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파일 업로드 'runs/train-cls/dduk_cls3'

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pinb 1 year ago
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commit 903c2ff888
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23
runs/train-cls/dduk_cls3/opt.yaml
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model: yolov5s-cls.pt
data: helmet_cls3
epochs: 80
batch_size: 504
imgsz: 224
nosave: false
cache: ram
device: ''
workers: 8
project: runs\train-cls
name: dduk_cls3
exist_ok: false
pretrained: true
optimizer: Adam
lr0: 0.001
decay: 5.0e-05
label_smoothing: 0.1
cutoff: null
dropout: null
verbose: false
seed: 0
local_rank: -1
save_dir: runs\train-cls\dduk_cls3

81
runs/train-cls/dduk_cls3/results.csv
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epoch, train/loss, val/loss, metrics/accuracy_top1, metrics/accuracy_top5, lr/0
0, 0.76833, 0.88951, 0.81386, 1, 0.00098763
1, 0.56961, 0.76173, 0.8272, 1, 0.00097525
2, 0.52156, 0.75181, 0.84346, 1, 0.00096288
3, 0.50394, 0.73263, 0.85304, 1, 0.0009505
4, 0.49153, 0.75253, 0.85167, 1, 0.00093813
5, 0.48479, 0.71112, 0.85783, 1, 0.00092575
6, 0.47884, 0.78273, 0.8296, 1, 0.00091337
7, 0.47331, 0.71544, 0.85697, 1, 0.000901
8, 0.46737, 0.76133, 0.82173, 1, 0.00088862
9, 0.46363, 0.71349, 0.86125, 1, 0.00087625
10, 0.46036, 0.74129, 0.84927, 1, 0.00086388
11, 0.46043, 0.70623, 0.85321, 1, 0.0008515
12, 0.45487, 0.73228, 0.84175, 1, 0.00083913
13, 0.45247, 0.75296, 0.84192, 1, 0.00082675
14, 0.4525, 0.73755, 0.83011, 1, 0.00081437
15, 0.44834, 0.68267, 0.87169, 1, 0.000802
16, 0.44707, 0.72935, 0.83165, 1, 0.00078963
17, 0.44355, 0.73995, 0.83285, 1, 0.00077725
18, 0.44291, 0.7031, 0.85817, 1, 0.00076488
19, 0.44263, 0.70539, 0.85663, 1, 0.0007525
20, 0.44351, 0.69666, 0.85543, 1, 0.00074013
21, 0.43801, 0.68528, 0.8645, 1, 0.00072775
22, 0.44098, 0.73154, 0.84808, 1, 0.00071537
23, 0.43726, 0.7072, 0.8556, 1, 0.000703
24, 0.43937, 0.71911, 0.84842, 1, 0.00069063
25, 0.43528, 0.6947, 0.85081, 1, 0.00067825
26, 0.43546, 0.71886, 0.85252, 1, 0.00066588
27, 0.43499, 0.69865, 0.85406, 1, 0.0006535
28, 0.43395, 0.68224, 0.86313, 1, 0.00064112
29, 0.43236, 0.68289, 0.86262, 1, 0.00062875
30, 0.43158, 0.73109, 0.84893, 1, 0.00061638
31, 0.43218, 0.69988, 0.86159, 1, 0.000604
32, 0.43354, 0.70973, 0.85577, 1, 0.00059163
33, 0.42806, 0.71899, 0.84842, 1, 0.00057925
34, 0.42888, 0.68793, 0.86518, 1, 0.00056687
35, 0.43093, 0.67981, 0.86792, 1, 0.0005545
36, 0.42497, 0.67168, 0.87032, 1, 0.00054212
37, 0.42323, 0.72155, 0.85064, 1, 0.00052975
38, 0.42387, 0.72693, 0.8426, 1, 0.00051737
