Issue with transfer learning Yolov4 #7785

sandeepmohanadasan · 2021-06-09T12:40:14Z

sandeepmohanadasan
Jun 9, 2021

I am trying to do fine-tuning of an exiting Yolov4 model

!./darknet detector train data/obj.data cfg/{OUTPUT_PREFIX}-yolov4-detector.cfg backup/yolov4-detector_pretrained.weights -dont_show -map

But it is not taking the new data set for the training. It is just saving the yolov4-detector_final.weights backup folder while running the above code as below

 CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  
 OpenCV version: 3.2.0
 Prepare additional network for mAP calculation...
 compute_capability = 750, cudnn_half = 0 
net.optimized_memory = 0 
mini_batch = 1, batch = 24, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF
   3 route  1 		                           ->  208 x 208 x  64 
   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF
   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF
   9 route  8 2 	                           ->  208 x 208 x 128 
  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF
  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  13 route  11 		                           ->  104 x 104 x 128 
  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF
  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF
  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
  22 route  21 12 	                           ->  104 x 104 x 128 
  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF
  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 route  24 		                           ->   52 x  52 x 256 
  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  53 route  52 25 	                           ->   52 x  52 x 256 
  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF
  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 route  55 		                           ->   26 x  26 x 512 
  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  84 route  83 56 	                           ->   26 x  26 x 512 
  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF
  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  88 route  86 		                           ->   13 x  13 x1024 
  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
 103 route  102 87 	                           ->   13 x  13 x1024 
 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF
 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF
 109 route  107 		                           ->   13 x  13 x 512 
 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF
 111 route  107 		                           ->   13 x  13 x 512 
 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF
 113 route  112 110 108 107 	                   ->   13 x  13 x2048 
 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF
 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
 119 route  85 		                           ->   26 x  26 x 512 
 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 121 route  120 118 	                           ->   26 x  26 x 512 
 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
 129 route  54 		                           ->   52 x  52 x 256 
 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 131 route  130 128 	                           ->   52 x  52 x 256 
 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 138 conv     33       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  33 0.046 BF
 139 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 
 140 route  136 		                           ->   52 x  52 x 128 
 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF
 142 route  141 126 	                           ->   26 x  26 x 512 
 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 149 conv     33       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  33 0.023 BF
 150 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 
 151 route  147 		                           ->   26 x  26 x 256 
 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF
 153 route  152 116 	                           ->   13 x  13 x1024 
 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 160 conv     33       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  33 0.011 BF
 161 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 
Total BFLOPS 59.599 
avg_outputs = 490435 
 Allocate additional workspace_size = 52.43 MB 
chips-yolov4-detector
 compute_capability = 750, cudnn_half = 0 
net.optimized_memory = 0 
mini_batch = 2, batch = 48, time_steps = 1, train = 1 
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF
   3 route  1 		                           ->  208 x 208 x  64 
   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF
   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF
   9 route  8 2 	                           ->  208 x 208 x 128 
  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF
  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  13 route  11 		                           ->  104 x 104 x 128 
  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF
  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF
  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
  22 route  21 12 	                           ->  104 x 104 x 128 
  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF
  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 route  24 		                           ->   52 x  52 x 256 
  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF
  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  53 route  52 25 	                           ->   52 x  52 x 256 
  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF
  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 route  55 		                           ->   26 x  26 x 512 
  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF
  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  84 route  83 56 	                           ->   26 x  26 x 512 
  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF
  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  88 route  86 		                           ->   13 x  13 x1024 
  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF
 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF
 103 route  102 87 	                           ->   13 x  13 x1024 
 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF
 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF
 109 route  107 		                           ->   13 x  13 x 512 
 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF
 111 route  107 		                           ->   13 x  13 x 512 
 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF
 113 route  112 110 108 107 	                   ->   13 x  13 x2048 
 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF
 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
 119 route  85 		                           ->   26 x  26 x 512 
 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 121 route  120 118 	                           ->   26 x  26 x 512 
 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
 129 route  54 		                           ->   52 x  52 x 256 
 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 131 route  130 128 	                           ->   52 x  52 x 256 
 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 138 conv     33       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  33 0.046 BF
 139 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 
 140 route  136 		                           ->   52 x  52 x 128 
 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF
 142 route  141 126 	                           ->   26 x  26 x 512 
 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
 149 conv     33       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  33 0.023 BF
 150 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 
 151 route  147 		                           ->   26 x  26 x 256 
 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF
 153 route  152 116 	                           ->   13 x  13 x1024 
 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
 160 conv     33       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  33 0.011 BF
 161 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 
Total BFLOPS 59.599 
avg_outputs = 490435 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from backup/chips-yolov4-detector_pretrained.weights...
 seen 64, trained: 576 K-images (9 Kilo-batches_64) 
Done! Loaded 162 layers from weights-file 
Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005
Saving weights to backup//yolov4-detector_final.weights
 Create 6 permanent cpu-threads

sandeepmohanadasan · 2021-06-10T09:48:07Z

sandeepmohanadasan
Jun 10, 2021
Author

Hi @AlexeyAB,

I have increased the batch size from 64 to 80 and it resolved. The estimated training time was 7 hrs for 140 images( even though I used stopbackward=1 in cfg file. Is there something wrong with this? It will be helpful if you can throw some light on this.

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sandeepmohanadasan Jun 10, 2021 Author

sandeepmohanadasan
Jun 9, 2021

sandeepmohanadasan
Jun 10, 2021
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