songbingle/le-yolo
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

[MF]修改文件

Browse Source
This commit is contained in:
songbingle 2025-06-06 14:35:16 +08:00
parent 47cc7c783b
commit fb574a319d
4 changed files with 77 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

75
runs/detect/train38/results.csv
View File

@ -12,3 +12,78 @@ epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),met
11,82.2757,0.39982,0.29848,0.79798,0.91385,0.6772,0.78622,0.60723,1.86969,1.09871,2.9569,0.00057664,0.00057664,0.00057664 11,82.2757,0.39982,0.29848,0.79798,0.91385,0.6772,0.78622,0.60723,1.86969,1.09871,2.9569,0.00057664,0.00057664,0.00057664
12,89.8078,0.42748,0.30629,0.80657,0.97466,0.65957,0.78702,0.60116,1.86946,1.17368,2.89691,0.00062377,0.00062377,0.00062377 12,89.8078,0.42748,0.30629,0.80657,0.97466,0.65957,0.78702,0.60116,1.86946,1.17368,2.89691,0.00062377,0.00062377,0.00062377
13,97.6266,0.38214,0.32367,0.82461,0.96398,0.65957,0.79028,0.59399,1.86126,1.28949,2.79979,0.000669712,0.000669712,0.000669712 13,97.6266,0.38214,0.32367,0.82461,0.96398,0.65957,0.79028,0.59399,1.86126,1.28949,2.79979,0.000669712,0.000669712,0.000669712
14,106.499,0.42681,0.32781,0.80701,0.96962,0.67911,0.78614,0.5826,1.88573,1.39743,2.70735,0.000714466,0.000714466,0.000714466
15,114.876,0.4124,0.32095,0.82154,0.96962,0.67911,0.78614,0.5826,1.88573,1.39743,2.70735,0.000758032,0.000758032,0.000758032
16,122.032,0.3156,0.27748,0.79804,0.96595,0.68085,0.79166,0.55548,1.94458,1.47053,2.64614,0.00080041,0.00080041,0.00080041
17,129.022,0.3842,0.32984,0.79767,0.96123,0.65957,0.76713,0.54811,1.98348,1.5629,2.71409,0.0008416,0.0008416,0.0008416
18,135.928,0.44695,0.33509,0.82407,0.96123,0.65957,0.76713,0.54811,1.98348,1.5629,2.71409,0.000881602,0.000881602,0.000881602
19,142.86,0.35319,0.27554,0.76457,1,0.63726,0.76094,0.555,1.97238,1.44968,2.86274,0.000920416,0.000920416,0.000920416
20,149.871,0.38547,0.29166,0.84841,0.99821,0.6383,0.74118,0.53996,1.98116,1.28991,2.96896,0.000958042,0.000958042,0.000958042
21,156.828,0.52653,0.38184,0.86419,0.99821,0.6383,0.74118,0.53996,1.98116,1.28991,2.96896,0.00099448,0.00099448,0.00099448
22,164.256,0.47429,0.33565,0.8048,0.96846,0.6534,0.74737,0.54459,2.00931,1.14153,3.0834,0.00102973,0.00102973,0.00102973
23,172.605,0.4727,0.38541,0.8592,0.96846,0.6534,0.74737,0.54459,2.00931,1.14153,3.0834,0.00106379,0.00106379,0.00106379
24,179.705,0.44564,0.33499,0.82782,0.92392,0.68085,0.76801,0.52843,2.00183,1.01742,3.12744,0.00109667,0.00109667,0.00109667
25,186.701,0.43184,0.3208,0.79865,0.92392,0.68085,0.76801,0.52843,2.00183,1.01742,3.12744,0.00112835,0.00112835,0.00112835
