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train_log.txt
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Total Epoch:300 Image_size:(256, 256) Training num:288 Validation num:72
[train] epoch:0 iter:0/18 0.00% lr:0.001000 loss:1.298646 ETA:0.0min
/content/UNetpp4HSI/utils/metric.py:24: RuntimeWarning: invalid value encountered in true_divide
acc_cls = np.diag(self.hist) / self.hist.sum(axis=1)
/content/UNetpp4HSI/utils/metric.py:26: RuntimeWarning: invalid value encountered in true_divide
iu = np.diag(self.hist) / (self.hist.sum(axis=1) + self.hist.sum(axis=0) - np.diag(self.hist))
[val] epoch:0 miou:0.20
[save] Best Model saved at epoch:0 =============================
[train] epoch:1 iter:0/18 0.00% lr:0.000753 loss:0.608553 ETA:0.0min
[val] epoch:1 miou:0.48
[save] Best Model saved at epoch:1 =============================
[train] epoch:2 iter:0/18 0.00% lr:0.000258 loss:0.584702 ETA:0.0min
[val] epoch:2 miou:0.61
[save] Best Model saved at epoch:2 =============================
[train] epoch:3 iter:0/18 0.00% lr:0.001000 loss:0.516121 ETA:0.0min
[val] epoch:3 miou:0.48
[train] epoch:4 iter:0/18 0.00% lr:0.000934 loss:0.651107 ETA:0.0min
[val] epoch:4 miou:0.56
[train] epoch:5 iter:0/18 0.00% lr:0.000753 loss:0.565354 ETA:0.0min
[val] epoch:5 miou:0.60
[train] epoch:6 iter:0/18 0.00% lr:0.000505 loss:0.603653 ETA:0.0min
[val] epoch:6 miou:0.68
[save] Best Model saved at epoch:6 =============================
[train] epoch:7 iter:0/18 0.00% lr:0.000258 loss:0.541594 ETA:0.0min
[val] epoch:7 miou:0.66
[train] epoch:8 iter:0/18 0.00% lr:0.000076 loss:0.540035 ETA:0.0min
[val] epoch:8 miou:0.68
[save] Best Model saved at epoch:8 =============================
[train] epoch:9 iter:0/18 0.00% lr:0.001000 loss:0.546340 ETA:1.0min
[val] epoch:9 miou:0.43
[train] epoch:10 iter:0/18 0.00% lr:0.000983 loss:0.564159 ETA:0.0min
[val] epoch:10 miou:0.62
[train] epoch:11 iter:0/18 0.00% lr:0.000934 loss:0.532628 ETA:0.0min
[val] epoch:11 miou:0.48
[train] epoch:12 iter:0/18 0.00% lr:0.000855 loss:0.540816 ETA:1.0min
[val] epoch:12 miou:0.60
[train] epoch:13 iter:0/18 0.00% lr:0.000753 loss:0.583533 ETA:1.0min
[val] epoch:13 miou:0.64
[train] epoch:14 iter:0/18 0.00% lr:0.000633 loss:0.495212 ETA:0.0min
[val] epoch:14 miou:0.51
[train] epoch:15 iter:0/18 0.00% lr:0.000505 loss:0.504033 ETA:1.0min
[val] epoch:15 miou:0.53
[train] epoch:16 iter:0/18 0.00% lr:0.000377 loss:0.495283 ETA:1.0min
[val] epoch:16 miou:0.62
[train] epoch:17 iter:0/18 0.00% lr:0.000258 loss:0.505063 ETA:1.0min
[val] epoch:17 miou:0.68
[train] epoch:18 iter:0/18 0.00% lr:0.000155 loss:0.467656 ETA:1.0min
[val] epoch:18 miou:0.66
[train] epoch:19 iter:0/18 0.00% lr:0.000076 loss:0.459948 ETA:0.0min
[val] epoch:19 miou:0.70
[save] Best Model saved at epoch:19 =============================
[train] epoch:20 iter:0/18 0.00% lr:0.000027 loss:0.444579 ETA:0.0min
[val] epoch:20 miou:0.70
