-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathresnet18.2019-01-11-4133.log
890 lines (890 loc) · 78.8 KB
/
resnet18.2019-01-11-4133.log
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
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 'resnet18'
=> Model : ResNet(
(conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=10, bias=True)
)
=> parameter : Namespace(arch='resnet18', batch_size=128, data='./', epochs=160, evaluate=False, lr=0.1, momentum=0.9, n_gpus=[0, 1], prefix='2019-01-11-4133', print_freq=200, resume='', save_dir='./', start_epoch=0, use_cuda=True, weight_decay=0.0001, workers=4)
[resnet18] :: 0/160 ----- [[2019-01-11 14:36:22]] [Need: 00:00:00]
Epoch: [0][0/391] Time 0.817 (0.817) Data 0.253 (0.253) Loss 2.4132 (2.4132) Prec@1 10.156 (10.156) Prec@5 49.219 (49.219)
Epoch: [0][200/391] Time 0.026 (0.030) Data 0.000 (0.001) Loss 1.9050 (2.5127) Prec@1 31.250 (14.544) Prec@5 90.625 (61.400)
Test: [0/79] Time 0.365 (0.365) Loss 1.7284 (1.7284) Prec@1 37.500 (37.500) Prec@5 89.844 (89.844)
* Prec@1 33.500 Prec@5 86.080 Error@1 66.500
[resnet18] :: 1/160 ----- [[2019-01-11 14:36:35]] [Need: 00:33:23]
Epoch: [1][0/391] Time 0.270 (0.270) Data 0.241 (0.241) Loss 1.6596 (1.6596) Prec@1 36.719 (36.719) Prec@5 86.719 (86.719)
Epoch: [1][200/391] Time 0.026 (0.027) Data 0.000 (0.001) Loss 1.3381 (1.4959) Prec@1 50.781 (44.325) Prec@5 92.969 (91.465)
Test: [0/79] Time 0.325 (0.325) Loss 1.3131 (1.3131) Prec@1 51.562 (51.562) Prec@5 95.312 (95.312)
* Prec@1 52.450 Prec@5 93.400 Error@1 47.550
[resnet18] :: 2/160 ----- [[2019-01-11 14:36:47]] [Need: 00:31:24]
Epoch: [2][0/391] Time 0.280 (0.280) Data 0.250 (0.250) Loss 1.3584 (1.3584) Prec@1 48.438 (48.438) Prec@5 92.969 (92.969)
Epoch: [2][200/391] Time 0.026 (0.027) Data 0.000 (0.001) Loss 0.8559 (1.1127) Prec@1 71.094 (60.191) Prec@5 98.438 (95.655)
Test: [0/79] Time 0.337 (0.337) Loss 0.8551 (0.8551) Prec@1 69.531 (69.531) Prec@5 94.531 (94.531)
* Prec@1 65.050 Prec@5 97.190 Error@1 34.950
[resnet18] :: 3/160 ----- [[2019-01-11 14:36:59]] [Need: 00:31:19]
Epoch: [3][0/391] Time 0.322 (0.322) Data 0.289 (0.289) Loss 0.8649 (0.8649) Prec@1 74.219 (74.219) Prec@5 96.875 (96.875)
Epoch: [3][200/391] Time 0.026 (0.028) Data 0.000 (0.002) Loss 0.9513 (0.8817) Prec@1 68.750 (68.968) Prec@5 95.312 (97.442)
Test: [0/79] Time 0.308 (0.308) Loss 0.7973 (0.7973) Prec@1 74.219 (74.219) Prec@5 96.875 (96.875)
* Prec@1 69.830 Prec@5 97.640 Error@1 30.170
[resnet18] :: 4/160 ----- [[2019-01-11 14:37:11]] [Need: 00:31:10]
Epoch: [4][0/391] Time 0.290 (0.290) Data 0.258 (0.258) Loss 0.7088 (0.7088) Prec@1 78.906 (78.906) Prec@5 97.656 (97.656)
Epoch: [4][200/391] Time 0.026 (0.028) Data 0.000 (0.001) Loss 0.7484 (0.7239) Prec@1 75.000 (74.883) Prec@5 95.312 (98.270)
Test: [0/79] Time 0.263 (0.263) Loss 0.7675 (0.7675) Prec@1 75.781 (75.781) Prec@5 98.438 (98.438)
* Prec@1 70.690 Prec@5 97.490 Error@1 29.310
[resnet18] :: 5/160 ----- [[2019-01-11 14:37:23]] [Need: 00:30:53]
Epoch: [5][0/391] Time 0.271 (0.271) Data 0.241 (0.241) Loss 0.5721 (0.5721) Prec@1 79.688 (79.688) Prec@5 99.219 (99.219)
Epoch: [5][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.5882 (0.6146) Prec@1 78.125 (78.463) Prec@5 99.219 (98.776)
Test: [0/79] Time 0.316 (0.316) Loss 0.6233 (0.6233) Prec@1 80.469 (80.469) Prec@5 97.656 (97.656)
* Prec@1 75.110 Prec@5 98.150 Error@1 24.890
[resnet18] :: 6/160 ----- [[2019-01-11 14:37:35]] [Need: 00:30:50]
Epoch: [6][0/391] Time 0.297 (0.297) Data 0.267 (0.267) Loss 0.5686 (0.5686) Prec@1 80.469 (80.469) Prec@5 100.000 (100.000)
Epoch: [6][200/391] Time 0.028 (0.028) Data 0.000 (0.002) Loss 0.6283 (0.5340) Prec@1 78.125 (81.386) Prec@5 97.656 (99.129)
Test: [0/79] Time 0.361 (0.361) Loss 0.6413 (0.6413) Prec@1 82.812 (82.812) Prec@5 97.656 (97.656)
* Prec@1 78.280 Prec@5 98.680 Error@1 21.720
[resnet18] :: 7/160 ----- [[2019-01-11 14:37:47]] [Need: 00:30:48]
Epoch: [7][0/391] Time 0.269 (0.269) Data 0.238 (0.238) Loss 0.5035 (0.5035) Prec@1 84.375 (84.375) Prec@5 100.000 (100.000)
Epoch: [7][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.5745 (0.4657) Prec@1 76.562 (83.881) Prec@5 100.000 (99.289)
Test: [0/79] Time 0.321 (0.321) Loss 0.6837 (0.6837) Prec@1 77.344 (77.344) Prec@5 98.438 (98.438)
* Prec@1 78.870 Prec@5 98.720 Error@1 21.130
[resnet18] :: 8/160 ----- [[2019-01-11 14:37:59]] [Need: 00:30:22]
Epoch: [8][0/391] Time 0.284 (0.284) Data 0.254 (0.254) Loss 0.4734 (0.4734) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
Epoch: [8][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.2324 (0.4151) Prec@1 92.188 (85.389) Prec@5 100.000 (99.394)
Test: [0/79] Time 0.338 (0.338) Loss 0.6425 (0.6425) Prec@1 83.594 (83.594) Prec@5 97.656 (97.656)
* Prec@1 78.780 Prec@5 98.660 Error@1 21.220
[resnet18] :: 9/160 ----- [[2019-01-11 14:38:11]] [Need: 00:29:40]
Epoch: [9][0/391] Time 0.305 (0.305) Data 0.275 (0.275) Loss 0.2413 (0.2413) Prec@1 92.188 (92.188) Prec@5 100.000 (100.000)
Epoch: [9][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.3746 (0.3620) Prec@1 86.719 (87.488) Prec@5 100.000 (99.561)
Test: [0/79] Time 0.263 (0.263) Loss 0.5051 (0.5051) Prec@1 84.375 (84.375) Prec@5 99.219 (99.219)
* Prec@1 80.010 Prec@5 98.880 Error@1 19.990
[resnet18] :: 10/160 ----- [[2019-01-11 14:38:23]] [Need: 00:29:58]
Epoch: [10][0/391] Time 0.277 (0.277) Data 0.245 (0.245) Loss 0.3201 (0.3201) Prec@1 92.188 (92.188) Prec@5 100.000 (100.000)
Epoch: [10][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.3000 (0.3186) Prec@1 89.844 (88.872) Prec@5 99.219 (99.712)
Test: [0/79] Time 0.336 (0.336) Loss 0.7207 (0.7207) Prec@1 78.906 (78.906) Prec@5 96.875 (96.875)
* Prec@1 79.230 Prec@5 98.660 Error@1 20.770
[resnet18] :: 11/160 ----- [[2019-01-11 14:38:34]] [Need: 00:29:13]
Epoch: [11][0/391] Time 0.311 (0.311) Data 0.283 (0.283) Loss 0.2327 (0.2327) Prec@1 91.406 (91.406) Prec@5 100.000 (100.000)
Epoch: [11][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.3308 (0.2862) Prec@1 89.062 (90.229) Prec@5 100.000 (99.767)
Test: [0/79] Time 0.311 (0.311) Loss 0.4883 (0.4883) Prec@1 87.500 (87.500) Prec@5 98.438 (98.438)
* Prec@1 80.710 Prec@5 98.930 Error@1 19.290
[resnet18] :: 12/160 ----- [[2019-01-11 14:38:47]] [Need: 00:29:59]
Epoch: [12][0/391] Time 0.315 (0.315) Data 0.285 (0.285) Loss 0.2361 (0.2361) Prec@1 91.406 (91.406) Prec@5 100.000 (100.000)
Epoch: [12][200/391] Time 0.026 (0.028) Data 0.000 (0.002) Loss 0.2244 (0.2587) Prec@1 93.750 (91.161) Prec@5 100.000 (99.821)
Test: [0/79] Time 0.324 (0.324) Loss 0.4042 (0.4042) Prec@1 87.500 (87.500) Prec@5 99.219 (99.219)
* Prec@1 81.240 Prec@5 99.090 Error@1 18.760
[resnet18] :: 13/160 ----- [[2019-01-11 14:38:59]] [Need: 00:29:42]
Epoch: [13][0/391] Time 0.297 (0.297) Data 0.270 (0.270) Loss 0.2233 (0.2233) Prec@1 92.188 (92.188) Prec@5 99.219 (99.219)
Epoch: [13][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1517 (0.2290) Prec@1 94.531 (92.114) Prec@5 100.000 (99.872)
Test: [0/79] Time 0.314 (0.314) Loss 0.5702 (0.5702) Prec@1 82.812 (82.812) Prec@5 98.438 (98.438)
* Prec@1 79.550 Prec@5 99.090 Error@1 20.450
[resnet18] :: 14/160 ----- [[2019-01-11 14:39:11]] [Need: 00:28:41]
Epoch: [14][0/391] Time 0.301 (0.301) Data 0.269 (0.269) Loss 0.1408 (0.1408) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000)
Epoch: [14][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.2755 (0.2102) Prec@1 92.188 (92.712) Prec@5 100.000 (99.880)
Test: [0/79] Time 0.334 (0.334) Loss 0.5972 (0.5972) Prec@1 82.812 (82.812) Prec@5 98.438 (98.438)
* Prec@1 80.290 Prec@5 98.880 Error@1 19.710
[resnet18] :: 15/160 ----- [[2019-01-11 14:39:22]] [Need: 00:28:33]
Epoch: [15][0/391] Time 0.301 (0.301) Data 0.282 (0.282) Loss 0.2027 (0.2027) Prec@1 93.750 (93.750) Prec@5 100.000 (100.000)
Epoch: [15][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1783 (0.1925) Prec@1 93.750 (93.272) Prec@5 100.000 (99.934)
Test: [0/79] Time 0.347 (0.347) Loss 0.4485 (0.4485) Prec@1 87.500 (87.500) Prec@5 99.219 (99.219)
* Prec@1 80.780 Prec@5 98.980 Error@1 19.220
[resnet18] :: 16/160 ----- [[2019-01-11 14:39:34]] [Need: 00:28:27]
Epoch: [16][0/391] Time 0.311 (0.311) Data 0.277 (0.277) Loss 0.1775 (0.1775) Prec@1 91.406 (91.406) Prec@5 100.000 (100.000)
Epoch: [16][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1175 (0.1796) Prec@1 95.312 (93.956) Prec@5 100.000 (99.911)
Test: [0/79] Time 0.314 (0.314) Loss 0.6244 (0.6244) Prec@1 81.250 (81.250) Prec@5 98.438 (98.438)
* Prec@1 81.070 Prec@5 98.650 Error@1 18.930
[resnet18] :: 17/160 ----- [[2019-01-11 14:39:46]] [Need: 00:28:08]
Epoch: [17][0/391] Time 0.298 (0.298) Data 0.271 (0.271) Loss 0.1383 (0.1383) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000)