26,193.691,0.49994,0.35061,0.87464,0.91297,0.68085,0.77001,0.53324,2.0311,1.03051,3.21046,0.00115885,0.00115885,0.00115885
27,200.771,0.54815,0.36418,0.83376,0.91297,0.68085,0.77001,0.53324,2.0311,1.03051,3.21046,0.00118816,0.00118816,0.00118816
28,207.934,0.50333,0.35966,0.79061,0.94038,0.67139,0.7661,0.53956,2.07114,1.12504,3.25993,0.00121628,0.00121628,0.00121628
29,214.848,0.47761,0.36002,0.83191,0.94038,0.67139,0.7661,0.53956,2.07114,1.12504,3.25993,0.00124322,0.00124322,0.00124322
30,221.794,0.5143,0.38193,0.84498,0.94038,0.67139,0.7661,0.53956,2.07114,1.12504,3.25993,0.00126896,0.00126896,0.00126896
31,228.805,0.47365,0.38134,0.84153,0.99483,0.6383,0.75333,0.5392,2.04771,1.15594,3.24232,0.00129352,0.00129352,0.00129352
32,235.783,0.47356,0.36006,0.84416,0.99483,0.6383,0.75333,0.5392,2.04771,1.15594,3.24232,0.00131689,0.00131689,0.00131689
33,242.987,0.46614,0.34128,0.81515,0.99564,0.6383,0.73368,0.52294,2.05179,1.28764,3.15833,0.00133907,0.00133907,0.00133907
34,250.096,0.51291,0.38233,0.82828,0.99564,0.6383,0.73368,0.52294,2.05179,1.28764,3.15833,0.0013466,0.0013466,0.0013466
35,257.093,0.46012,0.37924,0.79108,0.99564,0.6383,0.73368,0.52294,2.05179,1.28764,3.15833,0.0013268,0.0013268,0.0013268
36,264.125,0.47266,0.36559,0.83974,0.9946,0.6383,0.73594,0.49849,2.08888,1.47059,3.05636,0.001307,0.001307,0.001307
37,271.216,0.53193,0.36614,0.81035,0.9946,0.6383,0.73594,0.49849,2.08888,1.47059,3.05636,0.0012872,0.0012872,0.0012872
38,278.428,0.46391,0.37187,0.77822,0.9946,0.6383,0.73594,0.49849,2.08888,1.47059,3.05636,0.0012674,0.0012674,0.0012674
39,285.759,0.54892,0.3751,0.8077,0.96983,0.68405,0.77519,0.51542,2.06411,1.40878,2.99465,0.0012476,0.0012476,0.0012476
40,292.724,0.45425,0.36896,0.88718,0.96983,0.68405,0.77519,0.51542,2.06411,1.40878,2.99465,0.0012278,0.0012278,0.0012278
41,299.91,0.47381,0.37152,0.82568,1,0.71588,0.80283,0.56891,2.05245,1.4167,3.0392,0.001208,0.001208,0.001208
42,307.067,0.50159,0.38543,0.89871,1,0.71588,0.80283,0.56891,2.05245,1.4167,3.0392,0.0011882,0.0011882,0.0011882
43,314.157,0.51203,0.36832,0.845,1,0.71588,0.80283,0.56891,2.05245,1.4167,3.0392,0.0011684,0.0011684,0.0011684
44,321.43,0.42751,0.36484,0.82091,0.97078,0.7069,0.80667,0.58066,1.99761,1.36307,2.99403,0.0011486,0.0011486,0.0011486
45,328.674,0.37929,0.31113,0.8119,0.97078,0.7069,0.80667,0.58066,1.99761,1.36307,2.99403,0.0011288,0.0011288,0.0011288
46,335.945,0.45628,0.3585,0.81802,0.97078,0.7069,0.80667,0.58066,1.99761,1.36307,2.99403,0.001109,0.001109,0.001109
47,343.08,0.4815,0.35025,0.82786,0.92272,0.76219,0.80415,0.5949,1.92913,1.24708,2.95143,0.0010892,0.0010892,0.0010892
48,351.332,0.45424,0.33759,0.82806,0.92272,0.76219,0.80415,0.5949,1.92913,1.24708,2.95143,0.0010694,0.0010694,0.0010694
