I0413 09:48:59.041020 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:48:59.041031 22206 sgd_solver.cpp:106] Iteration 3520, lr = 0.0001
I0413 09:50:17.398932 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_3542.caffemodel
I0413 09:50:17.490676 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3542.solverstate
I0413 09:51:28.575299 22206 solver.cpp:242] Iteration 3560 (0.267493 iter/s, 149.537s/40 iter), loss = -6.43825e-20
I0413 09:51:28.575412 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:51:28.575422 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:51:28.575431 22206 sgd_solver.cpp:106] Iteration 3560, lr = 0.0001
I0413 09:53:57.796625 22206 solver.cpp:242] Iteration 3600 (0.268054 iter/s, 149.223s/40 iter), loss = -6.43825e-20
I0413 09:53:57.796737 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:53:57.796746 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:53:57.796758 22206 sgd_solver.cpp:106] Iteration 3600, lr = 0.0001
I0413 09:56:27.163589 22206 solver.cpp:242] Iteration 3640 (0.267793 iter/s, 149.369s/40 iter), loss = -6.43825e-20
I0413 09:56:27.163729 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:56:27.163738 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:56:27.163750 22206 sgd_solver.cpp:106] Iteration 3640, lr = 0.0001
I0413 09:58:56.335173 22206 solver.cpp:242] Iteration 3680 (0.268144 iter/s, 149.174s/40 iter), loss = -6.43825e-20
I0413 09:58:56.335278 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:58:56.335286 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:58:56.335299 22206 sgd_solver.cpp:106] Iteration 3680, lr = 0.0001
I0413 10:01:25.507982 22206 solver.cpp:242] Iteration 3720 (0.268141 iter/s, 149.175s/40 iter), loss = -6.43825e-20
I0413 10:01:25.508046 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:01:25.508055 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:01:25.508065 22206 sgd_solver.cpp:106] Iteration 3720, lr = 0.0001
I0413 10:03:54.554807 22206 solver.cpp:242] Iteration 3760 (0.268368 iter/s, 149.049s/40 iter), loss = -6.43825e-20
I0413 10:03:54.554925 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:03:54.554935 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:03:54.554946 22206 sgd_solver.cpp:106] Iteration 3760, lr = 0.0001
I0413 10:06:23.596776 22206 solver.cpp:242] Iteration 3800 (0.268377 iter/s, 149.044s/40 iter), loss = -6.43825e-20
I0413 10:06:23.596891 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:06:23.596902 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:06:23.596912 22206 sgd_solver.cpp:106] Iteration 3800, lr = 0.0001
I0413 10:08:52.669639 22206 solver.cpp:242] Iteration 3840 (0.268321 iter/s, 149.075s/40 iter), loss = -6.43825e-20
I0413 10:08:52.669754 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:08:52.669764 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:08:52.669773 22206 sgd_solver.cpp:106] Iteration 3840, lr = 0.0001
I0413 10:10:18.405055 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_3864.caffemodel
I0413 10:10:18.496944 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3864.solverstate
I0413 10:11:21.959678 22206 solver.cpp:242] Iteration 3880 (0.267931 iter/s, 149.292s/40 iter), loss = -6.43825e-20
I0413 10:11:21.959784 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:11:21.959792 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:11:21.959803 22206 sgd_solver.cpp:106] Iteration 3880, lr = 0.0001
I0413 10:13:51.061364 22206 solver.cpp:242] Iteration 3920 (0.268269 iter/s, 149.104s/40 iter), loss = -6.43825e-20
I0413 10:13:51.061491 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:13:51.061502 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:13:51.061514 22206 sgd_solver.cpp:106] Iteration 3920, lr = 0.0001
I0413 10:16:20.152899 22206 solver.cpp:242] Iteration 3960 (0.268288 iter/s, 149.094s/40 iter), loss = -6.43825e-20
I0413 10:16:20.152971 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:16:20.152978 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:16:20.152989 22206 sgd_solver.cpp:106] Iteration 3960, lr = 0.0001
I0413 10:18:49.210336 22206 solver.cpp:242] Iteration 4000 (0.268349 iter/s, 149.06s/40 iter), loss = -6.43825e-20
I0413 10:18:49.210407 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:18:49.210417 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:18:49.210427 22206 sgd_solver.cpp:106] Iteration 4000, lr = 0.0001
I0413 10:21:18.364866 22206 solver.cpp:242] Iteration 4040 (0.268174 iter/s, 149.157s/40 iter), loss = -6.43825e-20
I0413 10:21:18.365006 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:21:18.365016 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:21:18.365027 22206 sgd_solver.cpp:106] Iteration 4040, lr = 0.0001
I0413 10:23:47.478269 22206 solver.cpp:242] Iteration 4080 (0.268248 iter/s, 149.116s/40 iter), loss = -6.43825e-20
I0413 10:23:47.478392 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:23:47.478404 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:23:47.478413 22206 sgd_solver.cpp:106] Iteration 4080, lr = 0.0001
I0413 10:26:16.476501 22206 solver.cpp:242] Iteration 4120 (0.268456 iter/s, 149s/40 iter), loss = -6.43825e-20
I0413 10:26:16.476625 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:26:16.476635 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:26:16.476646 22206 sgd_solver.cpp:106] Iteration 4120, lr = 0.0001
I0413 10:28:45.564611 22206 solver.cpp:242] Iteration 4160 (0.268294 iter/s, 149.09s/40 iter), loss = -6.43825e-20
I0413 10:28:45.564725 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:28:45.564735 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:28:45.564745 22206 sgd_solver.cpp:106] Iteration 4160, lr = 0.0001
I0413 10:30:18.837824 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_4186.caffemodel
I0413 10:30:18.929880 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4186.solverstate
I0413 10:31:14.862416 22206 solver.cpp:242] Iteration 4200 (0.267917 iter/s, 149.3s/40 iter), loss = -6.43825e-20
I0413 10:31:14.862524 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:31:14.862534 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:31:14.862546 22206 sgd_solver.cpp:106] Iteration 4200, lr = 0.0001
I0413 10:33:43.922089 22206 solver.cpp:242] Iteration 4240 (0.268345 iter/s, 149.062s/40 iter), loss = -6.43825e-20
I0413 10:33:43.922157 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:33:43.922165 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:33:43.922175 22206 sgd_solver.cpp:106] Iteration 4240, lr = 0.0001
I0413 10:36:12.987992 22206 solver.cpp:242] Iteration 4280 (0.268334 iter/s, 149.068s/40 iter), loss = -6.43825e-20
I0413 10:36:12.988106 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:36:12.988116 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:36:12.988126 22206 sgd_solver.cpp:106] Iteration 4280, lr = 0.0001
I0413 10:38:42.116134 22206 solver.cpp:242] Iteration 4320 (0.268222 iter/s, 149.13s/40 iter), loss = -6.43825e-20
I0413 10:38:42.116241 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:38:42.116252 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:38:42.116263 22206 sgd_solver.cpp:106] Iteration 4320, lr = 0.0001
I0413 10:41:11.217193 22206 solver.cpp:242] Iteration 4360 (0.26827 iter/s, 149.103s/40 iter), loss = -6.43825e-20
I0413 10:41:11.217311 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:41:11.217321 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:41:11.217332 22206 sgd_solver.cpp:106] Iteration 4360, lr = 0.0001
I0413 10:43:40.140560 22206 solver.cpp:242] Iteration 4400 (0.268591 iter/s, 148.926s/40 iter), loss = -6.43825e-20
I0413 10:43:40.140676 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:43:40.140686 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:43:40.140697 22206 sgd_solver.cpp:106] Iteration 4400, lr = 0.0001
I0413 10:46:09.319939 22206 solver.cpp:242] Iteration 4440 (0.26813 iter/s, 149.182s/40 iter), loss = -6.43825e-20
I0413 10:46:09.320101 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:46:09.320112 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:46:09.320122 22206 sgd_solver.cpp:106] Iteration 4440, lr = 0.0001
I0413 10:48:38.501461 22206 solver.cpp:242] Iteration 4480 (0.268126 iter/s, 149.184s/40 iter), loss = -6.43825e-20
I0413 10:48:38.501576 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:48:38.501586 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:48:38.501597 22206 sgd_solver.cpp:106] Iteration 4480, lr = 0.0001
I0413 10:50:19.204316 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_4508.caffemodel
I0413 10:50:19.296655 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4508.solverstate
I0413 10:51:07.778781 22206 solver.cpp:242] Iteration 4520 (0.267954 iter/s, 149.279s/40 iter), loss = -6.43825e-20
I0413 10:51:07.778888 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:51:07.778899 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:51:07.778910 22206 sgd_solver.cpp:106] Iteration 4520, lr = 0.0001
I0413 10:53:36.721230 22206 solver.cpp:242] Iteration 4560 (0.268556 iter/s, 148.945s/40 iter), loss = -6.43825e-20
I0413 10:53:36.721348 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:53:36.721357 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:53:36.721369 22206 sgd_solver.cpp:106] Iteration 4560, lr = 0.0001
I0413 10:56:05.601032 22206 solver.cpp:242] Iteration 4600 (0.268669 iter/s, 148.882s/40 iter), loss = -6.43825e-20
I0413 10:56:05.601160 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:56:05.601171 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:56:05.601181 22206 sgd_solver.cpp:106] Iteration 4600, lr = 0.0001
I0413 10:58:34.820750 22206 solver.cpp:242] Iteration 4640 (0.268057 iter/s, 149.222s/40 iter), loss = -6.43825e-20
I0413 10:58:34.820994 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:58:34.821004 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:58:34.821015 22206 sgd_solver.cpp:106] Iteration 4640, lr = 0.0001
I0413 11:01:03.921664 22206 solver.cpp:242] Iteration 4680 (0.268271 iter/s, 149.103s/40 iter), loss = -6.43825e-20
I0413 11:01:03.921782 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:01:03.921792 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:01:03.921803 22206 sgd_solver.cpp:106] Iteration 4680, lr = 0.0001
I0413 11:03:33.017868 22206 solver.cpp:242] Iteration 4720 (0.268279 iter/s, 149.098s/40 iter), loss = -6.43825e-20
I0413 11:03:33.017990 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:03:33.018000 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:03:33.018012 22206 sgd_solver.cpp:106] Iteration 4720, lr = 0.0001
I0413 11:06:02.146970 22206 solver.cpp:242] Iteration 4760 (0.26822 iter/s, 149.131s/40 iter), loss = -6.43825e-20
I0413 11:06:02.147102 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:06:02.147112 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:06:02.147123 22206 sgd_solver.cpp:106] Iteration 4760, lr = 0.0001
I0413 11:08:31.073314 22206 solver.cpp:242] Iteration 4800 (0.268585 iter/s, 148.928s/40 iter), loss = -6.43825e-20
I0413 11:08:31.073456 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:08:31.073467 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:08:31.073477 22206 sgd_solver.cpp:106] Iteration 4800, lr = 0.0001
