Accuracy, mAP etc not displayed on graph

Hello!

Finally, I’m processing my photos on the AWS GPU and it appears to be working, with 2,700 photos it rattles through 300 epochs in just over 2 hours :)

However, the graph only displays loss bbox (train) and loss coverage (train) and nothing else. It would be really useful to see some accuracy stats eg mAP etc.

I’m using nvcr.io/nvidia/digits:18.10. Does version 18.11 fix this problem or is there something else I’m missing here? Is it because the training is just not converging at all?

Here’s the final section of the café log: … Thanks!

I1125 16:04:01.751603   122 solver.cpp:333]     [0.0] Iteration 63102 (8.37129 iter/s, 3.10585s/26 iter), 299.2/300.1ep, loss = 62.6671
I1125 16:04:01.751641   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 1.33363 (* 2 = 2.66725 loss)
I1125 16:04:01.751652   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 28.2475 (* 1 = 28.2475 loss)
I1125 16:04:01.751662   122 sgd_solver.cpp:180] [0.0] Iteration 63102, lr = 0.00133686, m = 0.9, lrm = 0.0133686, wd = 0.0001, gs = 1
I1125 16:04:04.856482   122 solver.cpp:333]     [0.0] Iteration 63128 (8.37389 iter/s, 3.10489s/26 iter), 299.3/300.1ep, loss = 47.7302
I1125 16:04:04.856518   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 0.612215 (* 2 = 1.22443 loss)
I1125 16:04:04.856529   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 14.7535 (* 1 = 14.7535 loss)
I1125 16:04:04.856539   122 sgd_solver.cpp:180] [0.0] Iteration 63128, lr = 0.00133575, m = 0.9, lrm = 0.0133575, wd = 0.0001, gs = 1
I1125 16:04:07.997928   122 solver.cpp:333]     [0.0] Iteration 63154 (8.27649 iter/s, 3.14143s/26 iter), 299.4/300.1ep, loss = 65.7498
I1125 16:04:07.998165   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 9.23087 (* 2 = 18.4617 loss)
I1125 16:04:07.998178   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 15.5358 (* 1 = 15.5358 loss)
I1125 16:04:07.998188   122 sgd_solver.cpp:180] [0.0] Iteration 63154, lr = 0.00133465, m = 0.9, lrm = 0.0133465, wd = 0.0001, gs = 1
I1125 16:04:11.123752   122 solver.cpp:333]     [0.0] Iteration 63180 (8.3178 iter/s, 3.12583s/26 iter), 299.6/300.1ep, loss = 86.7913
I1125 16:04:11.123792   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 3.1863 (* 2 = 6.3726 loss)
I1125 16:04:11.123802   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 48.6664 (* 1 = 48.6664 loss)
I1125 16:04:11.123812   122 sgd_solver.cpp:180] [0.0] Iteration 63180, lr = 0.00133354, m = 0.9, lrm = 0.0133354, wd = 0.0001, gs = 1
I1125 16:04:14.281015   122 solver.cpp:333]     [0.0] Iteration 63206 (8.23498 iter/s, 3.15726s/26 iter), 299.7/300.1ep, loss = 48.9389
I1125 16:04:14.281065   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 0.586028 (* 2 = 1.17206 loss)
I1125 16:04:14.281081   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 16.0145 (* 1 = 16.0145 loss)
I1125 16:04:14.281095   122 sgd_solver.cpp:180] [0.0] Iteration 63206, lr = 0.00133244, m = 0.9, lrm = 0.0133244, wd = 0.0001, gs = 1
I1125 16:04:17.436023   122 solver.cpp:333]     [0.0] Iteration 63232 (8.24083 iter/s, 3.15502s/26 iter), 299.8/300.1ep, loss = 62.0538
I1125 16:04:17.436064   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 1.52028 (* 2 = 3.04057 loss)
I1125 16:04:17.436074   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 27.2609 (* 1 = 27.2609 loss)