Epoch: [17][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.2282 (0.1625) Prec@1 91.406 (94.349) Prec@5 100.000 (99.911)
Test: [0/79] Time 0.352 (0.352) Loss 0.6469 (0.6469) Prec@1 83.594 (83.594) Prec@5 98.438 (98.438)
* Prec@1 81.210 Prec@5 98.810 Error@1 18.790
[resnet18] :: 18/160 ----- [[2019-01-11 14:39:58]] [Need: 00:28:05]
Epoch: [18][0/391] Time 0.300 (0.300) Data 0.269 (0.269) Loss 0.0828 (0.0828) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [18][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1698 (0.1532) Prec@1 95.312 (94.733) Prec@5 100.000 (99.965)
Test: [0/79] Time 0.310 (0.310) Loss 0.7372 (0.7372) Prec@1 77.344 (77.344) Prec@5 99.219 (99.219)
* Prec@1 81.160 Prec@5 98.890 Error@1 18.840
[resnet18] :: 19/160 ----- [[2019-01-11 14:40:10]] [Need: 00:27:46]
Epoch: [19][0/391] Time 0.316 (0.316) Data 0.290 (0.290) Loss 0.0983 (0.0983) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [19][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.2830 (0.1425) Prec@1 90.625 (95.002) Prec@5 100.000 (99.953)
Test: [0/79] Time 0.337 (0.337) Loss 0.4954 (0.4954) Prec@1 86.719 (86.719) Prec@5 98.438 (98.438)
* Prec@1 81.640 Prec@5 98.960 Error@1 18.360
[resnet18] :: 20/160 ----- [[2019-01-11 14:40:22]] [Need: 00:28:25]
Epoch: [20][0/391] Time 0.283 (0.283) Data 0.247 (0.247) Loss 0.1160 (0.1160) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000)
Epoch: [20][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1430 (0.1317) Prec@1 94.531 (95.367) Prec@5 100.000 (99.973)
Test: [0/79] Time 0.323 (0.323) Loss 0.6613 (0.6613) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 81.340 Prec@5 99.000 Error@1 18.660
[resnet18] :: 21/160 ----- [[2019-01-11 14:40:34]] [Need: 00:27:21]
Epoch: [21][0/391] Time 0.271 (0.271) Data 0.251 (0.251) Loss 0.1395 (0.1395) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000)
Epoch: [21][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1105 (0.1286) Prec@1 97.656 (95.658) Prec@5 100.000 (99.981)
Test: [0/79] Time 0.311 (0.311) Loss 0.5869 (0.5869) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 81.110 Prec@5 98.900 Error@1 18.890
[resnet18] :: 22/160 ----- [[2019-01-11 14:40:45]] [Need: 00:27:09]
Epoch: [22][0/391] Time 0.293 (0.293) Data 0.265 (0.265) Loss 0.0407 (0.0407) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [22][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0654 (0.1290) Prec@1 98.438 (95.359) Prec@5 100.000 (99.981)
Test: [0/79] Time 0.312 (0.312) Loss 0.5487 (0.5487) Prec@1 85.156 (85.156) Prec@5 100.000 (100.000)
* Prec@1 82.250 Prec@5 98.960 Error@1 17.750
[resnet18] :: 23/160 ----- [[2019-01-11 14:40:58]] [Need: 00:27:45]
Epoch: [23][0/391] Time 0.301 (0.301) Data 0.271 (0.271) Loss 0.1469 (0.1469) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000)
Epoch: [23][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1509 (0.1177) Prec@1 96.094 (95.853) Prec@5 100.000 (99.988)
Test: [0/79] Time 0.341 (0.341) Loss 0.5783 (0.5783) Prec@1 85.938 (85.938) Prec@5 96.875 (96.875)
* Prec@1 82.080 Prec@5 99.010 Error@1 17.920
[resnet18] :: 24/160 ----- [[2019-01-11 14:41:09]] [Need: 00:26:51]
Epoch: [24][0/391] Time 0.283 (0.283) Data 0.262 (0.262) Loss 0.0413 (0.0413) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [24][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0870 (0.1149) Prec@1 96.094 (95.950) Prec@5 100.000 (99.961)
Test: [0/79] Time 0.337 (0.337) Loss 0.5780 (0.5780) Prec@1 83.594 (83.594) Prec@5 98.438 (98.438)
* Prec@1 81.480 Prec@5 98.880 Error@1 18.520
[resnet18] :: 25/160 ----- [[2019-01-11 14:41:21]] [Need: 00:26:36]
Epoch: [25][0/391] Time 0.291 (0.291) Data 0.261 (0.261) Loss 0.0704 (0.0704) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000)
Epoch: [25][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1529 (0.1127) Prec@1 94.531 (96.078) Prec@5 100.000 (99.988)
Test: [0/79] Time 0.323 (0.323) Loss 0.5859 (0.5859) Prec@1 83.594 (83.594) Prec@5 97.656 (97.656)
* Prec@1 82.290 Prec@5 98.920 Error@1 17.710
[resnet18] :: 26/160 ----- [[2019-01-11 14:41:33]] [Need: 00:27:10]
Epoch: [26][0/391] Time 0.307 (0.307) Data 0.277 (0.277) Loss 0.0898 (0.0898) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [26][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1213 (0.1095) Prec@1 97.656 (96.273) Prec@5 100.000 (99.984)
Test: [0/79] Time 0.321 (0.321) Loss 0.5797 (0.5797) Prec@1 85.156 (85.156) Prec@5 98.438 (98.438)
* Prec@1 81.090 Prec@5 98.550 Error@1 18.910
[resnet18] :: 27/160 ----- [[2019-01-11 14:41:45]] [Need: 00:26:15]
Epoch: [27][0/391] Time 0.279 (0.279) Data 0.248 (0.248) Loss 0.0830 (0.0830) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000)
Epoch: [27][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1034 (0.1015) Prec@1 97.656 (96.475) Prec@5 100.000 (99.977)
Test: [0/79] Time 0.329 (0.329) Loss 0.4929 (0.4929) Prec@1 85.938 (85.938) Prec@5 99.219 (99.219)
* Prec@1 82.300 Prec@5 98.760 Error@1 17.700
[resnet18] :: 28/160 ----- [[2019-01-11 14:41:57]] [Need: 00:26:43]
Epoch: [28][0/391] Time 0.278 (0.278) Data 0.250 (0.250) Loss 0.1356 (0.1356) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000)
Epoch: [28][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0656 (0.0972) Prec@1 97.656 (96.685) Prec@5 100.000 (99.973)
Test: [0/79] Time 0.326 (0.326) Loss 0.7309 (0.7309) Prec@1 85.938 (85.938) Prec@5 96.875 (96.875)
* Prec@1 81.400 Prec@5 98.860 Error@1 18.600
[resnet18] :: 29/160 ----- [[2019-01-11 14:42:09]] [Need: 00:25:47]
Epoch: [29][0/391] Time 0.304 (0.304) Data 0.270 (0.270) Loss 0.0310 (0.0310) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [29][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1629 (0.0970) Prec@1 96.094 (96.696) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.331 (0.331) Loss 0.5488 (0.5488) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
* Prec@1 81.790 Prec@5 98.690 Error@1 18.210
[resnet18] :: 30/160 ----- [[2019-01-11 14:42:21]] [Need: 00:25:41]
Epoch: [30][0/391] Time 0.283 (0.283) Data 0.255 (0.255) Loss 0.0829 (0.0829) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [30][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0350 (0.0919) Prec@1 98.438 (96.758) Prec@5 100.000 (99.977)
Test: [0/79] Time 0.314 (0.314) Loss 0.5172 (0.5172) Prec@1 86.719 (86.719) Prec@5 99.219 (99.219)
* Prec@1 82.850 Prec@5 98.930 Error@1 17.150
[resnet18] :: 31/160 ----- [[2019-01-11 14:42:33]] [Need: 00:26:00]
Epoch: [31][0/391] Time 0.308 (0.308) Data 0.276 (0.276) Loss 0.0292 (0.0292) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [31][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0630 (0.0966) Prec@1 97.656 (96.762) Prec@5 100.000 (99.984)
Test: [0/79] Time 0.322 (0.322) Loss 0.4290 (0.4290) Prec@1 87.500 (87.500) Prec@5 99.219 (99.219)
* Prec@1 81.800 Prec@5 98.950 Error@1 18.200
[resnet18] :: 32/160 ----- [[2019-01-11 14:42:45]] [Need: 00:25:12]
Epoch: [32][0/391] Time 0.298 (0.298) Data 0.270 (0.270) Loss 0.0949 (0.0949) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000)
Epoch: [32][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1439 (0.0903) Prec@1 96.875 (96.926) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.321 (0.321) Loss 0.4854 (0.4854) Prec@1 85.938 (85.938) Prec@5 100.000 (100.000)
* Prec@1 82.060 Prec@5 98.730 Error@1 17.940
[resnet18] :: 33/160 ----- [[2019-01-11 14:42:57]] [Need: 00:25:05]
Epoch: [33][0/391] Time 0.271 (0.271) Data 0.245 (0.245) Loss 0.0550 (0.0550) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000)
Epoch: [33][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1620 (0.0853) Prec@1 94.531 (97.120) Prec@5 100.000 (99.996)
Test: [0/79] Time 0.311 (0.311) Loss 0.7296 (0.7296) Prec@1 79.688 (79.688) Prec@5 98.438 (98.438)
* Prec@1 81.800 Prec@5 98.670 Error@1 18.200
[resnet18] :: 34/160 ----- [[2019-01-11 14:43:09]] [Need: 00:24:51]
Epoch: [34][0/391] Time 0.318 (0.318) Data 0.283 (0.283) Loss 0.0227 (0.0227) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [34][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0811 (0.0849) Prec@1 96.875 (97.205) Prec@5 100.000 (99.984)
Test: [0/79] Time 0.329 (0.329) Loss 0.6233 (0.6233) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 82.340 Prec@5 98.860 Error@1 17.660
[resnet18] :: 35/160 ----- [[2019-01-11 14:43:21]] [Need: 00:24:44]
Epoch: [35][0/391] Time 0.318 (0.318) Data 0.289 (0.289) Loss 0.0341 (0.0341) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000)
Epoch: [35][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1198 (0.0850) Prec@1 95.312 (96.988) Prec@5 100.000 (99.988)
Test: [0/79] Time 0.319 (0.319) Loss 0.6217 (0.6217) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 81.820 Prec@5 98.620 Error@1 18.180