I0413 11:10:19.107978 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_4830.caffemodel
I0413 11:10:19.199867 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4830.solverstate
I0413 11:10:19.276968 22206 solver.cpp:362] Iteration 4830, Testing net (#0)
I0413 11:10:19.276990 22206 net.cpp:723] Ignoring source layer train_data
I0413 11:10:19.276995 22206 net.cpp:723] Ignoring source layer train_label
I0413 11:10:19.276999 22206 net.cpp:723] Ignoring source layer train_transform
I0413 11:10:37.890264 22206 solver.cpp:429]     Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:10:37.890288 22206 solver.cpp:429]     Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:10:37.890295 22206 solver.cpp:429]     Test net output #2: mAP = 0
I0413 11:10:37.890300 22206 solver.cpp:429]     Test net output #3: precision = 0
I0413 11:10:37.890305 22206 solver.cpp:429]     Test net output #4: recall = 0
I0413 11:11:18.855345 22206 solver.cpp:242] Iteration 4840 (0.238401 iter/s, 167.784s/40 iter), loss = -6.43825e-20
I0413 11:11:18.855469 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:11:18.855479 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:11:18.855490 22206 sgd_solver.cpp:106] Iteration 4840, lr = 0.0001
I0413 11:13:47.931879 22206 solver.cpp:242] Iteration 4880 (0.268315 iter/s, 149.079s/40 iter), loss = -6.43825e-20
I0413 11:13:47.931987 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:13:47.931996 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:13:47.932008 22206 sgd_solver.cpp:106] Iteration 4880, lr = 0.0001
I0413 11:16:17.053846 22206 solver.cpp:242] Iteration 4920 (0.268233 iter/s, 149.124s/40 iter), loss = -6.43825e-20
I0413 11:16:17.053958 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:16:17.053969 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:16:17.053980 22206 sgd_solver.cpp:106] Iteration 4920, lr = 0.0001
I0413 11:18:46.184485 22206 solver.cpp:242] Iteration 4960 (0.268217 iter/s, 149.133s/40 iter), loss = -6.43825e-20
I0413 11:18:46.184582 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:18:46.184592 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:18:46.184602 22206 sgd_solver.cpp:106] Iteration 4960, lr = 0.0001
I0413 11:21:15.249085 22206 solver.cpp:242] Iteration 5000 (0.268336 iter/s, 149.067s/40 iter), loss = -6.43825e-20
I0413 11:21:15.249160 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:21:15.249169 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:21:15.249181 22206 sgd_solver.cpp:106] Iteration 5000, lr = 0.0001
I0413 11:23:44.405105 22206 solver.cpp:242] Iteration 5040 (0.268172 iter/s, 149.158s/40 iter), loss = -6.43825e-20
I0413 11:23:44.405216 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:23:44.405226 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:23:44.405237 22206 sgd_solver.cpp:106] Iteration 5040, lr = 0.0001
I0413 11:26:13.267356 22206 solver.cpp:242] Iteration 5080 (0.268701 iter/s, 148.864s/40 iter), loss = -6.43825e-20
I0413 11:26:13.267424 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:26:13.267433 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:26:13.267443 22206 sgd_solver.cpp:106] Iteration 5080, lr = 0.0001
I0413 11:28:42.219928 22206 solver.cpp:242] Iteration 5120 (0.268538 iter/s, 148.955s/40 iter), loss = -6.43825e-20
I0413 11:28:42.220067 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:28:42.220077 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:28:42.220088 22206 sgd_solver.cpp:106] Iteration 5120, lr = 0.0001
I0413 11:30:37.844563 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_5152.caffemodel
I0413 11:30:37.936465 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5152.solverstate
I0413 11:31:11.507647 22206 solver.cpp:242] Iteration 5160 (0.267935 iter/s, 149.29s/40 iter), loss = -6.43825e-20
I0413 11:31:11.507751 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:31:11.507761 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:31:11.507772 22206 sgd_solver.cpp:106] Iteration 5160, lr = 0.0001
I0413 11:33:40.654230 22206 solver.cpp:242] Iteration 5200 (0.268189 iter/s, 149.149s/40 iter), loss = -6.43825e-20
I0413 11:33:40.654347 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:33:40.654357 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:33:40.654368 22206 sgd_solver.cpp:106] Iteration 5200, lr = 0.0001
I0413 11:36:09.591799 22206 solver.cpp:242] Iteration 5240 (0.268565 iter/s, 148.94s/40 iter), loss = -6.43825e-20
I0413 11:36:09.591915 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:36:09.591925 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:36:09.591936 22206 sgd_solver.cpp:106] Iteration 5240, lr = 0.0001
I0413 11:38:38.533434 22206 solver.cpp:242] Iteration 5280 (0.268558 iter/s, 148.944s/40 iter), loss = -6.43825e-20
I0413 11:38:38.533537 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:38:38.533547 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:38:38.533558 22206 sgd_solver.cpp:106] Iteration 5280, lr = 0.0001
I0413 11:41:07.509539 22206 solver.cpp:242] Iteration 5320 (0.268496 iter/s, 148.978s/40 iter), loss = -6.43825e-20
I0413 11:41:07.509606 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:41:07.509615 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:41:07.509626 22206 sgd_solver.cpp:106] Iteration 5320, lr = 0.0001
I0413 11:43:36.430819 22206 solver.cpp:242] Iteration 5360 (0.268594 iter/s, 148.923s/40 iter), loss = -6.43825e-20
I0413 11:43:36.430930 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:43:36.430940 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:43:36.430951 22206 sgd_solver.cpp:106] Iteration 5360, lr = 0.0001
I0413 11:46:05.434841 22206 solver.cpp:242] Iteration 5400 (0.268445 iter/s, 149.006s/40 iter), loss = -6.43825e-20
I0413 11:46:05.434968 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:46:05.434978 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:46:05.434988 22206 sgd_solver.cpp:106] Iteration 5400, lr = 0.0001
I0413 11:48:34.255635 22206 solver.cpp:242] Iteration 5440 (0.268776 iter/s, 148.823s/40 iter), loss = -6.43825e-20
I0413 11:48:34.255708 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:48:34.255717 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:48:34.255728 22206 sgd_solver.cpp:106] Iteration 5440, lr = 0.0001
I0413 11:50:37.201242 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_5474.caffemodel
I0413 11:50:37.292891 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5474.solverstate
I0413 11:51:03.436975 22206 solver.cpp:242] Iteration 5480 (0.268126 iter/s, 149.184s/40 iter), loss = -6.43825e-20
I0413 11:51:03.437016 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:51:03.437024 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:51:03.437034 22206 sgd_solver.cpp:106] Iteration 5480, lr = 0.0001
I0413 11:53:32.492372 22206 solver.cpp:242] Iteration 5520 (0.268353 iter/s, 149.058s/40 iter), loss = -6.43825e-20
I0413 11:53:32.492514 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:53:32.492525 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:53:32.492535 22206 sgd_solver.cpp:106] Iteration 5520, lr = 0.0001
I0413 11:56:01.845796 22206 solver.cpp:242] Iteration 5560 (0.267817 iter/s, 149.356s/40 iter), loss = -6.43825e-20
I0413 11:56:01.845908 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:56:01.845919 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:56:01.845930 22206 sgd_solver.cpp:106] Iteration 5560, lr = 0.0001
I0413 11:58:30.763732 22206 solver.cpp:242] Iteration 5600 (0.2686 iter/s, 148.92s/40 iter), loss = -6.43825e-20
I0413 11:58:30.763833 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:58:30.763842 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:58:30.763854 22206 sgd_solver.cpp:106] Iteration 5600, lr = 0.0001
I0413 12:00:59.810398 22206 solver.cpp:242] Iteration 5640 (0.268368 iter/s, 149.049s/40 iter), loss = -6.43825e-20
I0413 12:00:59.810508 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:00:59.810519 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:00:59.810530 22206 sgd_solver.cpp:106] Iteration 5640, lr = 0.0001
I0413 12:03:28.841716 22206 solver.cpp:242] Iteration 5680 (0.268396 iter/s, 149.033s/40 iter), loss = -6.43825e-20
I0413 12:03:28.841830 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:03:28.841840 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:03:28.841852 22206 sgd_solver.cpp:106] Iteration 5680, lr = 0.0001
I0413 12:05:57.901038 22206 solver.cpp:242] Iteration 5720 (0.268346 iter/s, 149.061s/40 iter), loss = -6.43825e-20
I0413 12:05:57.901135 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:05:57.901145 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:05:57.901156 22206 sgd_solver.cpp:106] Iteration 5720, lr = 0.0001
I0413 12:08:27.050364 22206 solver.cpp:242] Iteration 5760 (0.268184 iter/s, 149.152s/40 iter), loss = -6.43825e-20
I0413 12:08:27.050463 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:08:27.050473 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:08:27.050484 22206 sgd_solver.cpp:106] Iteration 5760, lr = 0.0001
I0413 12:10:37.396387 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_5796.caffemodel
I0413 12:10:37.488693 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5796.solverstate
I0413 12:10:56.210398 22206 solver.cpp:242] Iteration 5800 (0.268164 iter/s, 149.162s/40 iter), loss = -6.43825e-20
I0413 12:10:56.210441 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:10:56.210450 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:10:56.210460 22206 sgd_solver.cpp:106] Iteration 5800, lr = 0.0001
I0413 12:13:25.227550 22206 solver.cpp:242] Iteration 5840 (0.268422 iter/s, 149.019s/40 iter), loss = -6.43825e-20
I0413 12:13:25.227655 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:13:25.227665 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:13:25.227676 22206 sgd_solver.cpp:106] Iteration 5840, lr = 0.0001
I0413 12:15:54.367265 22206 solver.cpp:242] Iteration 5880 (0.268201 iter/s, 149.142s/40 iter), loss = -6.43825e-20
I0413 12:15:54.367399 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:15:54.367416 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:15:54.367434 22206 sgd_solver.cpp:106] Iteration 5880, lr = 0.0001
I0413 12:18:23.401654 22206 solver.cpp:242] Iteration 5920 (0.268391 iter/s, 149.037s/40 iter), loss = -6.43825e-20
I0413 12:18:23.401799 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:18:23.401809 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:18:23.401820 22206 sgd_solver.cpp:106] Iteration 5920, lr = 0.0001
I0413 12:20:52.562824 22206 solver.cpp:242] Iteration 5960 (0.268162 iter/s, 149.163s/40 iter), loss = -6.43825e-20
I0413 12:20:52.562935 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:20:52.562944 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:20:52.562955 22206 sgd_solver.cpp:106] Iteration 5960, lr = 0.0001
I0413 12:23:21.625830 22206 solver.cpp:242] Iteration 6000 (0.268339 iter/s, 149.065s/40 iter), loss = -6.43825e-20
I0413 12:23:21.625941 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:23:21.625950 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:23:21.625960 22206 sgd_solver.cpp:106] Iteration 6000, lr = 0.0001
I0413 12:25:50.510841 22206 solver.cpp:242] Iteration 6040 (0.26866 iter/s, 148.887s/40 iter), loss = -6.43825e-20
