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resnet.2019-01-11-5472.log
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PyThon version : 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56) [GCC 7.2.0]
PyTorch version : 0.4.1.post2
cuDNN version : 7102
Vision version : 0.2.1
=> creating model 'resnet'
=> parameter : Namespace(arch='resnet', batch_size=128, data='./', epochs=160, evaluate=False, lr=0.1, momentum=0.9, n_gpus=[0, 1], prefix='2019-01-11-5472', print_freq=200, resume='', save_dir='./', start_epoch=0, use_cuda=True, weight_decay=0.0001, workers=4)
[resnet] :: 0/160 ----- [[2019-01-11 03:27:42]] [Need: 00:00:00]
Epoch: [000][000/391] Time 4.394 (4.394) Data 0.272 (0.272) Loss 2.4004 (2.4004) Prec@1 11.719 (11.719) Prec@5 46.094 (46.094) [2019-01-11 03:27:46]
Epoch: [000][200/391] Time 0.085 (0.106) Data 0.000 (0.002) Loss 1.5322 (1.9513) Prec@1 42.969 (34.480) Prec@5 88.281 (82.910) [2019-01-11 03:28:03]
**Train** Prec@1 44.922 Prec@5 88.544 Error@1 55.078
**Test** Prec@1 61.310 Prec@5 95.860 Error@1 38.690
[resnet] :: 1/160 ----- [[2019-01-11 03:28:22]] [Need: 01:47:33]
Epoch: [001][000/391] Time 0.487 (0.487) Data 0.373 (0.373) Loss 1.1639 (1.1639) Prec@1 60.938 (60.938) Prec@5 92.969 (92.969) [2019-01-11 03:28:23]
Epoch: [001][200/391] Time 0.083 (0.084) Data 0.000 (0.002) Loss 0.9699 (1.0040) Prec@1 67.188 (64.591) Prec@5 97.656 (96.735) [2019-01-11 03:28:39]
**Train** Prec@1 66.224 Prec@5 96.990 Error@1 33.776
**Test** Prec@1 69.920 Prec@5 97.660 Error@1 30.080
[resnet] :: 2/160 ----- [[2019-01-11 03:28:58]] [Need: 01:34:41]
Epoch: [002][000/391] Time 0.478 (0.478) Data 0.380 (0.380) Loss 0.7801 (0.7801) Prec@1 68.750 (68.750) Prec@5 100.000 (100.000) [2019-01-11 03:28:59]
Epoch: [002][200/391] Time 0.088 (0.084) Data 0.000 (0.002) Loss 0.7602 (0.8070) Prec@1 73.438 (71.828) Prec@5 97.656 (97.870) [2019-01-11 03:29:15]
**Train** Prec@1 72.538 Prec@5 97.994 Error@1 27.462
**Test** Prec@1 68.680 Prec@5 97.740 Error@1 31.320
[resnet] :: 3/160 ----- [[2019-01-11 03:29:34]] [Need: 01:33:00]
Epoch: [003][000/391] Time 0.492 (0.492) Data 0.392 (0.392) Loss 0.6314 (0.6314) Prec@1 78.125 (78.125) Prec@5 98.438 (98.438) [2019-01-11 03:29:34]
Epoch: [003][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.5514 (0.6715) Prec@1 80.469 (76.671) Prec@5 100.000 (98.605) [2019-01-11 03:29:51]
**Train** Prec@1 77.200 Prec@5 98.654 Error@1 22.800
**Test** Prec@1 75.420 Prec@5 98.300 Error@1 24.580
[resnet] :: 4/160 ----- [[2019-01-11 03:30:10]] [Need: 01:33:48]
Epoch: [004][000/391] Time 0.539 (0.539) Data 0.433 (0.433) Loss 0.6389 (0.6389) Prec@1 78.906 (78.906) Prec@5 97.656 (97.656) [2019-01-11 03:30:10]
Epoch: [004][200/391] Time 0.084 (0.086) Data 0.000 (0.002) Loss 0.5213 (0.5722) Prec@1 85.156 (80.395) Prec@5 97.656 (98.919) [2019-01-11 03:30:27]
**Train** Prec@1 80.470 Prec@5 98.972 Error@1 19.530
**Test** Prec@1 76.860 Prec@5 98.490 Error@1 23.140
[resnet] :: 5/160 ----- [[2019-01-11 03:30:46]] [Need: 01:34:35]
Epoch: [005][000/391] Time 0.482 (0.482) Data 0.377 (0.377) Loss 0.5091 (0.5091) Prec@1 81.250 (81.250) Prec@5 99.219 (99.219) [2019-01-11 03:30:47]
Epoch: [005][200/391] Time 0.081 (0.086) Data 0.000 (0.002) Loss 0.5302 (0.5045) Prec@1 78.125 (82.474) Prec@5 100.000 (99.219) [2019-01-11 03:31:04]
**Train** Prec@1 82.488 Prec@5 99.166 Error@1 17.512
**Test** Prec@1 74.930 Prec@5 98.420 Error@1 25.070
[resnet] :: 6/160 ----- [[2019-01-11 03:31:23]] [Need: 01:32:58]
Epoch: [006][000/391] Time 0.500 (0.500) Data 0.394 (0.394) Loss 0.4397 (0.4397) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219) [2019-01-11 03:31:23]
Epoch: [006][200/391] Time 0.080 (0.086) Data 0.000 (0.002) Loss 0.5694 (0.4481) Prec@1 85.938 (84.616) Prec@5 97.656 (99.433) [2019-01-11 03:31:40]
**Train** Prec@1 84.300 Prec@5 99.406 Error@1 15.700
**Test** Prec@1 80.470 Prec@5 98.950 Error@1 19.530
[resnet] :: 7/160 ----- [[2019-01-11 03:31:59]] [Need: 01:32:15]
Epoch: [007][000/391] Time 0.509 (0.509) Data 0.407 (0.407) Loss 0.4038 (0.4038) Prec@1 85.156 (85.156) Prec@5 98.438 (98.438) [2019-01-11 03:31:59]
Epoch: [007][200/391] Time 0.084 (0.086) Data 0.000 (0.002) Loss 0.3947 (0.4061) Prec@1 86.719 (85.941) Prec@5 99.219 (99.487) [2019-01-11 03:32:16]
**Train** Prec@1 85.552 Prec@5 99.486 Error@1 14.448
**Test** Prec@1 81.730 Prec@5 99.000 Error@1 18.270
[resnet] :: 8/160 ----- [[2019-01-11 03:32:35]] [Need: 01:32:54]
Epoch: [008][000/391] Time 0.485 (0.485) Data 0.379 (0.379) Loss 0.4680 (0.4680) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219) [2019-01-11 03:32:36]
Epoch: [008][200/391] Time 0.092 (0.086) Data 0.000 (0.002) Loss 0.4280 (0.3743) Prec@1 85.156 (87.072) Prec@5 97.656 (99.553) [2019-01-11 03:32:53]
**Train** Prec@1 86.736 Prec@5 99.556 Error@1 13.264
**Test** Prec@1 81.080 Prec@5 98.860 Error@1 18.920
[resnet] :: 9/160 ----- [[2019-01-11 03:33:11]] [Need: 01:30:30]
Epoch: [009][000/391] Time 0.539 (0.539) Data 0.438 (0.438) Loss 0.2956 (0.2956) Prec@1 88.281 (88.281) Prec@5 99.219 (99.219) [2019-01-11 03:33:12]
Epoch: [009][200/391] Time 0.081 (0.086) Data 0.000 (0.002) Loss 0.4523 (0.3408) Prec@1 82.812 (88.126) Prec@5 99.219 (99.639) [2019-01-11 03:33:29]
**Train** Prec@1 87.708 Prec@5 99.630 Error@1 12.292
**Test** Prec@1 81.420 Prec@5 99.060 Error@1 18.580
[resnet] :: 10/160 ----- [[2019-01-11 03:33:48]] [Need: 01:30:31]
Epoch: [010][000/391] Time 0.485 (0.485) Data 0.393 (0.393) Loss 0.3565 (0.3565) Prec@1 91.406 (91.406) Prec@5 98.438 (98.438) [2019-01-11 03:33:48]
Epoch: [010][200/391] Time 0.085 (0.085) Data 0.000 (0.002) Loss 0.2885 (0.3146) Prec@1 89.844 (89.055) Prec@5 100.000 (99.720) [2019-01-11 03:34:05]
**Train** Prec@1 88.630 Prec@5 99.708 Error@1 11.370
**Test** Prec@1 80.770 Prec@5 98.720 Error@1 19.230
[resnet] :: 11/160 ----- [[2019-01-11 03:34:23]] [Need: 01:28:08]
Epoch: [011][000/391] Time 0.487 (0.487) Data 0.385 (0.385) Loss 0.3967 (0.3967) Prec@1 85.938 (85.938) Prec@5 100.000 (100.000) [2019-01-11 03:34:24]
Epoch: [011][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.3018 (0.2948) Prec@1 89.844 (89.638) Prec@5 100.000 (99.763) [2019-01-11 03:34:40]
**Train** Prec@1 89.190 Prec@5 99.740 Error@1 10.810
**Test** Prec@1 80.830 Prec@5 98.900 Error@1 19.170
[resnet] :: 12/160 ----- [[2019-01-11 03:34:59]] [Need: 01:27:38]
Epoch: [012][000/391] Time 0.476 (0.476) Data 0.376 (0.376) Loss 0.1902 (0.1902) Prec@1 92.188 (92.188) Prec@5 100.000 (100.000) [2019-01-11 03:34:59]
Epoch: [012][200/391] Time 0.090 (0.085) Data 0.000 (0.002) Loss 0.2727 (0.2773) Prec@1 89.062 (90.333) Prec@5 100.000 (99.848) [2019-01-11 03:35:16]
**Train** Prec@1 89.810 Prec@5 99.786 Error@1 10.190
**Test** Prec@1 80.270 Prec@5 98.730 Error@1 19.730
[resnet] :: 13/160 ----- [[2019-01-11 03:35:35]] [Need: 01:27:58]
Epoch: [013][000/391] Time 0.455 (0.455) Data 0.354 (0.354) Loss 0.2951 (0.2951) Prec@1 89.844 (89.844) Prec@5 100.000 (100.000) [2019-01-11 03:35:35]
Epoch: [013][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.2488 (0.2601) Prec@1 90.625 (90.990) Prec@5 100.000 (99.841) [2019-01-11 03:35:52]
**Train** Prec@1 90.432 Prec@5 99.834 Error@1 9.568
**Test** Prec@1 79.910 Prec@5 98.800 Error@1 20.090
[resnet] :: 14/160 ----- [[2019-01-11 03:36:10]] [Need: 01:26:55]
Epoch: [014][000/391] Time 0.449 (0.449) Data 0.350 (0.350) Loss 0.3119 (0.3119) Prec@1 86.719 (86.719) Prec@5 100.000 (100.000) [2019-01-11 03:36:11]
Epoch: [014][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.1762 (0.2471) Prec@1 95.312 (91.255) Prec@5 100.000 (99.845) [2019-01-11 03:36:27]
**Train** Prec@1 90.754 Prec@5 99.840 Error@1 9.246
**Test** Prec@1 80.120 Prec@5 98.760 Error@1 19.880
[resnet] :: 15/160 ----- [[2019-01-11 03:36:46]] [Need: 01:26:18]
Epoch: [015][000/391] Time 0.474 (0.474) Data 0.372 (0.372) Loss 0.2776 (0.2776) Prec@1 91.406 (91.406) Prec@5 100.000 (100.000) [2019-01-11 03:36:46]
Epoch: [015][200/391] Time 0.083 (0.086) Data 0.000 (0.002) Loss 0.0723 (0.2304) Prec@1 98.438 (91.970) Prec@5 100.000 (99.868) [2019-01-11 03:37:03]
**Train** Prec@1 91.390 Prec@5 99.828 Error@1 8.610
**Test** Prec@1 83.850 Prec@5 99.310 Error@1 16.150
[resnet] :: 16/160 ----- [[2019-01-11 03:37:22]] [Need: 01:27:27]
Epoch: [016][000/391] Time 0.533 (0.533) Data 0.430 (0.430) Loss 0.1405 (0.1405) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000) [2019-01-11 03:37:23]
Epoch: [016][200/391] Time 0.084 (0.086) Data 0.000 (0.002) Loss 0.3014 (0.2165) Prec@1 89.062 (92.436) Prec@5 100.000 (99.895) [2019-01-11 03:37:40]
**Train** Prec@1 91.828 Prec@5 99.886 Error@1 8.172
**Test** Prec@1 80.910 Prec@5 98.860 Error@1 19.090
[resnet] :: 17/160 ----- [[2019-01-11 03:37:59]] [Need: 01:25:56]
Epoch: [017][000/391] Time 0.529 (0.529) Data 0.429 (0.429) Loss 0.1387 (0.1387) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:37:59]
Epoch: [017][200/391] Time 0.086 (0.084) Data 0.000 (0.002) Loss 0.2375 (0.2062) Prec@1 90.625 (92.875) Prec@5 100.000 (99.883) [2019-01-11 03:38:15]