[resnet18] :: 36/160 ----- [[2019-01-11 14:43:33]] [Need: 00:24:35]
Epoch: [36][0/391] Time 0.295 (0.295) Data 0.269 (0.269) Loss 0.0529 (0.0529) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [36][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0396 (0.0850) Prec@1 98.438 (97.027) Prec@5 100.000 (99.996)
Test: [0/79] Time 0.321 (0.321) Loss 0.6533 (0.6533) Prec@1 84.375 (84.375) Prec@5 99.219 (99.219)
* Prec@1 83.040 Prec@5 98.890 Error@1 16.960
[resnet18] :: 37/160 ----- [[2019-01-11 14:43:45]] [Need: 00:24:51]
Epoch: [37][0/391] Time 0.328 (0.328) Data 0.299 (0.299) Loss 0.0740 (0.0740) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000)
Epoch: [37][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1323 (0.0851) Prec@1 96.875 (97.108) Prec@5 100.000 (99.988)
Test: [0/79] Time 0.335 (0.335) Loss 0.5039 (0.5039) Prec@1 87.500 (87.500) Prec@5 98.438 (98.438)
* Prec@1 82.460 Prec@5 98.820 Error@1 17.540
[resnet18] :: 38/160 ----- [[2019-01-11 14:43:57]] [Need: 00:24:09]
Epoch: [38][0/391] Time 0.300 (0.300) Data 0.267 (0.267) Loss 0.1011 (0.1011) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000)
Epoch: [38][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0596 (0.0860) Prec@1 97.656 (97.065) Prec@5 100.000 (99.984)
Test: [0/79] Time 0.313 (0.313) Loss 0.7878 (0.7878) Prec@1 79.688 (79.688) Prec@5 98.438 (98.438)
* Prec@1 81.270 Prec@5 98.700 Error@1 18.730
[resnet18] :: 39/160 ----- [[2019-01-11 14:44:08]] [Need: 00:23:50]
Epoch: [39][0/391] Time 0.300 (0.300) Data 0.270 (0.270) Loss 0.0694 (0.0694) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000)
Epoch: [39][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0791 (0.0914) Prec@1 98.438 (96.770) Prec@5 100.000 (99.996)
Test: [0/79] Time 0.321 (0.321) Loss 0.5995 (0.5995) Prec@1 84.375 (84.375) Prec@5 98.438 (98.438)
* Prec@1 82.620 Prec@5 98.720 Error@1 17.380
[resnet18] :: 40/160 ----- [[2019-01-11 14:44:20]] [Need: 00:23:41]
Epoch: [40][0/391] Time 0.269 (0.269) Data 0.243 (0.243) Loss 0.0620 (0.0620) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000)
Epoch: [40][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1161 (0.0908) Prec@1 96.094 (96.894) Prec@5 100.000 (99.988)
Test: [0/79] Time 0.312 (0.312) Loss 0.5608 (0.5608) Prec@1 87.500 (87.500) Prec@5 99.219 (99.219)
* Prec@1 82.910 Prec@5 98.870 Error@1 17.090
[resnet18] :: 41/160 ----- [[2019-01-11 14:44:32]] [Need: 00:23:27]
Epoch: [41][0/391] Time 0.282 (0.282) Data 0.252 (0.252) Loss 0.0344 (0.0344) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [41][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0588 (0.0797) Prec@1 97.656 (97.295) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.318 (0.318) Loss 0.3878 (0.3878) Prec@1 87.500 (87.500) Prec@5 100.000 (100.000)
* Prec@1 81.610 Prec@5 98.760 Error@1 18.390
[resnet18] :: 42/160 ----- [[2019-01-11 14:44:44]] [Need: 00:23:16]
Epoch: [42][0/391] Time 0.323 (0.323) Data 0.295 (0.295) Loss 0.0857 (0.0857) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000)
Epoch: [42][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0994 (0.0802) Prec@1 97.656 (97.279) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.332 (0.332) Loss 0.6241 (0.6241) Prec@1 85.938 (85.938) Prec@5 99.219 (99.219)
* Prec@1 82.120 Prec@5 98.750 Error@1 17.880
[resnet18] :: 43/160 ----- [[2019-01-11 14:44:56]] [Need: 00:23:07]
Epoch: [43][0/391] Time 0.299 (0.299) Data 0.270 (0.270) Loss 0.0512 (0.0512) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [43][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0857 (0.0871) Prec@1 97.656 (96.933) Prec@5 100.000 (99.996)
Test: [0/79] Time 0.310 (0.310) Loss 0.6700 (0.6700) Prec@1 84.375 (84.375) Prec@5 97.656 (97.656)
* Prec@1 82.090 Prec@5 98.630 Error@1 17.910
[resnet18] :: 44/160 ----- [[2019-01-11 14:45:08]] [Need: 00:22:54]
Epoch: [44][0/391] Time 0.260 (0.260) Data 0.238 (0.238) Loss 0.0616 (0.0616) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [44][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0578 (0.0716) Prec@1 98.438 (97.497) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.329 (0.329) Loss 0.7689 (0.7689) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 82.380 Prec@5 98.820 Error@1 17.620
[resnet18] :: 45/160 ----- [[2019-01-11 14:45:19]] [Need: 00:22:39]
Epoch: [45][0/391] Time 0.304 (0.304) Data 0.281 (0.281) Loss 0.0594 (0.0594) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [45][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1055 (0.0818) Prec@1 95.312 (97.151) Prec@5 100.000 (99.996)
Test: [0/79] Time 0.349 (0.349) Loss 0.6497 (0.6497) Prec@1 82.031 (82.031) Prec@5 99.219 (99.219)
* Prec@1 81.660 Prec@5 98.710 Error@1 18.340
[resnet18] :: 46/160 ----- [[2019-01-11 14:45:31]] [Need: 00:22:33]
Epoch: [46][0/391] Time 0.300 (0.300) Data 0.271 (0.271) Loss 0.0584 (0.0584) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [46][200/391] Time 0.028 (0.028) Data 0.000 (0.002) Loss 0.0339 (0.0799) Prec@1 98.438 (97.318) Prec@5 100.000 (99.984)
Test: [0/79] Time 0.314 (0.314) Loss 0.7212 (0.7212) Prec@1 82.812 (82.812) Prec@5 98.438 (98.438)
* Prec@1 81.460 Prec@5 98.800 Error@1 18.540
[resnet18] :: 47/160 ----- [[2019-01-11 14:45:43]] [Need: 00:22:15]
Epoch: [47][0/391] Time 0.308 (0.308) Data 0.277 (0.277) Loss 0.0304 (0.0304) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [47][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0257 (0.0713) Prec@1 100.000 (97.575) Prec@5 100.000 (99.988)
Test: [0/79] Time 0.321 (0.321) Loss 0.6193 (0.6193) Prec@1 82.812 (82.812) Prec@5 97.656 (97.656)
* Prec@1 81.790 Prec@5 98.820 Error@1 18.210
[resnet18] :: 48/160 ----- [[2019-01-11 14:45:55]] [Need: 00:22:09]
Epoch: [48][0/391] Time 0.301 (0.301) Data 0.266 (0.266) Loss 0.1230 (0.1230) Prec@1 95.312 (95.312) Prec@5 100.000 (100.000)
Epoch: [48][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1832 (0.0851) Prec@1 92.188 (97.155) Prec@5 100.000 (99.981)
Test: [0/79] Time 0.314 (0.314) Loss 0.6430 (0.6430) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 82.240 Prec@5 98.980 Error@1 17.760
[resnet18] :: 49/160 ----- [[2019-01-11 14:46:07]] [Need: 00:21:52]
Epoch: [49][0/391] Time 0.311 (0.311) Data 0.282 (0.282) Loss 0.0126 (0.0126) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [49][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0721 (0.0719) Prec@1 98.438 (97.493) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.317 (0.317) Loss 0.6165 (0.6165) Prec@1 82.812 (82.812) Prec@5 97.656 (97.656)
* Prec@1 82.760 Prec@5 98.920 Error@1 17.240
[resnet18] :: 50/160 ----- [[2019-01-11 14:46:19]] [Need: 00:21:40]
Epoch: [50][0/391] Time 0.275 (0.275) Data 0.247 (0.247) Loss 0.0145 (0.0145) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [50][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.1111 (0.0757) Prec@1 96.875 (97.322) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.359 (0.359) Loss 0.6528 (0.6528) Prec@1 80.469 (80.469) Prec@5 100.000 (100.000)
* Prec@1 82.530 Prec@5 98.800 Error@1 17.470
[resnet18] :: 51/160 ----- [[2019-01-11 14:46:30]] [Need: 00:21:31]
Epoch: [51][0/391] Time 0.285 (0.285) Data 0.254 (0.254) Loss 0.0458 (0.0458) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [51][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0282 (0.0758) Prec@1 99.219 (97.431) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.320 (0.320) Loss 0.7327 (0.7327) Prec@1 81.250 (81.250) Prec@5 99.219 (99.219)
* Prec@1 81.120 Prec@5 98.830 Error@1 18.880
[resnet18] :: 52/160 ----- [[2019-01-11 14:46:42]] [Need: 00:21:17]
Epoch: [52][0/391] Time 0.306 (0.306) Data 0.277 (0.277) Loss 0.0353 (0.0353) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [52][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1051 (0.0819) Prec@1 95.312 (97.151) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.334 (0.334) Loss 0.6491 (0.6491) Prec@1 79.688 (79.688) Prec@5 99.219 (99.219)
* Prec@1 82.020 Prec@5 98.810 Error@1 17.980
[resnet18] :: 53/160 ----- [[2019-01-11 14:46:54]] [Need: 00:21:08]
Epoch: [53][0/391] Time 0.298 (0.298) Data 0.270 (0.270) Loss 0.0607 (0.0607) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [53][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0601 (0.0737) Prec@1 96.875 (97.396) Prec@5 100.000 (99.996)
Test: [0/79] Time 0.325 (0.325) Loss 0.6966 (0.6966) Prec@1 85.156 (85.156) Prec@5 97.656 (97.656)
* Prec@1 81.870 Prec@5 98.910 Error@1 18.130
[resnet18] :: 54/160 ----- [[2019-01-11 14:47:06]] [Need: 00:20:53]
Epoch: [54][0/391] Time 0.297 (0.297) Data 0.270 (0.270) Loss 0.0505 (0.0505) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000)