I0413 12:25:50.510959 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:25:50.510969 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:25:50.510980 22206 sgd_solver.cpp:106] Iteration 6040, lr = 0.0001
I0413 12:28:19.695318 22206 solver.cpp:242] Iteration 6080 (0.26812 iter/s, 149.187s/40 iter), loss = -6.43825e-20
I0413 12:28:19.695426 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:28:19.695436 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:28:19.695448 22206 sgd_solver.cpp:106] Iteration 6080, lr = 0.0001
I0413 12:30:37.608603 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_6118.caffemodel
I0413 12:30:37.700292 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6118.solverstate
I0413 12:30:48.915585 22206 solver.cpp:242] Iteration 6120 (0.268056 iter/s, 149.222s/40 iter), loss = -6.43825e-20
I0413 12:30:48.915632 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:30:48.915639 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:30:48.915649 22206 sgd_solver.cpp:106] Iteration 6120, lr = 0.0001
I0413 12:33:18.196563 22206 solver.cpp:242] Iteration 6160 (0.267947 iter/s, 149.283s/40 iter), loss = -6.43825e-20
I0413 12:33:18.196668 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:33:18.196678 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:33:18.196689 22206 sgd_solver.cpp:106] Iteration 6160, lr = 0.0001
I0413 12:35:47.248986 22206 solver.cpp:242] Iteration 6200 (0.268358 iter/s, 149.055s/40 iter), loss = -6.43825e-20
I0413 12:35:47.249079 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:35:47.249089 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:35:47.249099 22206 sgd_solver.cpp:106] Iteration 6200, lr = 0.0001
I0413 12:38:16.190786 22206 solver.cpp:242] Iteration 6240 (0.268557 iter/s, 148.944s/40 iter), loss = -6.43825e-20
I0413 12:38:16.190904 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:38:16.190914 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:38:16.190924 22206 sgd_solver.cpp:106] Iteration 6240, lr = 0.0001
I0413 12:40:45.315899 22206 solver.cpp:242] Iteration 6280 (0.268227 iter/s, 149.127s/40 iter), loss = -6.43825e-20
I0413 12:40:45.316043 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:40:45.316054 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:40:45.316066 22206 sgd_solver.cpp:106] Iteration 6280, lr = 0.0001
I0413 12:43:14.356199 22206 solver.cpp:242] Iteration 6320 (0.26838 iter/s, 149.042s/40 iter), loss = -6.43825e-20
I0413 12:43:14.356298 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:43:14.356307 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:43:14.356318 22206 sgd_solver.cpp:106] Iteration 6320, lr = 0.0001
I0413 12:45:43.493907 22206 solver.cpp:242] Iteration 6360 (0.268205 iter/s, 149.14s/40 iter), loss = -6.43825e-20
I0413 12:45:43.494006 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:45:43.494016 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:45:43.494027 22206 sgd_solver.cpp:106] Iteration 6360, lr = 0.0001
I0413 12:48:12.582765 22206 solver.cpp:242] Iteration 6400 (0.268292 iter/s, 149.091s/40 iter), loss = -6.43825e-20
I0413 12:48:12.582886 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:48:12.582897 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:48:12.582908 22206 sgd_solver.cpp:106] Iteration 6400, lr = 1e-05
I0413 12:50:38.043826 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_6440.caffemodel
I0413 12:50:38.135321 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6440.solverstate
I0413 12:50:38.208289 22206 solver.cpp:362] Iteration 6440, Testing net (#0)
I0413 12:50:38.208309 22206 net.cpp:723] Ignoring source layer train_data
I0413 12:50:38.208314 22206 net.cpp:723] Ignoring source layer train_label
I0413 12:50:38.208317 22206 net.cpp:723] Ignoring source layer train_transform
I0413 12:50:56.811767 22206 solver.cpp:429]     Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:50:56.811790 22206 solver.cpp:429]     Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:50:56.811796 22206 solver.cpp:429]     Test net output #2: mAP = 0
I0413 12:50:56.811801 22206 solver.cpp:429]     Test net output #3: precision = 0
I0413 12:50:56.811805 22206 solver.cpp:429]     Test net output #4: recall = 0
I0413 12:51:00.520951 22206 solver.cpp:242] Iteration 6440 (0.238179 iter/s, 167.941s/40 iter), loss = -6.43825e-20
I0413 12:51:00.520995 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:51:00.521003 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:51:00.521014 22206 sgd_solver.cpp:106] Iteration 6440, lr = 1e-05
I0413 12:53:29.562093 22206 solver.cpp:242] Iteration 6480 (0.268378 iter/s, 149.043s/40 iter), loss = -6.43825e-20
I0413 12:53:29.562188 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:53:29.562198 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:53:29.562208 22206 sgd_solver.cpp:106] Iteration 6480, lr = 1e-05
I0413 12:55:58.574651 22206 solver.cpp:242] Iteration 6520 (0.26843 iter/s, 149.015s/40 iter), loss = -6.43825e-20
I0413 12:55:58.574770 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:55:58.574780 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:55:58.574791 22206 sgd_solver.cpp:106] Iteration 6520, lr = 1e-05
I0413 12:58:27.761814 22206 solver.cpp:242] Iteration 6560 (0.268116 iter/s, 149.189s/40 iter), loss = -6.43825e-20
I0413 12:58:27.761919 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:58:27.761929 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:58:27.761940 22206 sgd_solver.cpp:106] Iteration 6560, lr = 1e-05
I0413 13:00:56.901226 22206 solver.cpp:242] Iteration 6600 (0.268202 iter/s, 149.142s/40 iter), loss = -6.43825e-20
I0413 13:00:56.901410 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:00:56.901429 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:00:56.901448 22206 sgd_solver.cpp:106] Iteration 6600, lr = 1e-05
I0413 13:03:25.910713 22206 solver.cpp:242] Iteration 6640 (0.268435 iter/s, 149.012s/40 iter), loss = -6.43825e-20
I0413 13:03:25.910823 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:03:25.910832 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:03:25.910843 22206 sgd_solver.cpp:106] Iteration 6640, lr = 1e-05
I0413 13:05:55.244607 22206 solver.cpp:242] Iteration 6680 (0.267852 iter/s, 149.336s/40 iter), loss = -6.43825e-20
I0413 13:05:55.244720 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:05:55.244731 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:05:55.244742 22206 sgd_solver.cpp:106] Iteration 6680, lr = 1e-05
I0413 13:08:24.363471 22206 solver.cpp:242] Iteration 6720 (0.268238 iter/s, 149.121s/40 iter), loss = -6.43825e-20
I0413 13:08:24.363572 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:08:24.363582 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:08:24.363593 22206 sgd_solver.cpp:106] Iteration 6720, lr = 1e-05
I0413 13:10:53.399459 22206 solver.cpp:242] Iteration 6760 (0.268388 iter/s, 149.038s/40 iter), loss = -6.43825e-20
I0413 13:10:53.399587 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:10:53.399597 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:10:53.399607 22206 sgd_solver.cpp:106] Iteration 6760, lr = 1e-05
I0413 13:10:57.139369 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_6762.caffemodel
I0413 13:10:57.231012 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6762.solverstate
I0413 13:13:22.875146 22206 solver.cpp:242] Iteration 6800 (0.267598 iter/s, 149.478s/40 iter), loss = -6.43825e-20
I0413 13:13:22.875260 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:13:22.875272 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:13:22.875283 22206 sgd_solver.cpp:106] Iteration 6800, lr = 1e-05
I0413 13:15:51.919904 22206 solver.cpp:242] Iteration 6840 (0.268372 iter/s, 149.047s/40 iter), loss = -6.43825e-20
I0413 13:15:51.920022 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:15:51.920032 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:15:51.920043 22206 sgd_solver.cpp:106] Iteration 6840, lr = 1e-05
I0413 13:18:21.060111 22206 solver.cpp:242] Iteration 6880 (0.2682 iter/s, 149.142s/40 iter), loss = -6.43825e-20
I0413 13:18:21.060223 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:18:21.060233 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:18:21.060245 22206 sgd_solver.cpp:106] Iteration 6880, lr = 1e-05
I0413 13:20:50.300302 22206 solver.cpp:242] Iteration 6920 (0.26802 iter/s, 149.242s/40 iter), loss = -6.43825e-20
I0413 13:20:50.300415 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:20:50.300423 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:20:50.300434 22206 sgd_solver.cpp:106] Iteration 6920, lr = 1e-05
I0413 13:23:19.393328 22206 solver.cpp:242] Iteration 6960 (0.268285 iter/s, 149.095s/40 iter), loss = -6.43825e-20
I0413 13:23:19.393443 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:23:19.393453 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:23:19.393465 22206 sgd_solver.cpp:106] Iteration 6960, lr = 1e-05
I0413 13:25:48.366895 22206 solver.cpp:242] Iteration 7000 (0.2685 iter/s, 148.976s/40 iter), loss = -6.43825e-20
I0413 13:25:48.367045 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:25:48.367056 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:25:48.367066 22206 sgd_solver.cpp:106] Iteration 7000, lr = 1e-05
I0413 13:28:17.712879 22206 solver.cpp:242] Iteration 7040 (0.267831 iter/s, 149.348s/40 iter), loss = -6.43825e-20
I0413 13:28:17.713006 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:28:17.713016 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:28:17.713027 22206 sgd_solver.cpp:106] Iteration 7040, lr = 1e-05
I0413 13:30:47.053527 22206 solver.cpp:242] Iteration 7080 (0.26784 iter/s, 149.343s/40 iter), loss = -6.43825e-20
I0413 13:30:47.053652 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:30:47.053661 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:30:47.053673 22206 sgd_solver.cpp:106] Iteration 7080, lr = 1e-05
I0413 13:30:58.248433 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_7084.caffemodel
I0413 13:30:58.340050 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7084.solverstate
I0413 13:33:16.445003 22206 solver.cpp:242] Iteration 7120 (0.267749 iter/s, 149.394s/40 iter), loss = -6.43825e-20
I0413 13:33:16.445122 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:33:16.445132 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:33:16.445143 22206 sgd_solver.cpp:106] Iteration 7120, lr = 1e-05
I0413 13:35:45.404561 22206 solver.cpp:242] Iteration 7160 (0.268525 iter/s, 148.962s/40 iter), loss = -6.43825e-20
I0413 13:35:45.404664 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:35:45.404673 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:35:45.404685 22206 sgd_solver.cpp:106] Iteration 7160, lr = 1e-05
I0413 13:38:14.508970 22206 solver.cpp:242] Iteration 7200 (0.268264 iter/s, 149.107s/40 iter), loss = -6.43825e-20
I0413 13:38:14.509091 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:38:14.509101 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:38:14.509112 22206 sgd_solver.cpp:106] Iteration 7200, lr = 1e-05
I0413 13:40:43.527680 22206 solver.cpp:242] Iteration 7240 (0.268419 iter/s, 149.021s/40 iter), loss = -6.43825e-20
I0413 13:40:43.527781 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:40:43.527791 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:40:43.527801 22206 sgd_solver.cpp:106] Iteration 7240, lr = 1e-05