49,359.201,0.38503,0.3048,0.806,0.93388,0.7234,0.79352,0.5924,1.9135,1.25616,2.90943,0.0010496,0.0010496,0.0010496
50,366.32,0.464,0.34249,0.81729,0.93388,0.7234,0.79352,0.5924,1.9135,1.25616,2.90943,0.0010298,0.0010298,0.0010298
51,373.461,0.48833,0.34717,0.7905,0.93388,0.7234,0.79352,0.5924,1.9135,1.25616,2.90943,0.00101,0.00101,0.00101
52,380.648,0.47636,0.36862,0.80257,0.94022,0.74468,0.80726,0.57469,1.91869,1.12665,2.89025,0.0009902,0.0009902,0.0009902
53,387.542,0.4578,0.35366,0.82888,0.94022,0.74468,0.80726,0.57469,1.91869,1.12665,2.89025,0.0009704,0.0009704,0.0009704
54,394.494,0.42205,0.34785,0.80089,0.94022,0.74468,0.80726,0.57469,1.91869,1.12665,2.89025,0.0009506,0.0009506,0.0009506
55,401.52,0.562,0.40526,0.87108,0.93745,0.74468,0.80065,0.55536,1.9223,0.96134,2.90655,0.0009308,0.0009308,0.0009308
56,408.75,0.46482,0.35315,0.81293,0.93745,0.74468,0.80065,0.55536,1.9223,0.96134,2.90655,0.000911,0.000911,0.000911
57,416.803,0.44544,0.36121,0.81283,0.94423,0.72059,0.80358,0.55201,1.91904,0.83064,2.92761,0.0008912,0.0008912,0.0008912
58,424.973,0.41446,0.366,0.81556,0.94423,0.72059,0.80358,0.55201,1.91904,0.83064,2.92761,0.0008714,0.0008714,0.0008714
59,432.716,0.48247,0.37362,0.79318,0.94423,0.72059,0.80358,0.55201,1.91904,0.83064,2.92761,0.0008516,0.0008516,0.0008516
60,440.765,0.4712,0.36677,0.80058,0.9642,0.70213,0.80437,0.54307,1.91848,0.7855,2.94543,0.0008318,0.0008318,0.0008318
61,448.807,0.35863,0.34542,0.7641,0.9642,0.70213,0.80437,0.54307,1.91848,0.7855,2.94543,0.000812,0.000812,0.000812
62,457.031,0.47865,0.35305,0.84098,0.9642,0.70213,0.80437,0.54307,1.91848,0.7855,2.94543,0.0007922,0.0007922,0.0007922
63,465.076,0.42392,0.33701,0.83724,0.97058,0.70185,0.80611,0.53494,1.92839,0.77565,2.95173,0.0007724,0.0007724,0.0007724
64,472.861,0.50845,0.39243,0.86976,0.97058,0.70185,0.80611,0.53494,1.92839,0.77565,2.95173,0.0007526,0.0007526,0.0007526
65,481.002,0.42312,0.3432,0.82392,1,0.6755,0.80753,0.52921,1.94933,0.77359,2.95587,0.0007328,0.0007328,0.0007328
66,488.867,0.45348,0.35434,0.80292,1,0.6755,0.80753,0.52921,1.94933,0.77359,2.95587,0.000713,0.000713,0.000713
67,496.971,0.48175,0.35168,0.83863,1,0.6755,0.80753,0.52921,1.94933,0.77359,2.95587,0.0006932,0.0006932,0.0006932
68,504.96,0.44364,0.34708,0.83965,1,0.67986,0.82592,0.54254,1.95056,0.76533,2.97253,0.0006734,0.0006734,0.0006734
69,513.18,0.48863,0.39011,0.82441,1,0.67986,0.82592,0.54254,1.95056,0.76533,2.97253,0.0006536,0.0006536,0.0006536
70,521.073,0.34255,0.28066,0.79168,1,0.67986,0.82592,0.54254,1.95056,0.76533,2.97253,0.0006338,0.0006338,0.0006338
71,529.167,0.3535,0.30849,0.81125,0.99257,0.68085,0.82052,0.55738,1.93196,0.74637,2.96702,0.000614,0.000614,0.000614