Epoch: [54][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.1778 (0.0802) Prec@1 94.531 (97.365) Prec@5 100.000 (99.984)
Test: [0/79] Time 0.317 (0.317) Loss 0.8938 (0.8938) Prec@1 76.562 (76.562) Prec@5 97.656 (97.656)
* Prec@1 80.890 Prec@5 98.490 Error@1 19.110
[resnet18] :: 55/160 ----- [[2019-01-11 14:47:18]] [Need: 00:20:43]
Epoch: [55][0/391] Time 0.301 (0.301) Data 0.270 (0.270) Loss 0.1005 (0.1005) Prec@1 96.094 (96.094) Prec@5 100.000 (100.000)
Epoch: [55][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0259 (0.0796) Prec@1 99.219 (97.167) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.312 (0.312) Loss 0.7518 (0.7518) Prec@1 81.250 (81.250) Prec@5 98.438 (98.438)
* Prec@1 81.930 Prec@5 98.510 Error@1 18.070
[resnet18] :: 56/160 ----- [[2019-01-11 14:47:30]] [Need: 00:20:34]
Epoch: [56][0/391] Time 0.308 (0.308) Data 0.279 (0.279) Loss 0.0358 (0.0358) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [56][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0989 (0.0745) Prec@1 96.094 (97.489) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.319 (0.319) Loss 0.8962 (0.8962) Prec@1 80.469 (80.469) Prec@5 98.438 (98.438)
* Prec@1 81.630 Prec@5 98.790 Error@1 18.370
[resnet18] :: 57/160 ----- [[2019-01-11 14:47:42]] [Need: 00:20:19]
Epoch: [57][0/391] Time 0.307 (0.307) Data 0.277 (0.277) Loss 0.1202 (0.1202) Prec@1 94.531 (94.531) Prec@5 100.000 (100.000)
Epoch: [57][200/391] Time 0.029 (0.028) Data 0.000 (0.002) Loss 0.0248 (0.0689) Prec@1 99.219 (97.606) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.338 (0.338) Loss 0.6062 (0.6062) Prec@1 85.938 (85.938) Prec@5 100.000 (100.000)
* Prec@1 81.420 Prec@5 98.820 Error@1 18.580
[resnet18] :: 58/160 ----- [[2019-01-11 14:47:53]] [Need: 00:20:09]
Epoch: [58][0/391] Time 0.296 (0.296) Data 0.267 (0.267) Loss 0.1094 (0.1094) Prec@1 97.656 (97.656) Prec@5 100.000 (100.000)
Epoch: [58][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0847 (0.0730) Prec@1 96.875 (97.477) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.332 (0.332) Loss 0.6504 (0.6504) Prec@1 84.375 (84.375) Prec@5 99.219 (99.219)
* Prec@1 81.740 Prec@5 98.850 Error@1 18.260
[resnet18] :: 59/160 ----- [[2019-01-11 14:48:05]] [Need: 00:19:55]
Epoch: [59][0/391] Time 0.314 (0.314) Data 0.285 (0.285) Loss 0.0829 (0.0829) Prec@1 96.875 (96.875) Prec@5 100.000 (100.000)
Epoch: [59][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0838 (0.0720) Prec@1 96.094 (97.555) Prec@5 100.000 (99.992)
Test: [0/79] Time 0.326 (0.326) Loss 0.7264 (0.7264) Prec@1 82.031 (82.031) Prec@5 98.438 (98.438)
* Prec@1 82.520 Prec@5 99.060 Error@1 17.480
[resnet18] :: 60/160 ----- [[2019-01-11 14:48:17]] [Need: 00:19:45]
Epoch: [60][0/391] Time 0.307 (0.307) Data 0.279 (0.279) Loss 0.0496 (0.0496) Prec@1 98.438 (98.438) Prec@5 100.000 (100.000)
Epoch: [60][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0145 (0.0368) Prec@1 100.000 (98.807) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.323 (0.323) Loss 0.6172 (0.6172) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 85.650 Prec@5 99.330 Error@1 14.350
[resnet18] :: 61/160 ----- [[2019-01-11 14:48:29]] [Need: 00:20:03]
Epoch: [61][0/391] Time 0.273 (0.273) Data 0.245 (0.245) Loss 0.0130 (0.0130) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [61][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0068 (0.0134) Prec@1 100.000 (99.677) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.322 (0.322) Loss 0.6268 (0.6268) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 85.870 Prec@5 99.350 Error@1 14.130
[resnet18] :: 62/160 ----- [[2019-01-11 14:48:41]] [Need: 00:19:49]
Epoch: [62][0/391] Time 0.286 (0.286) Data 0.256 (0.256) Loss 0.0056 (0.0056) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [62][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0077 (0.0072) Prec@1 100.000 (99.860) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.333 (0.333) Loss 0.6328 (0.6328) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 85.910 Prec@5 99.350 Error@1 14.090
[resnet18] :: 63/160 ----- [[2019-01-11 14:48:54]] [Need: 00:19:38]
Epoch: [63][0/391] Time 0.296 (0.296) Data 0.266 (0.266) Loss 0.0161 (0.0161) Prec@1 99.219 (99.219) Prec@5 100.000 (100.000)
Epoch: [63][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0038 (0.0055) Prec@1 100.000 (99.903) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.327 (0.327) Loss 0.6514 (0.6514) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 85.940 Prec@5 99.310 Error@1 14.060
[resnet18] :: 64/160 ----- [[2019-01-11 14:49:06]] [Need: 00:19:24]
Epoch: [64][0/391] Time 0.298 (0.298) Data 0.269 (0.269) Loss 0.0051 (0.0051) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [64][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0031 (0.0040) Prec@1 100.000 (99.957) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.350 (0.350) Loss 0.6381 (0.6381) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.040 Prec@5 99.300 Error@1 13.960
[resnet18] :: 65/160 ----- [[2019-01-11 14:49:18]] [Need: 00:19:16]
Epoch: [65][0/391] Time 0.306 (0.306) Data 0.274 (0.274) Loss 0.0014 (0.0014) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [65][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0092 (0.0032) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.323 (0.323) Loss 0.6541 (0.6541) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.010 Prec@5 99.310 Error@1 13.990
[resnet18] :: 66/160 ----- [[2019-01-11 14:49:30]] [Need: 00:18:34]
Epoch: [66][0/391] Time 0.307 (0.307) Data 0.272 (0.272) Loss 0.0023 (0.0023) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [66][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0010 (0.0024) Prec@1 100.000 (99.984) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.330 (0.330) Loss 0.6827 (0.6827) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.150 Prec@5 99.290 Error@1 13.850
[resnet18] :: 67/160 ----- [[2019-01-11 14:49:42]] [Need: 00:18:45]
Epoch: [67][0/391] Time 0.292 (0.292) Data 0.261 (0.261) Loss 0.0013 (0.0013) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [67][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0023 (0.0021) Prec@1 100.000 (99.988) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.335 (0.335) Loss 0.6800 (0.6800) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.130 Prec@5 99.300 Error@1 13.870
[resnet18] :: 68/160 ----- [[2019-01-11 14:49:54]] [Need: 00:18:09]
Epoch: [68][0/391] Time 0.270 (0.270) Data 0.239 (0.239) Loss 0.0011 (0.0011) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [68][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0027 (0.0016) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.309 (0.309) Loss 0.6762 (0.6762) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.060 Prec@5 99.350 Error@1 13.940
[resnet18] :: 69/160 ----- [[2019-01-11 14:50:05]] [Need: 00:17:54]
Epoch: [69][0/391] Time 0.304 (0.304) Data 0.274 (0.274) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [69][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0011 (0.0017) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.326 (0.326) Loss 0.6828 (0.6828) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.000 Prec@5 99.290 Error@1 14.000
[resnet18] :: 70/160 ----- [[2019-01-11 14:50:17]] [Need: 00:17:47]
Epoch: [70][0/391] Time 0.301 (0.301) Data 0.272 (0.272) Loss 0.0021 (0.0021) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [70][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0009 (0.0014) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.349 (0.349) Loss 0.6819 (0.6819) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.120 Prec@5 99.320 Error@1 13.880
[resnet18] :: 71/160 ----- [[2019-01-11 14:50:29]] [Need: 00:17:38]
Epoch: [71][0/391] Time 0.300 (0.300) Data 0.267 (0.267) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [71][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0008 (0.0013) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.330 (0.330) Loss 0.6715 (0.6715) Prec@1 84.375 (84.375) Prec@5 100.000 (100.000)
* Prec@1 86.150 Prec@5 99.320 Error@1 13.850
[resnet18] :: 72/160 ----- [[2019-01-11 14:50:41]] [Need: 00:17:22]
Epoch: [72][0/391] Time 0.299 (0.299) Data 0.270 (0.270) Loss 0.0018 (0.0018) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [72][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0021 (0.0013) Prec@1 100.000 (99.992) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.320 (0.320) Loss 0.6865 (0.6865) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.050 Prec@5 99.310 Error@1 13.950
[resnet18] :: 73/160 ----- [[2019-01-11 14:50:53]] [Need: 00:17:12]
