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AlexeyAB / darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
http://pjreddie.com/darknet/
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Unused field while using yolov3 via c++ Detector class #4081

Open deimsdeutsch opened 4 years ago

deimsdeutsch commented 4 years ago

I am using the latest repo.

The following errors are not coming via ./darknet detector demo command line option. The following errors are happening when i load the code by making instance of the

Detector class.

        Detector *detector;

                                try
                                {
                detector = new Detector("./home.cfg","./home.weights",0);
                                }
Initializing Engine::
 Used GPU 0 
Couldn't find policy sgdr, going with constant
layer     filters    size              input                output
   0 conv     16  3 x 3 / 1   544 x 544 x   3   ->   544 x 544 x  16 0.256 BF
   1 max          2 x 2 / 2   544 x 544 x  16   ->   272 x 272 x  16 0.005 BF
   2 conv     32  3 x 3 / 1   272 x 272 x  16   ->   272 x 272 x  32 0.682 BF
   3 max          2 x 2 / 2   272 x 272 x  32   ->   136 x 136 x  32 0.002 BF
Unused field: 'antialiasing = 1'
   4 conv     64  3 x 3 / 1   136 x 136 x  32   ->   136 x 136 x  64 0.682 BF
   5 max          2 x 2 / 2   136 x 136 x  64   ->    68 x  68 x  64 0.001 BF
Unused field: 'antialiasing = 1'
   6 conv    128  3 x 3 / 1    68 x  68 x  64   ->    68 x  68 x 128 0.682 BF
   7 max          2 x 2 / 2    68 x  68 x 128   ->    34 x  34 x 128 0.001 BF
Unused field: 'antialiasing = 1'
   8 conv    256  3 x 3 / 1    34 x  34 x 128   ->    34 x  34 x 256 0.682 BF
   9 max          2 x 2 / 2    34 x  34 x 256   ->    17 x  17 x 256 0.000 BF
Unused field: 'antialiasing = 1'
  10 conv    512  3 x 3 / 1    17 x  17 x 256   ->    17 x  17 x 512 0.682 BF
  11 max          2 x 2 / 1    17 x  17 x 512   ->    17 x  17 x 512 0.001 BF
  12 conv   1024  3 x 3 / 1    17 x  17 x 512   ->    17 x  17 x1024 2.727 BF
  13 conv    256  1 x 1 / 1    17 x  17 x1024   ->    17 x  17 x 256 0.152 BF
  14 conv    512  3 x 3 / 1    17 x  17 x 256   ->    17 x  17 x 512 0.682 BF
Unused field: 'assisted_excitation = 4000'
  15 conv    128  1 x 1 / 1    17 x  17 x 512   ->    17 x  17 x 128 0.038 BF
  16 upsample            2x    17 x  17 x 128   ->    34 x  34 x 128
  17 route  16 8
  18 conv    128  1 x 1 / 1    34 x  34 x 384   ->    34 x  34 x 128 0.114 BF
  19 conv    256  3 x 3 / 1    34 x  34 x 128   ->    34 x  34 x 256 0.682 BF
  20 conv    128  1 x 1 / 1    34 x  34 x 256   ->    34 x  34 x 128 0.076 BF
  21 upsample            2x    34 x  34 x 128   ->    68 x  68 x 128
  22 route  21 6
  23 conv     64  1 x 1 / 1    68 x  68 x 256   ->    68 x  68 x  64 0.152 BF
  24 conv    128  3 x 3 / 1    68 x  68 x  64   ->    68 x  68 x 128 0.682 BF
  25 route  2
  26 max          16 x 16 / 16   272 x 272 x  32   ->    17 x  17 x  32 0.002 BF
  27 conv     64  1 x 1 / 1    17 x  17 x  32   ->    17 x  17 x  64 0.001 BF
  28 route  4
  29 max          8 x 8 / 8   136 x 136 x  64   ->    17 x  17 x  64 0.001 BF
  30 conv     64  1 x 1 / 1    17 x  17 x  64   ->    17 x  17 x  64 0.002 BF