72,536.836,0.40418,0.32751,0.79019,0.99257,0.68085,0.82052,0.55738,1.93196,0.74637,2.96702,0.0005942,0.0005942,0.0005942
73,545.062,0.39773,0.30971,0.82931,0.99279,0.68085,0.81672,0.56447,1.91114,0.73952,2.96043,0.0005744,0.0005744,0.0005744
74,552.938,0.4601,0.34044,0.78185,0.99279,0.68085,0.81672,0.56447,1.91114,0.73952,2.96043,0.0005546,0.0005546,0.0005546
75,560.969,0.38528,0.31725,0.80809,0.99279,0.68085,0.81672,0.56447,1.91114,0.73952,2.96043,0.0005348,0.0005348,0.0005348
76,568.853,0.39478,0.30513,0.80886,0.96073,0.65957,0.79222,0.56957,1.91622,0.74565,2.95303,0.000515,0.000515,0.000515
77,576.935,0.42554,0.32115,0.85212,0.96073,0.65957,0.79222,0.56957,1.91622,0.74565,2.95303,0.0004952,0.0004952,0.0004952
78,584.674,0.48494,0.35933,0.85259,0.96073,0.65957,0.79222,0.56957,1.91622,0.74565,2.95303,0.0004754,0.0004754,0.0004754
79,592.624,0.4278,0.35008,0.82484,0.96123,0.65957,0.79424,0.57532,1.91737,0.76654,2.93734,0.0004556,0.0004556,0.0004556
80,600.642,0.37041,0.31816,0.79067,0.96123,0.65957,0.79424,0.57532,1.91737,0.76654,2.93734,0.0004358,0.0004358,0.0004358
81,608.47,0.36081,0.307,0.85453,0.96414,0.65957,0.78839,0.57795,1.91893,0.79829,2.93012,0.000416,0.000416,0.000416
82,616.494,0.3691,0.31718,0.81064,0.96414,0.65957,0.78839,0.57795,1.91893,0.79829,2.93012,0.0003962,0.0003962,0.0003962
83,624.47,0.3826,0.29774,0.7411,0.96414,0.65957,0.78839,0.57795,1.91893,0.79829,2.93012,0.0003764,0.0003764,0.0003764
84,632.776,0.4513,0.33619,0.86849,0.96686,0.65957,0.78559,0.58502,1.92034,0.82476,2.92511,0.0003566,0.0003566,0.0003566
85,640.743,0.32959,0.2816,0.81304,0.96686,0.65957,0.78559,0.58502,1.92034,0.82476,2.92511,0.0003368,0.0003368,0.0003368
86,648.912,0.38921,0.31928,0.78851,0.96686,0.65957,0.78559,0.58502,1.92034,0.82476,2.92511,0.000317,0.000317,0.000317
87,657.266,0.44059,0.36016,0.81627,0.99835,0.68085,0.8143,0.59335,1.91094,0.84992,2.91374,0.0002972,0.0002972,0.0002972
88,665.579,0.37011,0.30439,0.82546,0.99835,0.68085,0.8143,0.59335,1.91094,0.84992,2.91374,0.0002774,0.0002774,0.0002774

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
12 11 82.2757 0.39982 0.29848 0.79798 0.91385 0.6772 0.78622 0.60723 1.86969 1.09871 2.9569 0.00057664 0.00057664 0.00057664
13 12 89.8078 0.42748 0.30629 0.80657 0.97466 0.65957 0.78702 0.60116 1.86946 1.17368 2.89691 0.00062377 0.00062377 0.00062377
14 13 97.6266 0.38214 0.32367 0.82461 0.96398 0.65957 0.79028 0.59399 1.86126 1.28949 2.79979 0.000669712 0.000669712 0.000669712
15 14 106.499 0.42681 0.32781 0.80701 0.96962 0.67911 0.78614 0.5826 1.88573 1.39743 2.70735 0.000714466 0.000714466 0.000714466