Epoch: [73][0/391] Time 0.300 (0.300) Data 0.269 (0.269) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [73][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0011) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.312 (0.312) Loss 0.6967 (0.6967) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.050 Prec@5 99.320 Error@1 13.950
[resnet18] :: 74/160 ----- [[2019-01-11 14:51:05]] [Need: 00:16:58]
Epoch: [74][0/391] Time 0.317 (0.317) Data 0.288 (0.288) Loss 0.0012 (0.0012) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [74][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0012 (0.0011) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.261 (0.261) Loss 0.6948 (0.6948) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.090 Prec@5 99.310 Error@1 13.910
[resnet18] :: 75/160 ----- [[2019-01-11 14:51:17]] [Need: 00:16:45]
Epoch: [75][0/391] Time 0.295 (0.295) Data 0.269 (0.269) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [75][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0010) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.333 (0.333) Loss 0.6890 (0.6890) Prec@1 84.375 (84.375) Prec@5 100.000 (100.000)
* Prec@1 86.110 Prec@5 99.360 Error@1 13.890
[resnet18] :: 76/160 ----- [[2019-01-11 14:51:28]] [Need: 00:16:36]
Epoch: [76][0/391] Time 0.277 (0.277) Data 0.247 (0.247) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [76][200/391] Time 0.026 (0.028) Data 0.000 (0.001) Loss 0.0021 (0.0009) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.361 (0.361) Loss 0.6941 (0.6941) Prec@1 84.375 (84.375) Prec@5 100.000 (100.000)
* Prec@1 86.160 Prec@5 99.340 Error@1 13.840
[resnet18] :: 77/160 ----- [[2019-01-11 14:51:41]] [Need: 00:16:48]
Epoch: [77][0/391] Time 0.296 (0.296) Data 0.276 (0.276) Loss 0.0012 (0.0012) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [77][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0009) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.354 (0.354) Loss 0.6908 (0.6908) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.130 Prec@5 99.330 Error@1 13.870
[resnet18] :: 78/160 ----- [[2019-01-11 14:51:52]] [Need: 00:16:12]
Epoch: [78][0/391] Time 0.282 (0.282) Data 0.254 (0.254) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [78][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0020 (0.0010) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.330 (0.330) Loss 0.6830 (0.6830) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.090 Prec@5 99.250 Error@1 13.910
[resnet18] :: 79/160 ----- [[2019-01-11 14:52:04]] [Need: 00:15:57]
Epoch: [79][0/391] Time 0.297 (0.297) Data 0.266 (0.266) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [79][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0015 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.328 (0.328) Loss 0.6939 (0.6939) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.190 Prec@5 99.290 Error@1 13.810
[resnet18] :: 80/160 ----- [[2019-01-11 14:52:16]] [Need: 00:16:14]
Epoch: [80][0/391] Time 0.306 (0.306) Data 0.273 (0.273) Loss 0.0009 (0.0009) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [80][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0007 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.337 (0.337) Loss 0.6950 (0.6950) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.160 Prec@5 99.330 Error@1 13.840
[resnet18] :: 81/160 ----- [[2019-01-11 14:52:28]] [Need: 00:15:39]
Epoch: [81][0/391] Time 0.309 (0.309) Data 0.277 (0.277) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [81][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.334 (0.334) Loss 0.6848 (0.6848) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.170 Prec@5 99.250 Error@1 13.830
[resnet18] :: 82/160 ----- [[2019-01-11 14:52:40]] [Need: 00:15:26]
Epoch: [82][0/391] Time 0.301 (0.301) Data 0.271 (0.271) Loss 0.0017 (0.0017) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [82][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.313 (0.313) Loss 0.6880 (0.6880) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.210 Prec@5 99.270 Error@1 13.790
[resnet18] :: 83/160 ----- [[2019-01-11 14:52:52]] [Need: 00:15:34]
Epoch: [83][0/391] Time 0.299 (0.299) Data 0.274 (0.274) Loss 0.0010 (0.0010) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [83][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0006 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.339 (0.339) Loss 0.6876 (0.6876) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.270 Prec@5 99.340 Error@1 13.730
[resnet18] :: 84/160 ----- [[2019-01-11 14:53:05]] [Need: 00:15:25]
Epoch: [84][0/391] Time 0.313 (0.313) Data 0.277 (0.277) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [84][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0008 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.317 (0.317) Loss 0.6946 (0.6946) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.250 Prec@5 99.310 Error@1 13.750
[resnet18] :: 85/160 ----- [[2019-01-11 14:53:16]] [Need: 00:14:50]
Epoch: [85][0/391] Time 0.296 (0.296) Data 0.268 (0.268) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [85][200/391] Time 0.025 (0.028) Data 0.000 (0.002) Loss 0.0006 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.320 (0.320) Loss 0.6964 (0.6964) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.130 Prec@5 99.290 Error@1 13.870
[resnet18] :: 86/160 ----- [[2019-01-11 14:53:28]] [Need: 00:14:37]
Epoch: [86][0/391] Time 0.314 (0.314) Data 0.278 (0.278) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [86][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0011 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.340 (0.340) Loss 0.6803 (0.6803) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.230 Prec@5 99.300 Error@1 13.770
[resnet18] :: 87/160 ----- [[2019-01-11 14:53:40]] [Need: 00:14:29]
Epoch: [87][0/391] Time 0.280 (0.280) Data 0.250 (0.250) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [87][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0006 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.323 (0.323) Loss 0.6854 (0.6854) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.190 Prec@5 99.260 Error@1 13.810
[resnet18] :: 88/160 ----- [[2019-01-11 14:53:52]] [Need: 00:14:09]
Epoch: [88][0/391] Time 0.277 (0.277) Data 0.246 (0.246) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [88][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0005 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.326 (0.326) Loss 0.6962 (0.6962) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.100 Prec@5 99.290 Error@1 13.900
[resnet18] :: 89/160 ----- [[2019-01-11 14:54:04]] [Need: 00:13:59]
Epoch: [89][0/391] Time 0.301 (0.301) Data 0.272 (0.272) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [89][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.327 (0.327) Loss 0.6864 (0.6864) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.160 Prec@5 99.310 Error@1 13.840
[resnet18] :: 90/160 ----- [[2019-01-11 14:54:16]] [Need: 00:13:49]
Epoch: [90][0/391] Time 0.321 (0.321) Data 0.280 (0.280) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [90][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.313 (0.313) Loss 0.6795 (0.6795) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.230 Prec@5 99.300 Error@1 13.770
[resnet18] :: 91/160 ----- [[2019-01-11 14:54:28]] [Need: 00:13:38]
Epoch: [91][0/391] Time 0.301 (0.301) Data 0.270 (0.270) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [91][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.326 (0.326) Loss 0.6927 (0.6927) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.250 Prec@5 99.300 Error@1 13.750
[resnet18] :: 92/160 ----- [[2019-01-11 14:54:39]] [Need: 00:13:27]
Epoch: [92][0/391] Time 0.301 (0.301) Data 0.271 (0.271) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [92][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.321 (0.321) Loss 0.6923 (0.6923) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.250 Prec@5 99.300 Error@1 13.750
[resnet18] :: 93/160 ----- [[2019-01-11 14:54:51]] [Need: 00:13:11]
Epoch: [93][0/391] Time 0.317 (0.317) Data 0.287 (0.287) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [93][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.317 (0.317) Loss 0.6832 (0.6832) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.140 Prec@5 99.320 Error@1 13.860
[resnet18] :: 94/160 ----- [[2019-01-11 14:55:03]] [Need: 00:13:01]
Epoch: [94][0/391] Time 0.299 (0.299) Data 0.272 (0.272) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [94][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0009 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.305 (0.305) Loss 0.6833 (0.6833) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.280 Prec@5 99.270 Error@1 13.720
