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songbingle 2025-06-05 15:36:42 +08:00
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@ -63,3 +63,39 @@ epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),met
62,768.657,0.48072,0.56784,0.88594,0.99859,0.97872,0.97504,0.87932,0.49053,0.75084,0.9037,0.0007922,0.0007922,0.0007922 62,768.657,0.48072,0.56784,0.88594,0.99859,0.97872,0.97504,0.87932,0.49053,0.75084,0.9037,0.0007922,0.0007922,0.0007922
63,780.841,0.55917,0.60833,0.90572,0.99866,0.97872,0.97504,0.8848,0.483,0.75222,0.90783,0.0007724,0.0007724,0.0007724 63,780.841,0.55917,0.60833,0.90572,0.99866,0.97872,0.97504,0.8848,0.483,0.75222,0.90783,0.0007724,0.0007724,0.0007724
64,794.139,0.49627,0.63279,0.93568,0.99871,0.97872,0.97504,0.88849,0.46154,0.71729,0.89663,0.0007526,0.0007526,0.0007526 64,794.139,0.49627,0.63279,0.93568,0.99871,0.97872,0.97504,0.88849,0.46154,0.71729,0.89663,0.0007526,0.0007526,0.0007526
65,809.183,0.5422,0.61979,0.91826,0.99831,0.97872,0.97504,0.88894,0.44554,0.69579,0.88535,0.0007328,0.0007328,0.0007328
66,826.529,0.52536,0.61141,0.88808,0.97735,0.97872,0.97504,0.88483,0.44802,0.71624,0.8918,0.000713,0.000713,0.000713
67,843.231,0.52884,0.60849,0.91693,0.97751,0.97872,0.97462,0.90006,0.49765,0.72076,0.9078,0.0006932,0.0006932,0.0006932
68,858.217,0.52012,0.60488,0.88413,0.9973,0.97872,0.97504,0.89056,0.47557,0.70697,0.89253,0.0006734,0.0006734,0.0006734
69,870.633,0.54421,0.60111,0.92961,0.99869,0.97872,0.97505,0.88974,0.43279,0.6473,0.89538,0.0006536,0.0006536,0.0006536
70,882.802,0.6041,0.62523,0.90733,0.99869,0.97872,0.97505,0.88974,0.43279,0.6473,0.89538,0.0006338,0.0006338,0.0006338
71,895.113,0.62387,0.5822,0.97853,0.99839,0.97872,0.97505,0.90479,0.44454,0.64819,0.89868,0.000614,0.000614,0.000614
72,907.536,0.49362,0.6171,0.86569,0.99867,0.97872,0.97505,0.908,0.43185,0.64718,0.89272,0.0005942,0.0005942,0.0005942
73,919.806,0.65596,0.65639,1.00239,0.99867,0.97872,0.97505,0.90561,0.4217,0.60938,0.87813,0.0005744,0.0005744,0.0005744
74,931.995,0.58737,0.58898,0.95303,0.99867,0.97872,0.97505,0.9173,0.40121,0.60192,0.86613,0.0005546,0.0005546,0.0005546
75,944.212,0.49581,0.5332,0.87874,0.99868,0.97872,0.97505,0.91972,0.39378,0.58595,0.86223,0.0005348,0.0005348,0.0005348
76,956.684,0.41264,0.74594,0.7891,0.99868,0.97872,0.97505,0.91334,0.38024,0.56515,0.86336,0.000515,0.000515,0.000515
77,969.06,0.5148,0.56391,0.91987,0.99873,0.97872,0.97505,0.90574,0.36714,0.55898,0.86153,0.0004952,0.0004952,0.0004952
78,981.215,0.44345,0.56514,0.87102,0.99873,0.97872,0.97505,0.90574,0.36714,0.55898,0.86153,0.0004754,0.0004754,0.0004754
