파일 업로드 'runs/train-cls/dduk_cls3'
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model: yolov5s-cls.pt
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data: helmet_cls3
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epochs: 80
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batch_size: 504
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imgsz: 224
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nosave: false
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cache: ram
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device: ''
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workers: 8
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project: runs\train-cls
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name: dduk_cls3
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exist_ok: false
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pretrained: true
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optimizer: Adam
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lr0: 0.001
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decay: 5.0e-05
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label_smoothing: 0.1
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cutoff: null
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dropout: null
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verbose: false
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seed: 0
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local_rank: -1
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save_dir: runs\train-cls\dduk_cls3
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epoch, train/loss, val/loss, metrics/accuracy_top1, metrics/accuracy_top5, lr/0
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0, 0.76833, 0.88951, 0.81386, 1, 0.00098763
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1, 0.56961, 0.76173, 0.8272, 1, 0.00097525
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2, 0.52156, 0.75181, 0.84346, 1, 0.00096288
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3, 0.50394, 0.73263, 0.85304, 1, 0.0009505
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4, 0.49153, 0.75253, 0.85167, 1, 0.00093813
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5, 0.48479, 0.71112, 0.85783, 1, 0.00092575
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6, 0.47884, 0.78273, 0.8296, 1, 0.00091337
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7, 0.47331, 0.71544, 0.85697, 1, 0.000901
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8, 0.46737, 0.76133, 0.82173, 1, 0.00088862
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9, 0.46363, 0.71349, 0.86125, 1, 0.00087625
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10, 0.46036, 0.74129, 0.84927, 1, 0.00086388
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11, 0.46043, 0.70623, 0.85321, 1, 0.0008515
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12, 0.45487, 0.73228, 0.84175, 1, 0.00083913
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13, 0.45247, 0.75296, 0.84192, 1, 0.00082675
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14, 0.4525, 0.73755, 0.83011, 1, 0.00081437
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15, 0.44834, 0.68267, 0.87169, 1, 0.000802
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16, 0.44707, 0.72935, 0.83165, 1, 0.00078963
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17, 0.44355, 0.73995, 0.83285, 1, 0.00077725
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18, 0.44291, 0.7031, 0.85817, 1, 0.00076488
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19, 0.44263, 0.70539, 0.85663, 1, 0.0007525
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20, 0.44351, 0.69666, 0.85543, 1, 0.00074013
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21, 0.43801, 0.68528, 0.8645, 1, 0.00072775
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22, 0.44098, 0.73154, 0.84808, 1, 0.00071537
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23, 0.43726, 0.7072, 0.8556, 1, 0.000703
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24, 0.43937, 0.71911, 0.84842, 1, 0.00069063
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25, 0.43528, 0.6947, 0.85081, 1, 0.00067825
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26, 0.43546, 0.71886, 0.85252, 1, 0.00066588
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27, 0.43499, 0.69865, 0.85406, 1, 0.0006535
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28, 0.43395, 0.68224, 0.86313, 1, 0.00064112
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29, 0.43236, 0.68289, 0.86262, 1, 0.00062875
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30, 0.43158, 0.73109, 0.84893, 1, 0.00061638
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31, 0.43218, 0.69988, 0.86159, 1, 0.000604
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32, 0.43354, 0.70973, 0.85577, 1, 0.00059163
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33, 0.42806, 0.71899, 0.84842, 1, 0.00057925
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34, 0.42888, 0.68793, 0.86518, 1, 0.00056687
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35, 0.43093, 0.67981, 0.86792, 1, 0.0005545
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36, 0.42497, 0.67168, 0.87032, 1, 0.00054212
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37, 0.42323, 0.72155, 0.85064, 1, 0.00052975
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38, 0.42387, 0.72693, 0.8426, 1, 0.00051737
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39, 0.42333, 0.68789, 0.85885, 1, 0.000505
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40, 0.42672, 0.68061, 0.86484, 1, 0.00049263
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41, 0.42602, 0.71904, 0.8426, 1, 0.00048025
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42, 0.42367, 0.67061, 0.86672, 1, 0.00046788
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43, 0.42397, 0.69298, 0.86245, 1, 0.0004555
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44, 0.42405, 0.69577, 0.85868, 1, 0.00044312
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45, 0.42383, 0.70744, 0.84927, 1, 0.00043075
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46, 0.42419, 0.71168, 0.85543, 1, 0.00041838
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47, 0.42105, 0.70456, 0.84996, 1, 0.000406
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48, 0.42149, 0.71426, 0.85064, 1, 0.00039362
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49, 0.4207, 0.72387, 0.84979, 1, 0.00038125
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50, 0.41919, 0.68517, 0.86501, 1, 0.00036888
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51, 0.41588, 0.68697, 0.858, 1, 0.0003565
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52, 0.41676, 0.67847, 0.87066, 1, 0.00034413
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53, 0.41357, 0.68348, 0.8698, 1, 0.00033175
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54, 0.41561, 0.6747, 0.87117, 1, 0.00031938
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55, 0.41378, 0.68828, 0.85577, 1, 0.000307
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56, 0.41204, 0.68438, 0.85954, 1, 0.00029462
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57, 0.41022, 0.69516, 0.86587, 1, 0.00028225
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58, 0.41145, 0.68544, 0.86313, 1, 0.00026987
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59, 0.41068, 0.70406, 0.85595, 1, 0.0002575
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60, 0.41089, 0.70139, 0.85577, 1, 0.00024513
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61, 0.40866, 0.69262, 0.85954, 1, 0.00023275
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62, 0.40811, 0.699, 0.85749, 1, 0.00022038
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63, 0.40757, 0.67834, 0.86536, 1, 0.000208
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64, 0.40896, 0.69648, 0.85201, 1, 0.00019562
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65, 0.40856, 0.71111, 0.85047, 1, 0.00018325
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66, 0.40829, 0.68339, 0.86724, 1, 0.00017087
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67, 0.40592, 0.70005, 0.85184, 1, 0.0001585
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68, 0.4078, 0.71784, 0.84551, 1, 0.00014612
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69, 0.40634, 0.69866, 0.85406, 1, 0.00013375
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70, 0.4051, 0.69982, 0.85201, 1, 0.00012138
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71, 0.40516, 0.68997, 0.85834, 1, 0.000109
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72, 0.40603, 0.69182, 0.85629, 1, 9.6625e-05
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73, 0.40428, 0.68093, 0.86262, 1, 8.425e-05
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74, 0.40516, 0.68302, 0.85868, 1, 7.1875e-05
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75, 0.40422, 0.6931, 0.85612, 1, 5.95e-05
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76, 0.40299, 0.68952, 0.86005, 1, 4.7125e-05
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77, 0.40328, 0.68088, 0.85766, 1, 3.475e-05
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78, 0.40284, 0.67786, 0.86159, 1, 2.2375e-05
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79, 0.40196, 0.68056, 0.86159, 1, 1e-05
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