  31 route  4
  32 max          8 x 8 / 4   136 x 136 x  64   ->    34 x  34 x  64 0.005 BF
Unused field: 'stride_x = 4'
Unused field: 'stride_y = 8'
  33 conv     64  1 x 1 / 2    34 x  34 x  64   ->    17 x  17 x  64 0.002 BF
Unused field: 'stride_x = 2'
Unused field: 'stride_y = 1'
  34 route  4
  35 max          8 x 8 / 8   136 x 136 x  64   ->    17 x  17 x  64 0.001 BF
Unused field: 'stride_x = 8'
Unused field: 'stride_y = 4'
  36 conv     64  1 x 1 / 1    17 x  17 x  64   ->    17 x  17 x  64 0.002 BF
Unused field: 'stride_x = 1'
Unused field: 'stride_y = 2'
  37 route  4
  38 max          8 x 8 / 8   136 x 136 x  64   ->    17 x  17 x  64 0.001 BF
  39 conv     64  1 x 1 / 1    17 x  17 x  64   ->    17 x  17 x  64 0.002 BF
  40 route  6
  41 max          4 x 4 / 4    68 x  68 x 128   ->    17 x  17 x 128 0.001 BF
  42 conv     64  1 x 1 / 1    17 x  17 x 128   ->    17 x  17 x  64 0.005 BF
  43 route  6
  44 max          4 x 4 / 2    68 x  68 x 128   ->    34 x  34 x 128 0.002 BF
Unused field: 'stride_x = 2'
Unused field: 'stride_y = 4'
  45 conv     64  1 x 1 / 2    34 x  34 x 128   ->    17 x  17 x  64 0.005 BF
Unused field: 'stride_x = 2'
Unused field: 'stride_y = 1'
  46 route  6
  47 max          4 x 4 / 4    68 x  68 x 128   ->    17 x  17 x 128 0.001 BF
Unused field: 'stride_x = 4'
Unused field: 'stride_y = 2'
  48 conv     64  1 x 1 / 1    17 x  17 x 128   ->    17 x  17 x  64 0.005 BF
Unused field: 'stride_x = 1'
Unused field: 'stride_y = 2'
  49 route  8
  50 max          2 x 2 / 2    34 x  34 x 256   ->    17 x  17 x 256 0.000 BF
  51 conv     64  1 x 1 / 1    17 x  17 x 256   ->    17 x  17 x  64 0.009 BF
  52 route  10
  53 conv     64  1 x 1 / 1    17 x  17 x 512   ->    17 x  17 x  64 0.019 BF
  54 route  53 51 48 45 42 39 36 33 30 27
  55 max          1 x 1 / 1    17 x  17 x 640   ->    17 x  17 x 640 0.000 BF
Unused field: 'maxpool_depth = 1'
Unused field: 'out_channels = 64'
  56 upsample            4x    17 x  17 x 640   ->    68 x  68 x 640
  57 route  56 24
  58 conv    128  3 x 3 / 1    68 x  68 x 768   ->    68 x  68 x 128 8.182 BF
  59 conv     30  1 x 1 / 1    68 x  68 x 128   ->    68 x  68 x  30 0.036 BF
  60 yolo
Unused field: 'iou_normalizer = 0.25'
Unused field: 'cls_normalizer = 1.0'
Unused field: 'iou_loss = giou'
Unused field: 'scale_x_y = 1.05'
  61 route  55
  62 upsample            2x    17 x  17 x 640   ->    34 x  34 x 640
  63 route  62 19
  64 conv    256  3 x 3 / 1    34 x  34 x 896   ->    34 x  34 x 256 4.773 BF
  65 conv     30  1 x 1 / 1    34 x  34 x 256   ->    34 x  34 x  30 0.018 BF
  66 yolo
Unused field: 'iou_normalizer = 0.25'
Unused field: 'cls_normalizer = 1.0'
Unused field: 'iou_loss = giou'
Unused field: 'scale_x_y = 1.1'
  67 route  55
  68 route  67 14
  69 conv    512  3 x 3 / 1    17 x  17 x1152   ->    17 x  17 x 512 3.068 BF
  70 conv     24  1 x 1 / 1    17 x  17 x 512   ->    17 x  17 x  24 0.007 BF
  71 yolo
Unused field: 'iou_normalizer = 0.25'
Unused field: 'cls_normalizer = 1.0'
Unused field: 'iou_loss = giou'
Unused field: 'scale_x_y = 1.2'
Total BFLOPS 25.129 
 Allocate additional workspace_size = 58.98 MB
dexception commented 4 years ago