**Train** Prec@1 92.206 Prec@5 99.866 Error@1 7.794
**Test** Prec@1 82.020 Prec@5 99.130 Error@1 17.980
[resnet] :: 18/160 ----- [[2019-01-11 03:38:34]] [Need: 01:23:45]
Epoch: [018][000/391] Time 0.541 (0.541) Data 0.445 (0.445) Loss 0.2298 (0.2298) Prec@1 92.188 (92.188) Prec@5 99.219 (99.219) [2019-01-11 03:38:34]
Epoch: [018][200/391] Time 0.084 (0.085) Data 0.000 (0.002) Loss 0.2282 (0.1997) Prec@1 91.406 (93.043) Prec@5 100.000 (99.922) [2019-01-11 03:38:51]
**Train** Prec@1 92.536 Prec@5 99.908 Error@1 7.464
**Test** Prec@1 81.650 Prec@5 99.150 Error@1 18.350
[resnet] :: 19/160 ----- [[2019-01-11 03:39:10]] [Need: 01:24:27]
Epoch: [019][000/391] Time 0.485 (0.485) Data 0.386 (0.386) Loss 0.1332 (0.1332) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 03:39:10]
Epoch: [019][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0772 (0.1937) Prec@1 98.438 (93.299) Prec@5 100.000 (99.938) [2019-01-11 03:39:27]
**Train** Prec@1 92.572 Prec@5 99.920 Error@1 7.428
**Test** Prec@1 79.780 Prec@5 98.700 Error@1 20.220
[resnet] :: 20/160 ----- [[2019-01-11 03:39:45]] [Need: 01:22:33]
Epoch: [020][000/391] Time 0.540 (0.540) Data 0.438 (0.438) Loss 0.1836 (0.1836) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000) [2019-01-11 03:39:46]
Epoch: [020][200/391] Time 0.084 (0.085) Data 0.000 (0.002) Loss 0.1899 (0.1860) Prec@1 92.969 (93.365) Prec@5 100.000 (99.926) [2019-01-11 03:40:02]
**Train** Prec@1 92.802 Prec@5 99.904 Error@1 7.198
**Test** Prec@1 81.590 Prec@5 98.970 Error@1 18.410
[resnet] :: 21/160 ----- [[2019-01-11 03:40:21]] [Need: 01:22:38]
Epoch: [021][000/391] Time 0.461 (0.461) Data 0.362 (0.362) Loss 0.2429 (0.2429) Prec@1 90.625 (90.625) Prec@5 100.000 (100.000) [2019-01-11 03:40:21]
Epoch: [021][200/391] Time 0.085 (0.084) Data 0.000 (0.002) Loss 0.2112 (0.1765) Prec@1 91.406 (93.583) Prec@5 100.000 (99.953) [2019-01-11 03:40:38]
**Train** Prec@1 93.162 Prec@5 99.920 Error@1 6.838
**Test** Prec@1 82.650 Prec@5 99.230 Error@1 17.350
[resnet] :: 22/160 ----- [[2019-01-11 03:40:56]] [Need: 01:21:27]
Epoch: [022][000/391] Time 0.472 (0.472) Data 0.374 (0.374) Loss 0.1668 (0.1668) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000) [2019-01-11 03:40:57]
Epoch: [022][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.2747 (0.1738) Prec@1 90.625 (93.905) Prec@5 100.000 (99.953) [2019-01-11 03:41:13]
**Train** Prec@1 93.466 Prec@5 99.938 Error@1 6.534
**Test** Prec@1 84.490 Prec@5 99.100 Error@1 15.510
[resnet] :: 23/160 ----- [[2019-01-11 03:41:32]] [Need: 01:22:00]
Epoch: [023][000/391] Time 0.485 (0.485) Data 0.383 (0.383) Loss 0.1471 (0.1471) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:41:33]
Epoch: [023][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.1815 (0.1668) Prec@1 93.750 (94.088) Prec@5 100.000 (99.942) [2019-01-11 03:41:49]
**Train** Prec@1 93.474 Prec@5 99.934 Error@1 6.526
**Test** Prec@1 80.810 Prec@5 98.830 Error@1 19.190
[resnet] :: 24/160 ----- [[2019-01-11 03:42:08]] [Need: 01:21:27]
Epoch: [024][000/391] Time 0.536 (0.536) Data 0.430 (0.430) Loss 0.1766 (0.1766) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000) [2019-01-11 03:42:09]
Epoch: [024][200/391] Time 0.078 (0.086) Data 0.000 (0.002) Loss 0.2717 (0.1639) Prec@1 92.188 (94.248) Prec@5 100.000 (99.938) [2019-01-11 03:42:25]
**Train** Prec@1 93.666 Prec@5 99.918 Error@1 6.334
**Test** Prec@1 82.870 Prec@5 99.200 Error@1 17.130
[resnet] :: 25/160 ----- [[2019-01-11 03:42:44]] [Need: 01:21:00]
Epoch: [025][000/391] Time 0.490 (0.490) Data 0.382 (0.382) Loss 0.1255 (0.1255) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000) [2019-01-11 03:42:45]
Epoch: [025][200/391] Time 0.081 (0.085) Data 0.000 (0.002) Loss 0.0948 (0.1571) Prec@1 96.875 (94.531) Prec@5 100.000 (99.942) [2019-01-11 03:43:01]
**Train** Prec@1 93.976 Prec@5 99.930 Error@1 6.024
**Test** Prec@1 83.350 Prec@5 99.120 Error@1 16.650
[resnet] :: 26/160 ----- [[2019-01-11 03:43:20]] [Need: 01:19:50]
Epoch: [026][000/391] Time 0.535 (0.535) Data 0.435 (0.435) Loss 0.1163 (0.1163) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:43:20]
Epoch: [026][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.1418 (0.1503) Prec@1 95.312 (94.729) Prec@5 100.000 (99.969) [2019-01-11 03:43:37]
**Train** Prec@1 94.150 Prec@5 99.962 Error@1 5.850
**Test** Prec@1 84.110 Prec@5 99.240 Error@1 15.890
[resnet] :: 27/160 ----- [[2019-01-11 03:43:56]] [Need: 01:19:08]
Epoch: [027][000/391] Time 0.535 (0.535) Data 0.435 (0.435) Loss 0.1272 (0.1272) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:43:56]
Epoch: [027][200/391] Time 0.084 (0.085) Data 0.000 (0.002) Loss 0.2646 (0.1499) Prec@1 89.844 (94.838) Prec@5 99.219 (99.977) [2019-01-11 03:44:13]
**Train** Prec@1 94.198 Prec@5 99.962 Error@1 5.802
**Test** Prec@1 81.620 Prec@5 98.910 Error@1 18.380
[resnet] :: 28/160 ----- [[2019-01-11 03:44:31]] [Need: 01:18:51]
Epoch: [028][000/391] Time 0.532 (0.532) Data 0.433 (0.433) Loss 0.0944 (0.0944) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 03:44:32]
Epoch: [028][200/391] Time 0.084 (0.085) Data 0.000 (0.002) Loss 0.2651 (0.1482) Prec@1 89.062 (94.679) Prec@5 100.000 (99.973) [2019-01-11 03:44:49]
**Train** Prec@1 94.266 Prec@5 99.952 Error@1 5.734
**Test** Prec@1 83.480 Prec@5 99.130 Error@1 16.520
[resnet] :: 29/160 ----- [[2019-01-11 03:45:07]] [Need: 01:17:49]
Epoch: [029][000/391] Time 0.535 (0.535) Data 0.434 (0.434) Loss 0.1855 (0.1855) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000) [2019-01-11 03:45:08]
Epoch: [029][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0640 (0.1348) Prec@1 99.219 (95.254) Prec@5 100.000 (99.984) [2019-01-11 03:45:24]
**Train** Prec@1 94.634 Prec@5 99.958 Error@1 5.366
**Test** Prec@1 84.660 Prec@5 99.140 Error@1 15.340
[resnet] :: 30/160 ----- [[2019-01-11 03:45:43]] [Need: 01:17:55]
Epoch: [030][000/391] Time 0.547 (0.547) Data 0.429 (0.429) Loss 0.1089 (0.1089) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000) [2019-01-11 03:45:44]
Epoch: [030][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.1485 (0.1442) Prec@1 92.969 (94.990) Prec@5 100.000 (99.953) [2019-01-11 03:46:00]
**Train** Prec@1 94.428 Prec@5 99.944 Error@1 5.572
**Test** Prec@1 83.740 Prec@5 99.250 Error@1 16.260
[resnet] :: 31/160 ----- [[2019-01-11 03:46:19]] [Need: 01:16:46]
Epoch: [031][000/391] Time 0.476 (0.476) Data 0.376 (0.376) Loss 0.0719 (0.0719) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000) [2019-01-11 03:46:19]
Epoch: [031][200/391] Time 0.081 (0.085) Data 0.000 (0.002) Loss 0.0976 (0.1407) Prec@1 95.312 (95.126) Prec@5 100.000 (99.988) [2019-01-11 03:46:36]
**Train** Prec@1 94.690 Prec@5 99.976 Error@1 5.310
**Test** Prec@1 83.790 Prec@5 99.070 Error@1 16.210
[resnet] :: 32/160 ----- [[2019-01-11 03:46:55]] [Need: 01:16:10]
Epoch: [032][000/391] Time 0.468 (0.468) Data 0.369 (0.369) Loss 0.1729 (0.1729) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000) [2019-01-11 03:46:55]
Epoch: [032][200/391] Time 0.084 (0.084) Data 0.000 (0.002) Loss 0.1541 (0.1370) Prec@1 96.094 (95.215) Prec@5 100.000 (99.981) [2019-01-11 03:47:11]
**Train** Prec@1 94.716 Prec@5 99.966 Error@1 5.284
**Test** Prec@1 82.890 Prec@5 99.050 Error@1 17.110
[resnet] :: 33/160 ----- [[2019-01-11 03:47:30]] [Need: 01:14:58]
Epoch: [033][000/391] Time 0.536 (0.536) Data 0.430 (0.430) Loss 0.1263 (0.1263) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:47:30]
Epoch: [033][200/391] Time 0.079 (0.086) Data 0.000 (0.002) Loss 0.1617 (0.1368) Prec@1 93.750 (95.079) Prec@5 99.219 (99.953) [2019-01-11 03:47:47]
**Train** Prec@1 94.718 Prec@5 99.962 Error@1 5.282
**Test** Prec@1 85.570 Prec@5 99.180 Error@1 14.430
[resnet] :: 34/160 ----- [[2019-01-11 03:48:06]] [Need: 01:16:19]
Epoch: [034][000/391] Time 0.489 (0.489) Data 0.396 (0.396) Loss 0.1381 (0.1381) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:48:07]
Epoch: [034][200/391] Time 0.084 (0.085) Data 0.000 (0.002) Loss 0.2461 (0.1243) Prec@1 93.750 (95.662) Prec@5 100.000 (99.981) [2019-01-11 03:48:23]
**Train** Prec@1 94.806 Prec@5 99.958 Error@1 5.194
**Test** Prec@1 83.300 Prec@5 99.120 Error@1 16.700
[resnet] :: 35/160 ----- [[2019-01-11 03:48:42]] [Need: 01:14:37]
Epoch: [035][000/391] Time 0.499 (0.499) Data 0.399 (0.399) Loss 0.1517 (0.1517) Prec@1 93.750 (93.750) Prec@5 100.000 (100.000) [2019-01-11 03:48:43]
Epoch: [035][200/391] Time 0.078 (0.085) Data 0.000 (0.002) Loss 0.1049 (0.1337) Prec@1 96.875 (95.297) Prec@5 100.000 (99.977) [2019-01-11 03:48:59]
**Train** Prec@1 94.834 Prec@5 99.952 Error@1 5.166
**Test** Prec@1 83.520 Prec@5 99.040 Error@1 16.480
[resnet] :: 36/160 ----- [[2019-01-11 03:49:18]] [Need: 01:14:16]
Epoch: [036][000/391] Time 0.541 (0.541) Data 0.438 (0.438) Loss 0.0921 (0.0921) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000) [2019-01-11 03:49:19]
Epoch: [036][200/391] Time 0.082 (0.088) Data 0.000 (0.002) Loss 0.1402 (0.1399) Prec@1 93.750 (95.126) Prec@5 100.000 (99.961) [2019-01-11 03:49:36]