I0413 13:43:12.574939 22206 solver.cpp:242] Iteration 7280 (0.268367 iter/s, 149.049s/40 iter), loss = -6.43825e-20
I0413 13:43:12.575076 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:43:12.575086 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:43:12.575098 22206 sgd_solver.cpp:106] Iteration 7280, lr = 1e-05
I0413 13:45:41.590486 22206 solver.cpp:242] Iteration 7320 (0.268425 iter/s, 149.018s/40 iter), loss = -6.43825e-20
I0413 13:45:41.590557 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:45:41.590566 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:45:41.590577 22206 sgd_solver.cpp:106] Iteration 7320, lr = 1e-05
I0413 13:48:10.610152 22206 solver.cpp:242] Iteration 7360 (0.268417 iter/s, 149.022s/40 iter), loss = -6.43825e-20
I0413 13:48:10.610265 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:48:10.610275 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:48:10.610286 22206 sgd_solver.cpp:106] Iteration 7360, lr = 1e-05
I0413 13:50:39.783463 22206 solver.cpp:242] Iteration 7400 (0.268141 iter/s, 149.175s/40 iter), loss = -6.43825e-20
I0413 13:50:39.783591 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:50:39.783601 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:50:39.783612 22206 sgd_solver.cpp:106] Iteration 7400, lr = 1e-05
I0413 13:50:58.456598 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_7406.caffemodel
I0413 13:50:58.548084 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7406.solverstate
I0413 13:53:09.213758 22206 solver.cpp:242] Iteration 7440 (0.26768 iter/s, 149.432s/40 iter), loss = -6.43825e-20
I0413 13:53:09.213860 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:53:09.213870 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:53:09.213881 22206 sgd_solver.cpp:106] Iteration 7440, lr = 1e-05
I0413 13:55:38.401008 22206 solver.cpp:242] Iteration 7480 (0.268116 iter/s, 149.189s/40 iter), loss = -6.43825e-20
I0413 13:55:38.401109 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:55:38.401119 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:55:38.401130 22206 sgd_solver.cpp:106] Iteration 7480, lr = 1e-05
I0413 13:58:07.487855 22206 solver.cpp:242] Iteration 7520 (0.268296 iter/s, 149.089s/40 iter), loss = -6.43825e-20
I0413 13:58:07.487967 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:58:07.487977 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:58:07.487988 22206 sgd_solver.cpp:106] Iteration 7520, lr = 1e-05
I0413 14:00:36.686660 22206 solver.cpp:242] Iteration 7560 (0.268095 iter/s, 149.201s/40 iter), loss = -6.43825e-20
I0413 14:00:36.686791 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:00:36.686801 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:00:36.686813 22206 sgd_solver.cpp:106] Iteration 7560, lr = 1e-05
I0413 14:03:05.957505 22206 solver.cpp:242] Iteration 7600 (0.267965 iter/s, 149.273s/40 iter), loss = -6.43825e-20
I0413 14:03:05.957608 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:03:05.957618 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:03:05.957629 22206 sgd_solver.cpp:106] Iteration 7600, lr = 1e-05
I0413 14:05:35.178156 22206 solver.cpp:242] Iteration 7640 (0.268056 iter/s, 149.223s/40 iter), loss = -6.43825e-20
I0413 14:05:35.178259 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:05:35.178268 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:05:35.178279 22206 sgd_solver.cpp:106] Iteration 7640, lr = 1e-05
I0413 14:08:04.350474 22206 solver.cpp:242] Iteration 7680 (0.268142 iter/s, 149.174s/40 iter), loss = -6.43825e-20
I0413 14:08:04.350613 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:08:04.350625 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:08:04.350634 22206 sgd_solver.cpp:106] Iteration 7680, lr = 1e-05
I0413 14:10:33.404428 22206 solver.cpp:242] Iteration 7720 (0.268355 iter/s, 149.056s/40 iter), loss = -6.43825e-20
I0413 14:10:33.404542 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:10:33.404552 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:10:33.404563 22206 sgd_solver.cpp:106] Iteration 7720, lr = 1e-05
I0413 14:10:59.537984 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_7728.caffemodel
I0413 14:10:59.629503 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7728.solverstate
I0413 14:13:02.751924 22206 solver.cpp:242] Iteration 7760 (0.267828 iter/s, 149.35s/40 iter), loss = -6.43825e-20
I0413 14:13:02.752023 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:13:02.752033 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:13:02.752044 22206 sgd_solver.cpp:106] Iteration 7760, lr = 1e-05
I0413 14:15:32.002921 22206 solver.cpp:242] Iteration 7800 (0.268001 iter/s, 149.253s/40 iter), loss = -6.43825e-20
I0413 14:15:32.003027 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:15:32.003038 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:15:32.003049 22206 sgd_solver.cpp:106] Iteration 7800, lr = 1e-05
I0413 14:18:01.238049 22206 solver.cpp:242] Iteration 7840 (0.26803 iter/s, 149.237s/40 iter), loss = -6.43825e-20
I0413 14:18:01.238193 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:18:01.238219 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:18:01.238237 22206 sgd_solver.cpp:106] Iteration 7840, lr = 1e-05
I0413 14:20:30.413532 22206 solver.cpp:242] Iteration 7880 (0.268137 iter/s, 149.178s/40 iter), loss = -6.43825e-20
I0413 14:20:30.413650 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:20:30.413661 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:20:30.413671 22206 sgd_solver.cpp:106] Iteration 7880, lr = 1e-05
I0413 14:22:59.518537 22206 solver.cpp:242] Iteration 7920 (0.268263 iter/s, 149.107s/40 iter), loss = -6.43825e-20
I0413 14:22:59.518635 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:22:59.518645 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:22:59.518656 22206 sgd_solver.cpp:106] Iteration 7920, lr = 1e-05
I0413 14:25:28.803967 22206 solver.cpp:242] Iteration 7960 (0.267939 iter/s, 149.288s/40 iter), loss = -6.43825e-20
I0413 14:25:28.804087 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:25:28.804098 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:25:28.804110 22206 sgd_solver.cpp:106] Iteration 7960, lr = 1e-05
I0413 14:27:57.851713 22206 solver.cpp:242] Iteration 8000 (0.268367 iter/s, 149.05s/40 iter), loss = -6.43825e-20
I0413 14:27:57.851835 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:27:57.851845 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:27:57.851856 22206 sgd_solver.cpp:106] Iteration 8000, lr = 1e-05
I0413 14:30:27.084547 22206 solver.cpp:242] Iteration 8040 (0.268034 iter/s, 149.235s/40 iter), loss = -6.43825e-20
I0413 14:30:27.084661 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:30:27.084671 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:30:27.084682 22206 sgd_solver.cpp:106] Iteration 8040, lr = 1e-05
I0413 14:31:00.638676 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_8050.caffemodel
I0413 14:31:00.730593 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8050.solverstate
I0413 14:31:00.803562 22206 solver.cpp:362] Iteration 8050, Testing net (#0)
I0413 14:31:00.803586 22206 net.cpp:723] Ignoring source layer train_data
I0413 14:31:00.803591 22206 net.cpp:723] Ignoring source layer train_label
I0413 14:31:00.803594 22206 net.cpp:723] Ignoring source layer train_transform
I0413 14:31:19.506942 22206 solver.cpp:429]     Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:31:19.506966 22206 solver.cpp:429]     Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:31:19.506973 22206 solver.cpp:429]     Test net output #2: mAP = 0
I0413 14:31:19.506978 22206 solver.cpp:429]     Test net output #3: precision = 0
I0413 14:31:19.506983 22206 solver.cpp:429]     Test net output #4: recall = 0
I0413 14:33:15.070456 22206 solver.cpp:242] Iteration 8080 (0.238112 iter/s, 167.988s/40 iter), loss = -6.43825e-20
I0413 14:33:15.070543 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:33:15.070551 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:33:15.070562 22206 sgd_solver.cpp:106] Iteration 8080, lr = 1e-05
I0413 14:35:44.386499 22206 solver.cpp:242] Iteration 8120 (0.267884 iter/s, 149.318s/40 iter), loss = -6.43825e-20
I0413 14:35:44.386603 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:35:44.386613 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:35:44.386625 22206 sgd_solver.cpp:106] Iteration 8120, lr = 1e-05
I0413 14:38:13.611971 22206 solver.cpp:242] Iteration 8160 (0.268047 iter/s, 149.228s/40 iter), loss = -6.43825e-20
I0413 14:38:13.612078 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:38:13.612088 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:38:13.612099 22206 sgd_solver.cpp:106] Iteration 8160, lr = 1e-05
I0413 14:40:42.722482 22206 solver.cpp:242] Iteration 8200 (0.268254 iter/s, 149.113s/40 iter), loss = -6.43825e-20
I0413 14:40:42.722584 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:40:42.722594 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:40:42.722604 22206 sgd_solver.cpp:106] Iteration 8200, lr = 1e-05
I0413 14:43:11.992506 22206 solver.cpp:242] Iteration 8240 (0.267967 iter/s, 149.272s/40 iter), loss = -6.43825e-20
I0413 14:43:11.992638 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:43:11.992650 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:43:11.992662 22206 sgd_solver.cpp:106] Iteration 8240, lr = 1e-05
I0413 14:45:41.087404 22206 solver.cpp:242] Iteration 8280 (0.268282 iter/s, 149.097s/40 iter), loss = -6.43825e-20
I0413 14:45:41.087527 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:45:41.087538 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:45:41.087548 22206 sgd_solver.cpp:106] Iteration 8280, lr = 1e-05
I0413 14:48:10.234676 22206 solver.cpp:242] Iteration 8320 (0.268188 iter/s, 149.149s/40 iter), loss = -6.43825e-20
I0413 14:48:10.234797 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:48:10.234807 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:48:10.234819 22206 sgd_solver.cpp:106] Iteration 8320, lr = 1e-05
I0413 14:50:39.711995 22206 solver.cpp:242] Iteration 8360 (0.267595 iter/s, 149.479s/40 iter), loss = -6.43825e-20
I0413 14:50:39.712088 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:50:39.712097 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:50:39.712108 22206 sgd_solver.cpp:106] Iteration 8360, lr = 1e-05
I0413 14:51:20.728899 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_8372.caffemodel
I0413 14:51:20.821074 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8372.solverstate
I0413 14:53:09.080492 22206 solver.cpp:242] Iteration 8400 (0.26779 iter/s, 149.371s/40 iter), loss = -6.43825e-20
I0413 14:53:09.080615 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:53:09.080626 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:53:09.080637 22206 sgd_solver.cpp:106] Iteration 8400, lr = 1e-05
I0413 14:55:38.304270 22206 solver.cpp:242] Iteration 8440 (0.26805 iter/s, 149.226s/40 iter), loss = -6.43825e-20
I0413 14:55:38.304374 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:55:38.304385 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:55:38.304396 22206 sgd_solver.cpp:106] Iteration 8440, lr = 1e-05
