无法打开标签文件 这个只有用MSCOCO才正常 Yolo

分类：编程问答

如何解决无法打开标签文件 这个只有用MSCOCO才正常 Yolo

我在 Yolo 的训练过程中遇到了这个错误。

.data 文件有

classes = 1
train = data/train.txt
valid = data/valid.txt
names = classes.names
backup = backup

train.txt 已放置在“data”文件夹中，其中包含完整的图像集。 train.txt 包含：

data/f1a92173-262b-42d1-b64e-213cdf6afdae.jpg
data/92ef9a59-d770-40b6-9ef5-2f909d6cc1c8.jpg
data/ea94af95-b492-4123-9fd7-04ef7ec79608.jpg
data/d288b5f6-f976-4fca-b7a1-b64cb6475d51.jpg
data/6e74d273-9bdb-4fb0-8a0c-edfda365df2e.jpg
data/a886d1b3-9373-46c6-9c65-b224c21cc4a1.jpg
data/89beedca-e0ce-45df-b840-218f54de8fae.jpg
data/3be0c383-ad5b-422e-a003-5676bc9d66df.jpg
data/04204973-a034-4a50-a43e-2ae45186aa50.jpg
data/aaabfc1b-6519-4c80-ad64-60612890501d.jpg
data/bb10de59-4206-46ca-b28d-c0750d460829.jpg
...

cfg 文件已被适当修改，用于运行训练的命令是：

!darknet/darknet detector train labelled_data.data darknet/cfg/yolov4_custom.cfg custom_weight/yolov4.conv.137 -dont_show