[resnet18] :: 95/160 ----- [[2019-01-11 14:55:15]] [Need: 00:13:07]
Epoch: [95][0/391] Time 0.314 (0.314) Data 0.288 (0.288) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [95][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0007 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.325 (0.325) Loss 0.6956 (0.6956) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.270 Prec@5 99.330 Error@1 13.730
[resnet18] :: 96/160 ----- [[2019-01-11 14:55:27]] [Need: 00:12:39]
Epoch: [96][0/391] Time 0.321 (0.321) Data 0.293 (0.293) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [96][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0004 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.317 (0.317) Loss 0.6835 (0.6835) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.290 Prec@5 99.200 Error@1 13.710
[resnet18] :: 97/160 ----- [[2019-01-11 14:55:39]] [Need: 00:12:45]
Epoch: [97][0/391] Time 0.311 (0.311) Data 0.280 (0.280) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [97][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.320 (0.320) Loss 0.6799 (0.6799) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.320 Prec@5 99.350 Error@1 13.680
[resnet18] :: 98/160 ----- [[2019-01-11 14:55:51]] [Need: 00:12:34]
Epoch: [98][0/391] Time 0.265 (0.265) Data 0.242 (0.242) Loss 0.0011 (0.0011) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [98][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0006 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.329 (0.329) Loss 0.6634 (0.6634) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.300 Prec@5 99.200 Error@1 13.700
[resnet18] :: 99/160 ----- [[2019-01-11 14:56:03]] [Need: 00:12:00]
Epoch: [99][0/391] Time 0.331 (0.331) Data 0.299 (0.299) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [99][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0004 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.316 (0.316) Loss 0.6775 (0.6775) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.260 Prec@5 99.250 Error@1 13.740
[resnet18] :: 100/160 ----- [[2019-01-11 14:56:15]] [Need: 00:11:51]
Epoch: [100][0/391] Time 0.325 (0.325) Data 0.293 (0.293) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [100][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.319 (0.319) Loss 0.6751 (0.6751) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.350 Prec@5 99.250 Error@1 13.650
[resnet18] :: 101/160 ----- [[2019-01-11 14:56:27]] [Need: 00:11:58]
Epoch: [101][0/391] Time 0.296 (0.296) Data 0.268 (0.268) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [101][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0025 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.322 (0.322) Loss 0.6817 (0.6817) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.330 Prec@5 99.280 Error@1 13.670
[resnet18] :: 102/160 ----- [[2019-01-11 14:56:39]] [Need: 00:11:27]
Epoch: [102][0/391] Time 0.296 (0.296) Data 0.273 (0.273) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [102][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.317 (0.317) Loss 0.6888 (0.6888) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.360 Prec@5 99.320 Error@1 13.640
[resnet18] :: 103/160 ----- [[2019-01-11 14:56:51]] [Need: 00:11:32]
Epoch: [103][0/391] Time 0.266 (0.266) Data 0.241 (0.241) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [103][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0004 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.341 (0.341) Loss 0.6713 (0.6713) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.310 Prec@5 99.260 Error@1 13.690
[resnet18] :: 104/160 ----- [[2019-01-11 14:57:03]] [Need: 00:11:02]
Epoch: [104][0/391] Time 0.304 (0.304) Data 0.278 (0.278) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [104][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.320 (0.320) Loss 0.6768 (0.6768) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.240 Prec@5 99.280 Error@1 13.760
[resnet18] :: 105/160 ----- [[2019-01-11 14:57:15]] [Need: 00:10:53]
Epoch: [105][0/391] Time 0.268 (0.268) Data 0.243 (0.243) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [105][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.328 (0.328) Loss 0.6844 (0.6844) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.260 Prec@5 99.300 Error@1 13.740
[resnet18] :: 106/160 ----- [[2019-01-11 14:57:27]] [Need: 00:10:39]
Epoch: [106][0/391] Time 0.276 (0.276) Data 0.248 (0.248) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [106][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0006 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.339 (0.339) Loss 0.6795 (0.6795) Prec@1 84.375 (84.375) Prec@5 99.219 (99.219)
* Prec@1 86.250 Prec@5 99.280 Error@1 13.750
[resnet18] :: 107/160 ----- [[2019-01-11 14:57:39]] [Need: 00:10:32]
Epoch: [107][0/391] Time 0.287 (0.287) Data 0.257 (0.257) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [107][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.337 (0.337) Loss 0.6705 (0.6705) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.350 Prec@5 99.280 Error@1 13.650
[resnet18] :: 108/160 ----- [[2019-01-11 14:57:51]] [Need: 00:10:18]
Epoch: [108][0/391] Time 0.315 (0.315) Data 0.285 (0.285) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [108][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0007 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.363 (0.363) Loss 0.6758 (0.6758) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.360 Prec@5 99.280 Error@1 13.640
[resnet18] :: 109/160 ----- [[2019-01-11 14:58:03]] [Need: 00:10:06]
Epoch: [109][0/391] Time 0.315 (0.315) Data 0.290 (0.290) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [109][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0008 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.354 (0.354) Loss 0.6656 (0.6656) Prec@1 82.031 (82.031) Prec@5 99.219 (99.219)
* Prec@1 86.370 Prec@5 99.250 Error@1 13.630
[resnet18] :: 110/160 ----- [[2019-01-11 14:58:15]] [Need: 00:10:07]
Epoch: [110][0/391] Time 0.264 (0.264) Data 0.235 (0.235) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [110][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0007 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.331 (0.331) Loss 0.6740 (0.6740) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.300 Prec@5 99.290 Error@1 13.700
[resnet18] :: 111/160 ----- [[2019-01-11 14:58:26]] [Need: 00:09:38]
Epoch: [111][0/391] Time 0.291 (0.291) Data 0.263 (0.263) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [111][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.330 (0.330) Loss 0.6793 (0.6793) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.210 Prec@5 99.270 Error@1 13.790
[resnet18] :: 112/160 ----- [[2019-01-11 14:58:38]] [Need: 00:09:30]
Epoch: [112][0/391] Time 0.297 (0.297) Data 0.267 (0.267) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [112][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.311 (0.311) Loss 0.6823 (0.6823) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.280 Prec@5 99.310 Error@1 13.720
[resnet18] :: 113/160 ----- [[2019-01-11 14:58:50]] [Need: 00:09:15]
Epoch: [113][0/391] Time 0.300 (0.300) Data 0.271 (0.271) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [113][200/391] Time 0.028 (0.028) Data 0.000 (0.002) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.369 (0.369) Loss 0.6854 (0.6854) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.290 Prec@5 99.280 Error@1 13.710
[resnet18] :: 114/160 ----- [[2019-01-11 14:59:02]] [Need: 00:09:08]
Epoch: [114][0/391] Time 0.272 (0.272) Data 0.243 (0.243) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [114][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0010 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.331 (0.331) Loss 0.6771 (0.6771) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.380 Prec@5 99.320 Error@1 13.620
[resnet18] :: 115/160 ----- [[2019-01-11 14:59:14]] [Need: 00:09:08]
Epoch: [115][0/391] Time 0.284 (0.284) Data 0.254 (0.254) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [115][200/391] Time 0.029 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.342 (0.342) Loss 0.6643 (0.6643) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.420 Prec@5 99.250 Error@1 13.580
[resnet18] :: 116/160 ----- [[2019-01-11 14:59:27]] [Need: 00:09:05]
Epoch: [116][0/391] Time 0.289 (0.289) Data 0.258 (0.258) Loss 0.0009 (0.0009) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [116][200/391] Time 0.027 (0.029) Data 0.000 (0.002) Loss 0.0008 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.353 (0.353) Loss 0.6744 (0.6744) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.450 Prec@5 99.250 Error@1 13.550