79,993.389,0.75259,0.88922,1.01904,0.99874,0.97872,0.97505,0.91994,0.35244,0.55994,0.85823,0.0004556,0.0004556,0.0004556
80,1005.69,0.56094,0.56148,0.91332,0.99871,0.97872,0.97505,0.93219,0.33951,0.54825,0.85353,0.0004358,0.0004358,0.0004358
81,1017.91,0.39504,0.7493,0.75912,0.99867,0.97872,0.97505,0.93406,0.32041,0.529,0.85223,0.000416,0.000416,0.000416
82,1030.32,0.42973,0.50872,0.82449,0.99864,0.97872,0.97505,0.9367,0.31864,0.50679,0.85244,0.0003962,0.0003962,0.0003962
83,1042.68,0.4585,0.47919,0.87823,0.99861,0.97872,0.97505,0.93873,0.32757,0.50275,0.85269,0.0003764,0.0003764,0.0003764
84,1055.16,0.4396,0.48395,0.86594,0.99859,0.97872,0.97505,0.93946,0.3301,0.50185,0.84862,0.0003566,0.0003566,0.0003566
85,1067.4,0.41895,0.48022,0.90105,0.99855,0.97872,0.97505,0.93956,0.32873,0.49971,0.83915,0.0003368,0.0003368,0.0003368
86,1079.79,0.37891,0.66838,0.73848,0.99855,0.97872,0.97505,0.93956,0.32873,0.49971,0.83915,0.000317,0.000317,0.000317
87,1092.18,0.48218,0.51309,0.89898,0.99852,0.97872,0.97505,0.94983,0.32133,0.49896,0.83179,0.0002972,0.0002972,0.0002972
88,1104.32,0.60411,0.59325,0.91111,0.99854,0.97872,0.97505,0.94761,0.3237,0.49831,0.82918,0.0002774,0.0002774,0.0002774
89,1116.51,0.37718,0.66303,0.7512,0.99852,0.97872,0.97505,0.9397,0.33159,0.50139,0.82825,0.0002576,0.0002576,0.0002576
90,1128.76,0.45675,0.54511,0.86681,0.99856,0.97872,0.97505,0.93146,0.33433,0.49946,0.82797,0.0002378,0.0002378,0.0002378
91,1140.95,0.42183,0.5796,0.93932,0.99855,0.97872,0.97505,0.93484,0.32953,0.49049,0.82598,0.000218,0.000218,0.000218
92,1153.16,0.3751,0.55246,0.84782,0.99855,0.97872,0.97505,0.933,0.326,0.49124,0.82383,0.0001982,0.0001982,0.0001982
93,1165.39,0.38557,0.5376,0.85563,0.99852,0.97872,0.97505,0.93231,0.32707,0.5014,0.82325,0.0001784,0.0001784,0.0001784
94,1177.82,0.35373,0.53711,0.85211,0.99852,0.97872,0.97505,0.93231,0.32707,0.5014,0.82325,0.0001586,0.0001586,0.0001586
95,1189.92,0.35459,0.52475,0.81646,0.99853,0.97872,0.97505,0.9336,0.32132,0.51149,0.82209,0.0001388,0.0001388,0.0001388
96,1202.11,0.38726,0.54125,0.87,0.99854,0.97872,0.97505,0.93324,0.31827,0.51172,0.82195,0.000119,0.000119,0.000119
97,1214.35,0.34727,0.53959,0.81035,0.99853,0.97872,0.97505,0.93164,0.30842,0.51247,0.82357,9.92e-05,9.92e-05,9.92e-05
98,1226.46,0.30893,0.69202,0.74366,0.99854,0.97872,0.97505,0.92958,0.30333,0.5146,0.82441,7.94e-05,7.94e-05,7.94e-05
99,1238.62,0.33106,0.53769,0.80986,0.99855,0.97872,0.97505,0.93077,0.29757,0.51586,0.82436,5.96e-05,5.96e-05,5.96e-05
100,1250.83,0.33754,0.50732,0.85718,0.99856,0.97872,0.97505,0.9327,0.29568,0.50961,0.82457,3.98e-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
