[CF]提交文件

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songbingle 2025-06-06 17:13:27 +08:00
parent 693257d9f3
commit 3c5e3abfa9
44 changed files with 174 additions and 2 deletions

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@ -42,3 +42,60 @@ epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),met
41,1014.57,0.59949,0.52836,0.90913,0.9976,0.97872,0.97507,0.87453,0.53395,0.60574,0.9754,0.001208,0.001208,0.001208 41,1014.57,0.59949,0.52836,0.90913,0.9976,0.97872,0.97507,0.87453,0.53395,0.60574,0.9754,0.001208,0.001208,0.001208
42,1037.9,0.60692,0.51077,0.91856,0.99765,0.97872,0.97507,0.86385,0.5848,0.6038,1.0358,0.0011882,0.0011882,0.0011882 42,1037.9,0.60692,0.51077,0.91856,0.99765,0.97872,0.97507,0.86385,0.5848,0.6038,1.0358,0.0011882,0.0011882,0.0011882
43,1063.52,0.61637,0.54559,0.92315,0.99601,0.97872,0.97507,0.86796,0.57281,0.6059,1.04494,0.0011684,0.0011684,0.0011684 43,1063.52,0.61637,0.54559,0.92315,0.99601,0.97872,0.97507,0.86796,0.57281,0.6059,1.04494,0.0011684,0.0011684,0.0011684
44,1092.41,0.56682,0.542,0.91691,0.99739,0.97872,0.97507,0.86212,0.56192,0.65041,1.06014,0.0011486,0.0011486,0.0011486
45,1117.06,0.64198,0.53349,0.96125,0.99455,0.97872,0.97507,0.86674,0.5539,0.69235,1.04594,0.0011288,0.0011288,0.0011288
46,1141.63,0.60128,0.53201,0.91766,0.97669,0.97872,0.97464,0.87465,0.57068,0.7196,1.03372,0.001109,0.001109,0.001109
47,1165.33,0.5519,0.58226,0.87766,0.99995,0.97872,0.97507,0.87923,0.57734,0.68746,1.04333,0.0010892,0.0010892,0.0010892
48,1190.51,0.61955,0.55435,0.92372,0.99828,0.97872,0.97508,0.86899,0.59967,0.62745,1.04996,0.0010694,0.0010694,0.0010694
49,1215.68,0.57611,0.5068,0.86542,0.99834,0.97872,0.97508,0.90285,0.53186,0.56817,1.04446,0.0010496,0.0010496,0.0010496
50,1239.24,0.52518,0.46535,0.87764,1,0.97829,0.97508,0.88767,0.54936,0.55594,1.03008,0.0010298,0.0010298,0.0010298
51,1263.25,0.60835,0.51077,0.90971,0.99781,0.97872,0.97508,0.89059,0.54906,0.57735,1.04816,0.00101,0.00101,0.00101
52,1287.64,0.57438,0.5124,0.92987,0.97851,0.96899,0.97423,0.88824,0.61444,0.54986,1.07954,0.0009902,0.0009902,0.0009902
53,1313.16,0.56937,0.48896,0.90152,0.99826,0.95745,0.97466,0.86378,0.60609,0.56536,1.047,0.0009704,0.0009704,0.0009704
54,1339.05,0.61594,0.51975,0.95916,0.97714,0.97872,0.97423,0.86971,0.60094,0.57078,1.05624,0.0009506,0.0009506,0.0009506
55,1365.76,0.57515,0.47696,0.89298,0.97705,0.97872,0.97423,0.87785,0.5651,0.58565,1.08914,0.0009308,0.0009308,0.0009308
56,1389.14,0.5068,0.45113,0.88907,0.97696,0.97872,0.97467,0.87046,0.58345,0.57728,1.12494,0.000911,0.000911,0.000911