16 15 114.876 0.4124 0.32095 0.82154 0.96962 0.67911 0.78614 0.5826 1.88573 1.39743 2.70735 0.000758032 0.000758032 0.000758032
17 16 122.032 0.3156 0.27748 0.79804 0.96595 0.68085 0.79166 0.55548 1.94458 1.47053 2.64614 0.00080041 0.00080041 0.00080041
18 17 129.022 0.3842 0.32984 0.79767 0.96123 0.65957 0.76713 0.54811 1.98348 1.5629 2.71409 0.0008416 0.0008416 0.0008416
19 18 135.928 0.44695 0.33509 0.82407 0.96123 0.65957 0.76713 0.54811 1.98348 1.5629 2.71409 0.000881602 0.000881602 0.000881602
20 19 142.86 0.35319 0.27554 0.76457 1 0.63726 0.76094 0.555 1.97238 1.44968 2.86274 0.000920416 0.000920416 0.000920416
21 20 149.871 0.38547 0.29166 0.84841 0.99821 0.6383 0.74118 0.53996 1.98116 1.28991 2.96896 0.000958042 0.000958042 0.000958042
22 21 156.828 0.52653 0.38184 0.86419 0.99821 0.6383 0.74118 0.53996 1.98116 1.28991 2.96896 0.00099448 0.00099448 0.00099448
23 22 164.256 0.47429 0.33565 0.8048 0.96846 0.6534 0.74737 0.54459 2.00931 1.14153 3.0834 0.00102973 0.00102973 0.00102973
24 23 172.605 0.4727 0.38541 0.8592 0.96846 0.6534 0.74737 0.54459 2.00931 1.14153 3.0834 0.00106379 0.00106379 0.00106379
25 24 179.705 0.44564 0.33499 0.82782 0.92392 0.68085 0.76801 0.52843 2.00183 1.01742 3.12744 0.00109667 0.00109667 0.00109667
26 25 186.701 0.43184 0.3208 0.79865 0.92392 0.68085 0.76801 0.52843 2.00183 1.01742 3.12744 0.00112835 0.00112835 0.00112835
27 26 193.691 0.49994 0.35061 0.87464 0.91297 0.68085 0.77001 0.53324 2.0311 1.03051 3.21046 0.00115885 0.00115885 0.00115885
28 27 200.771 0.54815 0.36418 0.83376 0.91297 0.68085 0.77001 0.53324 2.0311 1.03051 3.21046 0.00118816 0.00118816 0.00118816
29 28 207.934 0.50333 0.35966 0.79061 0.94038 0.67139 0.7661 0.53956 2.07114 1.12504 3.25993 0.00121628 0.00121628 0.00121628
30 29 214.848 0.47761 0.36002 0.83191 0.94038 0.67139 0.7661 0.53956 2.07114 1.12504 3.25993 0.00124322 0.00124322 0.00124322
31 30 221.794 0.5143 0.38193 0.84498 0.94038 0.67139 0.7661 0.53956 2.07114 1.12504 3.25993 0.00126896 0.00126896 0.00126896
32 31 228.805 0.47365 0.38134 0.84153 0.99483 0.6383 0.75333 0.5392 2.04771 1.15594 3.24232 0.00129352 0.00129352 0.00129352
33 32 235.783 0.47356 0.36006 0.84416 0.99483 0.6383 0.75333 0.5392 2.04771 1.15594 3.24232 0.00131689 0.00131689 0.00131689
34 33 242.987 0.46614 0.34128 0.81515 0.99564 0.6383 0.73368 0.52294 2.05179 1.28764 3.15833 0.00133907 0.00133907 0.00133907
35 34 250.096 0.51291 0.38233 0.82828 0.99564 0.6383 0.73368 0.52294 2.05179 1.28764 3.15833 0.0013466 0.0013466 0.0013466
36 35 257.093 0.46012 0.37924 0.79108 0.99564 0.6383 0.73368 0.52294 2.05179 1.28764 3.15833 0.0013268 0.0013268 0.0013268
37 36 264.125 0.47266 0.36559 0.83974 0.9946 0.6383 0.73594 0.49849 2.08888 1.47059 3.05636 0.001307 0.001307 0.001307
