[CF]提交文件

This commit is contained in:
songbingle 2025-06-06 15:14:32 +08:00
parent fb574a319d
commit 74acac6b03
262 changed files with 348 additions and 8 deletions

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task: detect
mode: train
model: C:\workspace\le-yolo\runs\detect\train31\weights\last.pt
data: data.yaml
epochs: 100
time: null
patience: 100
batch: 8
imgsz: 640
save: true
save_period: -1
cache: false
device: cpu
workers: 8
project: null
name: train39
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
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: true
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: true
opset: null
workspace: null
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
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
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
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erasing: 0.4
cfg: null
tracker: botsort.yaml
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72,595.537,0.65861,0.48503,0.86583,1,0.95084,0.98115,0.85285,0.69866,0.4426,1.00484,0.0005942,0.0005942,0.0005942
73,602.817,0.62082,0.47256,0.87628,1,0.95325,0.98141,0.86035,0.71151,0.44252,1.00846,0.0005744,0.0005744,0.0005744
74,609.831,0.7323,0.48117,0.83405,1,0.95325,0.98141,0.86035,0.71151,0.44252,1.00846,0.0005546,0.0005546,0.0005546
75,616.769,0.62003,0.46688,0.86447,1,0.95325,0.98141,0.86035,0.71151,0.44252,1.00846,0.0005348,0.0005348,0.0005348
76,623.743,0.61833,0.44657,0.84933,1,0.95454,0.98178,0.85258,0.73516,0.45188,1.01552,0.000515,0.000515,0.000515
77,630.676,0.67269,0.45762,0.90748,1,0.95454,0.98178,0.85258,0.73516,0.45188,1.01552,0.0004952,0.0004952,0.0004952
78,637.574,0.72824,0.4894,0.89513,1,0.95454,0.98178,0.85258,0.73516,0.45188,1.01552,0.0004754,0.0004754,0.0004754
79,644.561,0.69261,0.46224,0.87385,1,0.9561,0.98254,0.84663,0.76009,0.45704,1.02515,0.0004556,0.0004556,0.0004556
80,651.596,0.57778,0.44795,0.82494,1,0.9561,0.98254,0.84663,0.76009,0.45704,1.02515,0.0004358,0.0004358,0.0004358
81,658.727,0.54597,0.41587,0.90311,1,0.95555,0.98254,0.83214,0.78175,0.45756,1.03006,0.000416,0.000416,0.000416
82,665.81,0.59482,0.42393,0.84776,1,0.95555,0.98254,0.83214,0.78175,0.45756,1.03006,0.0003962,0.0003962,0.0003962
83,672.867,0.62783,0.42748,0.78693,1,0.95555,0.98254,0.83214,0.78175,0.45756,1.03006,0.0003764,0.0003764,0.0003764
84,679.989,0.61148,0.45573,0.90907,1,0.95071,0.9822,0.8217,0.79369,0.45374,1.03059,0.0003566,0.0003566,0.0003566
85,687.136,0.4667,0.35935,0.83128,1,0.95071,0.9822,0.8217,0.79369,0.45374,1.03059,0.0003368,0.0003368,0.0003368
86,694.254,0.5769,0.43019,0.82132,1,0.95071,0.9822,0.8217,0.79369,0.45374,1.03059,0.000317,0.000317,0.000317