57,1413.27,0.53803,0.45492,0.90522,0.97722,0.97872,0.97425,0.88975,0.49741,0.5502,1.03528,0.0008912,0.0008912,0.0008912
58,1441.19,0.608,0.4931,0.93643,0.97735,0.97872,0.97426,0.8931,0.50678,0.55762,0.99923,0.0008714,0.0008714,0.0008714
59,1468.55,0.54444,0.46445,0.95757,0.99848,0.97872,0.97512,0.89931,0.50644,0.53365,0.98802,0.0008516,0.0008516,0.0008516
60,1493.47,0.5672,0.47496,0.90847,0.99857,0.97872,0.97514,0.89933,0.48361,0.49757,0.94558,0.0008318,0.0008318,0.0008318
61,1519.16,0.56814,0.49767,0.89276,0.99854,0.97872,0.97515,0.8908,0.45367,0.44977,0.92588,0.000812,0.000812,0.000812
62,1545.68,0.54123,0.46468,0.92992,0.99867,0.97872,0.97514,0.89709,0.44947,0.45708,0.93801,0.0007922,0.0007922,0.0007922
63,1571.74,0.57246,0.46686,0.90762,0.99786,0.97872,0.97513,0.89482,0.46021,0.47385,0.94823,0.0007724,0.0007724,0.0007724
64,1597.8,0.54624,0.43858,0.87308,0.99844,0.97872,0.97516,0.89729,0.49468,0.46887,0.96013,0.0007526,0.0007526,0.0007526
65,1623.68,0.54364,0.44246,0.88962,0.99855,0.97872,0.97517,0.90365,0.51956,0.42429,0.95753,0.0007328,0.0007328,0.0007328
66,1649.18,0.46981,0.40913,0.87546,0.99847,0.97872,0.9752,0.90499,0.43039,0.40576,0.90702,0.000713,0.000713,0.000713
67,1673.24,0.52479,0.41543,0.86585,0.99841,0.97872,0.97522,0.89389,0.40141,0.42377,0.91541,0.0006932,0.0006932,0.0006932
68,1697.64,0.5616,0.43932,0.90982,0.99837,0.97872,0.97529,0.89919,0.49413,0.43951,0.98202,0.0006734,0.0006734,0.0006734
69,1723.64,0.52312,0.44072,0.90402,0.99831,0.97872,0.97536,0.89659,0.53139,0.44096,0.98169,0.0006536,0.0006536,0.0006536
70,1749.65,0.50496,0.41511,0.90988,0.99819,0.97872,0.97539,0.91645,0.45378,0.42096,0.92256,0.0006338,0.0006338,0.0006338
71,1777.56,0.51147,0.42693,0.86444,0.99821,0.97872,0.9754,0.92046,0.41803,0.38434,0.90736,0.000614,0.000614,0.000614
72,1806.81,0.50723,0.44087,0.88909,0.99845,0.97872,0.97545,0.9141,0.41854,0.38573,0.90434,0.0005942,0.0005942,0.0005942
73,1835.6,0.48878,0.42097,0.89505,0.99844,0.97872,0.97552,0.92009,0.40796,0.39896,0.89601,0.0005744,0.0005744,0.0005744
74,1861.75,0.52419,0.4239,0.8862,0.99859,0.97872,0.9756,0.92316,0.40554,0.41532,0.88737,0.0005546,0.0005546,0.0005546
75,1888.59,0.47155,0.38701,0.87038,0.99862,0.97872,0.9757,0.92891,0.38805,0.41992,0.8783,0.0005348,0.0005348,0.0005348
76,1915.19,0.48355,0.39834,0.86732,0.99865,0.97872,0.97579,0.93202,0.37531,0.41166,0.86651,0.000515,0.000515,0.000515
77,1941.02,0.52022,0.39644,0.90819,0.99874,0.97872,0.97588,0.93108,0.38676,0.42775,0.86735,0.0004952,0.0004952,0.0004952
78,1967.17,0.49991,0.39252,0.87213,0.99874,0.97872,0.97597,0.92747,0.39723,0.40431,0.87254,0.0004754,0.0004754,0.0004754