**Train** Prec@1 94.932 Prec@5 99.960 Error@1 5.068
**Test** Prec@1 81.250 Prec@5 98.710 Error@1 18.750
[resnet] :: 37/160 ----- [[2019-01-11 03:49:55]] [Need: 01:14:54]
Epoch: [037][000/391] Time 0.554 (0.554) Data 0.457 (0.457) Loss 0.0595 (0.0595) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000) [2019-01-11 03:49:55]
Epoch: [037][200/391] Time 0.082 (0.084) Data 0.000 (0.003) Loss 0.1999 (0.1316) Prec@1 92.188 (95.476) Prec@5 100.000 (99.973) [2019-01-11 03:50:12]
**Train** Prec@1 95.262 Prec@5 99.964 Error@1 4.738
**Test** Prec@1 83.240 Prec@5 99.110 Error@1 16.760
[resnet] :: 38/160 ----- [[2019-01-11 03:50:30]] [Need: 01:12:07]
Epoch: [038][000/391] Time 0.474 (0.474) Data 0.376 (0.376) Loss 0.0915 (0.0915) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 03:50:31]
Epoch: [038][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.1857 (0.1339) Prec@1 92.188 (95.402) Prec@5 100.000 (99.977) [2019-01-11 03:50:47]
**Train** Prec@1 95.120 Prec@5 99.966 Error@1 4.880
**Test** Prec@1 83.400 Prec@5 98.880 Error@1 16.600
[resnet] :: 39/160 ----- [[2019-01-11 03:51:06]] [Need: 01:11:54]
Epoch: [039][000/391] Time 0.476 (0.476) Data 0.377 (0.377) Loss 0.1249 (0.1249) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000) [2019-01-11 03:51:06]
Epoch: [039][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0963 (0.1187) Prec@1 95.312 (95.771) Prec@5 100.000 (99.992) [2019-01-11 03:51:23]
**Train** Prec@1 95.106 Prec@5 99.974 Error@1 4.894
**Test** Prec@1 83.120 Prec@5 99.080 Error@1 16.880
[resnet] :: 40/160 ----- [[2019-01-11 03:51:41]] [Need: 01:10:29]
Epoch: [040][000/391] Time 0.494 (0.494) Data 0.392 (0.392) Loss 0.1437 (0.1437) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:51:41]
Epoch: [040][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.0971 (0.1239) Prec@1 96.875 (95.717) Prec@5 100.000 (99.981) [2019-01-11 03:51:58]
**Train** Prec@1 95.046 Prec@5 99.980 Error@1 4.954
**Test** Prec@1 84.310 Prec@5 99.100 Error@1 15.690
[resnet] :: 41/160 ----- [[2019-01-11 03:52:17]] [Need: 01:11:12]
Epoch: [041][000/391] Time 0.480 (0.480) Data 0.380 (0.380) Loss 0.1039 (0.1039) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:52:17]
Epoch: [041][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.1609 (0.1211) Prec@1 94.531 (95.744) Prec@5 100.000 (99.984) [2019-01-11 03:52:34]
**Train** Prec@1 95.306 Prec@5 99.974 Error@1 4.694
**Test** Prec@1 84.480 Prec@5 99.290 Error@1 15.520
[resnet] :: 42/160 ----- [[2019-01-11 03:52:53]] [Need: 01:10:16]
Epoch: [042][000/391] Time 0.491 (0.491) Data 0.388 (0.388) Loss 0.1495 (0.1495) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:52:53]
Epoch: [042][200/391] Time 0.080 (0.086) Data 0.000 (0.002) Loss 0.2476 (0.1191) Prec@1 90.625 (95.872) Prec@5 100.000 (99.984) [2019-01-11 03:53:10]
**Train** Prec@1 95.302 Prec@5 99.968 Error@1 4.698
**Test** Prec@1 84.430 Prec@5 99.290 Error@1 15.570
[resnet] :: 43/160 ----- [[2019-01-11 03:53:28]] [Need: 01:09:52]
Epoch: [043][000/391] Time 0.483 (0.483) Data 0.387 (0.387) Loss 0.1459 (0.1459) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:53:29]
Epoch: [043][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.1155 (0.1143) Prec@1 96.094 (96.171) Prec@5 100.000 (99.984) [2019-01-11 03:53:45]
**Train** Prec@1 95.554 Prec@5 99.974 Error@1 4.446
**Test** Prec@1 83.930 Prec@5 99.120 Error@1 16.070
[resnet] :: 44/160 ----- [[2019-01-11 03:54:04]] [Need: 01:08:30]
Epoch: [044][000/391] Time 0.470 (0.470) Data 0.376 (0.376) Loss 0.0561 (0.0561) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000) [2019-01-11 03:54:04]
Epoch: [044][200/391] Time 0.087 (0.084) Data 0.000 (0.002) Loss 0.1103 (0.1196) Prec@1 96.094 (95.954) Prec@5 100.000 (99.965) [2019-01-11 03:54:21]
**Train** Prec@1 95.406 Prec@5 99.954 Error@1 4.594
**Test** Prec@1 81.660 Prec@5 98.970 Error@1 18.340
[resnet] :: 45/160 ----- [[2019-01-11 03:54:39]] [Need: 01:08:01]
Epoch: [045][000/391] Time 0.531 (0.531) Data 0.434 (0.434) Loss 0.1261 (0.1261) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 03:54:40]
Epoch: [045][200/391] Time 0.083 (0.084) Data 0.000 (0.002) Loss 0.1692 (0.1122) Prec@1 92.969 (96.175) Prec@5 100.000 (99.981) [2019-01-11 03:54:56]
**Train** Prec@1 95.492 Prec@5 99.968 Error@1 4.508
**Test** Prec@1 83.710 Prec@5 99.210 Error@1 16.290
[resnet] :: 46/160 ----- [[2019-01-11 03:55:15]] [Need: 01:07:27]
Epoch: [046][000/391] Time 0.487 (0.487) Data 0.380 (0.380) Loss 0.1205 (0.1205) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 03:55:15]
Epoch: [046][200/391] Time 0.077 (0.084) Data 0.000 (0.002) Loss 0.2108 (0.1132) Prec@1 94.531 (96.024) Prec@5 100.000 (99.984) [2019-01-11 03:55:32]
**Train** Prec@1 95.246 Prec@5 99.968 Error@1 4.754
**Test** Prec@1 82.100 Prec@5 99.000 Error@1 17.900
[resnet] :: 47/160 ----- [[2019-01-11 03:55:50]] [Need: 01:06:21]
Epoch: [047][000/391] Time 0.536 (0.536) Data 0.434 (0.434) Loss 0.1230 (0.1230) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000) [2019-01-11 03:55:51]
Epoch: [047][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.1073 (0.1196) Prec@1 95.312 (95.759) Prec@5 100.000 (99.973) [2019-01-11 03:56:07]
**Train** Prec@1 95.322 Prec@5 99.980 Error@1 4.678
**Test** Prec@1 84.450 Prec@5 99.220 Error@1 15.550
[resnet] :: 48/160 ----- [[2019-01-11 03:56:26]] [Need: 01:06:24]
Epoch: [048][000/391] Time 0.484 (0.484) Data 0.392 (0.392) Loss 0.0687 (0.0687) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 03:56:26]
Epoch: [048][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.1135 (0.1044) Prec@1 97.656 (96.401) Prec@5 100.000 (99.961) [2019-01-11 03:56:43]
**Train** Prec@1 95.812 Prec@5 99.962 Error@1 4.188
**Test** Prec@1 83.710 Prec@5 98.880 Error@1 16.290
[resnet] :: 49/160 ----- [[2019-01-11 03:57:01]] [Need: 01:05:43]
Epoch: [049][000/391] Time 0.534 (0.534) Data 0.431 (0.431) Loss 0.0785 (0.0785) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 03:57:02]
Epoch: [049][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.1845 (0.1119) Prec@1 93.750 (96.226) Prec@5 100.000 (99.969) [2019-01-11 03:57:18]
**Train** Prec@1 95.614 Prec@5 99.964 Error@1 4.386
**Test** Prec@1 80.100 Prec@5 98.690 Error@1 19.900
[resnet] :: 50/160 ----- [[2019-01-11 03:57:37]] [Need: 01:05:10]
Epoch: [050][000/391] Time 0.461 (0.461) Data 0.367 (0.367) Loss 0.1134 (0.1134) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000) [2019-01-11 03:57:37]
Epoch: [050][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.1580 (0.1118) Prec@1 93.750 (96.039) Prec@5 100.000 (99.973) [2019-01-11 03:57:54]
**Train** Prec@1 95.516 Prec@5 99.968 Error@1 4.484
**Test** Prec@1 84.890 Prec@5 99.190 Error@1 15.110
[resnet] :: 51/160 ----- [[2019-01-11 03:58:12]] [Need: 01:04:38]
Epoch: [051][000/391] Time 0.488 (0.488) Data 0.387 (0.387) Loss 0.0708 (0.0708) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000) [2019-01-11 03:58:13]
Epoch: [051][200/391] Time 0.085 (0.084) Data 0.000 (0.002) Loss 0.1675 (0.1222) Prec@1 93.750 (95.752) Prec@5 100.000 (99.961) [2019-01-11 03:58:29]
**Train** Prec@1 95.564 Prec@5 99.956 Error@1 4.436
**Test** Prec@1 83.670 Prec@5 98.970 Error@1 16.330
[resnet] :: 52/160 ----- [[2019-01-11 03:58:48]] [Need: 01:03:43]
Epoch: [052][000/391] Time 0.475 (0.475) Data 0.371 (0.371) Loss 0.1927 (0.1927) Prec@1 92.969 (92.969) Prec@5 100.000 (100.000) [2019-01-11 03:58:48]
Epoch: [052][200/391] Time 0.079 (0.085) Data 0.000 (0.002) Loss 0.1535 (0.1090) Prec@1 96.875 (96.133) Prec@5 100.000 (99.984) [2019-01-11 03:59:05]
**Train** Prec@1 95.670 Prec@5 99.958 Error@1 4.330
**Test** Prec@1 83.900 Prec@5 99.150 Error@1 16.100
[resnet] :: 53/160 ----- [[2019-01-11 03:59:23]] [Need: 01:03:39]
Epoch: [053][000/391] Time 0.495 (0.495) Data 0.394 (0.394) Loss 0.0684 (0.0684) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000) [2019-01-11 03:59:24]
Epoch: [053][200/391] Time 0.085 (0.085) Data 0.000 (0.002) Loss 0.1941 (0.1106) Prec@1 91.406 (96.144) Prec@5 100.000 (99.984) [2019-01-11 03:59:40]
**Train** Prec@1 95.732 Prec@5 99.982 Error@1 4.268
**Test** Prec@1 81.680 Prec@5 98.740 Error@1 18.320
[resnet] :: 54/160 ----- [[2019-01-11 03:59:59]] [Need: 01:03:27]
Epoch: [054][000/391] Time 0.484 (0.484) Data 0.379 (0.379) Loss 0.2165 (0.2165) Prec@1 92.188 (92.188) Prec@5 100.000 (100.000) [2019-01-11 04:00:00]
Epoch: [054][200/391] Time 0.090 (0.084) Data 0.000 (0.002) Loss 0.0406 (0.1133) Prec@1 99.219 (96.094) Prec@5 100.000 (99.984) [2019-01-11 04:00:16]
**Train** Prec@1 95.676 Prec@5 99.978 Error@1 4.324
**Test** Prec@1 83.630 Prec@5 99.220 Error@1 16.370
[resnet] :: 55/160 ----- [[2019-01-11 04:00:35]] [Need: 01:02:01]
Epoch: [055][000/391] Time 0.540 (0.540) Data 0.439 (0.439) Loss 0.1911 (0.1911) Prec@1 92.969 (92.969) Prec@5 100.000 (100.000) [2019-01-11 04:00:35]
Epoch: [055][200/391] Time 0.083 (0.086) Data 0.000 (0.002) Loss 0.0658 (0.1121) Prec@1 96.875 (96.214) Prec@5 100.000 (99.981) [2019-01-11 04:00:52]