38 37 271.216 0.53193 0.36614 0.81035 0.9946 0.6383 0.73594 0.49849 2.08888 1.47059 3.05636 0.0012872 0.0012872 0.0012872
39 38 278.428 0.46391 0.37187 0.77822 0.9946 0.6383 0.73594 0.49849 2.08888 1.47059 3.05636 0.0012674 0.0012674 0.0012674
40 39 285.759 0.54892 0.3751 0.8077 0.96983 0.68405 0.77519 0.51542 2.06411 1.40878 2.99465 0.0012476 0.0012476 0.0012476
41 40 292.724 0.45425 0.36896 0.88718 0.96983 0.68405 0.77519 0.51542 2.06411 1.40878 2.99465 0.0012278 0.0012278 0.0012278
42 41 299.91 0.47381 0.37152 0.82568 1 0.71588 0.80283 0.56891 2.05245 1.4167 3.0392 0.001208 0.001208 0.001208
43 42 307.067 0.50159 0.38543 0.89871 1 0.71588 0.80283 0.56891 2.05245 1.4167 3.0392 0.0011882 0.0011882 0.0011882
44 43 314.157 0.51203 0.36832 0.845 1 0.71588 0.80283 0.56891 2.05245 1.4167 3.0392 0.0011684 0.0011684 0.0011684
45 44 321.43 0.42751 0.36484 0.82091 0.97078 0.7069 0.80667 0.58066 1.99761 1.36307 2.99403 0.0011486 0.0011486 0.0011486
46 45 328.674 0.37929 0.31113 0.8119 0.97078 0.7069 0.80667 0.58066 1.99761 1.36307 2.99403 0.0011288 0.0011288 0.0011288
47 46 335.945 0.45628 0.3585 0.81802 0.97078 0.7069 0.80667 0.58066 1.99761 1.36307 2.99403 0.001109 0.001109 0.001109
48 47 343.08 0.4815 0.35025 0.82786 0.92272 0.76219 0.80415 0.5949 1.92913 1.24708 2.95143 0.0010892 0.0010892 0.0010892
49 48 351.332 0.45424 0.33759 0.82806 0.92272 0.76219 0.80415 0.5949 1.92913 1.24708 2.95143 0.0010694 0.0010694 0.0010694
50 49 359.201 0.38503 0.3048 0.806 0.93388 0.7234 0.79352 0.5924 1.9135 1.25616 2.90943 0.0010496 0.0010496 0.0010496
51 50 366.32 0.464 0.34249 0.81729 0.93388 0.7234 0.79352 0.5924 1.9135 1.25616 2.90943 0.0010298 0.0010298 0.0010298
52 51 373.461 0.48833 0.34717 0.7905 0.93388 0.7234 0.79352 0.5924 1.9135 1.25616 2.90943 0.00101 0.00101 0.00101
53 52 380.648 0.47636 0.36862 0.80257 0.94022 0.74468 0.80726 0.57469 1.91869 1.12665 2.89025 0.0009902 0.0009902 0.0009902
54 53 387.542 0.4578 0.35366 0.82888 0.94022 0.74468 0.80726 0.57469 1.91869 1.12665 2.89025 0.0009704 0.0009704 0.0009704
55 54 394.494 0.42205 0.34785 0.80089 0.94022 0.74468 0.80726 0.57469 1.91869 1.12665 2.89025 0.0009506 0.0009506 0.0009506
56 55 401.52 0.562 0.40526 0.87108 0.93745 0.74468 0.80065 0.55536 1.9223 0.96134 2.90655 0.0009308 0.0009308 0.0009308
57 56 408.75 0.46482 0.35315 0.81293 0.93745 0.74468 0.80065 0.55536 1.9223 0.96134 2.90655 0.000911 0.000911 0.000911
58 57 416.803 0.44544 0.36121 0.81283 0.94423 0.72059 0.80358 0.55201 1.91904 0.83064 2.92761 0.0008912 0.0008912 0.0008912
59 58 424.973 0.41446 0.366 0.81556 0.94423 0.72059 0.80358 0.55201 1.91904 0.83064 2.92761 0.0008714 0.0008714 0.0008714
