[MF]修改文件
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47cc7c783b
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fb574a319d
@ -12,3 +12,78 @@ epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),met
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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@ -20,7 +20,6 @@ class Yolov8Detect():
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save_folder = r'C:/workspace/le-yolo/data/labels/aut/'
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filename = os.path.splitext(os.path.basename(inputs))[0] + '.txt'
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save_path = os.path.join(save_folder, filename)
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txt_construct(save_path, label_text=label_text)
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def txt_construct(save_path, label_text):
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with open(save_path, 'w') as file:
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@ -36,7 +35,7 @@ def txt_construct(save_path, label_text):
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txt_file.write('\n')
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if __name__ == '__main__':
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model_path = r'C:\workspace\le-yolo\runs\detect\train34\weights\best.pt'
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model_path = r'C:\workspace\le-yolo\runs\detect\train38\weights\best.pt'
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model = Yolov8Detect(model_path)
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import glob
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image_path = glob.glob('../data/images/test/*.jpg')
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@ -1,7 +1,7 @@
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from ultralytics import YOLO
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import cv2
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import os
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model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\best.pt")
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model = YOLO(r"C:\workspace\le-yolo\runs\detect\train38\weights\best.pt")
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target_class = 0
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cap = cv2.VideoCapture('../res/1.mp4')
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if not os.path.exists('detected_objects'):
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