**Train** Prec@1 95.636 Prec@5 99.982 Error@1 4.364
**Test** Prec@1 82.260 Prec@5 99.040 Error@1 17.740
[resnet] :: 56/160 ----- [[2019-01-11 04:01:11]] [Need: 01:02:41]
Epoch: [056][000/391] Time 0.540 (0.540) Data 0.441 (0.441) Loss 0.1035 (0.1035) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000) [2019-01-11 04:01:11]
Epoch: [056][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0963 (0.1099) Prec@1 96.875 (96.035) Prec@5 100.000 (99.981) [2019-01-11 04:01:28]
**Train** Prec@1 95.626 Prec@5 99.982 Error@1 4.374
**Test** Prec@1 85.330 Prec@5 99.260 Error@1 14.670
[resnet] :: 57/160 ----- [[2019-01-11 04:01:47]] [Need: 01:01:10]
Epoch: [057][000/391] Time 0.477 (0.477) Data 0.378 (0.378) Loss 0.1299 (0.1299) Prec@1 97.656 (97.656) Prec@5 99.219 (99.219) [2019-01-11 04:01:47]
Epoch: [057][200/391] Time 0.089 (0.087) Data 0.000 (0.002) Loss 0.0516 (0.1102) Prec@1 100.000 (96.304) Prec@5 100.000 (99.984) [2019-01-11 04:02:04]
**Train** Prec@1 95.882 Prec@5 99.982 Error@1 4.118
**Test** Prec@1 82.970 Prec@5 99.290 Error@1 17.030
[resnet] :: 58/160 ----- [[2019-01-11 04:02:23]] [Need: 01:01:26]
Epoch: [058][000/391] Time 0.484 (0.484) Data 0.388 (0.388) Loss 0.1650 (0.1650) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000) [2019-01-11 04:02:23]
Epoch: [058][200/391] Time 0.084 (0.084) Data 0.000 (0.002) Loss 0.1253 (0.1122) Prec@1 96.875 (96.102) Prec@5 100.000 (99.977) [2019-01-11 04:02:40]
**Train** Prec@1 95.632 Prec@5 99.972 Error@1 4.368
**Test** Prec@1 84.620 Prec@5 99.250 Error@1 15.380
[resnet] :: 59/160 ----- [[2019-01-11 04:02:58]] [Need: 00:59:17]
Epoch: [059][000/391] Time 0.530 (0.530) Data 0.431 (0.431) Loss 0.0981 (0.0981) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000) [2019-01-11 04:02:58]
Epoch: [059][200/391] Time 0.085 (0.085) Data 0.000 (0.002) Loss 0.1078 (0.1045) Prec@1 95.312 (96.366) Prec@5 100.000 (99.988) [2019-01-11 04:03:15]
**Train** Prec@1 95.788 Prec@5 99.986 Error@1 4.212
**Test** Prec@1 84.170 Prec@5 99.250 Error@1 15.830
[resnet] :: 60/160 ----- [[2019-01-11 04:03:34]] [Need: 00:59:35]
Epoch: [060][000/391] Time 0.526 (0.526) Data 0.436 (0.436) Loss 0.1204 (0.1204) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000) [2019-01-11 04:03:34]
Epoch: [060][200/391] Time 0.086 (0.085) Data 0.000 (0.002) Loss 0.0152 (0.0524) Prec@1 99.219 (98.430) Prec@5 100.000 (99.996) [2019-01-11 04:03:51]
**Train** Prec@1 98.768 Prec@5 99.998 Error@1 1.232
**Test** Prec@1 89.670 Prec@5 99.610 Error@1 10.330
[resnet] :: 61/160 ----- [[2019-01-11 04:04:10]] [Need: 00:59:31]
Epoch: [061][000/391] Time 0.522 (0.522) Data 0.425 (0.425) Loss 0.0338 (0.0338) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000) [2019-01-11 04:04:10]
Epoch: [061][200/391] Time 0.091 (0.085) Data 0.000 (0.002) Loss 0.0359 (0.0191) Prec@1 99.219 (99.534) Prec@5 100.000 (100.000) [2019-01-11 04:04:27]
**Train** Prec@1 99.570 Prec@5 99.998 Error@1 0.430
**Test** Prec@1 89.980 Prec@5 99.570 Error@1 10.020
[resnet] :: 62/160 ----- [[2019-01-11 04:04:46]] [Need: 00:59:07]
Epoch: [062][000/391] Time 0.484 (0.484) Data 0.384 (0.384) Loss 0.0139 (0.0139) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:04:46]
Epoch: [062][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0024 (0.0117) Prec@1 100.000 (99.767) Prec@5 100.000 (100.000) [2019-01-11 04:05:03]
**Train** Prec@1 99.750 Prec@5 100.000 Error@1 0.250
**Test** Prec@1 90.250 Prec@5 99.560 Error@1 9.750
[resnet] :: 63/160 ----- [[2019-01-11 04:05:22]] [Need: 00:57:46]
Epoch: [063][000/391] Time 0.484 (0.484) Data 0.383 (0.383) Loss 0.0042 (0.0042) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:05:22]
Epoch: [063][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.0027 (0.0084) Prec@1 100.000 (99.872) Prec@5 100.000 (99.996) [2019-01-11 04:05:39]
**Train** Prec@1 99.874 Prec@5 99.998 Error@1 0.126
**Test** Prec@1 90.150 Prec@5 99.560 Error@1 9.850
[resnet] :: 64/160 ----- [[2019-01-11 04:05:57]] [Need: 00:56:57]
Epoch: [064][000/391] Time 0.541 (0.541) Data 0.440 (0.440) Loss 0.0075 (0.0075) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:05:58]
Epoch: [064][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0045 (0.0067) Prec@1 100.000 (99.883) Prec@5 100.000 (100.000) [2019-01-11 04:06:14]
**Train** Prec@1 99.904 Prec@5 100.000 Error@1 0.096
**Test** Prec@1 90.090 Prec@5 99.600 Error@1 9.910
[resnet] :: 65/160 ----- [[2019-01-11 04:06:33]] [Need: 00:56:33]
Epoch: [065][000/391] Time 0.474 (0.474) Data 0.377 (0.377) Loss 0.0241 (0.0241) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000) [2019-01-11 04:06:34]
Epoch: [065][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0028 (0.0045) Prec@1 100.000 (99.965) Prec@5 100.000 (100.000) [2019-01-11 04:06:50]
**Train** Prec@1 99.942 Prec@5 100.000 Error@1 0.058
**Test** Prec@1 90.300 Prec@5 99.590 Error@1 9.700
[resnet] :: 66/160 ----- [[2019-01-11 04:07:09]] [Need: 00:55:55]
Epoch: [066][000/391] Time 0.482 (0.482) Data 0.382 (0.382) Loss 0.0137 (0.0137) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:07:09]
Epoch: [066][200/391] Time 0.078 (0.084) Data 0.000 (0.002) Loss 0.0026 (0.0043) Prec@1 100.000 (99.946) Prec@5 100.000 (100.000) [2019-01-11 04:07:26]
**Train** Prec@1 99.946 Prec@5 100.000 Error@1 0.054
**Test** Prec@1 90.470 Prec@5 99.640 Error@1 9.530
[resnet] :: 67/160 ----- [[2019-01-11 04:07:44]] [Need: 00:55:11]
Epoch: [067][000/391] Time 0.475 (0.475) Data 0.375 (0.375) Loss 0.0012 (0.0012) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:07:45]
Epoch: [067][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0014 (0.0026) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:08:01]
**Train** Prec@1 99.966 Prec@5 100.000 Error@1 0.034
**Test** Prec@1 90.500 Prec@5 99.550 Error@1 9.500
[resnet] :: 68/160 ----- [[2019-01-11 04:08:20]] [Need: 00:54:38]
Epoch: [068][000/391] Time 0.499 (0.499) Data 0.405 (0.405) Loss 0.0021 (0.0021) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:08:20]
Epoch: [068][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0017 (0.0026) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:08:37]
**Train** Prec@1 99.974 Prec@5 100.000 Error@1 0.026
**Test** Prec@1 90.620 Prec@5 99.610 Error@1 9.380
[resnet] :: 69/160 ----- [[2019-01-11 04:08:56]] [Need: 00:54:14]
Epoch: [069][000/391] Time 0.491 (0.491) Data 0.392 (0.392) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:08:56]
Epoch: [069][200/391] Time 0.087 (0.085) Data 0.000 (0.002) Loss 0.0022 (0.0023) Prec@1 100.000 (99.981) Prec@5 100.000 (100.000) [2019-01-11 04:09:13]
**Train** Prec@1 99.982 Prec@5 100.000 Error@1 0.018
**Test** Prec@1 90.550 Prec@5 99.480 Error@1 9.450
[resnet] :: 70/160 ----- [[2019-01-11 04:09:31]] [Need: 00:53:31]
Epoch: [070][000/391] Time 0.479 (0.479) Data 0.378 (0.378) Loss 0.0014 (0.0014) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:09:32]
Epoch: [070][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0012 (0.0023) Prec@1 100.000 (99.969) Prec@5 100.000 (100.000) [2019-01-11 04:09:48]
**Train** Prec@1 99.980 Prec@5 100.000 Error@1 0.020
**Test** Prec@1 90.710 Prec@5 99.500 Error@1 9.290
[resnet] :: 71/160 ----- [[2019-01-11 04:10:07]] [Need: 00:53:14]
Epoch: [071][000/391] Time 0.493 (0.493) Data 0.386 (0.386) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:10:08]
Epoch: [071][200/391] Time 0.085 (0.086) Data 0.000 (0.002) Loss 0.0005 (0.0020) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:10:25]
**Train** Prec@1 99.982 Prec@5 100.000 Error@1 0.018
**Test** Prec@1 90.580 Prec@5 99.610 Error@1 9.420
[resnet] :: 72/160 ----- [[2019-01-11 04:10:43]] [Need: 00:52:29]
Epoch: [072][000/391] Time 0.491 (0.491) Data 0.389 (0.389) Loss 0.0023 (0.0023) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:10:44]
Epoch: [072][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0027 (0.0017) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:11:00]
**Train** Prec@1 99.986 Prec@5 100.000 Error@1 0.014
**Test** Prec@1 90.490 Prec@5 99.540 Error@1 9.510
[resnet] :: 73/160 ----- [[2019-01-11 04:11:18]] [Need: 00:51:15]
Epoch: [073][000/391] Time 0.504 (0.504) Data 0.404 (0.404) Loss 0.0015 (0.0015) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:11:19]
Epoch: [073][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0019 (0.0016) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:11:35]
**Train** Prec@1 99.982 Prec@5 100.000 Error@1 0.018
**Test** Prec@1 90.650 Prec@5 99.580 Error@1 9.350
[resnet] :: 74/160 ----- [[2019-01-11 04:11:54]] [Need: 00:50:37]
Epoch: [074][000/391] Time 0.504 (0.504) Data 0.407 (0.407) Loss 0.0009 (0.0009) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:11:54]
Epoch: [074][200/391] Time 0.084 (0.084) Data 0.000 (0.002) Loss 0.0020 (0.0016) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:12:11]
**Train** Prec@1 99.978 Prec@5 100.000 Error@1 0.022
**Test** Prec@1 90.620 Prec@5 99.550 Error@1 9.380
[resnet] :: 75/160 ----- [[2019-01-11 04:12:29]] [Need: 00:50:21]