Are you sure your using the latest repo ?

deimsdeutsch commented 4 years ago

@AlexeyAB I am using the latest repo. Am i doing something wrong ? I don't understand.

AlexeyAB commented 4 years ago

@deimsdeutsch Delete old Darknet library. And re-compile Darknet with LIBSO=1.

phonygene commented 4 years ago

@AlexeyAB I got similar issue,

I cloned latest repo to a new folder and change "LIBSO = 0" to "LIBSO = 1" in makefile then compiled it.

but still got 

Unused field: 'uc_normalizer = 0.07'
Unused field: 'beta1 = 0.6'

command :

darknet.exe detector train data.data csresnext50-panet-spp-original-optimal.cfg -map -gpus 0,1

log :

CUDA-version: 10000 (10010) Warning: CUDA-version is lower than Driver-version! , cuDNN: 7.6.1, CUDNN_HALF=1, GPU count: 2 OpenCV version: 4.1.0 0,1 Prepare additional network for mAP calculation... compute_capability = 750, cudnn_half = 1 net.optimized_memory = 0 batch = 1, time_steps = 1, train = 0 layer filters size/strd(dil) input output 0 conv 64 7 x 7/ 2 128 x 128 x 3 -> 64 x 64 x 64 0.077 BF 1 max 2x 2/ 2 64 x 64 x 64 -> 32 x 32 x 64 0.000 BF 2 conv 128 1 x 1/ 1 32 x 32 x 64 -> 32 x 32 x 128 0.017 BF 3 route 1 -> 32 x 32 x 64 4 conv 64 1 x 1/ 1 32 x 32 x 64 -> 32 x 32 x 64 0.008 BF 5 conv 128 1 x 1/ 1 32 x 32 x 64 -> 32 x 32 x 128 0.017 BF 6 conv 128/ 32 3 x 3/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.009 BF 7 conv 128 1 x 1/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.034 BF 8 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 32 x 32 x 128 0.000 BF ( 32 x 32 x 128) + ( 32 x 32 x 64) 9 conv 128 1 x 1/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.034 BF 10 conv 128/ 32 3 x 3/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.009 BF 11 conv 128 1 x 1/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.034 BF 12 Shortcut Layer: 8, wt = 0, wn = 0, outputs: 32 x 32 x 128 0.000 BF 13 conv 128 1 x 1/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.034 BF 14 conv 128/ 32 3 x 3/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.009 BF 15 conv 128 1 x 1/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.034 BF 16 Shortcut Layer: 12, wt = 0, wn = 0, outputs: 32 x 32 x 128 0.000 BF 17 conv 128 1 x 1/ 1 32 x 32 x 128 -> 32 x 32 x 128 0.034 BF 18 route 17 2 -> 32 x 32 x 256 19 conv 256 1 x 1/ 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BF 20 conv 256/ 32 3 x 3/ 2 32 x 32 x 256 -> 16 x 16 x 256 0.009 BF 21 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 22 route 20 -> 16 x 16 x 256 23 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 24 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 25 conv 256/ 32 3 x 3/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.009 BF 26 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 27 Shortcut Layer: 23, wt = 0, wn = 0, outputs: 16 x 16 x 256 0.000 BF 28 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 29 conv 256/ 32 3 x 3/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.009 BF 30 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 31 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 16 x 16 x 256 0.000 BF 32 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 33 conv 256/ 32 3 x 3/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.009 BF 34 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 