87,701.213,0.68288,0.46579,0.86345,1,0.94927,0.98182,0.81578,0.79518,0.44775,1.02581,0.0002972,0.0002972,0.0002972
88,708.131,0.57223,0.41781,0.87462,1,0.94927,0.98182,0.81578,0.79518,0.44775,1.02581,0.0002774,0.0002774,0.0002774
89,715.134,0.50767,0.39312,0.81386,1,0.94835,0.98161,0.81004,0.7902,0.44831,1.0204,0.0002576,0.0002576,0.0002576
90,722.388,0.43602,0.36527,0.84269,1,0.94835,0.98161,0.81004,0.7902,0.44831,1.0204,0.0002378,0.0002378,0.0002378
91,730.101,0.57163,0.40263,0.79787,1,0.94835,0.98161,0.81004,0.7902,0.44831,1.0204,0.000218,0.000218,0.000218
92,738.003,0.49167,0.37727,0.74126,0.98532,0.93617,0.98097,0.80921,0.80017,0.45399,1.02002,0.0001982,0.0001982,0.0001982
93,746.097,0.57529,0.35957,0.81114,0.98532,0.93617,0.98097,0.80921,0.80017,0.45399,1.02002,0.0001784,0.0001784,0.0001784
94,753.897,0.54845,0.38709,0.88162,0.98532,0.93617,0.98097,0.80921,0.80017,0.45399,1.02002,0.0001586,0.0001586,0.0001586
95,761.745,0.46782,0.3863,0.79275,0.99826,0.93617,0.9796,0.80719,0.81068,0.45651,1.02056,0.0001388,0.0001388,0.0001388
96,769.551,0.50454,0.37319,0.81969,0.99826,0.93617,0.9796,0.80719,0.81068,0.45651,1.02056,0.000119,0.000119,0.000119
97,777.175,0.42993,0.34901,0.84634,1,0.92937,0.97924,0.80633,0.81594,0.46386,1.02096,9.92e-05,9.92e-05,9.92e-05
98,784.374,0.4945,0.36026,0.74592,1,0.92937,0.97924,0.80633,0.81594,0.46386,1.02096,7.94e-05,7.94e-05,7.94e-05
99,791.271,0.44033,0.30951,0.75387,1,0.92937,0.97924,0.80633,0.81594,0.46386,1.02096,5.96e-05,5.96e-05,5.96e-05
100,798.13,0.44401,0.33636,0.78403,0.95732,0.95445,0.97866,0.80073,0.81647,0.46615,1.02064,3.98e-05,3.98e-05,3.98e-05
1 epoch time 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
2 1 8.05099 3.08701 4.63912 2.47347 0.99873 0.97872 0.97957 0.92621 0.33687 0.25896 0.79964 4e-05 4e-05 4e-05
3 2 15.1578 2.46069 4.50404 1.9441 0.99872 0.97872 0.97947 0.93288 0.31608 0.26053 0.79226 9.901e-05 9.901e-05 9.901e-05
4 3 22.7742 1.84504 2.0765 1.46089 0.99845 0.97872 0.97903 0.93305 0.30954 0.26278 0.7898 0.000156832 0.000156832 0.000156832
5 4 29.8536 1.95096 1.9146 1.45516 0.99786 0.97872 0.9778 0.92334 0.32629 0.27453 0.79304 0.000213466 0.000213466 0.000213466
6 5 38.1499 1.74336 1.52727 1.41879 0.9978 0.97872 0.97706 0.91884 0.34646 0.27787 0.79788 0.000268912 0.000268912 0.000268912
7 6 48.182 1.68535 1.55585 1.36754 0.99563 0.97872 0.97609 0.89605 0.40131 0.32387 0.80982 0.00032317 0.00032317 0.00032317
8 7 56.9729 1.28367 1.33211 1.08598 0.98752 0.97872 0.97591 0.88114 0.43404 0.35316 0.81705 0.00037624 0.00037624 0.00037624
9 8 68.6476 1.23225 0.92458 1.04869 0.97252 0.97872 0.97584 0.8729 0.4674 0.38174 0.82546 0.000428122 0.000428122 0.000428122