79,1993.36,0.46994,0.38264,0.87372,0.99871,0.97872,0.97598,0.94087,0.39048,0.38877,0.87239,0.0004556,0.0004556,0.0004556
80,2019.46,0.44306,0.34784,0.85531,0.99874,0.97872,0.97598,0.94111,0.37653,0.37271,0.85693,0.0004358,0.0004358,0.0004358
81,2045.91,0.52164,0.44215,0.90042,0.99874,0.97872,0.97605,0.93703,0.35099,0.35613,0.84375,0.000416,0.000416,0.000416
82,2071.09,0.43583,0.36948,0.86455,0.99875,0.97872,0.97614,0.93687,0.33748,0.34432,0.83893,0.0003962,0.0003962,0.0003962
83,2096.03,0.44289,0.36518,0.85191,0.99875,0.97872,0.97624,0.93707,0.3427,0.34866,0.84353,0.0003764,0.0003764,0.0003764
84,2122.84,0.47123,0.37938,0.86435,0.99877,0.97872,0.97633,0.93289,0.36886,0.34391,0.86271,0.0003566,0.0003566,0.0003566
85,2146.21,0.46956,0.37655,0.89478,0.99877,0.97872,0.97641,0.93346,0.35497,0.34737,0.85675,0.0003368,0.0003368,0.0003368
86,2169.49,0.44497,0.38846,0.79774,0.99875,0.97872,0.97663,0.93787,0.35528,0.32827,0.84843,0.000317,0.000317,0.000317
87,2192.97,0.42009,0.36714,0.84973,0.99875,0.97872,0.97668,0.94681,0.37703,0.32198,0.8541,0.0002972,0.0002972,0.0002972
88,2218.43,0.43938,0.36407,0.83415,0.99876,0.97872,0.97678,0.94578,0.40722,0.32622,0.86835,0.0002774,0.0002774,0.0002774
89,2241.97,0.42528,0.43108,0.82208,0.99881,0.97872,0.9769,0.94796,0.44235,0.33782,0.88634,0.0002576,0.0002576,0.0002576
90,2265.32,0.47437,0.3832,0.88705,0.99884,0.97872,0.97706,0.94442,0.42207,0.32059,0.87479,0.0002378,0.0002378,0.0002378
91,2289.85,0.37181,0.34761,0.84103,0.99886,0.97872,0.97701,0.94757,0.41277,0.32924,0.86992,0.000218,0.000218,0.000218
92,2312.97,0.34648,0.34359,0.826,0.99884,0.97872,0.97732,0.94171,0.39116,0.32631,0.86015,0.0001982,0.0001982,0.0001982
93,2335.99,0.36605,0.34218,0.84288,0.9988,0.97872,0.97724,0.94082,0.37981,0.3187,0.8523,0.0001784,0.0001784,0.0001784
94,2359.71,0.38169,0.33456,0.84097,0.99881,0.97872,0.97728,0.94485,0.36443,0.33128,0.84394,0.0001586,0.0001586,0.0001586
95,2383.48,0.37646,0.35094,0.83187,0.99881,0.97872,0.97726,0.94287,0.36169,0.32773,0.84267,0.0001388,0.0001388,0.0001388
96,2407.26,0.36623,0.33836,0.84097,0.99879,0.97872,0.97731,0.94201,0.34619,0.32886,0.83634,0.000119,0.000119,0.000119
97,2430.66,0.35889,0.34208,0.81934,0.99882,0.97872,0.97718,0.94312,0.32836,0.3326,0.83218,9.92e-05,9.92e-05,9.92e-05
98,2454.19,0.34079,0.32036,0.79553,0.99882,0.97872,0.97726,0.94541,0.3094,0.33484,0.83082,7.94e-05,7.94e-05,7.94e-05
99,2478.3,0.34663,0.32585,0.82711,0.99884,0.97872,0.9772,0.94542,0.30885,0.32567,0.82688,5.96e-05,5.96e-05,5.96e-05
100,2502.43,0.34408,0.31704,0.82257,0.99883,0.97872,0.97737,0.94894,0.30604,0.32063,0.82739,3.98e-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