35 Shortcut Layer: 31, wt = 0, wn = 0, outputs: 16 x 16 x 256 0.000 BF 36 conv 256 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 256 0.034 BF 37 route 36 21 -> 16 x 16 x 512 38 conv 512 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BF 39 conv 512/ 32 3 x 3/ 2 16 x 16 x 512 -> 8 x 8 x 512 0.009 BF 40 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 41 route 39 -> 8 x 8 x 512 42 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 43 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 44 conv 512/ 32 3 x 3/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.009 BF 45 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 46 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 8 x 8 x 512 0.000 BF 47 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 48 conv 512/ 32 3 x 3/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.009 BF 49 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 50 Shortcut Layer: 46, wt = 0, wn = 0, outputs: 8 x 8 x 512 0.000 BF 51 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 52 conv 512/ 32 3 x 3/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.009 BF 53 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 54 Shortcut Layer: 50, wt = 0, wn = 0, outputs: 8 x 8 x 512 0.000 BF 55 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 56 conv 512/ 32 3 x 3/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.009 BF 57 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 58 Shortcut Layer: 54, wt = 0, wn = 0, outputs: 8 x 8 x 512 0.000 BF 59 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 60 conv 512/ 32 3 x 3/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.009 BF 61 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 62 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 8 x 8 x 512 0.000 BF 63 conv 512 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 512 0.034 BF 64 route 63 40 -> 8 x 8 x1024 65 conv 1024 1 x 1/ 1 8 x 8 x1024 -> 8 x 8 x1024 0.134 BF 66 conv 1024/ 32 3 x 3/ 2 8 x 8 x1024 -> 4 x 4 x1024 0.009 BF 67 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 68 route 66 -> 4 x 4 x1024 69 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 70 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 71 conv 1024/ 32 3 x 3/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.009 BF 72 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 73 Shortcut Layer: 69, wt = 0, wn = 0, outputs: 4 x 4 x1024 0.000 BF 74 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 75 conv 1024/ 32 3 x 3/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.009 BF 76 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 77 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 4 x 4 x1024 0.000 BF 78 conv 1024 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x1024 0.034 BF 79 route 78 67 -> 4 x 4 x2048 80 conv 2048 1 x 1/ 1 4 x 4 x2048 -> 4 x 4 x2048 0.134 BF 81 conv 512 1 x 1/ 1 4 x 4 x2048 -> 4 x 4 x 512 0.034 BF 82 conv 1024 3 x 3/ 1 4 x 4 x 512 -> 4 x 4 x1024 0.151 BF 83 conv 512 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x 512 0.017 BF 84 max 5x 5/ 1 4 x 4 x 512 -> 4 x 4 x 512 0.000 BF 85 route 83 -> 4 x 4 x 512 86 max 9x 9/ 1 4 x 4 x 512 -> 4 x 4 x 512 0.001 BF 87 route 83 -> 4 x 4 x 512 88 max 13x13/ 1 4 x 4 x 512 -> 4 x 4 x 512 0.001 BF 