Epoch: [075][000/391] Time 0.504 (0.504) Data 0.406 (0.406) Loss 0.0012 (0.0012) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:12:30]
Epoch: [075][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.0011 (0.0015) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:12:46]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.770 Prec@5 99.590 Error@1 9.230
[resnet] :: 76/160 ----- [[2019-01-11 04:13:05]] [Need: 00:50:32]
Epoch: [076][000/391] Time 0.531 (0.531) Data 0.428 (0.428) Loss 0.0013 (0.0013) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:13:06]
Epoch: [076][200/391] Time 0.082 (0.086) Data 0.000 (0.002) Loss 0.0009 (0.0013) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:13:23]
**Train** Prec@1 99.992 Prec@5 100.000 Error@1 0.008
**Test** Prec@1 90.720 Prec@5 99.550 Error@1 9.280
[resnet] :: 77/160 ----- [[2019-01-11 04:13:42]] [Need: 00:50:02]
Epoch: [077][000/391] Time 0.506 (0.506) Data 0.404 (0.404) Loss 0.0031 (0.0031) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:13:42]
Epoch: [077][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0009 (0.0014) Prec@1 100.000 (99.973) Prec@5 100.000 (100.000) [2019-01-11 04:13:59]
**Train** Prec@1 99.978 Prec@5 100.000 Error@1 0.022
**Test** Prec@1 90.680 Prec@5 99.550 Error@1 9.320
[resnet] :: 78/160 ----- [[2019-01-11 04:14:17]] [Need: 00:48:43]
Epoch: [078][000/391] Time 0.505 (0.505) Data 0.405 (0.405) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:14:18]
Epoch: [078][200/391] Time 0.085 (0.084) Data 0.000 (0.002) Loss 0.0003 (0.0012) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:14:34]
**Train** Prec@1 99.988 Prec@5 100.000 Error@1 0.012
**Test** Prec@1 90.340 Prec@5 99.590 Error@1 9.660
[resnet] :: 79/160 ----- [[2019-01-11 04:14:53]] [Need: 00:47:55]
Epoch: [079][000/391] Time 0.494 (0.494) Data 0.389 (0.389) Loss 0.0033 (0.0033) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:14:53]
Epoch: [079][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0012) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:15:10]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.540 Prec@5 99.530 Error@1 9.460
[resnet] :: 80/160 ----- [[2019-01-11 04:15:28]] [Need: 00:47:10]
Epoch: [080][000/391] Time 0.478 (0.478) Data 0.383 (0.383) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:15:29]
Epoch: [080][200/391] Time 0.080 (0.083) Data 0.000 (0.002) Loss 0.0006 (0.0012) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:15:45]
**Train** Prec@1 99.992 Prec@5 100.000 Error@1 0.008
**Test** Prec@1 90.720 Prec@5 99.570 Error@1 9.280
[resnet] :: 81/160 ----- [[2019-01-11 04:16:03]] [Need: 00:46:26]
Epoch: [081][000/391] Time 0.498 (0.498) Data 0.408 (0.408) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:16:04]
Epoch: [081][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0029 (0.0012) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:16:20]
**Train** Prec@1 99.992 Prec@5 100.000 Error@1 0.008
**Test** Prec@1 90.910 Prec@5 99.600 Error@1 9.090
[resnet] :: 82/160 ----- [[2019-01-11 04:16:39]] [Need: 00:46:40]
Epoch: [082][000/391] Time 0.549 (0.549) Data 0.453 (0.453) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:16:40]
Epoch: [082][200/391] Time 0.084 (0.085) Data 0.000 (0.003) Loss 0.0013 (0.0009) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:16:56]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.840 Prec@5 99.560 Error@1 9.160
[resnet] :: 83/160 ----- [[2019-01-11 04:17:15]] [Need: 00:46:00]
Epoch: [083][000/391] Time 0.489 (0.489) Data 0.393 (0.393) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:17:16]
Epoch: [083][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0005 (0.0009) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:17:32]
**Train** Prec@1 99.996 Prec@5 100.000 Error@1 0.004
**Test** Prec@1 90.590 Prec@5 99.540 Error@1 9.410
[resnet] :: 84/160 ----- [[2019-01-11 04:17:50]] [Need: 00:44:41]
Epoch: [084][000/391] Time 0.478 (0.478) Data 0.382 (0.382) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:17:51]
Epoch: [084][200/391] Time 0.081 (0.085) Data 0.000 (0.002) Loss 0.0013 (0.0011) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:18:08]
**Train** Prec@1 99.984 Prec@5 100.000 Error@1 0.016
**Test** Prec@1 90.750 Prec@5 99.610 Error@1 9.250
[resnet] :: 85/160 ----- [[2019-01-11 04:18:26]] [Need: 00:44:28]
Epoch: [085][000/391] Time 0.535 (0.535) Data 0.437 (0.437) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:18:27]
Epoch: [085][200/391] Time 0.082 (0.085) Data 0.000 (0.002) Loss 0.0006 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:18:43]
**Train** Prec@1 99.994 Prec@5 100.000 Error@1 0.006
**Test** Prec@1 90.690 Prec@5 99.570 Error@1 9.310
[resnet] :: 86/160 ----- [[2019-01-11 04:19:02]] [Need: 00:44:26]
Epoch: [086][000/391] Time 0.505 (0.505) Data 0.410 (0.410) Loss 0.0182 (0.0182) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000) [2019-01-11 04:19:03]
Epoch: [086][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.0004 (0.0009) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:19:19]
**Train** Prec@1 99.996 Prec@5 100.000 Error@1 0.004
**Test** Prec@1 90.560 Prec@5 99.600 Error@1 9.440
[resnet] :: 87/160 ----- [[2019-01-11 04:19:38]] [Need: 00:43:40]
Epoch: [087][000/391] Time 0.483 (0.483) Data 0.380 (0.380) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:19:38]
Epoch: [087][200/391] Time 0.091 (0.084) Data 0.000 (0.002) Loss 0.0004 (0.0012) Prec@1 100.000 (99.969) Prec@5 100.000 (100.000) [2019-01-11 04:19:55]
**Train** Prec@1 99.976 Prec@5 100.000 Error@1 0.024
**Test** Prec@1 90.740 Prec@5 99.590 Error@1 9.260
[resnet] :: 88/160 ----- [[2019-01-11 04:20:13]] [Need: 00:42:31]
Epoch: [088][000/391] Time 0.541 (0.541) Data 0.440 (0.440) Loss 0.0017 (0.0017) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:20:14]
Epoch: [088][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.0021 (0.0009) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:20:31]
**Train** Prec@1 99.992 Prec@5 100.000 Error@1 0.008
**Test** Prec@1 90.730 Prec@5 99.530 Error@1 9.270
[resnet] :: 89/160 ----- [[2019-01-11 04:20:49]] [Need: 00:42:23]
Epoch: [089][000/391] Time 0.483 (0.483) Data 0.380 (0.380) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:20:50]
Epoch: [089][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.0002 (0.0009) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:21:06]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.410 Prec@5 99.520 Error@1 9.590
[resnet] :: 90/160 ----- [[2019-01-11 04:21:25]] [Need: 00:41:31]
Epoch: [090][000/391] Time 0.539 (0.539) Data 0.437 (0.437) Loss 0.0017 (0.0017) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:21:25]
Epoch: [090][200/391] Time 0.086 (0.084) Data 0.000 (0.002) Loss 0.0005 (0.0010) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:21:42]
**Train** Prec@1 99.992 Prec@5 100.000 Error@1 0.008
**Test** Prec@1 90.630 Prec@5 99.550 Error@1 9.370
[resnet] :: 91/160 ----- [[2019-01-11 04:22:00]] [Need: 00:40:52]
Epoch: [091][000/391] Time 0.495 (0.495) Data 0.392 (0.392) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:22:01]
Epoch: [091][200/391] Time 0.082 (0.085) Data 0.000 (0.002) Loss 0.0006 (0.0013) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:22:18]
**Train** Prec@1 99.988 Prec@5 100.000 Error@1 0.012
**Test** Prec@1 90.830 Prec@5 99.570 Error@1 9.170
[resnet] :: 92/160 ----- [[2019-01-11 04:22:36]] [Need: 00:40:24]
Epoch: [092][000/391] Time 0.491 (0.491) Data 0.398 (0.398) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:22:37]
Epoch: [092][200/391] Time 0.084 (0.084) Data 0.000 (0.002) Loss 0.0002 (0.0008) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:22:53]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.600 Prec@5 99.570 Error@1 9.400
[resnet] :: 93/160 ----- [[2019-01-11 04:23:12]] [Need: 00:39:43]
Epoch: [093][000/391] Time 0.486 (0.486) Data 0.386 (0.386) Loss 0.0016 (0.0016) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:23:12]
Epoch: [093][200/391] Time 0.087 (0.087) Data 0.000 (0.002) Loss 0.0005 (0.0008) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:23:29]
**Train** Prec@1 99.988 Prec@5 100.000 Error@1 0.012
**Test** Prec@1 90.550 Prec@5 99.520 Error@1 9.450
[resnet] :: 94/160 ----- [[2019-01-11 04:23:48]] [Need: 00:39:44]
Epoch: [094][000/391] Time 0.453 (0.453) Data 0.355 (0.355) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:23:48]
Epoch: [094][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0002 (0.0008) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:24:05]
**Train** Prec@1 99.984 Prec@5 100.000 Error@1 0.016
**Test** Prec@1 90.840 Prec@5 99.560 Error@1 9.160
[resnet] :: 95/160 ----- [[2019-01-11 04:24:23]] [Need: 00:38:29]
Epoch: [095][000/391] Time 0.496 (0.496) Data 0.398 (0.398) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:24:24]
Epoch: [095][200/391] Time 0.079 (0.085) Data 0.000 (0.002) Loss 0.0039 (0.0011) Prec@1 100.000 (99.981) Prec@5 100.000 (100.000) [2019-01-11 04:24:40]