10 9 82.1946 1.26315 1.09128 1.0546 0.97278 0.97872 0.97541 0.86466 0.49433 0.39865 0.83439 0.000478816 0.000478816 0.000478816
11 10 95.5895 1.35572 1.02586 1.21323 0.97442 0.97872 0.97542 0.83928 0.5252 0.41573 0.84421 0.000528322 0.000528322 0.000528322
12 11 103.532 1.02691 0.83799 0.98294 0.9903 0.97872 0.97589 0.8313 0.55198 0.42833 0.85926 0.00057664 0.00057664 0.00057664
13 12 112.085 1.28294 0.92146 1.06731 0.99405 0.97872 0.97604 0.8288 0.57685 0.43572 0.87867 0.00062377 0.00062377 0.00062377
14 13 122.077 1.06833 0.89229 1.04773 0.99508 0.97872 0.97631 0.82443 0.6109 0.4317 0.90223 0.000669712 0.000669712 0.000669712
15 14 132.589 1.12603 0.87877 1.02198 0.99565 0.97872 0.97654 0.82325 0.64119 0.43448 0.92173 0.000714466 0.000714466 0.000714466
16 15 140.248 1.02289 0.75528 0.96673 0.99565 0.97872 0.97654 0.82325 0.64119 0.43448 0.92173 0.000758032 0.000758032 0.000758032
17 16 147.525 0.85424 0.60841 0.92139 0.98506 0.97872 0.9766 0.81427 0.67706 0.4478 0.9446 0.00080041 0.00080041 0.00080041
18 17 154.88 0.90578 0.61856 0.9107 0.989 0.97872 0.97662 0.82414 0.69243 0.45087 0.95217 0.0008416 0.0008416 0.0008416
19 18 161.821 0.96076 0.71651 0.95162 0.989 0.97872 0.97662 0.82414 0.69243 0.45087 0.95217 0.000881602 0.000881602 0.000881602
20 19 169.516 0.88202 0.62736 0.92089 0.9785 0.97872 0.97658 0.81591 0.72171 0.4678 0.96504 0.000920416 0.000920416 0.000920416
21 20 176.651 0.94913 0.63657 0.95928 0.97989 0.97872 0.97662 0.80929 0.75268 0.48808 0.97481 0.000958042 0.000958042 0.000958042
22 21 183.778 0.92887 0.76227 0.99112 0.97989 0.97872 0.97662 0.80929 0.75268 0.48808 0.97481 0.00099448 0.00099448 0.00099448
23 22 191.148 0.95056 0.67689 0.94819 0.97432 0.97872 0.97692 0.81686 0.806 0.47243 0.99886 0.00102973 0.00102973 0.00102973
24 23 198.909 0.99177 0.74002 1.04211 0.97432 0.97872 0.97692 0.81686 0.806 0.47243 0.99886 0.00106379 0.00106379 0.00106379
25 24 206.71 0.92616 0.7085 0.92564 0.99519 0.97872 0.97751 0.79378 0.81311 0.47027 0.99999 0.00109667 0.00109667 0.00109667
26 25 214.335 0.84966 0.59276 0.89505 0.99519 0.97872 0.97751 0.79378 0.81311 0.47027 0.99999 0.00112835 0.00112835 0.00112835
27 26 221.916 0.94191 0.61676 0.98478 0.99726 0.97872 0.97826 0.80123 0.79436 0.47312 1.001 0.00115885 0.00115885 0.00115885
28 27 229.19 0.84957 0.64949 0.92447 0.99726 0.97872 0.97826 0.80123 0.79436 0.47312 1.001 0.00118816 0.00118816 0.00118816
29 28 236.38 0.90009 0.59759 0.88303 0.99763 0.97872 0.97927 0.80503 0.78604 0.49629 1.01165 0.00121628 0.00121628 0.00121628
30 29 243.491 0.91629 0.6076 0.92722 0.99763 0.97872 0.97927 0.80503 0.78604 0.49629 1.01165 0.00124322 0.00124322 0.00124322