I0413 14:58:07.588831 22206 solver.cpp:242] Iteration 8480 (0.267941 iter/s, 149.287s/40 iter), loss = -6.43825e-20
I0413 14:58:07.588932 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:58:07.588943 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:58:07.588955 22206 sgd_solver.cpp:106] Iteration 8480, lr = 1e-05
I0413 15:00:36.969568 22206 solver.cpp:242] Iteration 8520 (0.267768 iter/s, 149.383s/40 iter), loss = -6.43825e-20
I0413 15:00:36.969687 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:00:36.969698 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:00:36.969708 22206 sgd_solver.cpp:106] Iteration 8520, lr = 1e-05
I0413 15:03:06.332303 22206 solver.cpp:242] Iteration 8560 (0.267801 iter/s, 149.365s/40 iter), loss = -6.43825e-20
I0413 15:03:06.332402 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:03:06.332412 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:03:06.332423 22206 sgd_solver.cpp:106] Iteration 8560, lr = 1e-05
I0413 15:05:35.588589 22206 solver.cpp:242] Iteration 8600 (0.267992 iter/s, 149.258s/40 iter), loss = -6.43825e-20
I0413 15:05:35.588707 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:05:35.588717 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:05:35.588728 22206 sgd_solver.cpp:106] Iteration 8600, lr = 1e-05
I0413 15:08:04.741399 22206 solver.cpp:242] Iteration 8640 (0.268178 iter/s, 149.155s/40 iter), loss = -6.43825e-20
I0413 15:08:04.741468 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:08:04.741478 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:08:04.741488 22206 sgd_solver.cpp:106] Iteration 8640, lr = 1e-05
I0413 15:10:34.081730 22206 solver.cpp:242] Iteration 8680 (0.267841 iter/s, 149.342s/40 iter), loss = -6.43825e-20
I0413 15:10:34.081846 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:10:34.081857 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:10:34.081869 22206 sgd_solver.cpp:106] Iteration 8680, lr = 1e-05
I0413 15:11:22.549012 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_8694.caffemodel
I0413 15:11:22.642297 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8694.solverstate
I0413 15:13:03.606060 22206 solver.cpp:242] Iteration 8720 (0.267511 iter/s, 149.526s/40 iter), loss = -6.43825e-20
I0413 15:13:03.606163 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:13:03.606173 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:13:03.606184 22206 sgd_solver.cpp:106] Iteration 8720, lr = 1e-05
I0413 15:15:33.144889 22206 solver.cpp:242] Iteration 8760 (0.267485 iter/s, 149.541s/40 iter), loss = -6.43825e-20
I0413 15:15:33.144995 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:15:33.145006 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:15:33.145016 22206 sgd_solver.cpp:106] Iteration 8760, lr = 1e-05
I0413 15:18:02.505872 22206 solver.cpp:242] Iteration 8800 (0.267804 iter/s, 149.363s/40 iter), loss = -6.43825e-20
I0413 15:18:02.506003 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:18:02.506013 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:18:02.506026 22206 sgd_solver.cpp:106] Iteration 8800, lr = 1e-05
I0413 15:20:32.117471 22206 solver.cpp:242] Iteration 8840 (0.267355 iter/s, 149.614s/40 iter), loss = -6.43825e-20
I0413 15:20:32.117594 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:20:32.117605 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:20:32.117616 22206 sgd_solver.cpp:106] Iteration 8840, lr = 1e-05
I0413 15:23:01.613730 22206 solver.cpp:242] Iteration 8880 (0.267561 iter/s, 149.498s/40 iter), loss = -6.43825e-20
I0413 15:23:01.613893 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:23:01.613903 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:23:01.613915 22206 sgd_solver.cpp:106] Iteration 8880, lr = 1e-05
I0413 15:25:31.309424 22206 solver.cpp:242] Iteration 8920 (0.267205 iter/s, 149.698s/40 iter), loss = -6.43825e-20
I0413 15:25:31.309556 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:25:31.309566 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:25:31.309579 22206 sgd_solver.cpp:106] Iteration 8920, lr = 1e-05
I0413 15:28:01.191401 22206 solver.cpp:242] Iteration 8960 (0.266873 iter/s, 149.884s/40 iter), loss = -6.43825e-20
I0413 15:28:01.191548 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:28:01.191558 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:28:01.191572 22206 sgd_solver.cpp:106] Iteration 8960, lr = 1e-05
I0413 15:30:31.321321 22206 solver.cpp:242] Iteration 9000 (0.266432 iter/s, 150.132s/40 iter), loss = -6.43825e-20
I0413 15:30:31.321535 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:30:31.321545 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:30:31.321558 22206 sgd_solver.cpp:106] Iteration 9000, lr = 1e-05
I0413 15:31:27.611608 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_9016.caffemodel
I0413 15:31:27.705700 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9016.solverstate
I0413 15:33:01.407253 22206 solver.cpp:242] Iteration 9040 (0.26651 iter/s, 150.088s/40 iter), loss = -6.43825e-20
I0413 15:33:01.407387 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:33:01.407397 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:33:01.407411 22206 sgd_solver.cpp:106] Iteration 9040, lr = 1e-05
I0413 15:35:31.367146 22206 solver.cpp:242] Iteration 9080 (0.266734 iter/s, 149.962s/40 iter), loss = -6.43825e-20
I0413 15:35:31.367285 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:35:31.367297 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:35:31.367310 22206 sgd_solver.cpp:106] Iteration 9080, lr = 1e-05
I0413 15:38:01.585503 22206 solver.cpp:242] Iteration 9120 (0.266275 iter/s, 150.22s/40 iter), loss = -6.43825e-20
I0413 15:38:01.585613 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:38:01.585623 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:38:01.585635 22206 sgd_solver.cpp:106] Iteration 9120, lr = 1e-05
I0413 15:40:31.827421 22206 solver.cpp:242] Iteration 9160 (0.266233 iter/s, 150.244s/40 iter), loss = -6.43825e-20
I0413 15:40:31.827508 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:40:31.827517 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:40:31.827530 22206 sgd_solver.cpp:106] Iteration 9160, lr = 1e-05
I0413 15:43:01.989017 22206 solver.cpp:242] Iteration 9200 (0.266376 iter/s, 150.164s/40 iter), loss = -6.43825e-20
I0413 15:43:01.989157 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:43:01.989168 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:43:01.989181 22206 sgd_solver.cpp:106] Iteration 9200, lr = 1e-05
I0413 15:45:32.128882 22206 solver.cpp:242] Iteration 9240 (0.266414 iter/s, 150.142s/40 iter), loss = -6.43825e-20
I0413 15:45:32.128963 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:45:32.128973 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:45:32.128984 22206 sgd_solver.cpp:106] Iteration 9240, lr = 1e-05
I0413 15:48:01.950661 22206 solver.cpp:242] Iteration 9280 (0.26698 iter/s, 149.824s/40 iter), loss = -6.43825e-20
I0413 15:48:01.950836 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:48:01.950848 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:48:01.950861 22206 sgd_solver.cpp:106] Iteration 9280, lr = 1e-05
I0413 15:50:31.726364 22206 solver.cpp:242] Iteration 9320 (0.267062 iter/s, 149.778s/40 iter), loss = -6.43825e-20
I0413 15:50:31.726516 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:50:31.726527 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:50:31.726542 22206 sgd_solver.cpp:106] Iteration 9320, lr = 1e-05
I0413 15:51:35.415482 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_9338.caffemodel
I0413 15:51:35.509249 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9338.solverstate
I0413 15:53:01.691311 22206 solver.cpp:242] Iteration 9360 (0.266725 iter/s, 149.967s/40 iter), loss = -6.43825e-20
I0413 15:53:01.691435 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:53:01.691445 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:53:01.691457 22206 sgd_solver.cpp:106] Iteration 9360, lr = 1e-05
I0413 15:55:31.599824 22206 solver.cpp:242] Iteration 9400 (0.266826 iter/s, 149.911s/40 iter), loss = -6.43825e-20
I0413 15:55:31.599951 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:55:31.599961 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:55:31.599973 22206 sgd_solver.cpp:106] Iteration 9400, lr = 1e-05
I0413 15:58:01.239950 22206 solver.cpp:242] Iteration 9440 (0.267304 iter/s, 149.642s/40 iter), loss = -6.43825e-20
I0413 15:58:01.240053 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:58:01.240063 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:58:01.240074 22206 sgd_solver.cpp:106] Iteration 9440, lr = 1e-05
I0413 16:00:31.107925 22206 solver.cpp:242] Iteration 9480 (0.266898 iter/s, 149.87s/40 iter), loss = -6.43825e-20
I0413 16:00:31.108005 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:00:31.108013 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:00:31.108026 22206 sgd_solver.cpp:106] Iteration 9480, lr = 1e-05
I0413 16:03:01.155339 22206 solver.cpp:242] Iteration 9520 (0.266578 iter/s, 150.05s/40 iter), loss = -6.43825e-20
I0413 16:03:01.155455 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:03:01.155465 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:03:01.155478 22206 sgd_solver.cpp:106] Iteration 9520, lr = 1e-05
I0413 16:05:31.172293 22206 solver.cpp:242] Iteration 9560 (0.266633 iter/s, 150.019s/40 iter), loss = -6.43825e-20
I0413 16:05:31.172405 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:05:31.172415 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:05:31.172428 22206 sgd_solver.cpp:106] Iteration 9560, lr = 1e-05
I0413 16:08:01.182947 22206 solver.cpp:242] Iteration 9600 (0.266644 iter/s, 150.013s/40 iter), loss = -6.43825e-20
I0413 16:08:01.183090 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:08:01.183101 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:08:01.183115 22206 sgd_solver.cpp:106] Iteration 9600, lr = 1e-06
I0413 16:10:31.031286 22206 solver.cpp:242] Iteration 9640 (0.266933 iter/s, 149.85s/40 iter), loss = -6.43825e-20
I0413 16:10:31.031422 22206 solver.cpp:261]     Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:10:31.031432 22206 solver.cpp:261]     Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:10:31.031445 22206 sgd_solver.cpp:106] Iteration 9640, lr = 1e-06
I0413 16:11:42.178346 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_9660.caffemodel
I0413 16:11:42.270309 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9660.solverstate
I0413 16:11:42.342959 22206 solver.cpp:362] Iteration 9660, Testing net (#0)
I0413 16:11:42.342983 22206 net.cpp:723] Ignoring source layer train_data
I0413 16:11:42.342988 22206 net.cpp:723] Ignoring source layer train_label
I0413 16:11:42.342991 22206 net.cpp:723] Ignoring source layer train_transform
I0413 16:12:01.052938 22206 solver.cpp:429]     Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:12:01.052963 22206 solver.cpp:429]     Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:12:01.052969 22206 solver.cpp:429]     Test net output #2: mAP = 0
I0413 16:12:01.052974 22206 solver.cpp:429]     Test net output #3: precision = 0
I0413 16:12:01.052979 22206 solver.cpp:429]     Test net output #4: recall = 0
I0413 16:12:01.052984 22206 solver.cpp:347] Optimization Done.
I0413 16:12:01.052987 22206 caffe.cpp:234] Optimization Done.