39, 0.42333, 0.68789, 0.85885, 1, 0.000505
40, 0.42672, 0.68061, 0.86484, 1, 0.00049263
41, 0.42602, 0.71904, 0.8426, 1, 0.00048025
42, 0.42367, 0.67061, 0.86672, 1, 0.00046788
43, 0.42397, 0.69298, 0.86245, 1, 0.0004555
44, 0.42405, 0.69577, 0.85868, 1, 0.00044312
45, 0.42383, 0.70744, 0.84927, 1, 0.00043075
46, 0.42419, 0.71168, 0.85543, 1, 0.00041838
47, 0.42105, 0.70456, 0.84996, 1, 0.000406
48, 0.42149, 0.71426, 0.85064, 1, 0.00039362
49, 0.4207, 0.72387, 0.84979, 1, 0.00038125
50, 0.41919, 0.68517, 0.86501, 1, 0.00036888
51, 0.41588, 0.68697, 0.858, 1, 0.0003565
52, 0.41676, 0.67847, 0.87066, 1, 0.00034413
53, 0.41357, 0.68348, 0.8698, 1, 0.00033175
54, 0.41561, 0.6747, 0.87117, 1, 0.00031938
55, 0.41378, 0.68828, 0.85577, 1, 0.000307
56, 0.41204, 0.68438, 0.85954, 1, 0.00029462
57, 0.41022, 0.69516, 0.86587, 1, 0.00028225
58, 0.41145, 0.68544, 0.86313, 1, 0.00026987
59, 0.41068, 0.70406, 0.85595, 1, 0.0002575
60, 0.41089, 0.70139, 0.85577, 1, 0.00024513
61, 0.40866, 0.69262, 0.85954, 1, 0.00023275
62, 0.40811, 0.699, 0.85749, 1, 0.00022038
63, 0.40757, 0.67834, 0.86536, 1, 0.000208
64, 0.40896, 0.69648, 0.85201, 1, 0.00019562
65, 0.40856, 0.71111, 0.85047, 1, 0.00018325
66, 0.40829, 0.68339, 0.86724, 1, 0.00017087
67, 0.40592, 0.70005, 0.85184, 1, 0.0001585
68, 0.4078, 0.71784, 0.84551, 1, 0.00014612
69, 0.40634, 0.69866, 0.85406, 1, 0.00013375
70, 0.4051, 0.69982, 0.85201, 1, 0.00012138
71, 0.40516, 0.68997, 0.85834, 1, 0.000109
72, 0.40603, 0.69182, 0.85629, 1, 9.6625e-05
73, 0.40428, 0.68093, 0.86262, 1, 8.425e-05
74, 0.40516, 0.68302, 0.85868, 1, 7.1875e-05
75, 0.40422, 0.6931, 0.85612, 1, 5.95e-05
76, 0.40299, 0.68952, 0.86005, 1, 4.7125e-05
77, 0.40328, 0.68088, 0.85766, 1, 3.475e-05
78, 0.40284, 0.67786, 0.86159, 1, 2.2375e-05
79, 0.40196, 0.68056, 0.86159, 1, 1e-05
1 epoch train/loss val/loss metrics/accuracy_top1 metrics/accuracy_top5 lr/0
2 0 0.76833 0.88951 0.81386 1 0.00098763
3 1 0.56961 0.76173 0.8272 1 0.00097525
4 2 0.52156 0.75181 0.84346 1 0.00096288
5 3 0.50394 0.73263 0.85304 1 0.0009505
6 4 0.49153 0.75253 0.85167 1 0.00093813
7 5 0.48479 0.71112 0.85783 1 0.00092575
8 6 0.47884 0.78273 0.8296 1 0.00091337
9 7 0.47331 0.71544 0.85697 1 0.000901
10 8 0.46737 0.76133 0.82173 1 0.00088862
11 9 0.46363 0.71349 0.86125 1 0.00087625
12 10 0.46036 0.74129 0.84927 1 0.00086388
13 11 0.46043 0.70623 0.85321 1 0.0008515
14 12 0.45487 0.73228 0.84175 1 0.00083913
15 13 0.45247 0.75296 0.84192 1 0.00082675
16 14 0.4525 0.73755 0.83011 1 0.00081437
17 15 0.44834 0.68267 0.87169 1 0.000802
18 16 0.44707 0.72935 0.83165 1 0.00078963
19 17 0.44355 0.73995 0.83285 1 0.00077725
20 18 0.44291 0.7031 0.85817 1 0.00076488
