训练期间yolov4自定义对象检测错误

时间:2020-08-25 14:18:37

标签: python opencv yolo darknet

我在自定义数据集上训练yolov4时收到以下错误:

C:\yolo_v4\yolo_v4_mask_detection\darknet\build\darknet\x64>darknet.exe detector train data/obj.data cfg/yolo-obj.cfg yolov4.conv.137  CUDA-version: 10010 (11000), cuDNN: 7.6.5, GPU count: 1  OpenCV version: 4.1.0 valid: Using default 'data/train.txt' yolo-obj  0 : compute_capability = 750, cudnn_half = 0, GPU: GeForce RTX 2070 Super with Max-Q Design net.optimized_memory = 0 mini_batch = 4, batch = 64, 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     21       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  21 0.029 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     21       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  21
0.015 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     21       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  21
0.007 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 59.570 avg_outputs = 489910  Allocate additional workspace_size = 52.43 MB Loading weights from yolov4.conv.137...  seen 64, trained: 0 K-images (0 Kilo-batches_64) Done! Loaded 137 layers from weights-file Learning Rate: 0.001, Momentum: 0.949, Decay:
0.0005  Detection layer: 139 - type = 27  Detection layer: 150 - type = 27  Detection layer: 161 - type = 27  If error occurs - run training with flag: -dont_show Resizing, random_coef = 1.40

 608 x 608  Create 6 permanent cpu-threads Cannot load image data/obj/asian_mask246.txt Cannot load image data/obj/asian_mask74.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe3645.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_207.txt

 Error in load_data_detection() - OpenCV Cannot load image  Error in load_data_detection() - OpenCV data/obj/maskframe2070.txt Cannot load image data/obj/new_227.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe2790.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/42.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe2385.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe8685.txt

 Error in load_data_detection() - OpenCV

 Error in load_data_detection() - OpenCV Cannot load image data/obj/crowd_mask181.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_151.txt Cannot load image data/obj/maskframe105.txt

 Error in load_data_detection() - OpenCV

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask278.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe3675.txtCannot load image data/obj/new_85.txt


 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask209.txt

 Error in load_data_detection() - OpenCV Cannot load image  Error in load_data_detection() - OpenCV data/obj/asian_mask192.txt Cannot load image  Error in load_data_detection() - OpenCV data/obj/asian_mask36.txt Cannot load image data/obj/asian_mask87.txt

 Error in load_data_detection() - OpenCV

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe1500.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask253.txtCannot load image data/obj/crowd_mask39.txt


 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_116.txt

 Error in load_data_detection() - OpenCV

 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_1.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_124.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask8.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask28.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/81.txt Cannot load image data/obj/maskframe6045.txt

 Error in load_data_detection() - OpenCV

 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_162.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask220.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe2280.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe4965.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask111.txt Cannot load image data/obj/new_65.txt

 Error in load_data_detection() - OpenCV

 Error in load_data_detection() - OpenCV Cannot load image data/obj/60.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/asian_mask65.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/37.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/new_234.txt

 Error in load_data_detection() - OpenCV Cannot load image data/obj/maskframe5325.txt

 Error in load_data_detection() - OpenCV

C:\yolo_v4\yolo_v4_mask_detection\darknet\build\darknet\x64>darknet.exe detector train data/obj.data cfg/yolo-obj.cfg yolov4.conv.137

我被困在这里,我是新手,我认为问题出在将数据集转换为yolov4格式,因为我使用了以下代码:

import os
import random

imgspath = 'C:/yolo_v4/yolo_v4_mask_detection/darknet/build/darknet/x64/data/obj'
path = 'data/obj/'


images = []
for i in os.listdir(imgspath):
    temp = path+i
    images.append(temp)
# train and test split... adjust it if necessary
trainlen = round(len(images)*.80)
testlen = round(len(images)*.20)
#print('total, train, test dataset size -',trainlen+testlen,trainlen,testlen)
random.shuffle(images)
test = images[:testlen]

train = images[testlen:]

with open('train.txt', 'w') as f:
    for item in train:
        f.write("%s\n" % item)
with open('test.txt', 'w') as f:
    for item in test:
        f.write("%s\n" % item)

我认为该程序是错误的,将不胜感激。

2 个答案:

答案 0 :(得分:1)

这是文件路径的问题,只需检查一下即可。

答案 1 :(得分:1)

我还不知道如何解决,但我知道是什么原因造成的。

我尝试使用 6 通道图像进行训练,但 Yolo 内部使用 OpenCV,目前无法读取超过 3 通道的图像。

如果不是这种情况,则必须是以下之一

  1. 检查train.txtobj.data文件是否正确配置
  2. 检查您是否可以在 python > opencv 中打开和读取数据集中抛出错误的文件。