1.0975, 1.3666, 1.5238, 0.71738, 0.63053, 0.70708, 0.48114, 1.1247, 1.1347, 1.6316, 0.00037039, 0.00037039, 0.00037039
37, 1.0978, 1.3558, 1.525, 0.79357, 0.62456, 0.74372, 0.50797, 1.0883, 1.0885, 1.6011, 0.00036057, 0.00036057, 0.00036057
38, 1.0956, 1.3594, 1.5259, 0.75235, 0.64187, 0.73529, 0.50795, 1.0879, 1.0711, 1.603, 0.00035075, 0.00035075, 0.00035075
39, 1.0783, 1.327, 1.5053, 0.76556, 0.63619, 0.73629, 0.50883, 1.0833, 1.067, 1.5981, 0.00034094, 0.00034094, 0.00034094
40, 1.0904, 1.3405, 1.5252, 0.76977, 0.66331, 0.7618, 0.5298, 1.0679, 1.062, 1.591, 0.00033112, 0.00033112, 0.00033112
41, 1.0832, 1.3186, 1.5119, 0.77606, 0.65205, 0.75525, 0.52875, 1.0561, 1.04, 1.5721, 0.0003213, 0.0003213, 0.0003213
42, 1.076, 1.3241, 1.5055, 0.7809, 0.66753, 0.7575, 0.53674, 1.0499, 1.0406, 1.5679, 0.00031148, 0.00031148, 0.00031148
43, 1.0636, 1.3094, 1.4996, 0.78695, 0.70415, 0.78465, 0.55208, 1.0302, 1.0249, 1.5554, 0.00030167, 0.00030167, 0.00030167
44, 1.0657, 1.2992, 1.5006, 0.76796, 0.69882, 0.78106, 0.55793, 1.0235, 1.01, 1.547, 0.00029185, 0.00029185, 0.00029185
45, 1.0516, 1.2788, 1.4849, 0.76804, 0.68947, 0.77423, 0.54193, 1.0347, 1.0169, 1.5545, 0.00028203, 0.00028203, 0.00028203
46, 1.0447, 1.2599, 1.4786, 0.76548, 0.70777, 0.78697, 0.55245, 1.0042, 1.0011, 1.5319, 0.00027221, 0.00027221, 0.00027221
47, 1.0389, 1.2531, 1.4754, 0.79237, 0.70125, 0.7886, 0.55636, 1.004, 0.98191, 1.5235, 0.0002624, 0.0002624, 0.0002624
48, 1.0395, 1.2434, 1.4788, 0.83748, 0.67824, 0.79986, 0.58049, 0.98729, 0.95892, 1.5102, 0.00025258, 0.00025258, 0.00025258
49, 1.0497, 1.2658, 1.4853, 0.81021, 0.6926, 0.8096, 0.57719, 0.9882, 0.9557, 1.5045, 0.00024276, 0.00024276, 0.00024276
50, 1.0272, 1.23, 1.462, 0.80619, 0.70858, 0.80318, 0.58335, 0.97643, 0.94805, 1.4989, 0.00023294, 0.00023294, 0.00023294
51, 1.0202, 1.228, 1.4646, 0.82875, 0.6988, 0.80751, 0.58193, 0.97004, 0.94682, 1.4929, 0.00022313, 0.00022313, 0.00022313
52, 1.0166, 1.2224, 1.4641, 0.85802, 0.70881, 0.82189, 0.60725, 0.95412, 0.9438, 1.477, 0.00021331, 0.00021331, 0.00021331
53, 1.0117, 1.1956, 1.4538, 0.8497, 0.70844, 0.81575, 0.59618, 0.95287, 0.92471, 1.4776, 0.00020349, 0.00020349, 0.00020349
54, 1.002, 1.2102, 1.4541, 0.81828, 0.73601, 0.82009, 0.60322, 0.94575, 0.91469, 1.4675, 0.00019367, 0.00019367, 0.00019367