63 62 768.657 0.48072 0.56784 0.88594 0.99859 0.97872 0.97504 0.87932 0.49053 0.75084 0.9037 0.0007922 0.0007922 0.0007922
64 63 780.841 0.55917 0.60833 0.90572 0.99866 0.97872 0.97504 0.8848 0.483 0.75222 0.90783 0.0007724 0.0007724 0.0007724
65 64 794.139 0.49627 0.63279 0.93568 0.99871 0.97872 0.97504 0.88849 0.46154 0.71729 0.89663 0.0007526 0.0007526 0.0007526
66 65 809.183 0.5422 0.61979 0.91826 0.99831 0.97872 0.97504 0.88894 0.44554 0.69579 0.88535 0.0007328 0.0007328 0.0007328
67 66 826.529 0.52536 0.61141 0.88808 0.97735 0.97872 0.97504 0.88483 0.44802 0.71624 0.8918 0.000713 0.000713 0.000713
68 67 843.231 0.52884 0.60849 0.91693 0.97751 0.97872 0.97462 0.90006 0.49765 0.72076 0.9078 0.0006932 0.0006932 0.0006932
69 68 858.217 0.52012 0.60488 0.88413 0.9973 0.97872 0.97504 0.89056 0.47557 0.70697 0.89253 0.0006734 0.0006734 0.0006734
70 69 870.633 0.54421 0.60111 0.92961 0.99869 0.97872 0.97505 0.88974 0.43279 0.6473 0.89538 0.0006536 0.0006536 0.0006536
71 70 882.802 0.6041 0.62523 0.90733 0.99869 0.97872 0.97505 0.88974 0.43279 0.6473 0.89538 0.0006338 0.0006338 0.0006338
72 71 895.113 0.62387 0.5822 0.97853 0.99839 0.97872 0.97505 0.90479 0.44454 0.64819 0.89868 0.000614 0.000614 0.000614
73 72 907.536 0.49362 0.6171 0.86569 0.99867 0.97872 0.97505 0.908 0.43185 0.64718 0.89272 0.0005942 0.0005942 0.0005942
74 73 919.806 0.65596 0.65639 1.00239 0.99867 0.97872 0.97505 0.90561 0.4217 0.60938 0.87813 0.0005744 0.0005744 0.0005744
75 74 931.995 0.58737 0.58898 0.95303 0.99867 0.97872 0.97505 0.9173 0.40121 0.60192 0.86613 0.0005546 0.0005546 0.0005546
76 75 944.212 0.49581 0.5332 0.87874 0.99868 0.97872 0.97505 0.91972 0.39378 0.58595 0.86223 0.0005348 0.0005348 0.0005348
77 76 956.684 0.41264 0.74594 0.7891 0.99868 0.97872 0.97505 0.91334 0.38024 0.56515 0.86336 0.000515 0.000515 0.000515
78 77 969.06 0.5148 0.56391 0.91987 0.99873 0.97872 0.97505 0.90574 0.36714 0.55898 0.86153 0.0004952 0.0004952 0.0004952
79 78 981.215 0.44345 0.56514 0.87102 0.99873 0.97872 0.97505 0.90574 0.36714 0.55898 0.86153 0.0004754 0.0004754 0.0004754
80 79 993.389 0.75259 0.88922 1.01904 0.99874 0.97872 0.97505 0.91994 0.35244 0.55994 0.85823 0.0004556 0.0004556 0.0004556
81 80 1005.69 0.56094 0.56148 0.91332 0.99871 0.97872 0.97505 0.93219 0.33951 0.54825 0.85353 0.0004358 0.0004358 0.0004358
82 81 1017.91 0.39504 0.7493 0.75912 0.99867 0.97872 0.97505 0.93406 0.32041 0.529 0.85223 0.000416 0.000416 0.000416
83 82 1030.32 0.42973 0.50872 0.82449 0.99864 0.97872 0.97505 0.9367 0.31864 0.50679 0.85244 0.0003962 0.0003962 0.0003962