31 30 250.534 0.93007 0.66743 0.9456 0.99763 0.97872 0.97927 0.80503 0.78604 0.49629 1.01165 0.00126896 0.00126896 0.00126896
32 31 257.511 0.8715 0.6157 0.97078 0.99632 0.97872 0.97991 0.81824 0.76204 0.50663 1.01729 0.00129352 0.00129352 0.00129352
33 32 264.421 0.84777 0.55078 0.9736 0.99632 0.97872 0.97991 0.81824 0.76204 0.50663 1.01729 0.00131689 0.00131689 0.00131689
34 33 271.671 0.81371 0.57039 0.91919 0.9973 0.97872 0.98015 0.81515 0.74225 0.48704 1.01098 0.00133907 0.00133907 0.00133907
35 34 279.687 0.77948 0.55724 0.88746 0.9973 0.97872 0.98015 0.81515 0.74225 0.48704 1.01098 0.0013466 0.0013466 0.0013466
36 35 293.705 0.75269 0.48255 0.89272 0.9973 0.97872 0.98015 0.81515 0.74225 0.48704 1.01098 0.0013268 0.0013268 0.0013268
37 36 306.842 0.80379 0.51012 0.92243 0.99642 0.97872 0.9809 0.8098 0.76236 0.47592 1.01976 0.001307 0.001307 0.001307
38 37 320.053 0.72632 0.5175 0.86363 0.99642 0.97872 0.9809 0.8098 0.76236 0.47592 1.01976 0.0012872 0.0012872 0.0012872
39 38 333.277 0.72061 0.53349 0.8412 0.99642 0.97872 0.9809 0.8098 0.76236 0.47592 1.01976 0.0012674 0.0012674 0.0012674
40 39 346.469 0.87859 0.55712 0.87717 0.99505 0.97872 0.98069 0.78544 0.80495 0.4677 1.03678 0.0012476 0.0012476 0.0012476
41 40 359.89 0.91341 0.59301 1.01974 0.99505 0.97872 0.98069 0.78544 0.80495 0.4677 1.03678 0.0012278 0.0012278 0.0012278
42 41 366.935 0.75513 0.51061 0.88556 1 0.95422 0.98021 0.78506 0.80459 0.46562 1.04436 0.001208 0.001208 0.001208
43 42 374.026 0.91481 0.61156 0.99506 1 0.95422 0.98021 0.78506 0.80459 0.46562 1.04436 0.0011882 0.0011882 0.0011882
44 43 381.109 0.84519 0.55324 0.91298 1 0.95422 0.98021 0.78506 0.80459 0.46562 1.04436 0.0011684 0.0011684 0.0011684
45 44 388.077 0.77871 0.48867 0.88476 1 0.9516 0.97914 0.79469 0.79953 0.45695 1.053 0.0011486 0.0011486 0.0011486
46 45 395.128 0.74964 0.48431 0.8694 1 0.9516 0.97914 0.79469 0.79953 0.45695 1.053 0.0011288 0.0011288 0.0011288
47 46 402.805 0.79558 0.53265 0.89319 1 0.9516 0.97914 0.79469 0.79953 0.45695 1.053 0.001109 0.001109 0.001109
48 47 409.988 0.71298 0.51497 0.87547 0.93712 0.97872 0.97751 0.8207 0.77792 0.45788 1.05422 0.0010892 0.0010892 0.0010892
49 48 416.934 0.97885 0.58382 1.06605 0.93712 0.97872 0.97751 0.8207 0.77792 0.45788 1.05422 0.0010694 0.0010694 0.0010694
50 49 423.881 0.70935 0.46637 0.87752 1 0.93577 0.97715 0.82756 0.74962 0.45473 1.03603 0.0010496 0.0010496 0.0010496
51 50 430.849 0.90399 0.52955 0.94775 1 0.93577 0.97715 0.82756 0.74962 0.45473 1.03603 0.0010298 0.0010298 0.0010298
52 51 437.843 0.78062 0.50465 0.8672 1 0.93577 0.97715 0.82756 0.74962 0.45473 1.03603 0.00101 0.00101 0.00101
53 52 445.492 0.7881 0.531 0.85657 1 0.9506 0.97679 0.81465 0.73267 0.45672 1.02154 0.0009902 0.0009902 0.0009902