42 41 1014.57 0.59949 0.52836 0.90913 0.9976 0.97872 0.97507 0.87453 0.53395 0.60574 0.9754 0.001208 0.001208 0.001208
43 42 1037.9 0.60692 0.51077 0.91856 0.99765 0.97872 0.97507 0.86385 0.5848 0.6038 1.0358 0.0011882 0.0011882 0.0011882
44 43 1063.52 0.61637 0.54559 0.92315 0.99601 0.97872 0.97507 0.86796 0.57281 0.6059 1.04494 0.0011684 0.0011684 0.0011684
45 44 1092.41 0.56682 0.542 0.91691 0.99739 0.97872 0.97507 0.86212 0.56192 0.65041 1.06014 0.0011486 0.0011486 0.0011486
46 45 1117.06 0.64198 0.53349 0.96125 0.99455 0.97872 0.97507 0.86674 0.5539 0.69235 1.04594 0.0011288 0.0011288 0.0011288
47 46 1141.63 0.60128 0.53201 0.91766 0.97669 0.97872 0.97464 0.87465 0.57068 0.7196 1.03372 0.001109 0.001109 0.001109
48 47 1165.33 0.5519 0.58226 0.87766 0.99995 0.97872 0.97507 0.87923 0.57734 0.68746 1.04333 0.0010892 0.0010892 0.0010892
49 48 1190.51 0.61955 0.55435 0.92372 0.99828 0.97872 0.97508 0.86899 0.59967 0.62745 1.04996 0.0010694 0.0010694 0.0010694
50 49 1215.68 0.57611 0.5068 0.86542 0.99834 0.97872 0.97508 0.90285 0.53186 0.56817 1.04446 0.0010496 0.0010496 0.0010496
51 50 1239.24 0.52518 0.46535 0.87764 1 0.97829 0.97508 0.88767 0.54936 0.55594 1.03008 0.0010298 0.0010298 0.0010298
52 51 1263.25 0.60835 0.51077 0.90971 0.99781 0.97872 0.97508 0.89059 0.54906 0.57735 1.04816 0.00101 0.00101 0.00101
53 52 1287.64 0.57438 0.5124 0.92987 0.97851 0.96899 0.97423 0.88824 0.61444 0.54986 1.07954 0.0009902 0.0009902 0.0009902
54 53 1313.16 0.56937 0.48896 0.90152 0.99826 0.95745 0.97466 0.86378 0.60609 0.56536 1.047 0.0009704 0.0009704 0.0009704
55 54 1339.05 0.61594 0.51975 0.95916 0.97714 0.97872 0.97423 0.86971 0.60094 0.57078 1.05624 0.0009506 0.0009506 0.0009506
56 55 1365.76 0.57515 0.47696 0.89298 0.97705 0.97872 0.97423 0.87785 0.5651 0.58565 1.08914 0.0009308 0.0009308 0.0009308
57 56 1389.14 0.5068 0.45113 0.88907 0.97696 0.97872 0.97467 0.87046 0.58345 0.57728 1.12494 0.000911 0.000911 0.000911
58 57 1413.27 0.53803 0.45492 0.90522 0.97722 0.97872 0.97425 0.88975 0.49741 0.5502 1.03528 0.0008912 0.0008912 0.0008912
59 58 1441.19 0.608 0.4931 0.93643 0.97735 0.97872 0.97426 0.8931 0.50678 0.55762 0.99923 0.0008714 0.0008714 0.0008714
60 59 1468.55 0.54444 0.46445 0.95757 0.99848 0.97872 0.97512 0.89931 0.50644 0.53365 0.98802 0.0008516 0.0008516 0.0008516
61 60 1493.47 0.5672 0.47496 0.90847 0.99857 0.97872 0.97514 0.89933 0.48361 0.49757 0.94558 0.0008318 0.0008318 0.0008318
62 61 1519.16 0.56814 0.49767 0.89276 0.99854 0.97872 0.97515 0.8908 0.45367 0.44977 0.92588 0.000812 0.000812 0.000812