**Train** Prec@1 99.984 Prec@5 100.000 Error@1 0.016
**Test** Prec@1 90.720 Prec@5 99.520 Error@1 9.280
[resnet] :: 96/160 ----- [[2019-01-11 04:24:59]] [Need: 00:38:15]
Epoch: [096][000/391] Time 0.530 (0.530) Data 0.424 (0.424) Loss 0.0017 (0.0017) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:25:00]
Epoch: [096][200/391] Time 0.079 (0.085) Data 0.000 (0.002) Loss 0.0006 (0.0009) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:25:16]
**Train** Prec@1 99.986 Prec@5 100.000 Error@1 0.014
**Test** Prec@1 90.690 Prec@5 99.470 Error@1 9.310
[resnet] :: 97/160 ----- [[2019-01-11 04:25:35]] [Need: 00:37:23]
Epoch: [097][000/391] Time 0.490 (0.490) Data 0.391 (0.391) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:25:35]
Epoch: [097][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0007 (0.0010) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:25:52]
**Train** Prec@1 99.992 Prec@5 100.000 Error@1 0.008
**Test** Prec@1 90.300 Prec@5 99.520 Error@1 9.700
[resnet] :: 98/160 ----- [[2019-01-11 04:26:10]] [Need: 00:36:44]
Epoch: [098][000/391] Time 0.480 (0.480) Data 0.381 (0.381) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:26:11]
Epoch: [098][200/391] Time 0.080 (0.083) Data 0.000 (0.002) Loss 0.0003 (0.0009) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:26:27]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.800 Prec@5 99.560 Error@1 9.200
[resnet] :: 99/160 ----- [[2019-01-11 04:26:46]] [Need: 00:35:57]
Epoch: [099][000/391] Time 0.493 (0.493) Data 0.396 (0.396) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:26:46]
Epoch: [099][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0008 (0.0011) Prec@1 100.000 (99.973) Prec@5 100.000 (100.000) [2019-01-11 04:27:03]
**Train** Prec@1 99.978 Prec@5 100.000 Error@1 0.022
**Test** Prec@1 90.590 Prec@5 99.490 Error@1 9.410
[resnet] :: 100/160 ----- [[2019-01-11 04:27:21]] [Need: 00:35:36]
Epoch: [100][000/391] Time 0.550 (0.550) Data 0.447 (0.447) Loss 0.0011 (0.0011) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:27:22]
Epoch: [100][200/391] Time 0.078 (0.085) Data 0.000 (0.002) Loss 0.0011 (0.0010) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:27:38]
**Train** Prec@1 99.984 Prec@5 100.000 Error@1 0.016
**Test** Prec@1 90.430 Prec@5 99.610 Error@1 9.570
[resnet] :: 101/160 ----- [[2019-01-11 04:27:57]] [Need: 00:35:20]
Epoch: [101][000/391] Time 0.547 (0.547) Data 0.445 (0.445) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:27:58]
Epoch: [101][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.0003 (0.0013) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:28:14]
**Train** Prec@1 99.978 Prec@5 100.000 Error@1 0.022
**Test** Prec@1 90.490 Prec@5 99.550 Error@1 9.510
[resnet] :: 102/160 ----- [[2019-01-11 04:28:33]] [Need: 00:34:23]
Epoch: [102][000/391] Time 0.495 (0.495) Data 0.392 (0.392) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:28:33]
Epoch: [102][200/391] Time 0.084 (0.086) Data 0.000 (0.002) Loss 0.0004 (0.0014) Prec@1 100.000 (99.973) Prec@5 100.000 (100.000) [2019-01-11 04:28:50]
**Train** Prec@1 99.976 Prec@5 100.000 Error@1 0.024
**Test** Prec@1 90.180 Prec@5 99.550 Error@1 9.820
[resnet] :: 103/160 ----- [[2019-01-11 04:29:09]] [Need: 00:33:58]
Epoch: [103][000/391] Time 0.512 (0.512) Data 0.411 (0.411) Loss 0.0028 (0.0028) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:29:09]
Epoch: [103][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0005 (0.0011) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:29:26]
**Train** Prec@1 99.982 Prec@5 100.000 Error@1 0.018
**Test** Prec@1 90.600 Prec@5 99.480 Error@1 9.400
[resnet] :: 104/160 ----- [[2019-01-11 04:29:45]] [Need: 00:33:36]
Epoch: [104][000/391] Time 0.544 (0.544) Data 0.451 (0.451) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:29:45]
Epoch: [104][200/391] Time 0.081 (0.086) Data 0.000 (0.003) Loss 0.0003 (0.0009) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:30:02]
**Train** Prec@1 99.988 Prec@5 100.000 Error@1 0.012
**Test** Prec@1 90.440 Prec@5 99.540 Error@1 9.560
[resnet] :: 105/160 ----- [[2019-01-11 04:30:21]] [Need: 00:33:06]
Epoch: [105][000/391] Time 0.491 (0.491) Data 0.392 (0.392) Loss 0.0010 (0.0010) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:30:21]
Epoch: [105][200/391] Time 0.085 (0.084) Data 0.000 (0.002) Loss 0.0006 (0.0014) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:30:38]
**Train** Prec@1 99.966 Prec@5 100.000 Error@1 0.034
**Test** Prec@1 90.590 Prec@5 99.410 Error@1 9.410
[resnet] :: 106/160 ----- [[2019-01-11 04:30:57]] [Need: 00:32:19]
Epoch: [106][000/391] Time 0.541 (0.541) Data 0.442 (0.442) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:30:57]
Epoch: [106][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0008 (0.0015) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:31:14]
**Train** Prec@1 99.976 Prec@5 100.000 Error@1 0.024
**Test** Prec@1 90.450 Prec@5 99.510 Error@1 9.550
[resnet] :: 107/160 ----- [[2019-01-11 04:31:32]] [Need: 00:31:20]
Epoch: [107][000/391] Time 0.539 (0.539) Data 0.442 (0.442) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:31:33]
Epoch: [107][200/391] Time 0.080 (0.083) Data 0.000 (0.002) Loss 0.0013 (0.0013) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:31:49]
**Train** Prec@1 99.974 Prec@5 100.000 Error@1 0.026
**Test** Prec@1 90.220 Prec@5 99.500 Error@1 9.780
[resnet] :: 108/160 ----- [[2019-01-11 04:32:07]] [Need: 00:30:21]
Epoch: [108][000/391] Time 0.498 (0.498) Data 0.389 (0.389) Loss 0.0024 (0.0024) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:32:08]
Epoch: [108][200/391] Time 0.083 (0.084) Data 0.000 (0.002) Loss 0.0014 (0.0010) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:32:24]
**Train** Prec@1 99.982 Prec@5 100.000 Error@1 0.018
**Test** Prec@1 90.420 Prec@5 99.470 Error@1 9.580
[resnet] :: 109/160 ----- [[2019-01-11 04:32:43]] [Need: 00:30:04]
Epoch: [109][000/391] Time 0.554 (0.554) Data 0.458 (0.458) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:32:43]
Epoch: [109][200/391] Time 0.082 (0.087) Data 0.000 (0.003) Loss 0.0009 (0.0010) Prec@1 100.000 (99.981) Prec@5 100.000 (100.000) [2019-01-11 04:33:00]
**Train** Prec@1 99.982 Prec@5 100.000 Error@1 0.018
**Test** Prec@1 90.550 Prec@5 99.570 Error@1 9.450
[resnet] :: 110/160 ----- [[2019-01-11 04:33:19]] [Need: 00:30:10]
Epoch: [110][000/391] Time 0.565 (0.565) Data 0.468 (0.468) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:33:19]
Epoch: [110][200/391] Time 0.082 (0.086) Data 0.000 (0.003) Loss 0.0007 (0.0014) Prec@1 100.000 (99.957) Prec@5 100.000 (100.000) [2019-01-11 04:33:36]
**Train** Prec@1 99.968 Prec@5 100.000 Error@1 0.032
**Test** Prec@1 90.280 Prec@5 99.470 Error@1 9.720
[resnet] :: 111/160 ----- [[2019-01-11 04:33:55]] [Need: 00:29:38]
Epoch: [111][000/391] Time 0.490 (0.490) Data 0.391 (0.391) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:33:56]
Epoch: [111][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0017 (0.0016) Prec@1 100.000 (99.953) Prec@5 100.000 (100.000) [2019-01-11 04:34:12]
**Train** Prec@1 99.966 Prec@5 100.000 Error@1 0.034
**Test** Prec@1 90.130 Prec@5 99.370 Error@1 9.870
[resnet] :: 112/160 ----- [[2019-01-11 04:34:30]] [Need: 00:28:19]
Epoch: [112][000/391] Time 0.559 (0.559) Data 0.461 (0.461) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:34:31]
Epoch: [112][200/391] Time 0.080 (0.085) Data 0.000 (0.003) Loss 0.0007 (0.0012) Prec@1 100.000 (99.977) Prec@5 100.000 (100.000) [2019-01-11 04:34:48]
**Train** Prec@1 99.966 Prec@5 100.000 Error@1 0.034
**Test** Prec@1 90.620 Prec@5 99.540 Error@1 9.380
[resnet] :: 113/160 ----- [[2019-01-11 04:35:06]] [Need: 00:28:10]
Epoch: [113][000/391] Time 0.554 (0.554) Data 0.462 (0.462) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:35:07]
Epoch: [113][200/391] Time 0.080 (0.086) Data 0.000 (0.003) Loss 0.0003 (0.0019) Prec@1 100.000 (99.946) Prec@5 100.000 (100.000) [2019-01-11 04:35:24]
**Train** Prec@1 99.952 Prec@5 100.000 Error@1 0.048
**Test** Prec@1 90.220 Prec@5 99.540 Error@1 9.780
[resnet] :: 114/160 ----- [[2019-01-11 04:35:43]] [Need: 00:27:47]
Epoch: [114][000/391] Time 0.497 (0.497) Data 0.394 (0.394) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:35:43]
Epoch: [114][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0005 (0.0021) Prec@1 100.000 (99.953) Prec@5 100.000 (100.000) [2019-01-11 04:36:00]
**Train** Prec@1 99.950 Prec@5 100.000 Error@1 0.050
**Test** Prec@1 90.220 Prec@5 99.470 Error@1 9.780
[resnet] :: 115/160 ----- [[2019-01-11 04:36:18]] [Need: 00:26:31]
Epoch: [115][000/391] Time 0.482 (0.482) Data 0.379 (0.379) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:36:19]
Epoch: [115][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0013 (0.0020) Prec@1 100.000 (99.961) Prec@5 100.000 (100.000) [2019-01-11 04:36:35]
**Train** Prec@1 99.954 Prec@5 100.000 Error@1 0.046
**Test** Prec@1 90.310 Prec@5 99.470 Error@1 9.690
[resnet] :: 116/160 ----- [[2019-01-11 04:36:53]] [Need: 00:25:48]