我正在使用 Colab 运行模型。

我得到的错误是：（竞争日志）

CUDA-version: 10010 (11020),cuDNN: 7.6.5,GPU count: 1  
 OpenCV version: 3.2.0
yolov4_custom
 0 : compute_capability = 750,cudnn_half = 0,GPU: Tesla T4 
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    608 x 608 x   1 ->  608 x 608 x  32 0.213 BF
   1 conv     64       3 x 3/ 2    608 x 608 x  32 ->  304 x 304 x  64 3.407 BF
   2 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   3 route  1                                  ->  304 x 304 x  64 
   4 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   5 conv     32       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  32 0.379 BF
   6 conv     64       3 x 3/ 1    304 x 304 x  32 ->  304 x 304 x  64 3.407 BF
   7 Shortcut Layer: 4,wt = 0,wn = 0,outputs: 304 x 304 x  64 0.006 BF
   8 conv     64       1 x 1/ 1    304 x 304 x  64 ->  304 x 304 x  64 0.757 BF
   9 route  8 2                                ->  304 x 304 x 128 
  10 conv     64       1 x 1/ 1    304 x 304 x 128 ->  304 x 304 x  64 1.514 BF
  11 conv    128       3 x 3/ 2    304 x 304 x  64 ->  152 x 152 x 128 3.407 BF
  12 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF
  13 route  11                                 ->  152 x 152 x 128 
  14 conv     64       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x  64 0.379 BF
  15 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF
  16 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF
  17 Shortcut Layer: 14,outputs: 152 x 152 x  64 0.001 BF
  18 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF
  19 conv     64       3 x 3/ 1    152 x 152 x  64 ->  152 x 152 x  64 1.703 BF
  20 Shortcut Layer: 17,outputs: 152 x 152 x  64 0.001 BF
  21 conv     64       1 x 1/ 1    152 x 152 x  64 ->  152 x 152 x  64 0.189 BF
  22 route  21 12                              ->  152 x 152 x 128 
  23 conv    128       1 x 1/ 1    152 x 152 x 128 ->  152 x 152 x 128 0.757 BF
  24 conv    256       3 x 3/ 2    152 x 152 x 128 ->   76 x  76 x 256 3.407 BF
  25 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
  26 route  24                                 ->   76 x  76 x 256 
  27 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
  28 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  29 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  30 Shortcut Layer: 27,outputs:  76 x  76 x 128 0.001 BF
  31 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  32 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  33 Shortcut Layer: 30,outputs:  76 x  76 x 128 0.001 BF
  34 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  35 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  36 Shortcut Layer: 33,outputs:  76 x  76 x 128 0.001 BF
  37 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  38 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  39 Shortcut Layer: 36,outputs:  76 x  76 x 128 0.001 BF
  40 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  41 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  42 Shortcut Layer: 39,outputs:  76 x  76 x 128 0.001 BF
  43 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  44 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  45 Shortcut Layer: 42,outputs:  76 x  76 x 128 0.001 BF
  46 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  47 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  48 Shortcut Layer: 45,outputs:  76 x  76 x 128 0.001 BF
  49 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  50 conv    128       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 128 1.703 BF
  51 Shortcut Layer: 48,outputs:  76 x  76 x 128 0.001 BF
  52 conv    128       1 x 1/ 1     76 x  76 x 128 ->   76 x  76 x 128 0.189 BF
  53 route  52 25                              ->   76 x  76 x 256 
  54 conv    256       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 256 0.757 BF
  55 conv    512       3 x 3/ 2     76 x  76 x 256 ->   38 x  38 x 512 3.407 BF
  56 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
  57 route  55                                 ->   38 x  38 x 512 
  58 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
  59 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  60 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  61 Shortcut Layer: 58,outputs:  38 x  38 x 256 0.000 BF
  62 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  63 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  64 Shortcut Layer: 61,outputs:  38 x  38 x 256 0.000 BF
  65 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  66 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  67 Shortcut Layer: 64,outputs:  38 x  38 x 256 0.000 BF
  68 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  69 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  70 Shortcut Layer: 67,outputs:  38 x  38 x 256 0.000 BF
  71 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  72 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  73 Shortcut Layer: 70,outputs:  38 x  38 x 256 0.000 BF
  74 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  75 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  76 Shortcut Layer: 73,outputs:  38 x  38 x 256 0.000 BF
  77 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  78 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  79 Shortcut Layer: 76,outputs:  38 x  38 x 256 0.000 BF
  80 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  81 conv    256       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 256 1.703 BF
  82 Shortcut Layer: 79,outputs:  38 x  38 x 256 0.000 BF
  83 conv    256       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 256 0.189 BF
  84 route  83 56                              ->   38 x  38 x 512 
  85 conv    512       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 512 0.757 BF
  86 conv   1024       3 x 3/ 2     38 x  38 x 512 ->   19 x  19 x1024 3.407 BF
  87 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
  88 route  86                                 ->   19 x  19 x1024 
  89 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
  90 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
  91 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
  92 Shortcut Layer: 89,outputs:  19 x  19 x 512 0.000 BF
  93 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
  94 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
  95 Shortcut Layer: 92,outputs:  19 x  19 x 512 0.000 BF
  96 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
  97 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
  98 Shortcut Layer: 95,outputs:  19 x  19 x 512 0.000 BF
  99 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
 100 conv    512       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x 512 1.703 BF
 101 Shortcut Layer: 98,outputs:  19 x  19 x 512 0.000 BF
 102 conv    512       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.189 BF
 103 route  102 87                             ->   19 x  19 x1024 
 104 conv   1024       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x1024 0.757 BF
 105 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 106 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 107 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 108 max                5x 5/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.005 BF
 109 route  107                                    ->   19 x  19 x 512 
 110 max                9x 9/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.015 BF
 111 route  107                                    ->   19 x  19 x 512 
 112 max               13x13/ 1     19 x  19 x 512 ->   19 x  19 x 512 0.031 BF
 113 route  112 110 108 107                        ->   19 x  19 x2048 
 114 conv    512       1 x 1/ 1     19 x  19 x2048 ->   19 x  19 x 512 0.757 BF
 115 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 116 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 117 conv    256       1 x 1/ 1     19 x  19 x 512 ->   19 x  19 x 256 0.095 BF
 118 upsample                 2x    19 x  19 x 256 ->   38 x  38 x 256
 119 route  85                                 ->   38 x  38 x 512 
 120 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 121 route  120 118                                ->   38 x  38 x 512 
 122 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 123 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 124 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 125 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 126 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 127 conv    128       1 x 1/ 1     38 x  38 x 256 ->   38 x  38 x 128 0.095 BF
 128 upsample                 2x    38 x  38 x 128 ->   76 x  76 x 128
 129 route  54                                 ->   76 x  76 x 256 
 130 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 131 route  130 128                                ->   76 x  76 x 256 
 132 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 133 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF
 134 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 135 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF
 136 conv    128       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x 128 0.379 BF
 137 conv    256       3 x 3/ 1     76 x  76 x 128 ->   76 x  76 x 256 3.407 BF
 138 conv     18       1 x 1/ 1     76 x  76 x 256 ->   76 x  76 x  18 0.053 BF
 139 yolo
[yolo] params: iou loss: ciou (4),iou_norm: 0.07,obj_norm: 1.00,cls_norm: 1.00,delta_norm: 1.00,scale_x_y: 1.20
nms_kind: greedynms (1),beta = 0.600000 
 140 route  136                                    ->   76 x  76 x 128 
 141 conv    256       3 x 3/ 2     76 x  76 x 128 ->   38 x  38 x 256 0.852 BF
 142 route  141 126                                ->   38 x  38 x 512 
 143 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 144 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 145 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 146 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 147 conv    256       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x 256 0.379 BF
 148 conv    512       3 x 3/ 1     38 x  38 x 256 ->   38 x  38 x 512 3.407 BF
 149 conv     18       1 x 1/ 1     38 x  38 x 512 ->   38 x  38 x  18 0.027 BF
 150 yolo
[yolo] params: iou loss: ciou (4),scale_x_y: 1.10
nms_kind: greedynms (1),beta = 0.600000 
 151 route  147                                    ->   38 x  38 x 256 
 152 conv    512       3 x 3/ 2     38 x  38 x 256 ->   19 x  19 x 512 0.852 BF
 153 route  152 116                                ->   19 x  19 x1024 
 154 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 155 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 156 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 157 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 158 conv    512       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x 512 0.379 BF
 159 conv   1024       3 x 3/ 1     19 x  19 x 512 ->   19 x  19 x1024 3.407 BF
 160 conv     18       1 x 1/ 1     19 x  19 x1024 ->   19 x  19 x  18 0.013 BF
 161 yolo
[yolo] params: iou loss: ciou (4),scale_x_y: 1.05
nms_kind: greedynms (1),beta = 0.600000 
Unused field: 'yolov4-custom.cfg = (null)'
Unused field: 'OpenwithGoogleDocs = (null)'
Total BFLOPS 126.806 
avg_outputs = 1046213 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from custom_weight/yolov4.conv.137...
 seen 64,trained: 0 K-images (0 Kilo-batches_64) 