63 62 1545.68 0.54123 0.46468 0.92992 0.99867 0.97872 0.97514 0.89709 0.44947 0.45708 0.93801 0.0007922 0.0007922 0.0007922
64 63 1571.74 0.57246 0.46686 0.90762 0.99786 0.97872 0.97513 0.89482 0.46021 0.47385 0.94823 0.0007724 0.0007724 0.0007724
65 64 1597.8 0.54624 0.43858 0.87308 0.99844 0.97872 0.97516 0.89729 0.49468 0.46887 0.96013 0.0007526 0.0007526 0.0007526
66 65 1623.68 0.54364 0.44246 0.88962 0.99855 0.97872 0.97517 0.90365 0.51956 0.42429 0.95753 0.0007328 0.0007328 0.0007328
67 66 1649.18 0.46981 0.40913 0.87546 0.99847 0.97872 0.9752 0.90499 0.43039 0.40576 0.90702 0.000713 0.000713 0.000713
68 67 1673.24 0.52479 0.41543 0.86585 0.99841 0.97872 0.97522 0.89389 0.40141 0.42377 0.91541 0.0006932 0.0006932 0.0006932
69 68 1697.64 0.5616 0.43932 0.90982 0.99837 0.97872 0.97529 0.89919 0.49413 0.43951 0.98202 0.0006734 0.0006734 0.0006734
70 69 1723.64 0.52312 0.44072 0.90402 0.99831 0.97872 0.97536 0.89659 0.53139 0.44096 0.98169 0.0006536 0.0006536 0.0006536
71 70 1749.65 0.50496 0.41511 0.90988 0.99819 0.97872 0.97539 0.91645 0.45378 0.42096 0.92256 0.0006338 0.0006338 0.0006338
72 71 1777.56 0.51147 0.42693 0.86444 0.99821 0.97872 0.9754 0.92046 0.41803 0.38434 0.90736 0.000614 0.000614 0.000614
73 72 1806.81 0.50723 0.44087 0.88909 0.99845 0.97872 0.97545 0.9141 0.41854 0.38573 0.90434 0.0005942 0.0005942 0.0005942
74 73 1835.6 0.48878 0.42097 0.89505 0.99844 0.97872 0.97552 0.92009 0.40796 0.39896 0.89601 0.0005744 0.0005744 0.0005744
75 74 1861.75 0.52419 0.4239 0.8862 0.99859 0.97872 0.9756 0.92316 0.40554 0.41532 0.88737 0.0005546 0.0005546 0.0005546
76 75 1888.59 0.47155 0.38701 0.87038 0.99862 0.97872 0.9757 0.92891 0.38805 0.41992 0.8783 0.0005348 0.0005348 0.0005348
77 76 1915.19 0.48355 0.39834 0.86732 0.99865 0.97872 0.97579 0.93202 0.37531 0.41166 0.86651 0.000515 0.000515 0.000515
78 77 1941.02 0.52022 0.39644 0.90819 0.99874 0.97872 0.97588 0.93108 0.38676 0.42775 0.86735 0.0004952 0.0004952 0.0004952
79 78 1967.17 0.49991 0.39252 0.87213 0.99874 0.97872 0.97597 0.92747 0.39723 0.40431 0.87254 0.0004754 0.0004754 0.0004754
80 79 1993.36 0.46994 0.38264 0.87372 0.99871 0.97872 0.97598 0.94087 0.39048 0.38877 0.87239 0.0004556 0.0004556 0.0004556
81 80 2019.46 0.44306 0.34784 0.85531 0.99874 0.97872 0.97598 0.94111 0.37653 0.37271 0.85693 0.0004358 0.0004358 0.0004358
82 81 2045.91 0.52164 0.44215 0.90042 0.99874 0.97872 0.97605 0.93703 0.35099 0.35613 0.84375 0.000416 0.000416 0.000416
83 82 2071.09 0.43583 0.36948 0.86455 0.99875 0.97872 0.97614 0.93687 0.33748 0.34432 0.83893 0.0003962 0.0003962 0.0003962
84 83 2096.03 0.44289 0.36518 0.85191 0.99875 0.97872 0.97624 0.93707 0.3427 0.34866 0.84353 0.0003764 0.0003764 0.0003764