Now iam getting a accuracy graph as mentioned.How can i improve MAP value.

What param/model/dataset changes helped in increasing the accuracy to the
current value?
On Apr 20, 2017 6:31 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:

Now iam getting a accuracy graph as mentioned.How can i improve MAP value.
[image: screen shot 2017-04-20 at 6 30 06 am]
https://cloud.githubusercontent.com/assets/26631312/25208518/e02ba350-2592-11e7-87a9-8b99cb4cb9ec.png
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Please sent your custom detectnet being used now.I will change in it and sent you back.Please try on it.If possible,sent a image of yours being treated or please let me know the properties of the image being used.I hope i can help you.

On 20-Apr-2017, at 6:42 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
What param/model/dataset changes helped in increasing the accuracy to the
current value?
On Apr 20, 2017 6:31 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:

Now iam getting a accuracy graph as mentioned.How can i improve MAP value.
[image: screen shot 2017-04-20 at 6 30 06 am]
https://cloud.githubusercontent.com/assets/26631312/25208518/e02ba350-2592-11e7-87a9-8b99cb4cb9ec.png
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I'm using the customNetwork that I sent you before. Can you change that and
send it back? I'll try it out
Thanks!
On Apr 20, 2017 6:46 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:
Please sent your custom detectnet being used now.I will change in it and
sent you back.Please try on it.If possible,sent a image of yours being
treated or please let me know the properties of the image being used.I hope
i can help you.

On 20-Apr-2017, at 6:42 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
What param/model/dataset changes helped in increasing the accuracy to the
current value?
On Apr 20, 2017 6:31 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:

Now iam getting a accuracy graph as mentioned.How can i improve MAP
value.
[image: screen shot 2017-04-20 at 6 30 06 am]
<https://cloud.githubusercontent.com/assets/26631312/25208518/e02ba350-
2592-11e7-87a9-8b99cb4cb9ec.png>

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As mentioned before, my images are 1248x384. 2000 train and 500 test.
On Apr 20, 2017 6:46 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:

Please sent your custom detectnet being used now.I will change in it and
sent you back.Please try on it.If possible,sent a image of yours being
treated or please let me know the properties of the image being used.I hope
i can help you.

On 20-Apr-2017, at 6:42 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
What param/model/dataset changes helped in increasing the accuracy to the
current value?
On Apr 20, 2017 6:31 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:

Now iam getting a accuracy graph as mentioned.How can i improve MAP
value.
[image: screen shot 2017-04-20 at 6 30 06 am]
<https://cloud.githubusercontent.com/assets/
26631312/25208518/e02ba350-2592-11e7-87a9-8b99cb4cb9ec.png>
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I hope it will work for you too.Trying using this and please also let me know your status.Thank you

On 20-Apr-2017, at 6:49 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
I'm using the customNetwork that I sent you before. Can you change that and
send it back? I'll try it out
Thanks!
On Apr 20, 2017 6:46 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:
Please sent your custom detectnet being used now.I will change in it and
sent you back.Please try on it.If possible,sent a image of yours being
treated or please let me know the properties of the image being used.I hope
i can help you.