54 53 452.471 0.72769 0.50645 0.90281 1 0.9506 0.97679 0.81465 0.73267 0.45672 1.02154 0.0009704 0.0009704 0.0009704
55 54 459.43 0.67691 0.52939 0.85715 1 0.9506 0.97679 0.81465 0.73267 0.45672 1.02154 0.0009506 0.0009506 0.0009506
56 55 466.493 0.81266 0.51969 0.97958 1 0.95161 0.97697 0.8214 0.72123 0.46119 1.01505 0.0009308 0.0009308 0.0009308
57 56 473.436 0.75862 0.51562 0.8744 1 0.95161 0.97697 0.8214 0.72123 0.46119 1.01505 0.000911 0.000911 0.000911
58 57 480.569 0.71539 0.51707 0.86088 0.99823 0.95745 0.9781 0.83103 0.70299 0.46113 1.00601 0.0008912 0.0008912 0.0008912
59 58 487.524 0.67848 0.51332 0.87617 0.99823 0.95745 0.9781 0.83103 0.70299 0.46113 1.00601 0.0008714 0.0008714 0.0008714
60 59 494.487 0.75964 0.50496 0.87115 0.99823 0.95745 0.9781 0.83103 0.70299 0.46113 1.00601 0.0008516 0.0008516 0.0008516
61 60 501.508 0.7562 0.53104 0.90018 1 0.95255 0.97847 0.84478 0.70646 0.45664 1.00404 0.0008318 0.0008318 0.0008318
62 61 508.47 0.64516 0.49596 0.83724 1 0.95255 0.97847 0.84478 0.70646 0.45664 1.00404 0.000812 0.000812 0.000812
63 62 515.355 0.74277 0.46382 0.91008 1 0.95255 0.97847 0.84478 0.70646 0.45664 1.00404 0.0007922 0.0007922 0.0007922
64 63 522.272 0.67324 0.47101 0.91204 1 0.95284 0.97909 0.84146 0.70837 0.45882 1.00294 0.0007724 0.0007724 0.0007724
65 64 529.232 0.74781 0.5243 0.95031 1 0.95284 0.97909 0.84146 0.70837 0.45882 1.00294 0.0007526 0.0007526 0.0007526
66 65 536.165 0.61743 0.45203 0.86371 0.97162 0.95745 0.97946 0.84115 0.71462 0.45658 1.00647 0.0007328 0.0007328 0.0007328
67 66 543.991 0.71992 0.47095 0.88408 0.97162 0.95745 0.97946 0.84115 0.71462 0.45658 1.00647 0.000713 0.000713 0.000713
68 67 557.603 0.7744 0.53847 0.91985 0.97162 0.95745 0.97946 0.84115 0.71462 0.45658 1.00647 0.0006932 0.0006932 0.0006932
69 68 567.209 0.60403 0.46667 0.87803 1 0.95161 0.9809 0.84227 0.70377 0.44955 1.00578 0.0006734 0.0006734 0.0006734
70 69 574.6 0.73263 0.53092 0.88304 1 0.95161 0.9809 0.84227 0.70377 0.44955 1.00578 0.0006536 0.0006536 0.0006536
71 70 581.492 0.59546 0.41799 0.83985 1 0.95161 0.9809 0.84227 0.70377 0.44955 1.00578 0.0006338 0.0006338 0.0006338
72 71 588.407 0.56452 0.4604 0.85643 1 0.95084 0.98115 0.85285 0.69866 0.4426 1.00484 0.000614 0.000614 0.000614
73 72 595.537 0.65861 0.48503 0.86583 1 0.95084 0.98115 0.85285 0.69866 0.4426 1.00484 0.0005942 0.0005942 0.0005942
74 73 602.817 0.62082 0.47256 0.87628 1 0.95325 0.98141 0.86035 0.71151 0.44252 1.00846 0.0005744 0.0005744 0.0005744
75 74 609.831 0.7323 0.48117 0.83405 1 0.95325 0.98141 0.86035 0.71151 0.44252 1.00846 0.0005546 0.0005546 0.0005546