85 84 2122.84 0.47123 0.37938 0.86435 0.99877 0.97872 0.97633 0.93289 0.36886 0.34391 0.86271 0.0003566 0.0003566 0.0003566
86 85 2146.21 0.46956 0.37655 0.89478 0.99877 0.97872 0.97641 0.93346 0.35497 0.34737 0.85675 0.0003368 0.0003368 0.0003368
87 86 2169.49 0.44497 0.38846 0.79774 0.99875 0.97872 0.97663 0.93787 0.35528 0.32827 0.84843 0.000317 0.000317 0.000317
88 87 2192.97 0.42009 0.36714 0.84973 0.99875 0.97872 0.97668 0.94681 0.37703 0.32198 0.8541 0.0002972 0.0002972 0.0002972
89 88 2218.43 0.43938 0.36407 0.83415 0.99876 0.97872 0.97678 0.94578 0.40722 0.32622 0.86835 0.0002774 0.0002774 0.0002774
90 89 2241.97 0.42528 0.43108 0.82208 0.99881 0.97872 0.9769 0.94796 0.44235 0.33782 0.88634 0.0002576 0.0002576 0.0002576
91 90 2265.32 0.47437 0.3832 0.88705 0.99884 0.97872 0.97706 0.94442 0.42207 0.32059 0.87479 0.0002378 0.0002378 0.0002378
92 91 2289.85 0.37181 0.34761 0.84103 0.99886 0.97872 0.97701 0.94757 0.41277 0.32924 0.86992 0.000218 0.000218 0.000218
93 92 2312.97 0.34648 0.34359 0.826 0.99884 0.97872 0.97732 0.94171 0.39116 0.32631 0.86015 0.0001982 0.0001982 0.0001982
94 93 2335.99 0.36605 0.34218 0.84288 0.9988 0.97872 0.97724 0.94082 0.37981 0.3187 0.8523 0.0001784 0.0001784 0.0001784
95 94 2359.71 0.38169 0.33456 0.84097 0.99881 0.97872 0.97728 0.94485 0.36443 0.33128 0.84394 0.0001586 0.0001586 0.0001586
96 95 2383.48 0.37646 0.35094 0.83187 0.99881 0.97872 0.97726 0.94287 0.36169 0.32773 0.84267 0.0001388 0.0001388 0.0001388
97 96 2407.26 0.36623 0.33836 0.84097 0.99879 0.97872 0.97731 0.94201 0.34619 0.32886 0.83634 0.000119 0.000119 0.000119
98 97 2430.66 0.35889 0.34208 0.81934 0.99882 0.97872 0.97718 0.94312 0.32836 0.3326 0.83218 9.92e-05 9.92e-05 9.92e-05
99 98 2454.19 0.34079 0.32036 0.79553 0.99882 0.97872 0.97726 0.94541 0.3094 0.33484 0.83082 7.94e-05 7.94e-05 7.94e-05
100 99 2478.3 0.34663 0.32585 0.82711 0.99884 0.97872 0.9772 0.94542 0.30885 0.32567 0.82688 5.96e-05 5.96e-05 5.96e-05
101 100 2502.43 0.34408 0.31704 0.82257 0.99883 0.97872 0.97737 0.94894 0.30604 0.32063 0.82739 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: train43
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\train43

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@ -0,0 +1,10 @@
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
1,24.3756,1.21014,3.1702,1.19727,0.00313,0.97872,0.79623,0.64322,0.52877,3.00181,0.80078,0.00026,0.00026,0.00026
2,48.2621,0.81396,2.37685,0.8738,0.00313,0.97872,0.91786,0.75913,0.59196,2.94612,0.82206,0.000534654,0.000534654,0.000534654