84 83 1042.68 0.4585 0.47919 0.87823 0.99861 0.97872 0.97505 0.93873 0.32757 0.50275 0.85269 0.0003764 0.0003764 0.0003764
85 84 1055.16 0.4396 0.48395 0.86594 0.99859 0.97872 0.97505 0.93946 0.3301 0.50185 0.84862 0.0003566 0.0003566 0.0003566
86 85 1067.4 0.41895 0.48022 0.90105 0.99855 0.97872 0.97505 0.93956 0.32873 0.49971 0.83915 0.0003368 0.0003368 0.0003368
87 86 1079.79 0.37891 0.66838 0.73848 0.99855 0.97872 0.97505 0.93956 0.32873 0.49971 0.83915 0.000317 0.000317 0.000317
88 87 1092.18 0.48218 0.51309 0.89898 0.99852 0.97872 0.97505 0.94983 0.32133 0.49896 0.83179 0.0002972 0.0002972 0.0002972
89 88 1104.32 0.60411 0.59325 0.91111 0.99854 0.97872 0.97505 0.94761 0.3237 0.49831 0.82918 0.0002774 0.0002774 0.0002774
90 89 1116.51 0.37718 0.66303 0.7512 0.99852 0.97872 0.97505 0.9397 0.33159 0.50139 0.82825 0.0002576 0.0002576 0.0002576
91 90 1128.76 0.45675 0.54511 0.86681 0.99856 0.97872 0.97505 0.93146 0.33433 0.49946 0.82797 0.0002378 0.0002378 0.0002378
92 91 1140.95 0.42183 0.5796 0.93932 0.99855 0.97872 0.97505 0.93484 0.32953 0.49049 0.82598 0.000218 0.000218 0.000218
93 92 1153.16 0.3751 0.55246 0.84782 0.99855 0.97872 0.97505 0.933 0.326 0.49124 0.82383 0.0001982 0.0001982 0.0001982
94 93 1165.39 0.38557 0.5376 0.85563 0.99852 0.97872 0.97505 0.93231 0.32707 0.5014 0.82325 0.0001784 0.0001784 0.0001784
95 94 1177.82 0.35373 0.53711 0.85211 0.99852 0.97872 0.97505 0.93231 0.32707 0.5014 0.82325 0.0001586 0.0001586 0.0001586
96 95 1189.92 0.35459 0.52475 0.81646 0.99853 0.97872 0.97505 0.9336 0.32132 0.51149 0.82209 0.0001388 0.0001388 0.0001388
97 96 1202.11 0.38726 0.54125 0.87 0.99854 0.97872 0.97505 0.93324 0.31827 0.51172 0.82195 0.000119 0.000119 0.000119
98 97 1214.35 0.34727 0.53959 0.81035 0.99853 0.97872 0.97505 0.93164 0.30842 0.51247 0.82357 9.92e-05 9.92e-05 9.92e-05
99 98 1226.46 0.30893 0.69202 0.74366 0.99854 0.97872 0.97505 0.92958 0.30333 0.5146 0.82441 7.94e-05 7.94e-05 7.94e-05
100 99 1238.62 0.33106 0.53769 0.80986 0.99855 0.97872 0.97505 0.93077 0.29757 0.51586 0.82436 5.96e-05 5.96e-05 5.96e-05
101 100 1250.83 0.33754 0.50732 0.85718 0.99856 0.97872 0.97505 0.9327 0.29568 0.50961 0.82457 3.98e-05 3.98e-05 3.98e-05

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4
src/test.py
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@ -1,3 +1,3 @@
from ultralytics import YOLO from ultralytics import YOLO
model = YOLO(r"C:\workspace\le-yolo\runs\detect\train22\weights\best.pt") model = YOLO(r"C:\workspace\le-yolo\runs\detect\train26\weights\best.pt")
results = model.predict("../res/1.mp4", show=True, save=True) results = model.predict("../res/2.mp4", show=True, save=True)