[save] Best Model saved at epoch:20 =============================
[train] epoch:21 iter:0/18 0.00% lr:0.001000 loss:0.509863 ETA:0.0min
[val] epoch:21 miou:0.56
[train] epoch:22 iter:0/18 0.00% lr:0.000996 loss:0.494684 ETA:1.0min
[val] epoch:22 miou:0.44
[train] epoch:23 iter:0/18 0.00% lr:0.000983 loss:0.604542 ETA:1.0min
[val] epoch:23 miou:0.60
[train] epoch:24 iter:0/18 0.00% lr:0.000962 loss:0.524073 ETA:0.0min
[val] epoch:24 miou:0.47
[train] epoch:25 iter:0/18 0.00% lr:0.000934 loss:0.495123 ETA:0.0min
[val] epoch:25 miou:0.55
[train] epoch:26 iter:0/18 0.00% lr:0.000898 loss:0.467956 ETA:1.0min
[val] epoch:26 miou:0.43
[train] epoch:27 iter:0/18 0.00% lr:0.000855 loss:0.506727 ETA:0.0min
[val] epoch:27 miou:0.64
[train] epoch:28 iter:0/18 0.00% lr:0.000806 loss:0.447962 ETA:0.0min
[val] epoch:28 miou:0.55
[train] epoch:29 iter:0/18 0.00% lr:0.000753 loss:0.479995 ETA:1.0min
[val] epoch:29 miou:0.49
[train] epoch:30 iter:0/18 0.00% lr:0.000694 loss:0.502672 ETA:0.0min
[val] epoch:30 miou:0.55
[train] epoch:31 iter:0/18 0.00% lr:0.000633 loss:0.458210 ETA:1.0min
[val] epoch:31 miou:0.61
[train] epoch:32 iter:0/18 0.00% lr:0.000570 loss:0.538401 ETA:1.0min
[val] epoch:32 miou:0.68
[train] epoch:33 iter:0/18 0.00% lr:0.000505 loss:0.414547 ETA:1.0min
[val] epoch:33 miou:0.67
[train] epoch:34 iter:0/18 0.00% lr:0.000440 loss:0.498295 ETA:0.0min
[val] epoch:34 miou:0.62
[train] epoch:35 iter:0/18 0.00% lr:0.000377 loss:0.475281 ETA:0.0min
[val] epoch:35 miou:0.70
[train] epoch:36 iter:0/18 0.00% lr:0.000316 loss:0.509569 ETA:0.0min
[val] epoch:36 miou:0.61
[train] epoch:37 iter:0/18 0.00% lr:0.000258 loss:0.503784 ETA:0.0min
[val] epoch:37 miou:0.67
[train] epoch:38 iter:0/18 0.00% lr:0.000204 loss:0.485153 ETA:0.0min
[val] epoch:38 miou:0.70
[train] epoch:39 iter:0/18 0.00% lr:0.000155 loss:0.447527 ETA:0.0min
[val] epoch:39 miou:0.71
[save] Best Model saved at epoch:39 =============================
[train] epoch:40 iter:0/18 0.00% lr:0.000112 loss:0.473721 ETA:1.0min
[val] epoch:40 miou:0.73
[save] Best Model saved at epoch:40 =============================
[train] epoch:41 iter:0/18 0.00% lr:0.000076 loss:0.460936 ETA:1.0min
[val] epoch:41 miou:0.72
[train] epoch:42 iter:0/18 0.00% lr:0.000048 loss:0.480137 ETA:0.0min
[val] epoch:42 miou:0.73
[train] epoch:43 iter:0/18 0.00% lr:0.000027 loss:0.458055 ETA:1.0min
[val] epoch:43 miou:0.73
[save] Best Model saved at epoch:43 =============================
[train] epoch:44 iter:0/18 0.00% lr:0.000014 loss:0.467917 ETA:1.0min
[val] epoch:44 miou:0.73
[train] epoch:45 iter:0/18 0.00% lr:0.001000 loss:0.465569 ETA:0.0min
[val] epoch:45 miou:0.48
[train] epoch:46 iter:0/18 0.00% lr:0.000999 loss:0.536657 ETA:1.0min
[val] epoch:46 miou:0.66
[train] epoch:47 iter:0/18 0.00% lr:0.000996 loss:0.589849 ETA:1.0min
[val] epoch:47 miou:0.54
[train] epoch:48 iter:0/18 0.00% lr:0.000990 loss:0.495660 ETA:0.0min
[val] epoch:48 miou:0.61