3,73.8232,0.90479,1.60795,0.97833,1,0.22824,0.93879,0.66689,0.75898,2.86271,0.91755,0.000803764,0.000803764,0.000803764
4,97.3313,0.8895,1.99802,0.91206,0.93431,0.30285,0.74521,0.55539,1.24471,3.01773,1.14351,0.00106733,0.00106733,0.00106733
5,120.563,1.04688,1.71324,1.04767,1,0.24535,0.84327,0.51189,1.29624,2.99114,1.2371,0.00132535,0.00132535,0.00132535
6,144.566,1.01511,1.47643,1.01558,0.58375,0.55319,0.61104,0.35898,1.30424,2.7948,1.37665,0.00157783,0.00157783,0.00157783
7,167.911,0.9864,1.49348,1.04347,0.44618,0.4972,0.5101,0.32791,1.17259,2.53619,1.17558,0.00182476,0.00182476,0.00182476
8,191.533,0.95161,1.72087,0.96641,0.35612,0.3617,0.35199,0.21519,1.13552,2.86189,1.32933,0.0018614,0.0018614,0.0018614
9,214.965,0.88668,1.27559,1.01199,0.94045,0.85106,0.92187,0.71277,0.93981,2.32268,1.10274,0.0018416,0.0018416,0.0018416
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 24.3756 1.21014 3.1702 1.19727 0.00313 0.97872 0.79623 0.64322 0.52877 3.00181 0.80078 0.00026 0.00026 0.00026
3 2 48.2621 0.81396 2.37685 0.8738 0.00313 0.97872 0.91786 0.75913 0.59196 2.94612 0.82206 0.000534654 0.000534654 0.000534654
4 3 73.8232 0.90479 1.60795 0.97833 1 0.22824 0.93879 0.66689 0.75898 2.86271 0.91755 0.000803764 0.000803764 0.000803764
5 4 97.3313 0.8895 1.99802 0.91206 0.93431 0.30285 0.74521 0.55539 1.24471 3.01773 1.14351 0.00106733 0.00106733 0.00106733
6 5 120.563 1.04688 1.71324 1.04767 1 0.24535 0.84327 0.51189 1.29624 2.99114 1.2371 0.00132535 0.00132535 0.00132535
7 6 144.566 1.01511 1.47643 1.01558 0.58375 0.55319 0.61104 0.35898 1.30424 2.7948 1.37665 0.00157783 0.00157783 0.00157783
8 7 167.911 0.9864 1.49348 1.04347 0.44618 0.4972 0.5101 0.32791 1.17259 2.53619 1.17558 0.00182476 0.00182476 0.00182476
9 8 191.533 0.95161 1.72087 0.96641 0.35612 0.3617 0.35199 0.21519 1.13552 2.86189 1.32933 0.0018614 0.0018614 0.0018614
10 9 214.965 0.88668 1.27559 1.01199 0.94045 0.85106 0.92187 0.71277 0.93981 2.32268 1.10274 0.0018416 0.0018416 0.0018416

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@ -3,7 +3,7 @@ train: images/train
val: images/val val: images/val
test: images/test test: images/test
nc: 1 nc: 1
names: [ 'person' ] names: [ 'aaa' ]
augment: augment:
flipud: 0.5 # 50% 概率进行垂直翻转 flipud: 0.5 # 50% 概率进行垂直翻转
fliplr: 0.5 # 50% 概率进行水平翻转 fliplr: 0.5 # 50% 概率进行水平翻转

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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\train41\weights\best.pt") model = YOLO(r"C:\workspace\le-yolo\runs\detect\train42\weights\best.pt")
results = model.predict("../res/5.mp4", show=True, save=True) results = model.predict("../res/5.mp4", show=True, save=True)