76 75 616.769 0.62003 0.46688 0.86447 1 0.95325 0.98141 0.86035 0.71151 0.44252 1.00846 0.0005348 0.0005348 0.0005348
77 76 623.743 0.61833 0.44657 0.84933 1 0.95454 0.98178 0.85258 0.73516 0.45188 1.01552 0.000515 0.000515 0.000515
78 77 630.676 0.67269 0.45762 0.90748 1 0.95454 0.98178 0.85258 0.73516 0.45188 1.01552 0.0004952 0.0004952 0.0004952
79 78 637.574 0.72824 0.4894 0.89513 1 0.95454 0.98178 0.85258 0.73516 0.45188 1.01552 0.0004754 0.0004754 0.0004754
80 79 644.561 0.69261 0.46224 0.87385 1 0.9561 0.98254 0.84663 0.76009 0.45704 1.02515 0.0004556 0.0004556 0.0004556
81 80 651.596 0.57778 0.44795 0.82494 1 0.9561 0.98254 0.84663 0.76009 0.45704 1.02515 0.0004358 0.0004358 0.0004358
82 81 658.727 0.54597 0.41587 0.90311 1 0.95555 0.98254 0.83214 0.78175 0.45756 1.03006 0.000416 0.000416 0.000416
83 82 665.81 0.59482 0.42393 0.84776 1 0.95555 0.98254 0.83214 0.78175 0.45756 1.03006 0.0003962 0.0003962 0.0003962
84 83 672.867 0.62783 0.42748 0.78693 1 0.95555 0.98254 0.83214 0.78175 0.45756 1.03006 0.0003764 0.0003764 0.0003764
85 84 679.989 0.61148 0.45573 0.90907 1 0.95071 0.9822 0.8217 0.79369 0.45374 1.03059 0.0003566 0.0003566 0.0003566
86 85 687.136 0.4667 0.35935 0.83128 1 0.95071 0.9822 0.8217 0.79369 0.45374 1.03059 0.0003368 0.0003368 0.0003368
87 86 694.254 0.5769 0.43019 0.82132 1 0.95071 0.9822 0.8217 0.79369 0.45374 1.03059 0.000317 0.000317 0.000317
88 87 701.213 0.68288 0.46579 0.86345 1 0.94927 0.98182 0.81578 0.79518 0.44775 1.02581 0.0002972 0.0002972 0.0002972
89 88 708.131 0.57223 0.41781 0.87462 1 0.94927 0.98182 0.81578 0.79518 0.44775 1.02581 0.0002774 0.0002774 0.0002774
90 89 715.134 0.50767 0.39312 0.81386 1 0.94835 0.98161 0.81004 0.7902 0.44831 1.0204 0.0002576 0.0002576 0.0002576
91 90 722.388 0.43602 0.36527 0.84269 1 0.94835 0.98161 0.81004 0.7902 0.44831 1.0204 0.0002378 0.0002378 0.0002378
92 91 730.101 0.57163 0.40263 0.79787 1 0.94835 0.98161 0.81004 0.7902 0.44831 1.0204 0.000218 0.000218 0.000218
93 92 738.003 0.49167 0.37727 0.74126 0.98532 0.93617 0.98097 0.80921 0.80017 0.45399 1.02002 0.0001982 0.0001982 0.0001982
94 93 746.097 0.57529 0.35957 0.81114 0.98532 0.93617 0.98097 0.80921 0.80017 0.45399 1.02002 0.0001784 0.0001784 0.0001784
95 94 753.897 0.54845 0.38709 0.88162 0.98532 0.93617 0.98097 0.80921 0.80017 0.45399 1.02002 0.0001586 0.0001586 0.0001586
96 95 761.745 0.46782 0.3863 0.79275 0.99826 0.93617 0.9796 0.80719 0.81068 0.45651 1.02056 0.0001388 0.0001388 0.0001388
97 96 769.551 0.50454 0.37319 0.81969 0.99826 0.93617 0.9796 0.80719 0.81068 0.45651 1.02056 0.000119 0.000119 0.000119
98 97 777.175 0.42993 0.34901 0.84634 1 0.92937 0.97924 0.80633 0.81594 0.46386 1.02096 9.92e-05 9.92e-05 9.92e-05