[train] epoch:49 iter:0/18 0.00% lr:0.000983 loss:0.494312 ETA:1.0min
[val] epoch:49 miou:0.61
[train] epoch:50 iter:0/18 0.00% lr:0.000974 loss:0.461874 ETA:1.0min
[val] epoch:50 miou:0.67
[train] epoch:51 iter:0/18 0.00% lr:0.000962 loss:0.501030 ETA:0.0min
[val] epoch:51 miou:0.66
[train] epoch:52 iter:0/18 0.00% lr:0.000949 loss:0.496741 ETA:1.0min
[val] epoch:52 miou:0.64
[train] epoch:53 iter:0/18 0.00% lr:0.000934 loss:0.517377 ETA:0.0min
[val] epoch:53 miou:0.67
[train] epoch:54 iter:0/18 0.00% lr:0.000917 loss:0.502428 ETA:0.0min
[val] epoch:54 miou:0.63
[train] epoch:55 iter:0/18 0.00% lr:0.000898 loss:0.482452 ETA:0.0min
[val] epoch:55 miou:0.63
[train] epoch:56 iter:0/18 0.00% lr:0.000877 loss:0.452691 ETA:0.0min
[val] epoch:56 miou:0.62
[train] epoch:57 iter:0/18 0.00% lr:0.000855 loss:0.445051 ETA:0.0min
[val] epoch:57 miou:0.51
[train] epoch:58 iter:0/18 0.00% lr:0.000831 loss:0.512788 ETA:1.0min
[val] epoch:58 miou:0.57
[train] epoch:59 iter:0/18 0.00% lr:0.000806 loss:0.474625 ETA:0.0min
[val] epoch:59 miou:0.69
[train] epoch:60 iter:0/18 0.00% lr:0.000780 loss:0.505021 ETA:0.0min
[val] epoch:60 miou:0.55
[train] epoch:61 iter:0/18 0.00% lr:0.000753 loss:0.468570 ETA:1.0min
[val] epoch:61 miou:0.66
[train] epoch:62 iter:0/18 0.00% lr:0.000724 loss:0.460377 ETA:0.0min
[val] epoch:62 miou:0.68
[train] epoch:63 iter:0/18 0.00% lr:0.000694 loss:0.441485 ETA:0.0min
[val] epoch:63 miou:0.64
[train] epoch:64 iter:0/18 0.00% lr:0.000664 loss:0.464575 ETA:0.0min
[val] epoch:64 miou:0.59
[train] epoch:65 iter:0/18 0.00% lr:0.000633 loss:0.448032 ETA:0.0min
[val] epoch:65 miou:0.71
[train] epoch:66 iter:0/18 0.00% lr:0.000602 loss:0.466045 ETA:0.0min
[val] epoch:66 miou:0.55
[train] epoch:67 iter:0/18 0.00% lr:0.000570 loss:0.481568 ETA:0.0min
[val] epoch:67 miou:0.70
[train] epoch:68 iter:0/18 0.00% lr:0.000537 loss:0.442118 ETA:1.0min
[val] epoch:68 miou:0.70
[train] epoch:69 iter:0/18 0.00% lr:0.000505 loss:0.468497 ETA:1.0min
[val] epoch:69 miou:0.58
[train] epoch:70 iter:0/18 0.00% lr:0.000473 loss:0.475232 ETA:0.0min
[val] epoch:70 miou:0.63
[train] epoch:71 iter:0/18 0.00% lr:0.000440 loss:0.461815 ETA:1.0min
[val] epoch:71 miou:0.68
[train] epoch:72 iter:0/18 0.00% lr:0.000408 loss:0.436941 ETA:0.0min
[val] epoch:72 miou:0.72
[train] epoch:73 iter:0/18 0.00% lr:0.000377 loss:0.453873 ETA:1.0min
[val] epoch:73 miou:0.71
[train] epoch:74 iter:0/18 0.00% lr:0.000346 loss:0.429512 ETA:1.0min
[val] epoch:74 miou:0.72
[train] epoch:75 iter:0/18 0.00% lr:0.000316 loss:0.413471 ETA:1.0min
[val] epoch:75 miou:0.69
[train] epoch:76 iter:0/18 0.00% lr:0.000286 loss:0.462462 ETA:0.0min
[val] epoch:76 miou:0.73
[train] epoch:77 iter:0/18 0.00% lr:0.000258 loss:0.432255 ETA:1.0min
[val] epoch:77 miou:0.71
[train] epoch:78 iter:0/18 0.00% lr:0.000230 loss:0.416684 ETA:0.0min
[val] epoch:78 miou:0.74
[save] Best Model saved at epoch:78 =============================