89 route 88 86 84 83 -> 4 x 4 x2048 90 conv 512 1 x 1/ 1 4 x 4 x2048 -> 4 x 4 x 512 0.034 BF 91 conv 1024 3 x 3/ 1 4 x 4 x 512 -> 4 x 4 x1024 0.151 BF 92 conv 512 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x 512 0.017 BF 93 conv 256 1 x 1/ 1 4 x 4 x 512 -> 4 x 4 x 256 0.004 BF 94 upsample 2x 4 x 4 x 256 -> 8 x 8 x 256 95 route 65 -> 8 x 8 x1024 96 conv 256 1 x 1/ 1 8 x 8 x1024 -> 8 x 8 x 256 0.034 BF 97 route 96 94 -> 8 x 8 x 512 98 conv 256 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BF 99 conv 512 3 x 3/ 1 8 x 8 x 256 -> 8 x 8 x 512 0.151 BF 100 conv 256 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BF 101 conv 512 3 x 3/ 1 8 x 8 x 256 -> 8 x 8 x 512 0.151 BF 102 conv 256 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BF 103 conv 128 1 x 1/ 1 8 x 8 x 256 -> 8 x 8 x 128 0.004 BF 104 upsample 2x 8 x 8 x 128 -> 16 x 16 x 128 105 route 38 -> 16 x 16 x 512 106 conv 128 1 x 1/ 1 16 x 16 x 512 -> 16 x 16 x 128 0.034 BF 107 route 106 104 -> 16 x 16 x 256 108 conv 128 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 128 0.017 BF 109 conv 256 3 x 3/ 1 16 x 16 x 128 -> 16 x 16 x 256 0.151 BF 110 conv 128 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 128 0.017 BF 111 conv 256 3 x 3/ 1 16 x 16 x 128 -> 16 x 16 x 256 0.151 BF 112 conv 128 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 128 0.017 BF 113 conv 256 3 x 3/ 1 16 x 16 x 128 -> 16 x 16 x 256 0.151 BF 114 conv 21 1 x 1/ 1 16 x 16 x 256 -> 16 x 16 x 21 0.003 BF 115 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20 nms_kind: greedynms (1), beta = 0.600000 Unused field: 'uc_normalizer = 0.07' Unused field: 'beta1 = 0.6' 116 route 112 -> 16 x 16 x 128 117 conv 256 3 x 3/ 2 16 x 16 x 128 -> 8 x 8 x 256 0.038 BF 118 route 117 102 -> 8 x 8 x 512 119 conv 256 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BF 120 conv 512 3 x 3/ 1 8 x 8 x 256 -> 8 x 8 x 512 0.151 BF 121 conv 256 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BF 122 conv 512 3 x 3/ 1 8 x 8 x 256 -> 8 x 8 x 512 0.151 BF 123 conv 256 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BF 124 conv 512 3 x 3/ 1 8 x 8 x 256 -> 8 x 8 x 512 0.151 BF 125 conv 21 1 x 1/ 1 8 x 8 x 512 -> 8 x 8 x 21 0.001 BF 126 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10 nms_kind: greedynms (1), beta = 0.600000 Unused field: 'uc_normalizer = 0.07' Unused field: 'beta1 = 0.6' 127 route 123 -> 8 x 8 x 256 128 conv 512 3 x 3/ 2 8 x 8 x 256 -> 4 x 4 x 512 0.038 BF 129 route 128 92 -> 4 x 4 x1024 130 conv 512 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x 512 0.017 BF 131 conv 1024 3 x 3/ 1 4 x 4 x 512 -> 4 x 4 x1024 0.151 BF 132 conv 512 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x 512 0.017 BF 133 conv 1024 3 x 3/ 1 4 x 4 x 512 -> 4 x 4 x1024 0.151 BF 134 conv 512 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x 512 0.017 BF 135 conv 1024 3 x 3/ 1 4 x 4 x 512 -> 4 x 4 x1024 0.151 BF 136 conv 21 1 x 1/ 1 4 x 4 x1024 -> 4 x 4 x 21 0.001 BF 137 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Unused field: 'uc_normalizer = 0.07' Unused field: 'beta1 = 0.6' Total BFLOPS 4.405 avg_outputs = 48809 Allocate additional workspace_size = 52.43 MB

AlexeyAB commented 4 years ago

@phonygene

Unused field: 'uc_normalizer = 0.07' Unused field: 'beta1 = 0.6'

This is normal.