[resnet18] :: 117/160 ----- [[2019-01-11 14:59:39]] [Need: 00:08:56]
Epoch: [117][0/391] Time 0.294 (0.294) Data 0.264 (0.264) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [117][200/391] Time 0.028 (0.029) Data 0.000 (0.001) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.356 (0.356) Loss 0.6701 (0.6701) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.440 Prec@5 99.240 Error@1 13.560
[resnet18] :: 118/160 ----- [[2019-01-11 14:59:51]] [Need: 00:08:33]
Epoch: [118][0/391] Time 0.293 (0.293) Data 0.265 (0.265) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [118][200/391] Time 0.028 (0.029) Data 0.000 (0.001) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.373 (0.373) Loss 0.6560 (0.6560) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.450 Prec@5 99.300 Error@1 13.550
[resnet18] :: 119/160 ----- [[2019-01-11 15:00:04]] [Need: 00:08:20]
Epoch: [119][0/391] Time 0.306 (0.306) Data 0.276 (0.276) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [119][200/391] Time 0.028 (0.029) Data 0.000 (0.002) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.374 (0.374) Loss 0.6764 (0.6764) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.350 Prec@5 99.240 Error@1 13.650
[resnet18] :: 120/160 ----- [[2019-01-11 15:00:16]] [Need: 00:08:10]
Epoch: [120][0/391] Time 0.294 (0.294) Data 0.260 (0.260) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [120][200/391] Time 0.027 (0.029) Data 0.000 (0.001) Loss 0.0005 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.374 (0.374) Loss 0.6650 (0.6650) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.320 Prec@5 99.240 Error@1 13.680
[resnet18] :: 121/160 ----- [[2019-01-11 15:00:28]] [Need: 00:07:55]
Epoch: [121][0/391] Time 0.296 (0.296) Data 0.273 (0.273) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [121][200/391] Time 0.028 (0.029) Data 0.000 (0.002) Loss 0.0005 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.290 (0.290) Loss 0.6718 (0.6718) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.370 Prec@5 99.290 Error@1 13.630
[resnet18] :: 122/160 ----- [[2019-01-11 15:00:40]] [Need: 00:07:41]
Epoch: [122][0/391] Time 0.305 (0.305) Data 0.269 (0.269) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [122][200/391] Time 0.028 (0.029) Data 0.000 (0.002) Loss 0.0011 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.354 (0.354) Loss 0.6759 (0.6759) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.410 Prec@5 99.240 Error@1 13.590
[resnet18] :: 123/160 ----- [[2019-01-11 15:00:52]] [Need: 00:07:30]
Epoch: [123][0/391] Time 0.323 (0.323) Data 0.293 (0.293) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [123][200/391] Time 0.028 (0.029) Data 0.000 (0.002) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.359 (0.359) Loss 0.6747 (0.6747) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.460 Prec@5 99.190 Error@1 13.540
[resnet18] :: 124/160 ----- [[2019-01-11 15:01:05]] [Need: 00:07:31]
Epoch: [124][0/391] Time 0.306 (0.306) Data 0.273 (0.273) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [124][200/391] Time 0.027 (0.029) Data 0.000 (0.001) Loss 0.0005 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.371 (0.371) Loss 0.6641 (0.6641) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.400 Prec@5 99.250 Error@1 13.600
[resnet18] :: 125/160 ----- [[2019-01-11 15:01:17]] [Need: 00:07:06]
Epoch: [125][0/391] Time 0.305 (0.305) Data 0.270 (0.270) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [125][200/391] Time 0.027 (0.029) Data 0.000 (0.002) Loss 0.0017 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.352 (0.352) Loss 0.6652 (0.6652) Prec@1 82.031 (82.031) Prec@5 99.219 (99.219)
* Prec@1 86.460 Prec@5 99.170 Error@1 13.540
[resnet18] :: 126/160 ----- [[2019-01-11 15:01:29]] [Need: 00:06:57]
Epoch: [126][0/391] Time 0.262 (0.262) Data 0.227 (0.227) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [126][200/391] Time 0.028 (0.029) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.352 (0.352) Loss 0.6768 (0.6768) Prec@1 84.375 (84.375) Prec@5 99.219 (99.219)
* Prec@1 86.240 Prec@5 99.280 Error@1 13.760
[resnet18] :: 127/160 ----- [[2019-01-11 15:01:42]] [Need: 00:06:42]
Epoch: [127][0/391] Time 0.296 (0.296) Data 0.270 (0.270) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [127][200/391] Time 0.028 (0.029) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.343 (0.343) Loss 0.6646 (0.6646) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.310 Prec@5 99.270 Error@1 13.690
[resnet18] :: 128/160 ----- [[2019-01-11 15:01:54]] [Need: 00:06:30]
Epoch: [128][0/391] Time 0.321 (0.321) Data 0.291 (0.291) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [128][200/391] Time 0.029 (0.029) Data 0.000 (0.002) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.367 (0.367) Loss 0.6533 (0.6533) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.390 Prec@5 99.250 Error@1 13.610
[resnet18] :: 129/160 ----- [[2019-01-11 15:02:06]] [Need: 00:06:20]
Epoch: [129][0/391] Time 0.288 (0.288) Data 0.249 (0.249) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [129][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.388 (0.388) Loss 0.6603 (0.6603) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.330 Prec@5 99.220 Error@1 13.670
[resnet18] :: 130/160 ----- [[2019-01-11 15:02:18]] [Need: 00:05:57]
Epoch: [130][0/391] Time 0.262 (0.262) Data 0.232 (0.232) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [130][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.362 (0.362) Loss 0.6626 (0.6626) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.410 Prec@5 99.210 Error@1 13.590
[resnet18] :: 131/160 ----- [[2019-01-11 15:02:30]] [Need: 00:05:43]
Epoch: [131][0/391] Time 0.258 (0.258) Data 0.228 (0.228) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [131][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.353 (0.353) Loss 0.6681 (0.6681) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.360 Prec@5 99.270 Error@1 13.640
[resnet18] :: 132/160 ----- [[2019-01-11 15:02:42]] [Need: 00:05:31]
Epoch: [132][0/391] Time 0.266 (0.266) Data 0.231 (0.231) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [132][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.368 (0.368) Loss 0.6509 (0.6509) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.350 Prec@5 99.230 Error@1 13.650
[resnet18] :: 133/160 ----- [[2019-01-11 15:02:54]] [Need: 00:05:20]
Epoch: [133][0/391] Time 0.266 (0.266) Data 0.235 (0.235) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [133][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0006 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.347 (0.347) Loss 0.6742 (0.6742) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.430 Prec@5 99.250 Error@1 13.570
[resnet18] :: 134/160 ----- [[2019-01-11 15:03:05]] [Need: 00:05:07]
Epoch: [134][0/391] Time 0.262 (0.262) Data 0.229 (0.229) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [134][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0013 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.375 (0.375) Loss 0.6671 (0.6671) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.510 Prec@5 99.250 Error@1 13.490
[resnet18] :: 135/160 ----- [[2019-01-11 15:03:18]] [Need: 00:05:03]
Epoch: [135][0/391] Time 0.261 (0.261) Data 0.231 (0.231) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [135][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.347 (0.347) Loss 0.6769 (0.6769) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.260 Prec@5 99.250 Error@1 13.740
[resnet18] :: 136/160 ----- [[2019-01-11 15:03:29]] [Need: 00:04:43]
Epoch: [136][0/391] Time 0.331 (0.331) Data 0.299 (0.299) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [136][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.340 (0.340) Loss 0.6706 (0.6706) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.410 Prec@5 99.300 Error@1 13.590
[resnet18] :: 137/160 ----- [[2019-01-11 15:03:41]] [Need: 00:04:33]
Epoch: [137][0/391] Time 0.300 (0.300) Data 0.276 (0.276) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [137][200/391] Time 0.023 (0.028) Data 0.000 (0.002) Loss 0.0002 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.329 (0.329) Loss 0.6593 (0.6593) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.360 Prec@5 99.280 Error@1 13.640
[resnet18] :: 138/160 ----- [[2019-01-11 15:03:53]] [Need: 00:04:21]
Epoch: [138][0/391] Time 0.295 (0.295) Data 0.267 (0.267) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [138][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0004 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.323 (0.323) Loss 0.6611 (0.6611) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.230 Prec@5 99.260 Error@1 13.770