60 59 432.716 0.48247 0.37362 0.79318 0.94423 0.72059 0.80358 0.55201 1.91904 0.83064 2.92761 0.0008516 0.0008516 0.0008516
61 60 440.765 0.4712 0.36677 0.80058 0.9642 0.70213 0.80437 0.54307 1.91848 0.7855 2.94543 0.0008318 0.0008318 0.0008318
62 61 448.807 0.35863 0.34542 0.7641 0.9642 0.70213 0.80437 0.54307 1.91848 0.7855 2.94543 0.000812 0.000812 0.000812
63 62 457.031 0.47865 0.35305 0.84098 0.9642 0.70213 0.80437 0.54307 1.91848 0.7855 2.94543 0.0007922 0.0007922 0.0007922
64 63 465.076 0.42392 0.33701 0.83724 0.97058 0.70185 0.80611 0.53494 1.92839 0.77565 2.95173 0.0007724 0.0007724 0.0007724
65 64 472.861 0.50845 0.39243 0.86976 0.97058 0.70185 0.80611 0.53494 1.92839 0.77565 2.95173 0.0007526 0.0007526 0.0007526
66 65 481.002 0.42312 0.3432 0.82392 1 0.6755 0.80753 0.52921 1.94933 0.77359 2.95587 0.0007328 0.0007328 0.0007328
67 66 488.867 0.45348 0.35434 0.80292 1 0.6755 0.80753 0.52921 1.94933 0.77359 2.95587 0.000713 0.000713 0.000713
68 67 496.971 0.48175 0.35168 0.83863 1 0.6755 0.80753 0.52921 1.94933 0.77359 2.95587 0.0006932 0.0006932 0.0006932
69 68 504.96 0.44364 0.34708 0.83965 1 0.67986 0.82592 0.54254 1.95056 0.76533 2.97253 0.0006734 0.0006734 0.0006734
70 69 513.18 0.48863 0.39011 0.82441 1 0.67986 0.82592 0.54254 1.95056 0.76533 2.97253 0.0006536 0.0006536 0.0006536
71 70 521.073 0.34255 0.28066 0.79168 1 0.67986 0.82592 0.54254 1.95056 0.76533 2.97253 0.0006338 0.0006338 0.0006338
72 71 529.167 0.3535 0.30849 0.81125 0.99257 0.68085 0.82052 0.55738 1.93196 0.74637 2.96702 0.000614 0.000614 0.000614
73 72 536.836 0.40418 0.32751 0.79019 0.99257 0.68085 0.82052 0.55738 1.93196 0.74637 2.96702 0.0005942 0.0005942 0.0005942
74 73 545.062 0.39773 0.30971 0.82931 0.99279 0.68085 0.81672 0.56447 1.91114 0.73952 2.96043 0.0005744 0.0005744 0.0005744
75 74 552.938 0.4601 0.34044 0.78185 0.99279 0.68085 0.81672 0.56447 1.91114 0.73952 2.96043 0.0005546 0.0005546 0.0005546
76 75 560.969 0.38528 0.31725 0.80809 0.99279 0.68085 0.81672 0.56447 1.91114 0.73952 2.96043 0.0005348 0.0005348 0.0005348
77 76 568.853 0.39478 0.30513 0.80886 0.96073 0.65957 0.79222 0.56957 1.91622 0.74565 2.95303 0.000515 0.000515 0.000515
78 77 576.935 0.42554 0.32115 0.85212 0.96073 0.65957 0.79222 0.56957 1.91622 0.74565 2.95303 0.0004952 0.0004952 0.0004952
79 78 584.674 0.48494 0.35933 0.85259 0.96073 0.65957 0.79222 0.56957 1.91622 0.74565 2.95303 0.0004754 0.0004754 0.0004754
80 79 592.624 0.4278 0.35008 0.82484 0.96123 0.65957 0.79424 0.57532 1.91737 0.76654 2.93734 0.0004556 0.0004556 0.0004556
81 80 600.642 0.37041 0.31816 0.79067 0.96123 0.65957 0.79424 0.57532 1.91737 0.76654 2.93734 0.0004358 0.0004358 0.0004358