On 20-Apr-2017, at 6:42 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
What param/model/dataset changes helped in increasing the accuracy to the
current value?
On Apr 20, 2017 6:31 AM, "sulthanashafi"<EMAIL_ADDRESS>wrote:

Now iam getting a accuracy graph as mentioned.How can i improve MAP
value.
[image: screen shot 2017-04-20 at 6 30 06 am]
<https://cloud.githubusercontent.com/assets/26631312/25208518/e02ba350-
2592-11e7-87a9-8b99cb4cb9ec.png>

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}
}
}
######################################################################
End of convolutional network
######################################################################
Convert bboxes
layer {
name: "bbox_mask"
type: "Eltwise"
bottom: "bboxes"
bottom: "coverage-block"
top: "bboxes-masked"
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: "val" }
}
layer {
name: "bbox-norm"
type: "Eltwise"
bottom: "bboxes-masked"
bottom: "size-block"
top: "bboxes-masked-norm"
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: "val" }
}
layer {
name: "bbox-obj-norm"
type: "Eltwise"
bottom: "bboxes-masked-norm"
bottom: "obj-block"
top: "bboxes-obj-masked-norm"
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: "val" }
}
Loss layers
layer {
name: "bbox_loss"
type: "L1Loss"
bottom: "bboxes-obj-masked-norm"
bottom: "bbox-obj-label-norm"
top: "loss_bbox"
loss_weight: 2
include { phase: TRAIN }
include { phase: TEST stage: "val" }
}
layer {
name: "coverage_loss"
type: "EuclideanLoss"
bottom: "coverage"
bottom: "coverage-label"
top: "loss_coverage"
include { phase: TRAIN }
include { phase: TEST stage: "val" }
}
Cluster bboxes
layer {
type: 'Python'
name: 'cluster'
bottom: 'coverage'
bottom: 'bboxes'
top: 'bbox-list'
python_param {
module: 'caffe.layers.detectnet.clustering'
layer: 'ClusterDetections'
param_str : '1248, 352, 16, 0.06, 2, 0.025, 10'
}
include: { phase: TEST }
}
Calculate mean average precision
layer {
type: 'Python'
name: 'cluster_gt'
bottom: 'coverage-label'
bottom: 'bbox-label'
top: 'bbox-list-label'
python_param {
module: 'caffe.layers.detectnet.clustering'
layer: 'ClusterGroundtruth'
param_str : '1248, 352, 16'
}
include: { phase: TEST stage: "val" }
}
layer {
type: 'Python'
name: 'score'
bottom: 'bbox-list-label'
bottom: 'bbox-list'
top: 'bbox-list-scored'
python_param {
module: 'caffe.layers.detectnet.mean_ap'
layer: 'ScoreDetections'
}
include: { phase: TEST stage: "val" }
}
layer {
type: 'Python'
name: 'mAP'
bottom: 'bbox-list-scored'
top: 'mAP'
top: 'precision'
top: 'recall'
python_param {
module: 'caffe.layers.detectnet.mean_ap'
layer: 'mAP'
param_str : '1248, 352, 16'
}
include: { phase: TEST stage: "val" }
}

Also try using with and without custom class mentioned.Use ADAM Solver itself.

On 20-Apr-2017, at 6:57 AM, Sulthana Shafi<EMAIL_ADDRESS>wrote:
<customNetworkchange.txt>
I hope it will work for you too.Trying using this and please also let me know your status.Thank you

On 20-Apr-2017, at 6:49 AM, shreyasramesh<EMAIL_ADDRESS><EMAIL_ADDRESS>wrote:
I'm using the customNetwork that I sent you before. Can you change that and
send it back? I'll try it out
Thanks!
On Apr 20, 2017 6:46 AM, "sulthanashafi"<EMAIL_ADDRESS><EMAIL_ADDRESS>wrote:
Please sent your custom detectnet being used now.I will change in it and
sent you back.Please try on it.If possible,sent a image of yours being
treated or please let me know the properties of the image being used.I hope
i can help you.

On 20-Apr-2017, at 6:42 AM, shreyasramesh<EMAIL_ADDRESS><EMAIL_ADDRESS>wrote:
What param/model/dataset changes helped in increasing the accuracy to the
current value?
On Apr 20, 2017 6:31 AM, "sulthanashafi"<EMAIL_ADDRESS><EMAIL_ADDRESS>wrote:

Now iam getting a accuracy graph as mentioned.How can i improve MAP
value.
[image: screen shot 2017-04-20 at 6 30 06 am]
<https://cloud.githubusercontent.com/assets/26631312/25208518/e02ba350- https://cloud.githubusercontent.com/assets/26631312/25208518/e02ba350-
2592-11e7-87a9-8b99cb4cb9ec.png>

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@sulthanashafi https://github.com/NVIDIA/DIGITS/issues/1011 provides details on how you could improve your accuracy

Hi,Are you able to get boundingbox now.Then,may i know about your accuracy graph.
Thank you

On 21-Apr-2017, at 9:00 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
@sulthanashafi https://github.com/sulthanashafi #1011 https://github.com/NVIDIA/DIGITS/issues/1011 provides details on how you could improve your accuracy
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Hello, it did not work for me. Actually didn't get even classifier and coverage output.

Oh ..If you could share more about your image type /details being used.Then i will also try to find it out for you .

On 23-Apr-2017, at 11:30 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
Hello, it did not work for me. Actually didn't get even classifier and coverage output.
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Hi
I have exactly the same problem. All mAPs and recalls are zero but loss is decreasing. Does anyone managed to solve it?

Hi,Have you got the boundingbox for your system.May i please know how to take the coverage value alone from detectnet .

On 23-Apr-2017, at 11:30 AM, shreyasramesh<EMAIL_ADDRESS>wrote:
Hello, it did not work for me. Actually didn't get even classifier and coverage output.
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anyone finetuned the network for 2 class detection.Please help me

THE SOLUTION
We got the same problem, that mAP always stays at 0. For us, the problem was the way we created our custom dataset.
As the first poster mentioned, for every image, e.g. 0001.jpg he has a label called 0001.txt with this content:
car 0.0 0 0.0 12.0 172.0 402.0 318.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Problem: Original Kitti Dataset looks like this:
Car 0.96 0 -0.86 0.00 201.73 304.22 369.00 1.50 1.78 3.69 -3.16 1.68 3.36 -1.56
Cyclist 0.00 1 -1.60 991.74 147.41 1029.71 217.49 1.72 0.78 1.71 10.46 0.91 18.32 -1.08
Car 0.00 0 -0.02 760.94 163.22 878.26 207.91 1.44 1.64 3.68 7.22 0.90 24.06 0.27
DontCare -1 -1 -10 418.79 163.43 449.65 188.12 -1 -1 -1 -1000 -1000 -1000 -10
DontCare -1 -1 -10 600.71 159.32 702.49 176.82 -1 -1 -1 -1000 -1000 -1000 -10

Solution: You have to write "Car" and not "car" in your custom classes configuration in the dataset. Otherwise, all classes wich are not recognized in the dataset class mapping will be mapped to DontCare. The result is, that mAP will always be zero, because there are no classes besides background.

How to fix?
Name classes the same way as in Original Kitti dataset or change your class mapping to match your classes (also respect upper/lower case). E.g. if you have a custom dataset with class "Bear" and the following label file:
Bear 0.0 0 -1 9.0 229.0 57.0 260.0 -1 -1 -1 -1 -1 -1 -1

Set the custom class in your dataset to the following: DontCare,Bear
Now training actually works and mAP is 0.8 and higher.

Hi ,Thanks for your tip.Iam now working on multiple object detection,after
getting a satisfactory result from single object detection in Digits.I have
tried your suggestion too.But it is not learning for two class.Do you have
any other suggestion please.
On Mon, Oct 2, 2017 at 4:39 PM, plieningerweb<EMAIL_ADDRESS>wrote:

THE SOLUTION
We got the same problem, that mAP always stays at 0. For us, the problem
was the way we created our custom dataset.
As the first poster mentioned, for every image, e.g. 0001.jpg he has a
label called 0001.txt with this content:
car 0.0 0 0.0 12.0 172.0 402.0 318.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Problem: Original Kitti Dataset looks like this:
Car 0.96 0 -0.86 0.00 201.73 304.22 369.00 1.50 1.78 3.69 -3.16 1.68 3.36 -1.56
Cyclist 0.00 1 -1.60 991.74 147.41 1029.71 217.49 1.72 0.78 1.71 10.46 0.91 18.32 -1.08
Car 0.00 0 -0.02 760.94 163.22 878.26 207.91 1.44 1.64 3.68 7.22 0.90 24.06 0.27
DontCare -1 -1 -10 418.79 163.43 449.65 188.12 -1 -1 -1 -1000 -1000 -1000 -10
DontCare -1 -1 -10 600.71 159.32 702.49 176.82 -1 -1 -1 -1000 -1000 -1000 -10
Solution: You have to write "Car" and not "car" in your custom classes
configuration in the dataset. Otherwise, all classes wich are not
recognized in the dataset class mapping will be mapped to DontCare. The
result is, that mAP will always be zero, because there are no classes
besides background.
[image: screenshot from 2017-10-02 11-33-38]
https://user-images.githubusercontent.com/481107/31071904-1e06b0e8-a766-11e7-95f4-3bbc2fc230c7.png
How to fix?
Name classes the same way as in Original Kitti dataset or change your
class mapping to match your classes (also respect upper/lower case). E.g.
if you have a custom dataset with class "Bear" and the following label file:
Bear 0.0 0 -1 9.0 229.0 57.0 260.0 -1 -1 -1 -1 -1 -1 -1
Set the custom class in your dataset to the following: DontCare,Bear
Now training actually works and mAP is 0.8 and higher.
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Array not validating.

i have the dynamic input like below how to validate like this. Only first field is validate
<input type="text" name="arrival_time[]" required>
<input type="text" name="arrival_time[]" required>
<input type="text" name="arrival_time[]" required>

The name attribute must be unique to the form, as this is how the plugin keeps track of all input elements. However, it is allowed to use the same name for radio buttons and checkboxs.
So each group of radio or checkbox elements will share the same name since the value of this grouping represents a single piece of the form data.
They were many requests in the past from the community to add support for it, but the lack for time and/or people who are willing to implement it results in what you see now.
I would like to work on it, but my current situation doesn't help with that. So I promise nothing.