[resnet18] :: 139/160 ----- [[2019-01-11 15:04:05]] [Need: 00:04:09]
Epoch: [139][0/391] Time 0.282 (0.282) Data 0.248 (0.248) Loss 0.0006 (0.0006) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [139][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.306 (0.306) Loss 0.6622 (0.6622) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.290 Prec@5 99.250 Error@1 13.710
[resnet18] :: 140/160 ----- [[2019-01-11 15:04:17]] [Need: 00:03:56]
Epoch: [140][0/391] Time 0.293 (0.293) Data 0.265 (0.265) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [140][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0006 (0.0005) Prec@1 100.000 (99.996) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.326 (0.326) Loss 0.6791 (0.6791) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.500 Prec@5 99.310 Error@1 13.500
[resnet18] :: 141/160 ----- [[2019-01-11 15:04:29]] [Need: 00:03:45]
Epoch: [141][0/391] Time 0.303 (0.303) Data 0.269 (0.269) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [141][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0002 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.317 (0.317) Loss 0.6740 (0.6740) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.420 Prec@5 99.300 Error@1 13.580
[resnet18] :: 142/160 ----- [[2019-01-11 15:04:40]] [Need: 00:03:33]
Epoch: [142][0/391] Time 0.266 (0.266) Data 0.236 (0.236) Loss 0.0007 (0.0007) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [142][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.336 (0.336) Loss 0.6744 (0.6744) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.320 Prec@5 99.290 Error@1 13.680
[resnet18] :: 143/160 ----- [[2019-01-11 15:04:52]] [Need: 00:03:20]
Epoch: [143][0/391] Time 0.300 (0.300) Data 0.270 (0.270) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [143][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.335 (0.335) Loss 0.6637 (0.6637) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.300 Prec@5 99.290 Error@1 13.700
[resnet18] :: 144/160 ----- [[2019-01-11 15:05:04]] [Need: 00:03:09]
Epoch: [144][0/391] Time 0.270 (0.270) Data 0.243 (0.243) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [144][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.335 (0.335) Loss 0.6620 (0.6620) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.320 Prec@5 99.290 Error@1 13.680
[resnet18] :: 145/160 ----- [[2019-01-11 15:05:16]] [Need: 00:02:57]
Epoch: [145][0/391] Time 0.287 (0.287) Data 0.250 (0.250) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [145][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.346 (0.346) Loss 0.6488 (0.6488) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.570 Prec@5 99.260 Error@1 13.430
[resnet18] :: 146/160 ----- [[2019-01-11 15:05:28]] [Need: 00:02:50]
Epoch: [146][0/391] Time 0.275 (0.275) Data 0.242 (0.242) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [146][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.317 (0.317) Loss 0.6588 (0.6588) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.520 Prec@5 99.290 Error@1 13.480
[resnet18] :: 147/160 ----- [[2019-01-11 15:05:40]] [Need: 00:02:33]
Epoch: [147][0/391] Time 0.312 (0.312) Data 0.284 (0.284) Loss 0.0002 (0.0002) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [147][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0002 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.319 (0.319) Loss 0.6639 (0.6639) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.250 Prec@5 99.310 Error@1 13.750
[resnet18] :: 148/160 ----- [[2019-01-11 15:05:52]] [Need: 00:02:22]
Epoch: [148][0/391] Time 0.268 (0.268) Data 0.242 (0.242) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [148][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.307 (0.307) Loss 0.6663 (0.6663) Prec@1 84.375 (84.375) Prec@5 100.000 (100.000)
* Prec@1 86.370 Prec@5 99.290 Error@1 13.630
[resnet18] :: 149/160 ----- [[2019-01-11 15:06:04]] [Need: 00:02:09]
Epoch: [149][0/391] Time 0.325 (0.325) Data 0.292 (0.292) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [149][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.336 (0.336) Loss 0.6836 (0.6836) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.380 Prec@5 99.300 Error@1 13.620
[resnet18] :: 150/160 ----- [[2019-01-11 15:06:16]] [Need: 00:01:59]
Epoch: [150][0/391] Time 0.298 (0.298) Data 0.268 (0.268) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [150][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0007 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.327 (0.327) Loss 0.6528 (0.6528) Prec@1 82.031 (82.031) Prec@5 100.000 (100.000)
* Prec@1 86.310 Prec@5 99.200 Error@1 13.690
[resnet18] :: 151/160 ----- [[2019-01-11 15:06:27]] [Need: 00:01:46]
Epoch: [151][0/391] Time 0.269 (0.269) Data 0.240 (0.240) Loss 0.0009 (0.0009) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [151][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.301 (0.301) Loss 0.6626 (0.6626) Prec@1 83.594 (83.594) Prec@5 100.000 (100.000)
* Prec@1 86.390 Prec@5 99.270 Error@1 13.610
[resnet18] :: 152/160 ----- [[2019-01-11 15:06:39]] [Need: 00:01:34]
Epoch: [152][0/391] Time 0.315 (0.315) Data 0.285 (0.285) Loss 0.0010 (0.0010) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [152][200/391] Time 0.026 (0.028) Data 0.000 (0.002) Loss 0.0003 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.350 (0.350) Loss 0.6547 (0.6547) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.240 Prec@5 99.280 Error@1 13.760
[resnet18] :: 153/160 ----- [[2019-01-11 15:06:51]] [Need: 00:01:23]
Epoch: [153][0/391] Time 0.297 (0.297) Data 0.269 (0.269) Loss 0.0025 (0.0025) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [153][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0002 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.372 (0.372) Loss 0.6699 (0.6699) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.360 Prec@5 99.280 Error@1 13.640
[resnet18] :: 154/160 ----- [[2019-01-11 15:07:03]] [Need: 00:01:11]
Epoch: [154][0/391] Time 0.280 (0.280) Data 0.253 (0.253) Loss 0.0005 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [154][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0002 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.337 (0.337) Loss 0.6619 (0.6619) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.410 Prec@5 99.280 Error@1 13.590
[resnet18] :: 155/160 ----- [[2019-01-11 15:07:15]] [Need: 00:00:59]
Epoch: [155][0/391] Time 0.298 (0.298) Data 0.270 (0.270) Loss 0.0008 (0.0008) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [155][200/391] Time 0.025 (0.028) Data 0.000 (0.002) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.326 (0.326) Loss 0.6750 (0.6750) Prec@1 82.031 (82.031) Prec@5 99.219 (99.219)
* Prec@1 86.360 Prec@5 99.230 Error@1 13.640
[resnet18] :: 156/160 ----- [[2019-01-11 15:07:27]] [Need: 00:00:47]
Epoch: [156][0/391] Time 0.309 (0.309) Data 0.278 (0.278) Loss 0.0014 (0.0014) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [156][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0007 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.333 (0.333) Loss 0.6607 (0.6607) Prec@1 82.812 (82.812) Prec@5 99.219 (99.219)
* Prec@1 86.380 Prec@5 99.270 Error@1 13.620
[resnet18] :: 157/160 ----- [[2019-01-11 15:07:39]] [Need: 00:00:35]
Epoch: [157][0/391] Time 0.281 (0.281) Data 0.251 (0.251) Loss 0.0004 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [157][200/391] Time 0.027 (0.028) Data 0.000 (0.001) Loss 0.0003 (0.0005) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.259 (0.259) Loss 0.6666 (0.6666) Prec@1 83.594 (83.594) Prec@5 99.219 (99.219)
* Prec@1 86.300 Prec@5 99.190 Error@1 13.700
[resnet18] :: 158/160 ----- [[2019-01-11 15:07:50]] [Need: 00:00:23]
Epoch: [158][0/391] Time 0.301 (0.301) Data 0.274 (0.274) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [158][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0005 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.403 (0.403) Loss 0.6568 (0.6568) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.340 Prec@5 99.210 Error@1 13.660
[resnet18] :: 159/160 ----- [[2019-01-11 15:08:02]] [Need: 00:00:11]
Epoch: [159][0/391] Time 0.313 (0.313) Data 0.280 (0.280) Loss 0.0003 (0.0003) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Epoch: [159][200/391] Time 0.027 (0.028) Data 0.000 (0.002) Loss 0.0007 (0.0004) Prec@1 100.000 (100.000) Prec@5 100.000 (100.000)
Test: [0/79] Time 0.337 (0.337) Loss 0.6566 (0.6566) Prec@1 82.812 (82.812) Prec@5 100.000 (100.000)
* Prec@1 86.420 Prec@5 99.290 Error@1 13.580