82 81 608.47 0.36081 0.307 0.85453 0.96414 0.65957 0.78839 0.57795 1.91893 0.79829 2.93012 0.000416 0.000416 0.000416
83 82 616.494 0.3691 0.31718 0.81064 0.96414 0.65957 0.78839 0.57795 1.91893 0.79829 2.93012 0.0003962 0.0003962 0.0003962
84 83 624.47 0.3826 0.29774 0.7411 0.96414 0.65957 0.78839 0.57795 1.91893 0.79829 2.93012 0.0003764 0.0003764 0.0003764
85 84 632.776 0.4513 0.33619 0.86849 0.96686 0.65957 0.78559 0.58502 1.92034 0.82476 2.92511 0.0003566 0.0003566 0.0003566
86 85 640.743 0.32959 0.2816 0.81304 0.96686 0.65957 0.78559 0.58502 1.92034 0.82476 2.92511 0.0003368 0.0003368 0.0003368
87 86 648.912 0.38921 0.31928 0.78851 0.96686 0.65957 0.78559 0.58502 1.92034 0.82476 2.92511 0.000317 0.000317 0.000317
88 87 657.266 0.44059 0.36016 0.81627 0.99835 0.68085 0.8143 0.59335 1.91094 0.84992 2.91374 0.0002972 0.0002972 0.0002972
89 88 665.579 0.37011 0.30439 0.82546 0.99835 0.68085 0.8143 0.59335 1.91094 0.84992 2.91374 0.0002774 0.0002774 0.0002774

BIN
runs/detect/train38/weights/last.pt
View File

Binary file not shown.

3
src/labels.py
View File

@ -20,7 +20,6 @@ class Yolov8Detect():
save_folder = r'C:/workspace/le-yolo/data/labels/aut/' save_folder = r'C:/workspace/le-yolo/data/labels/aut/'
filename = os.path.splitext(os.path.basename(inputs))[0] + '.txt' filename = os.path.splitext(os.path.basename(inputs))[0] + '.txt'
save_path = os.path.join(save_folder, filename) save_path = os.path.join(save_folder, filename)
txt_construct(save_path, label_text=label_text) txt_construct(save_path, label_text=label_text)
def txt_construct(save_path, label_text): def txt_construct(save_path, label_text):
with open(save_path, 'w') as file: with open(save_path, 'w') as file:
@ -36,7 +35,7 @@ def txt_construct(save_path, label_text):
txt_file.write('\n') txt_file.write('\n')
if __name__ == '__main__': if __name__ == '__main__':
model_path = r'C:\workspace\le-yolo\runs\detect\train34\weights\best.pt' model_path = r'C:\workspace\le-yolo\runs\detect\train38\weights\best.pt'
model = Yolov8Detect(model_path) model = Yolov8Detect(model_path)
import glob import glob
image_path = glob.glob('../data/images/test/*.jpg') image_path = glob.glob('../data/images/test/*.jpg')

2
src/pic.py
View File

@ -1,7 +1,7 @@
from ultralytics import YOLO from ultralytics import YOLO
import cv2 import cv2
import os import os
model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\best.pt") model = YOLO(r"C:\workspace\le-yolo\runs\detect\train38\weights\best.pt")
target_class = 0 target_class = 0
cap = cv2.VideoCapture('../res/1.mp4') cap = cv2.VideoCapture('../res/1.mp4')
if not os.path.exists('detected_objects'): if not os.path.exists('detected_objects'):