I manually applied this https://github.com/jquery-validation/jquery-validation/pull/2000
And it works for me.


Remove empty query variable from request when writing blueprint this way

I am using the Polls api to show my use case. It has one Resource section and under it two Action sections with different URI. It seems valid to me and the drafter doesn't complain either.

FORMAT: 1A
HOST: https://polls.apiblueprint.org/

# Polls

## Questions [/questions]

### List all questions [GET /questions{?page}]

+ Parameters
    + page (optional, number) - The page of questions to return

+ Response 200

## View a question detail  [GET /questions/{question_id}]

+ Parameters
    + question_id (required, number, `1`) ... ID of the Question in form of an integer

+ Response 200

But when I leave the page query empty, it still includes the default value of page=page in the request:

Hey @hassansin! Thank you very much for reporting this. We'll track it as a bug internally and it will be scheduled for fixing soon.

Awesome. Thanks!


When should dotnet build --runtime flag be used?

When should this runtime flag be used? Is it a general flag which applies to all use cases just to optimize the generated binaries for specific platform? Specifically, is it only used to build self-contained binaries or it can also be used to build app which runs on dotnet core SDK/Runtime?
EDIT by thraka to add doc metdata

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 910cd365-d63a-00dc-417c-db017f83bf6f
Version Independent ID: 5e495ddd-140e-de5c-a793-d4a9f88be51e
Content: dotnet build command - .NET Core CLI
Content Source: docs/core/tools/dotnet-build.md
Product: dotnet-core
Technology: dotnet-cli
GitHub Login: @mairaw
Microsoft Alias: mairaw

According to the RID article linked, the runtime identifier helps the engine pick OS specific dependencies like nuget packages. I also think it generates the appropriate binary.


Map swap

Create method to swap sites
Create API to swap sites. API is POST /map/v1/swapSites
Add test for API
Add tests for swapping sites over
Add tests for the create API

This change is

Some comments, but I can revisit after.
Previously, katheris (Katherine Stanley) wrote…
> #### Map swap
>
> Create method to swap sites
>
> Create API to swap sites. API is POST /map/v1/swapSites
>
> Add test for API
>
> Add tests for swapping sites over
>
> Add tests for the create API
>
>
>

Reviewed 2 of 7 files at r2, 14 of 14 files at r3.
Review status: all files reviewed at latest revision, 2 unresolved discussions.

map-app/src/main/java/net/wasdev/gameon/map/couchdb/SiteDocuments.java, line 81 [r3] (raw file):

public class SiteDocuments {

    @Resource(lookup="sweepId")

Assume this is temporary

map-app/src/main/java/net/wasdev/gameon/map/couchdb/SiteSwapper.java, line 23 [r3] (raw file):

import net.wasdev.gameon.map.models.Site;

public interface SiteSwapper {

Not sure why this interface is required.

Comments from Reviewable

Reviewed 2 of 7 files at r2, 11 of 14 files at r3.
Review status: all files reviewed at latest revision, 2 unresolved discussions.

map-app/src/main/java/net/wasdev/gameon/map/couchdb/SiteSwapper.java, line 23 [r3] (raw file):
Previously, ebullient (Erin Schnabel) wrote…
> Not sure why this interface is required.
Looks like it's related to the way that the auth privs are granted by the createPolicyForUser thing below... not sure it's entirely obvious at first glance.. looks like the auth stuff expects to be able to restrict access to resources, and access to this interface is used to protect the swapRooms stuff back in MapRepository.. I think it's all a bit odd tho, because this interface is never 'used' as an interface, just as a marker.. It could just have easily been a constant without a method on it ?? (it's forced to be a class by the way 'AccessCertainResourcesPolicy' expects a collection of those)

Comments from Reviewable

Review status: 14 of 16 files reviewed at latest revision, 2 unresolved discussions.

map-app/src/main/java/net/wasdev/gameon/map/couchdb/SiteDocuments.java, line 81 [r3] (raw file):
Previously, ebullient (Erin Schnabel) wrote…
> Assume this is temporary
Good catch, I added this in because I thought I would be using it for auth but didn't use it so I have removed it.

map-app/src/main/java/net/wasdev/gameon/map/couchdb/SiteSwapper.java, line 23 [r3] (raw file):
Previously, BarDweller wrote…
> Looks like it's related to the way that the auth privs are granted by the createPolicyForUser thing below... not sure it's entirely obvious at first glance.. looks like the auth stuff expects to be able to restrict access to resources, and access to this interface is used to protect the swapRooms stuff back in MapRepository.. I think it's all a bit odd tho, because this interface is never 'used' as an interface, just as a marker.. It could just have easily been a constant without a method on it ?? (it's forced to be a class by the way 'AccessCertainResourcesPolicy' expects a collection of those)
Ozzy is right, I am using it so that I didn't have to refactor the way that 'AccessCertainResourcesPolicy'  works. I'm not sure that it is the most intuitive way to do it, but it means we don't have to have two different systems. It may be that we want to refactor it at some point. I've added a comment on the class to explain.

Comments from Reviewable

Previously, ebullient (Erin Schnabel) wrote…
> Some comments, but I can revisit after.

Reviewed 2 of 2 files at r4.
Review status: all files reviewed at latest revision, 1 unresolved discussion.

Comments from Reviewable

Reviewed 1 of 14 files at r3, 2 of 2 files at r4.
Review status: all files reviewed at latest revision, 1 unresolved discussion.

Comments from Reviewable


Notes repeating at the start of the song

Describe your bug here.
On both Ourple Guy V3 and FNF: Poopshitters mod, the first 3 notes of the song keep repeating.
https://github.com/ShadowMario/FNF-PsychEngine/assets/107628201/060b50c6-b8ba-41a9-9a52-e90863b7f2ca
heres a showcase using the "too far" song from the Poopshitters mod
Command Prompt/Terminal logs (if existing)
No response
Are you modding a build from source or with Lua?
Lua
What is your build target?
Windows
Did you edit anything in this build? If so, mention or summarize your changes.
No

by the way, i am not compiling. i have simply downloaded the mentioned mods.

oh. i dont have sound on this computer, could that be it?


flameshot: fix executable path in dbus service

Motivation for this change
also some minor tweaks that makes the package more robust.
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Assured whether relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

We could probably get rid of most usages of services.dbus.packages by just pulling everything from environment.systemPackages instead by default.


bugFixGulpVersion

exactly as described here...
https://github.com/JeremyEnglert/JointsWP/issues/369

I've signed it.


Where can I get an AccessToken from an sample?

How do I get an external data source connection configuration from a virtual entity?

Podrobnosti dokumentu
⚠ Neupravujte tuto část. Vyžaduje se k propojení problémů docs.microsoft.com ➟ GitHub.

ID: f38df8a3-58d1-6cd9-2dac-4ea6c857f01e
Version Independent ID: 7bb3e8c6-067d-321b-375b-c1f9752e91b8
Content: Sample: Generic virtual entity data provider plug-in (Common Data Service) - Power Apps
Content Source: powerapps-docs/developer/common-data-service/virtual-entities/sample-generic-ve-plugin.md
Service: powerapps
GitHub Login: @Sunil-Garg
Microsoft Alias: pehecke

@vaclavrozsypal  Thanks for your feedback. We are closing this issue because we are either unable to find relevant information or reproduce this. If you're still experiencing the issue described here, please reopen or create a new issue.


Update scodec-bits to 1.1.23

Updates org.scodec:scodec-bits from 1.1.22 to 1.1.23.
GitHub Release Notes - Changelog - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "org.scodec", artifactId = "scodec-bits" } ]

labels: library-update, semver-patch

Codecov Report

Merging #85 (11d941e) into master (8b31961) will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##            master       #85   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files            3         3
  Lines           47        47
=========================================
  Hits            47        47

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Function "Search" on the Homepage

Story: When I open AM application, the function «Search» is first what I see on the Homepage, so that I can quickly start to search.
Acceptance criteria:

[x] The "Search" button is on the Top of the Homepage.
[x] The "Search" button can be selected
[x] The brief description (Tittle) is available above the button
-  EN: Find what You are looking for
-  DE: Finde was Du suchst
[x] Under the title the user hat quicke overview of the categories
no search funktionality will be implement here

Tasks:

[x] Adjust title

The container accommodation should be closed per default, so that the user have quick overview of all category.


Charts[Network]: Ensure support for external gossip (external network nodes)

we need to support external IP address for the network-node(x) service, the current solution does not provide that. This is so that an external consensus node can gossip with consensus nodes running within a fullstack-deployment k8s cluster.

[ ] create a regular network node service, the existing one is headless
[ ] expose the gossip port to be consumed by the external network node(s)
[ ] set the service to LoadBalancer type
[ ] required by https://github.com/hashgraph/solo/issues/306

:tada: This issue has been resolved in version 0.26.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Rename Stack.sizing to Stack.fit

The enum is called StackFit, so the property name "fit" makes more sense.

@Hixie @apwilson

I was just thinking this morning that there was a chance I'd forgotten to rename the property itself...
LGTM. Thanks.