Epoch: [116][000/391] Time 0.468 (0.468) Data 0.366 (0.366) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:36:54]
Epoch: [116][200/391] Time 0.083 (0.084) Data 0.000 (0.002) Loss 0.0002 (0.0023) Prec@1 100.000 (99.942) Prec@5 100.000 (100.000) [2019-01-11 04:37:10]
**Train** Prec@1 99.946 Prec@5 100.000 Error@1 0.054
**Test** Prec@1 89.900 Prec@5 99.560 Error@1 10.100
[resnet] :: 117/160 ----- [[2019-01-11 04:37:29]] [Need: 00:25:18]
Epoch: [117][000/391] Time 0.540 (0.540) Data 0.444 (0.444) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:37:29]
Epoch: [117][200/391] Time 0.084 (0.086) Data 0.000 (0.002) Loss 0.0170 (0.0021) Prec@1 99.219 (99.946) Prec@5 100.000 (100.000) [2019-01-11 04:37:46]
**Train** Prec@1 99.954 Prec@5 100.000 Error@1 0.046
**Test** Prec@1 90.100 Prec@5 99.490 Error@1 9.900
[resnet] :: 118/160 ----- [[2019-01-11 04:38:04]] [Need: 00:24:51]
Epoch: [118][000/391] Time 0.481 (0.481) Data 0.381 (0.381) Loss 0.0055 (0.0055) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:38:05]
Epoch: [118][200/391] Time 0.081 (0.085) Data 0.000 (0.002) Loss 0.0007 (0.0016) Prec@1 100.000 (99.961) Prec@5 100.000 (100.000) [2019-01-11 04:38:21]
**Train** Prec@1 99.954 Prec@5 100.000 Error@1 0.046
**Test** Prec@1 90.460 Prec@5 99.490 Error@1 9.540
[resnet] :: 119/160 ----- [[2019-01-11 04:38:40]] [Need: 00:24:26]
Epoch: [119][000/391] Time 0.481 (0.481) Data 0.382 (0.382) Loss 0.0011 (0.0011) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:38:40]
Epoch: [119][200/391] Time 0.086 (0.086) Data 0.000 (0.002) Loss 0.0031 (0.0016) Prec@1 100.000 (99.961) Prec@5 100.000 (100.000) [2019-01-11 04:38:57]
**Train** Prec@1 99.964 Prec@5 100.000 Error@1 0.036
**Test** Prec@1 90.140 Prec@5 99.470 Error@1 9.860
[resnet] :: 120/160 ----- [[2019-01-11 04:39:16]] [Need: 00:23:50]
Epoch: [120][000/391] Time 0.547 (0.547) Data 0.445 (0.445) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:39:16]
Epoch: [120][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.0002 (0.0009) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000) [2019-01-11 04:39:33]
**Train** Prec@1 99.990 Prec@5 100.000 Error@1 0.010
**Test** Prec@1 90.840 Prec@5 99.540 Error@1 9.160
[resnet] :: 121/160 ----- [[2019-01-11 04:39:51]] [Need: 00:23:13]
Epoch: [121][000/391] Time 0.538 (0.538) Data 0.438 (0.438) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:39:52]
Epoch: [121][200/391] Time 0.079 (0.086) Data 0.000 (0.002) Loss 0.0002 (0.0005) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:40:09]
**Train** Prec@1 99.996 Prec@5 100.000 Error@1 0.004
**Test** Prec@1 90.910 Prec@5 99.590 Error@1 9.090
[resnet] :: 122/160 ----- [[2019-01-11 04:40:27]] [Need: 00:22:49]
Epoch: [122][000/391] Time 0.546 (0.546) Data 0.442 (0.442) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:40:28]
Epoch: [122][200/391] Time 0.083 (0.085) Data 0.000 (0.002) Loss 0.0001 (0.0005) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:40:44]
**Train** Prec@1 99.994 Prec@5 100.000 Error@1 0.006
**Test** Prec@1 91.070 Prec@5 99.550 Error@1 8.930
[resnet] :: 123/160 ----- [[2019-01-11 04:41:04]] [Need: 00:22:19]
Epoch: [123][000/391] Time 0.549 (0.549) Data 0.448 (0.448) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:41:04]
Epoch: [123][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0006 (0.0005) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:41:20]
**Train** Prec@1 99.994 Prec@5 100.000 Error@1 0.006
**Test** Prec@1 90.960 Prec@5 99.610 Error@1 9.040
[resnet] :: 124/160 ----- [[2019-01-11 04:41:39]] [Need: 00:21:14]
Epoch: [124][000/391] Time 0.506 (0.506) Data 0.403 (0.403) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:41:39]
Epoch: [124][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0008 (0.0004) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:41:56]
**Train** Prec@1 99.994 Prec@5 100.000 Error@1 0.006
**Test** Prec@1 90.940 Prec@5 99.590 Error@1 9.060
[resnet] :: 125/160 ----- [[2019-01-11 04:42:14]] [Need: 00:20:39]
Epoch: [125][000/391] Time 0.496 (0.496) Data 0.394 (0.394) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:42:15]
Epoch: [125][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0005) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000) [2019-01-11 04:42:31]
**Train** Prec@1 99.994 Prec@5 100.000 Error@1 0.006
**Test** Prec@1 90.900 Prec@5 99.570 Error@1 9.100
[resnet] :: 126/160 ----- [[2019-01-11 04:42:50]] [Need: 00:20:00]
Epoch: [126][000/391] Time 0.548 (0.548) Data 0.453 (0.453) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:42:50]
Epoch: [126][200/391] Time 0.084 (0.085) Data 0.000 (0.003) Loss 0.0007 (0.0004) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:43:07]
**Train** Prec@1 99.994 Prec@5 100.000 Error@1 0.006
**Test** Prec@1 91.010 Prec@5 99.540 Error@1 8.990
[resnet] :: 127/160 ----- [[2019-01-11 04:43:25]] [Need: 00:19:38]
Epoch: [127][000/391] Time 0.511 (0.511) Data 0.406 (0.406) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:43:26]
Epoch: [127][200/391] Time 0.084 (0.084) Data 0.000 (0.002) Loss 0.0007 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:43:42]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.000 Prec@5 99.580 Error@1 9.000
[resnet] :: 128/160 ----- [[2019-01-11 04:44:01]] [Need: 00:18:53]
Epoch: [128][000/391] Time 0.452 (0.452) Data 0.355 (0.355) Loss 0.0027 (0.0027) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:44:01]
Epoch: [128][200/391] Time 0.085 (0.084) Data 0.000 (0.002) Loss 0.0002 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:44:18]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.100 Prec@5 99.550 Error@1 8.900
[resnet] :: 129/160 ----- [[2019-01-11 04:44:37]] [Need: 00:18:45]
Epoch: [129][000/391] Time 0.489 (0.489) Data 0.386 (0.386) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:44:38]
Epoch: [129][200/391] Time 0.082 (0.083) Data 0.000 (0.002) Loss 0.0002 (0.0005) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000) [2019-01-11 04:44:54]
**Train** Prec@1 99.996 Prec@5 100.000 Error@1 0.004
**Test** Prec@1 91.070 Prec@5 99.590 Error@1 8.930
[resnet] :: 130/160 ----- [[2019-01-11 04:45:12]] [Need: 00:17:31]
Epoch: [130][000/391] Time 0.515 (0.515) Data 0.410 (0.410) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:45:13]
Epoch: [130][200/391] Time 0.082 (0.085) Data 0.000 (0.002) Loss 0.0005 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:45:29]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.080 Prec@5 99.570 Error@1 8.920
[resnet] :: 131/160 ----- [[2019-01-11 04:45:48]] [Need: 00:17:10]
Epoch: [131][000/391] Time 0.476 (0.476) Data 0.375 (0.375) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:45:48]
Epoch: [131][200/391] Time 0.077 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:46:05]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.010 Prec@5 99.620 Error@1 8.990
[resnet] :: 132/160 ----- [[2019-01-11 04:46:23]] [Need: 00:16:32]
Epoch: [132][000/391] Time 0.497 (0.497) Data 0.405 (0.405) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:46:24]
Epoch: [132][200/391] Time 0.083 (0.086) Data 0.000 (0.002) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:46:40]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.020 Prec@5 99.540 Error@1 8.980
[resnet] :: 133/160 ----- [[2019-01-11 04:46:59]] [Need: 00:16:08]
Epoch: [133][000/391] Time 0.557 (0.557) Data 0.452 (0.452) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:47:00]
Epoch: [133][200/391] Time 0.081 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:47:16]
**Train** Prec@1 99.996 Prec@5 100.000 Error@1 0.004
**Test** Prec@1 91.070 Prec@5 99.540 Error@1 8.930
[resnet] :: 134/160 ----- [[2019-01-11 04:47:34]] [Need: 00:15:19]
Epoch: [134][000/391] Time 0.453 (0.453) Data 0.358 (0.358) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:47:35]
Epoch: [134][200/391] Time 0.086 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0003) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:47:51]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.010 Prec@5 99.550 Error@1 8.990
[resnet] :: 135/160 ----- [[2019-01-11 04:48:10]] [Need: 00:14:45]
Epoch: [135][000/391] Time 0.480 (0.480) Data 0.382 (0.382) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:48:10]
Epoch: [135][200/391] Time 0.084 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:48:27]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.000 Prec@5 99.520 Error@1 9.000
[resnet] :: 136/160 ----- [[2019-01-11 04:48:45]] [Need: 00:14:04]
Epoch: [136][000/391] Time 0.549 (0.549) Data 0.458 (0.458) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:48:46]
Epoch: [136][200/391] Time 0.084 (0.086) Data 0.000 (0.003) Loss 0.0001 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:49:02]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.030 Prec@5 99.560 Error@1 8.970
[resnet] :: 137/160 ----- [[2019-01-11 04:49:21]] [Need: 00:13:47]
Epoch: [137][000/391] Time 0.480 (0.480) Data 0.382 (0.382) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:49:21]