99 98 784.374 0.4945 0.36026 0.74592 1 0.92937 0.97924 0.80633 0.81594 0.46386 1.02096 7.94e-05 7.94e-05 7.94e-05
100 99 791.271 0.44033 0.30951 0.75387 1 0.92937 0.97924 0.80633 0.81594 0.46386 1.02096 5.96e-05 5.96e-05 5.96e-05
101 100 798.13 0.44401 0.33636 0.78403 0.95732 0.95445 0.97866 0.80073 0.81647 0.46615 1.02064 3.98e-05 3.98e-05 3.98e-05

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@ -0,0 +1,105 @@
task: detect
mode: train
model: yolov8n.pt
data: data.yaml
epochs: 100
time: null
patience: 100
batch: 8
imgsz: 640
save: true
save_period: -1
cache: false
device: cpu
workers: 8
project: null
name: train40
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
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: true
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: true
opset: null
workspace: null
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
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
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: C:\workspace\le-yolo\runs\detect\train40

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@ -0,0 +1,6 @@
epoch,time,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
1,17.6253,1.58673,4.02966,1.4629,0.00306,0.95745,0.25147,0.21701,0.59831,3.4472,0.93484,0.00016,0.00016,0.00016
2,34.157,0.97453,2.5837,1.00845,1,0.29022,0.93511,0.7284,0.72794,2.90934,0.85501,0.000336634,0.000336634,0.000336634
3,50.8526,0.84409,2.14524,0.93347,0.01141,0.97872,0.95462,0.65143,0.72545,2.96458,0.86384,0.000509704,0.000509704,0.000509704
4,68.8891,0.92823,1.93303,0.99896,1,0.17404,0.8576,0.66888,0.93772,3.1365,0.99018,0.00067921,0.00067921,0.00067921
5,88.2008,0.86695,1.72383,0.99086,1,0.14981,0.89164,0.65259,0.85707,3.08949,0.94664,0.000845152,0.000845152,0.000845152
1 epoch time 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
2 1 17.6253 1.58673 4.02966 1.4629 0.00306 0.95745 0.25147 0.21701 0.59831 3.4472 0.93484 0.00016 0.00016 0.00016
3 2 34.157 0.97453 2.5837 1.00845 1 0.29022 0.93511 0.7284 0.72794 2.90934 0.85501 0.000336634 0.000336634 0.000336634
4 3 50.8526 0.84409 2.14524 0.93347 0.01141 0.97872 0.95462 0.65143 0.72545 2.96458 0.86384 0.000509704 0.000509704 0.000509704
5 4 68.8891 0.92823 1.93303 0.99896 1 0.17404 0.8576 0.66888 0.93772 3.1365 0.99018 0.00067921 0.00067921 0.00067921
6 5 88.2008 0.86695 1.72383 0.99086 1 0.14981 0.89164 0.65259 0.85707 3.08949 0.94664 0.000845152 0.000845152 0.000845152

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@ -1,7 +1,7 @@
path: C:\workspace\le-yolo\data
train: images/test
train: images/train
val: images/val
test: images/train
test: images/test
nc: 1
names: [ 'person' ]
augment:

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