[train] epoch:79 iter:0/18 0.00% lr:0.000204 loss:0.456103 ETA:0.0min
[val] epoch:79 miou:0.69
[train] epoch:80 iter:0/18 0.00% lr:0.000179 loss:0.467209 ETA:0.0min
[val] epoch:80 miou:0.74
[train] epoch:81 iter:0/18 0.00% lr:0.000155 loss:0.429815 ETA:1.0min
[val] epoch:81 miou:0.70
[train] epoch:82 iter:0/18 0.00% lr:0.000133 loss:0.434991 ETA:1.0min
[val] epoch:82 miou:0.74
[save] Best Model saved at epoch:82 =============================
[train] epoch:83 iter:0/18 0.00% lr:0.000112 loss:0.434141 ETA:1.0min
[val] epoch:83 miou:0.76
[save] Best Model saved at epoch:83 =============================
[train] epoch:84 iter:0/18 0.00% lr:0.000093 loss:0.432685 ETA:1.0min
[val] epoch:84 miou:0.75
[train] epoch:85 iter:0/18 0.00% lr:0.000076 loss:0.425503 ETA:1.0min
[val] epoch:85 miou:0.76
[train] epoch:86 iter:0/18 0.00% lr:0.000061 loss:0.423549 ETA:1.0min
[val] epoch:86 miou:0.75
[train] epoch:87 iter:0/18 0.00% lr:0.000048 loss:0.406003 ETA:1.0min
[val] epoch:87 miou:0.76
[save] Best Model saved at epoch:87 =============================
[train] epoch:88 iter:0/18 0.00% lr:0.000036 loss:0.438535 ETA:1.0min
[val] epoch:88 miou:0.76
[save] Best Model saved at epoch:88 =============================
[train] epoch:89 iter:0/18 0.00% lr:0.000027 loss:0.402479 ETA:1.0min
[val] epoch:89 miou:0.76
[train] epoch:90 iter:0/18 0.00% lr:0.000020 loss:0.453454 ETA:1.0min
[val] epoch:90 miou:0.76
[save] Best Model saved at epoch:90 =============================
[train] epoch:91 iter:0/18 0.00% lr:0.000014 loss:0.458732 ETA:1.0min
[val] epoch:91 miou:0.76
[train] epoch:92 iter:0/18 0.00% lr:0.000011 loss:0.421396 ETA:1.0min
[val] epoch:92 miou:0.76
[train] epoch:93 iter:0/18 0.00% lr:0.001000 loss:0.411580 ETA:1.0min
[val] epoch:93 miou:0.54
[train] epoch:94 iter:0/18 0.00% lr:0.001000 loss:0.475551 ETA:1.0min
[val] epoch:94 miou:0.63
[train] epoch:95 iter:0/18 0.00% lr:0.000999 loss:0.431127 ETA:0.0min
[val] epoch:95 miou:0.65
[train] epoch:96 iter:0/18 0.00% lr:0.000998 loss:0.469577 ETA:1.0min
[val] epoch:96 miou:0.55
[train] epoch:97 iter:0/18 0.00% lr:0.000996 loss:0.483072 ETA:1.0min
[val] epoch:97 miou:0.64
[train] epoch:98 iter:0/18 0.00% lr:0.000993 loss:0.461989 ETA:0.0min
[val] epoch:98 miou:0.48
[train] epoch:99 iter:0/18 0.00% lr:0.000990 loss:0.451418 ETA:0.0min
[val] epoch:99 miou:0.62
[train] epoch:100 iter:0/18 0.00% lr:0.000987 loss:0.436380 ETA:0.0min
[val] epoch:100 miou:0.54
[train] epoch:101 iter:0/18 0.00% lr:0.000983 loss:0.445901 ETA:1.0min
[val] epoch:101 miou:0.64
[train] epoch:102 iter:0/18 0.00% lr:0.000979 loss:0.453209 ETA:1.0min
[val] epoch:102 miou:0.63
[train] epoch:103 iter:0/18 0.00% lr:0.000974 loss:0.460259 ETA:1.0min
[val] epoch:103 miou:0.40
[train] epoch:104 iter:0/18 0.00% lr:0.000968 loss:0.510828 ETA:1.0min
[val] epoch:104 miou:0.62
[train] epoch:105 iter:0/18 0.00% lr:0.000962 loss:0.568980 ETA:0.0min
[val] epoch:105 miou:0.62