55, 1.0109, 1.2077, 1.4553, 0.84589, 0.73919, 0.83307, 0.61589, 0.94098, 0.90046, 1.4588, 0.00018385, 0.00018385, 0.00018385
56, 0.99031, 1.1758, 1.4389, 0.85519, 0.7212, 0.83421, 0.61717, 0.92411, 0.89088, 1.4473, 0.00017404, 0.00017404, 0.00017404
57, 0.99767, 1.1813, 1.444, 0.8606, 0.72936, 0.84081, 0.61989, 0.92766, 0.88689, 1.4469, 0.00016422, 0.00016422, 0.00016422
58, 1.005, 1.1828, 1.4459, 0.85553, 0.74149, 0.83972, 0.62351, 0.92472, 0.8919, 1.4439, 0.0001544, 0.0001544, 0.0001544
59, 0.99548, 1.1655, 1.4382, 0.87673, 0.73738, 0.84715, 0.62791, 0.91504, 0.87232, 1.4347, 0.00014458, 0.00014458, 0.00014458
60, 0.98489, 1.1526, 1.432, 0.84774, 0.76033, 0.84579, 0.62803, 0.91397, 0.86337, 1.4359, 0.00013477, 0.00013477, 0.00013477
61, 0.98179, 1.146, 1.4298, 0.85791, 0.74316, 0.84895, 0.63315, 0.90232, 0.84648, 1.4224, 0.00012495, 0.00012495, 0.00012495
62, 0.97031, 1.1357, 1.425, 0.86577, 0.75392, 0.85344, 0.63537, 0.89817, 0.85546, 1.4215, 0.00011513, 0.00011513, 0.00011513
63, 0.94965, 0.96612, 1.5097, 0.83429, 0.76914, 0.84767, 0.62405, 0.89128, 0.8572, 1.4103, 0.00010531, 0.00010531, 0.00010531
64, 0.92291, 0.9029, 1.4843, 0.85587, 0.76157, 0.85575, 0.63278, 0.87426, 0.84049, 1.3917, 9.5498e-05, 9.5498e-05, 9.5498e-05
65, 0.90134, 0.87851, 1.4778, 0.87574, 0.74225, 0.8583, 0.64823, 0.8564, 0.81978, 1.3785, 8.568e-05, 8.568e-05, 8.568e-05
66, 0.88992, 0.85875, 1.4538, 0.88084, 0.76256, 0.86349, 0.65391, 0.84161, 0.81003, 1.3641, 7.5862e-05, 7.5862e-05, 7.5862e-05
67, 0.86798, 0.84018, 1.4374, 0.87852, 0.76189, 0.86538, 0.65835, 0.83561, 0.80201, 1.3562, 6.6045e-05, 6.6045e-05, 6.6045e-05
68, 0.87422, 0.83475, 1.4435, 0.86943, 0.78194, 0.87303, 0.66794, 0.83074, 0.79291, 1.3505, 5.6227e-05, 5.6227e-05, 5.6227e-05
69, 0.85585, 0.82052, 1.4258, 0.88106, 0.77257, 0.87397, 0.66248, 0.82326, 0.78925, 1.3436, 4.641e-05, 4.641e-05, 4.641e-05
70, 0.8568, 0.81792, 1.4226, 0.89624, 0.76665, 0.87613, 0.66944, 0.81948, 0.7759, 1.3381, 3.6592e-05, 3.6592e-05, 3.6592e-05
71, 0.85176, 0.80666, 1.4226, 0.86772, 0.78302, 0.8753, 0.67055, 0.81357, 0.77657, 1.3324, 2.6775e-05, 2.6775e-05, 2.6775e-05
72, 0.85717, 0.81365, 1.4255, 0.88814, 0.78351, 0.87834, 0.67415, 0.80891, 0.77274, 1.3284, 1.6957e-05, 1.6957e-05, 1.6957e-05