21 19 0.44263 0.70539 0.85663 1 0.0007525
22 20 0.44351 0.69666 0.85543 1 0.00074013
23 21 0.43801 0.68528 0.8645 1 0.00072775
24 22 0.44098 0.73154 0.84808 1 0.00071537
25 23 0.43726 0.7072 0.8556 1 0.000703
26 24 0.43937 0.71911 0.84842 1 0.00069063
27 25 0.43528 0.6947 0.85081 1 0.00067825
28 26 0.43546 0.71886 0.85252 1 0.00066588
29 27 0.43499 0.69865 0.85406 1 0.0006535
30 28 0.43395 0.68224 0.86313 1 0.00064112
31 29 0.43236 0.68289 0.86262 1 0.00062875
32 30 0.43158 0.73109 0.84893 1 0.00061638
33 31 0.43218 0.69988 0.86159 1 0.000604
34 32 0.43354 0.70973 0.85577 1 0.00059163
35 33 0.42806 0.71899 0.84842 1 0.00057925
36 34 0.42888 0.68793 0.86518 1 0.00056687
37 35 0.43093 0.67981 0.86792 1 0.0005545
38 36 0.42497 0.67168 0.87032 1 0.00054212
39 37 0.42323 0.72155 0.85064 1 0.00052975
40 38 0.42387 0.72693 0.8426 1 0.00051737
41 39 0.42333 0.68789 0.85885 1 0.000505
42 40 0.42672 0.68061 0.86484 1 0.00049263
43 41 0.42602 0.71904 0.8426 1 0.00048025
44 42 0.42367 0.67061 0.86672 1 0.00046788
45 43 0.42397 0.69298 0.86245 1 0.0004555
46 44 0.42405 0.69577 0.85868 1 0.00044312
47 45 0.42383 0.70744 0.84927 1 0.00043075
48 46 0.42419 0.71168 0.85543 1 0.00041838
49 47 0.42105 0.70456 0.84996 1 0.000406
50 48 0.42149 0.71426 0.85064 1 0.00039362
51 49 0.4207 0.72387 0.84979 1 0.00038125
52 50 0.41919 0.68517 0.86501 1 0.00036888
53 51 0.41588 0.68697 0.858 1 0.0003565
54 52 0.41676 0.67847 0.87066 1 0.00034413
55 53 0.41357 0.68348 0.8698 1 0.00033175
56 54 0.41561 0.6747 0.87117 1 0.00031938
57 55 0.41378 0.68828 0.85577 1 0.000307
58 56 0.41204 0.68438 0.85954 1 0.00029462
59 57 0.41022 0.69516 0.86587 1 0.00028225
60 58 0.41145 0.68544 0.86313 1 0.00026987
61 59 0.41068 0.70406 0.85595 1 0.0002575
62 60 0.41089 0.70139 0.85577 1 0.00024513
63 61 0.40866 0.69262 0.85954 1 0.00023275
64 62 0.40811 0.699 0.85749 1 0.00022038
65 63 0.40757 0.67834 0.86536 1 0.000208
66 64 0.40896 0.69648 0.85201 1 0.00019562
67 65 0.40856 0.71111 0.85047 1 0.00018325
68 66 0.40829 0.68339 0.86724 1 0.00017087
69 67 0.40592 0.70005 0.85184 1 0.0001585
70 68 0.4078 0.71784 0.84551 1 0.00014612
71 69 0.40634 0.69866 0.85406 1 0.00013375
72 70 0.4051 0.69982 0.85201 1 0.00012138
73 71 0.40516 0.68997 0.85834 1 0.000109
74 72 0.40603 0.69182 0.85629 1 9.6625e-05
75 73 0.40428 0.68093 0.86262 1 8.425e-05
76 74 0.40516 0.68302 0.85868 1 7.1875e-05
77 75 0.40422 0.6931 0.85612 1 5.95e-05
78 76 0.40299 0.68952 0.86005 1 4.7125e-05
79 77 0.40328 0.68088 0.85766 1 3.475e-05
80 78 0.40284 0.67786 0.86159 1 2.2375e-05
81 79 0.40196 0.68056 0.86159 1 1e-05

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