[train] epoch:106 iter:0/18 0.00% lr:0.000956 loss:0.485236 ETA:1.0min
[val] epoch:106 miou:0.38
[train] epoch:107 iter:0/18 0.00% lr:0.000949 loss:0.447774 ETA:1.0min
[val] epoch:107 miou:0.50
[train] epoch:108 iter:0/18 0.00% lr:0.000942 loss:0.446568 ETA:0.0min
[val] epoch:108 miou:0.61
[train] epoch:109 iter:0/18 0.00% lr:0.000934 loss:0.528812 ETA:1.0min
[val] epoch:109 miou:0.68
[train] epoch:110 iter:0/18 0.00% lr:0.000925 loss:0.448818 ETA:0.0min
[val] epoch:110 miou:0.66
[train] epoch:111 iter:0/18 0.00% lr:0.000917 loss:0.446413 ETA:0.0min
[val] epoch:111 miou:0.59
[train] epoch:112 iter:0/18 0.00% lr:0.000907 loss:0.433258 ETA:0.0min
[val] epoch:112 miou:0.59
[train] epoch:113 iter:0/18 0.00% lr:0.000898 loss:0.433807 ETA:0.0min
[val] epoch:113 miou:0.68
[train] epoch:114 iter:0/18 0.00% lr:0.000888 loss:0.410807 ETA:0.0min
[val] epoch:114 miou:0.49
[train] epoch:115 iter:0/18 0.00% lr:0.000877 loss:0.419984 ETA:0.0min
[val] epoch:115 miou:0.66
[train] epoch:116 iter:0/18 0.00% lr:0.000866 loss:0.416805 ETA:0.0min
[val] epoch:116 miou:0.63
[train] epoch:117 iter:0/18 0.00% lr:0.000855 loss:0.421674 ETA:0.0min
[val] epoch:117 miou:0.66
[train] epoch:118 iter:0/18 0.00% lr:0.000843 loss:0.441228 ETA:0.0min
[val] epoch:118 miou:0.61
[train] epoch:119 iter:0/18 0.00% lr:0.000831 loss:0.419378 ETA:0.0min
[val] epoch:119 miou:0.62
[train] epoch:120 iter:0/18 0.00% lr:0.000819 loss:0.477386 ETA:0.0min
[val] epoch:120 miou:0.58
[train] epoch:121 iter:0/18 0.00% lr:0.000806 loss:0.464510 ETA:0.0min
[val] epoch:121 miou:0.49
[train] epoch:122 iter:0/18 0.00% lr:0.000793 loss:0.459284 ETA:0.0min
[val] epoch:122 miou:0.66
[train] epoch:123 iter:0/18 0.00% lr:0.000780 loss:0.522136 ETA:0.0min
[val] epoch:123 miou:0.54
[train] epoch:124 iter:0/18 0.00% lr:0.000766 loss:0.481015 ETA:0.0min
[val] epoch:124 miou:0.68
[train] epoch:125 iter:0/18 0.00% lr:0.000753 loss:0.450052 ETA:0.0min
[val] epoch:125 miou:0.64
[train] epoch:126 iter:0/18 0.00% lr:0.000738 loss:0.426244 ETA:0.0min
[val] epoch:126 miou:0.66
[train] epoch:127 iter:0/18 0.00% lr:0.000724 loss:0.453585 ETA:0.0min
[val] epoch:127 miou:0.59
[train] epoch:128 iter:0/18 0.00% lr:0.000709 loss:0.440740 ETA:0.0min
[val] epoch:128 miou:0.71
[train] epoch:129 iter:0/18 0.00% lr:0.000694 loss:0.450140 ETA:0.0min
[val] epoch:129 miou:0.71
[train] epoch:130 iter:0/18 0.00% lr:0.000679 loss:0.420588 ETA:0.0min
[val] epoch:130 miou:0.56
[train] epoch:131 iter:0/18 0.00% lr:0.000664 loss:0.435038 ETA:0.0min
[val] epoch:131 miou:0.68
[train] epoch:132 iter:0/18 0.00% lr:0.000649 loss:0.445721 ETA:0.0min
[val] epoch:132 miou:0.72
[train] epoch:133 iter:0/18 0.00% lr:0.000633 loss:0.430097 ETA:0.0min
[val] epoch:133 miou:0.58
[train] epoch:134 iter:0/18 0.00% lr:0.000617 loss:0.388567 ETA:0.0min
[val] epoch:134 miou:0.68
[train] epoch:135 iter:0/18 0.00% lr:0.000602 loss:0.402002 ETA:0.0min
[val] epoch:135 miou:0.73
[train] epoch:136 iter:0/18 0.00% lr:0.000586 loss:0.410016 ETA:0.0min