Epoch: [137][200/391] Time 0.087 (0.084) Data 0.000 (0.002) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:49:38]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 90.960 Prec@5 99.550 Error@1 9.040
[resnet] :: 138/160 ----- [[2019-01-11 04:49:56]] [Need: 00:12:57]
Epoch: [138][000/391] Time 0.499 (0.499) Data 0.399 (0.399) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:49:57]
Epoch: [138][200/391] Time 0.082 (0.085) Data 0.000 (0.002) Loss 0.0002 (0.0003) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:50:13]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 90.970 Prec@5 99.540 Error@1 9.030
[resnet] :: 139/160 ----- [[2019-01-11 04:50:32]] [Need: 00:12:36]
Epoch: [139][000/391] Time 0.501 (0.501) Data 0.400 (0.400) Loss 0.0021 (0.0021) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:50:33]
Epoch: [139][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:50:49]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.050 Prec@5 99.550 Error@1 8.950
[resnet] :: 140/160 ----- [[2019-01-11 04:51:08]] [Need: 00:11:54]
Epoch: [140][000/391] Time 0.573 (0.573) Data 0.470 (0.470) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:51:09]
Epoch: [140][200/391] Time 0.083 (0.084) Data 0.000 (0.003) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:51:25]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 90.980 Prec@5 99.540 Error@1 9.020
[resnet] :: 141/160 ----- [[2019-01-11 04:51:44]] [Need: 00:11:15]
Epoch: [141][000/391] Time 0.552 (0.552) Data 0.451 (0.451) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:51:44]
Epoch: [141][200/391] Time 0.083 (0.084) Data 0.000 (0.003) Loss 0.0001 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:52:01]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 90.960 Prec@5 99.580 Error@1 9.040
[resnet] :: 142/160 ----- [[2019-01-11 04:52:19]] [Need: 00:10:38]
Epoch: [142][000/391] Time 0.554 (0.554) Data 0.457 (0.457) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:52:20]
Epoch: [142][200/391] Time 0.083 (0.087) Data 0.000 (0.003) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:52:37]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 90.970 Prec@5 99.570 Error@1 9.030
[resnet] :: 143/160 ----- [[2019-01-11 04:52:55]] [Need: 00:10:16]
Epoch: [143][000/391] Time 0.501 (0.501) Data 0.401 (0.401) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:52:56]
Epoch: [143][200/391] Time 0.080 (0.085) Data 0.000 (0.002) Loss 0.0001 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:53:13]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.110 Prec@5 99.550 Error@1 8.890
[resnet] :: 144/160 ----- [[2019-01-11 04:53:31]] [Need: 00:09:35]
Epoch: [144][000/391] Time 0.513 (0.513) Data 0.411 (0.411) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:53:32]
Epoch: [144][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:53:48]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.030 Prec@5 99.540 Error@1 8.970
[resnet] :: 145/160 ----- [[2019-01-11 04:54:07]] [Need: 00:08:52]
Epoch: [145][000/391] Time 0.497 (0.497) Data 0.393 (0.393) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:54:07]
Epoch: [145][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0000 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:54:24]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 90.920 Prec@5 99.570 Error@1 9.080
[resnet] :: 146/160 ----- [[2019-01-11 04:54:43]] [Need: 00:08:21]
Epoch: [146][000/391] Time 0.497 (0.497) Data 0.397 (0.397) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:54:43]
Epoch: [146][200/391] Time 0.085 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:55:00]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.110 Prec@5 99.590 Error@1 8.890
[resnet] :: 147/160 ----- [[2019-01-11 04:55:18]] [Need: 00:07:43]
Epoch: [147][000/391] Time 0.491 (0.491) Data 0.393 (0.393) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:55:19]
Epoch: [147][200/391] Time 0.082 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:55:35]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.130 Prec@5 99.580 Error@1 8.870
[resnet] :: 148/160 ----- [[2019-01-11 04:55:54]] [Need: 00:07:09]
Epoch: [148][000/391] Time 0.548 (0.548) Data 0.449 (0.449) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:55:55]
Epoch: [148][200/391] Time 0.081 (0.085) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:56:11]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.030 Prec@5 99.570 Error@1 8.970
[resnet] :: 149/160 ----- [[2019-01-11 04:56:30]] [Need: 00:06:34]
Epoch: [149][000/391] Time 0.532 (0.532) Data 0.428 (0.428) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:56:31]
Epoch: [149][200/391] Time 0.081 (0.085) Data 0.000 (0.002) Loss 0.0004 (0.0004) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:56:47]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 90.980 Prec@5 99.520 Error@1 9.020
[resnet] :: 150/160 ----- [[2019-01-11 04:57:06]] [Need: 00:05:59]
Epoch: [150][000/391] Time 0.496 (0.496) Data 0.394 (0.394) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:57:06]
Epoch: [150][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0003 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:57:23]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.010 Prec@5 99.540 Error@1 8.990
[resnet] :: 151/160 ----- [[2019-01-11 04:57:41]] [Need: 00:05:16]
Epoch: [151][000/391] Time 0.499 (0.499) Data 0.407 (0.407) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:57:42]
Epoch: [151][200/391] Time 0.080 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:57:58]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.040 Prec@5 99.600 Error@1 8.960
[resnet] :: 152/160 ----- [[2019-01-11 04:58:17]] [Need: 00:04:43]
Epoch: [152][000/391] Time 0.553 (0.553) Data 0.455 (0.455) Loss 0.0000 (0.0000) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:58:17]
Epoch: [152][200/391] Time 0.078 (0.085) Data 0.000 (0.003) Loss 0.0002 (0.0003) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 04:58:34]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 90.990 Prec@5 99.570 Error@1 9.010
[resnet] :: 153/160 ----- [[2019-01-11 04:58:52]] [Need: 00:04:09]
Epoch: [153][000/391] Time 0.553 (0.553) Data 0.447 (0.447) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:58:53]
Epoch: [153][200/391] Time 0.084 (0.085) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:59:09]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.180 Prec@5 99.570 Error@1 8.820
[resnet] :: 154/160 ----- [[2019-01-11 04:59:28]] [Need: 00:03:36]
Epoch: [154][000/391] Time 0.564 (0.564) Data 0.467 (0.467) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:59:29]
Epoch: [154][200/391] Time 0.081 (0.085) Data 0.000 (0.003) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 04:59:45]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.050 Prec@5 99.570 Error@1 8.950
[resnet] :: 155/160 ----- [[2019-01-11 05:00:04]] [Need: 00:02:57]
Epoch: [155][000/391] Time 0.549 (0.549) Data 0.442 (0.442) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:00:04]
Epoch: [155][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:00:21]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 90.960 Prec@5 99.570 Error@1 9.040
[resnet] :: 156/160 ----- [[2019-01-11 05:00:40]] [Need: 00:02:23]
Epoch: [156][000/391] Time 0.483 (0.483) Data 0.386 (0.386) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:00:40]
Epoch: [156][200/391] Time 0.078 (0.084) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 05:00:57]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.270 Prec@5 99.540 Error@1 8.730
[resnet] :: 157/160 ----- [[2019-01-11 05:01:16]] [Need: 00:01:47]
Epoch: [157][000/391] Time 0.490 (0.490) Data 0.392 (0.392) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:01:16]
Epoch: [157][200/391] Time 0.082 (0.085) Data 0.000 (0.002) Loss 0.0002 (0.0002) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000) [2019-01-11 05:01:33]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 90.940 Prec@5 99.530 Error@1 9.060
[resnet] :: 158/160 ----- [[2019-01-11 05:01:51]] [Need: 00:01:11]
Epoch: [158][000/391] Time 0.494 (0.494) Data 0.395 (0.395) Loss 0.0001 (0.0001) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:01:52]
Epoch: [158][200/391] Time 0.082 (0.085) Data 0.000 (0.002) Loss 0.0001 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:02:08]
**Train** Prec@1 100.000 Prec@5 100.000 Error@1 0.000
**Test** Prec@1 91.120 Prec@5 99.540 Error@1 8.880
[resnet] :: 159/160 ----- [[2019-01-11 05:02:27]] [Need: 00:00:35]
Epoch: [159][000/391] Time 0.493 (0.493) Data 0.391 (0.391) Loss 0.0038 (0.0038) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:02:28]
Epoch: [159][200/391] Time 0.079 (0.084) Data 0.000 (0.002) Loss 0.0005 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000) [2019-01-11 05:02:44]
**Train** Prec@1 99.998 Prec@5 100.000 Error@1 0.002
**Test** Prec@1 91.130 Prec@5 99.560 Error@1 8.870