I1125 16:04:17.436085   122 sgd_solver.cpp:180] [0.0] Iteration 63232, lr = 0.00133133, m = 0.9, lrm = 0.0133133, wd = 0.0001, gs = 1
I1125 16:04:18.395835   164 data_reader.cpp:321] Restarting data pre-fetching
I1125 16:04:18.546618   166 data_reader.cpp:321] Restarting data pre-fetching
I1125 16:04:20.560073   122 solver.cpp:333]     [0.0] Iteration 63258 (8.32254 iter/s, 3.12405s/26 iter), 299.9/300.1ep, loss = 47.1324
I1125 16:04:20.560114   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 0.808282 (* 2 = 1.61656 loss)
I1125 16:04:20.560124   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 13.7635 (* 1 = 13.7635 loss)
I1125 16:04:20.560135   122 sgd_solver.cpp:180] [0.0] Iteration 63258, lr = 0.00133023, m = 0.9, lrm = 0.0133023, wd = 0.0001, gs = 1
I1125 16:04:23.663071   122 solver.cpp:333]     [0.0] Iteration 63284 (8.37897 iter/s, 3.10301s/26 iter), 300.1/300.1ep, loss = 53.6399
I1125 16:04:23.663107   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 1.36236 (* 2 = 2.72472 loss)
I1125 16:04:23.663117   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 19.1629 (* 1 = 19.1629 loss)
I1125 16:04:23.663127   122 sgd_solver.cpp:180] [0.0] Iteration 63284, lr = 0.00132913, m = 0.9, lrm = 0.0132913, wd = 0.0001, gs = 1
I1125 16:04:25.453852   122 solver.cpp:333]     [0.0] Iteration 63300 (8.37628 iter/s, 1.79077s/15 iter), 300.1/300.1ep, loss = 68.1711
I1125 16:04:25.453889   122 solver.cpp:361]     [0.0]     Train net output #0: loss_bbox = 1.57427 (* 2 = 3.14854 loss)
I1125 16:04:25.453899   122 solver.cpp:361]     [0.0]     Train net output #1: loss_coverage = 33.2703 (* 1 = 33.2703 loss)
I1125 16:04:25.453910   122 solver.cpp:769] Snapshotting to binary proto file snapshot_iter_63300.caffemodel
I1125 16:04:25.496446   122 sgd_solver.cpp:419] Snapshotting solver state to binary proto file snapshot_iter_63300.solverstate
I1125 16:04:25.533502   122 solver.cpp:501] Iteration 63300, Testing net (#0)
I1125 16:04:33.152792   149 data_reader.cpp:321] Restarting data pre-fetching
I1125 16:04:33.300627   146 data_reader.cpp:321] Restarting data pre-fetching
I1125 16:04:34.276348   122 solver.cpp:588]     (0.0)    Test net output #0: loss_bbox = 1.08538 (* 2 = 2.17077 loss)
I1125 16:04:34.276383   122 solver.cpp:588]     (0.0)    Test net output #1: loss_coverage = 26.1989 (* 1 = 26.1989 loss)
I1125 16:04:34.276424   122 solver.cpp:588]     (0.0)    Test net output #2: mAP = 0
I1125 16:04:34.276433   122 solver.cpp:588]     (0.0)    Test net output #3: precision = 0
I1125 16:04:34.276438   122 solver.cpp:588]     (0.0)    Test net output #4: recall = 0
I1125 16:04:34.276461   122 caffe.cpp:265] Solver performance on device 0: 8.144 * 10 = 81.44 img/sec (63300 itr in 7772 sec)
I1125 16:04:34.276476   122 caffe.cpp:269] Optimization Done in 2h 10m 51s

Screenshot of graph:

The fix was in Merge pull request #2141 from IsaacYangSLA/fix_caffe_parser · NVIDIA/DIGITS@9f7bece · GitHub.

Hello!
I follow the guide in DIGITS/README.md at master · NVIDIA/DIGITS · GitHub, and finished the training of object detection. However, there is no boxes displayed when I test any picture. Besides, I also cannot find map and accuracy.

The task is finished in PC, Ubuntu16.04

Can you give me some advice? Thanks!