[val] epoch:136 miou:0.71
[train] epoch:137 iter:0/18 0.00% lr:0.000570 loss:0.403799 ETA:1.0min
[val] epoch:137 miou:0.73
[train] epoch:138 iter:0/18 0.00% lr:0.000554 loss:0.393954 ETA:0.0min
[val] epoch:138 miou:0.72
[train] epoch:139 iter:0/18 0.00% lr:0.000537 loss:0.441305 ETA:0.0min
[val] epoch:139 miou:0.49
[train] epoch:140 iter:0/18 0.00% lr:0.000521 loss:0.442146 ETA:0.0min
[val] epoch:140 miou:0.69
[train] epoch:141 iter:0/18 0.00% lr:0.000505 loss:0.422556 ETA:0.0min
[val] epoch:141 miou:0.74
[train] epoch:142 iter:0/18 0.00% lr:0.000489 loss:0.447942 ETA:0.0min
[val] epoch:142 miou:0.67
[train] epoch:143 iter:0/18 0.00% lr:0.000473 loss:0.407557 ETA:0.0min
[val] epoch:143 miou:0.75
[train] epoch:144 iter:0/18 0.00% lr:0.000456 loss:0.391522 ETA:0.0min
[val] epoch:144 miou:0.65
[train] epoch:145 iter:0/18 0.00% lr:0.000440 loss:0.439583 ETA:0.0min
[val] epoch:145 miou:0.74
[train] epoch:146 iter:0/18 0.00% lr:0.000424 loss:0.413712 ETA:0.0min
[val] epoch:146 miou:0.73
[train] epoch:147 iter:0/18 0.00% lr:0.000408 loss:0.447944 ETA:0.0min
[val] epoch:147 miou:0.73
[train] epoch:148 iter:0/18 0.00% lr:0.000393 loss:0.407280 ETA:0.0min
[val] epoch:148 miou:0.71
[train] epoch:149 iter:0/18 0.00% lr:0.000377 loss:0.395474 ETA:0.0min
[val] epoch:149 miou:0.74
[train] epoch:150 iter:0/18 0.00% lr:0.000361 loss:0.414775 ETA:0.0min
[val] epoch:150 miou:0.73
[train] epoch:151 iter:0/18 0.00% lr:0.000346 loss:0.403500 ETA:0.0min
[val] epoch:151 miou:0.76
[save] Best Model saved at epoch:151 =============================
[train] epoch:152 iter:0/18 0.00% lr:0.000331 loss:0.412620 ETA:0.0min
[val] epoch:152 miou:0.75
[train] epoch:153 iter:0/18 0.00% lr:0.000316 loss:0.398648 ETA:0.0min
[val] epoch:153 miou:0.76
[save] Best Model saved at epoch:153 =============================
[train] epoch:154 iter:0/18 0.00% lr:0.000301 loss:0.485497 ETA:0.0min
[val] epoch:154 miou:0.78
[save] Best Model saved at epoch:154 =============================
[train] epoch:155 iter:0/18 0.00% lr:0.000286 loss:0.421260 ETA:0.0min
[val] epoch:155 miou:0.78
[train] epoch:156 iter:0/18 0.00% lr:0.000272 loss:0.415341 ETA:0.0min
[val] epoch:156 miou:0.77
[train] epoch:157 iter:0/18 0.00% lr:0.000258 loss:0.371491 ETA:0.0min
[val] epoch:157 miou:0.68
[train] epoch:158 iter:0/18 0.00% lr:0.000244 loss:0.401613 ETA:0.0min
[val] epoch:158 miou:0.78
[train] epoch:159 iter:0/18 0.00% lr:0.000230 loss:0.373275 ETA:0.0min
[val] epoch:159 miou:0.79
[save] Best Model saved at epoch:159 =============================
[train] epoch:160 iter:0/18 0.00% lr:0.000217 loss:0.403322 ETA:0.0min
[val] epoch:160 miou:0.79
[train] epoch:161 iter:0/18 0.00% lr:0.000204 loss:0.386873 ETA:0.0min
[val] epoch:161 miou:0.79
[train] epoch:162 iter:0/18 0.00% lr:0.000191 loss:0.450794 ETA:0.0min
[val] epoch:162 miou:0.78
[train] epoch:163 iter:0/18 0.00% lr:0.000179 loss:0.401750 ETA:0.0min