 Warning: Unexpected end of wights-file! l.rolling_mean - l.index = 137 
Done! Loaded 137 layers from weights-file 
Learning Rate: 0.001,Momentum: 0.949,Decay: 0.0005
 Detection layer: 139 - type = 28 
 Detection layer: 150 - type = 28 
 Detection layer: 161 - type = 28 
Resizing,random_coef = 1.40 

 896 x 896 
 Create 6 permanent cpu-threads 
Cannot load image data/dd300129-d6f1-4ff4-8245-6baf4dfca61d.jpg
Cannot load image data/a2269867-3a23-4cd4-80ec-8c0f917f5986.jpg

 Error in load_data_detection() - OpenCV 

 Error in load_data_detection() - OpenCV 
Can't open label file. (This can be normal only if you use MSCOCO): data/0bbc209a-2a7a-43cb-8664-d6a337dee4a6.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/442b890c-34e0-4007-b085-c81ef82bb78c.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/c8138fcd-28e9-4a7d-a637-05d56816c864.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/b62a6a88-6ad1-43cf-ad59-6d171e89b265.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/78d2518f-eeb8-4234-ab5a-6f41e02b5692.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/f5310d22-83c1-4051-b114-843c10c67749.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/74f69a44-9224-4c14-a6a4-cffef6d1a757.txt 
Cannot load image data/643a2621-274d-4c11-a609-507c044fda00.jpg

 Error in load_data_detection() - OpenCV 
Can't open label file. (This can be normal only if you use MSCOCO): data/8b6d9d85-61c3-4469-b7ca-5dff4047815d.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/06ba1322-0274-47e0-b7eb-07c11d96360f.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/ba8516cc-aa1b-4f6c-ac7a-e34c5b2cda39.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/bcb1273c-d917-4a7b-b882-928223847250.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/8b2e2e6b-422f-4196-9209-10c722b2e860.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/efb0bc45-0769-4e87-9724-071a776f4059.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/983ad361-c6a6-43c5-9aa2-c17b758a3901.txt 
Cannot load image data/42122fc5-12ce-4840-ae13-5f70fffdc6e5.jpg

 Error in load_data_detection() - OpenCV 
Can't open label file. (This can be normal only if you use MSCOCO): data/7e64761d-6224-4888-8976-75453b8fc842.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/b62a6a88-6ad1-43cf-ad59-6d171e89b265.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/4ed5b2b2-9aa1-49f2-84e6-4f634744039e.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/1352a7f3-1f0d-4f1f-af05-daa5d7f8624a.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/46f37e46-e4cd-4677-875b-1f1b28cfc70d.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/195c5e5f-7058-48c5-8fe9-67cffd60c9b3.txt 
Cannot load image data/c9ebce2f-0799-422a-9714-37c8880bf93a.jpg

 Error in load_data_detection() - OpenCV 
Can't open label file. (This can be normal only if you use MSCOCO): data/34034bf2-1e42-45e7-aad2-80df607373bb.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/fedfb751-4365-4ea3-a4d9-ac761534aca3.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/7e64761d-6224-4888-8976-75453b8fc842.txt 
Cannot load image data/ebc3e487-6b56-41d3-847d-bb5b04297b9e.jpg
Can't open label file. (This can be normal only if you use MSCOCO): data/7981fc95-1551-4867-8681-2f2d8afc07d8.txt 

 Error in load_data_detection() - OpenCV 
Can't open label file. (This can be normal only if you use MSCOCO): data/8b2543f1-89e6-41d2-8ede-ab0f9b797f41.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/377c27f9-b004-40b4-95b3-dc79b067314c.txt 
Cannot load image data/f27e1121-c394-4577-80c6-762f47e916b2.jpg

 Error in load_data_detection() - OpenCV 
Can't open label file. (This can be normal only if you use MSCOCO): data/9b5ca700-e7f9-4c72-ae12-c1ff11d6b0b3.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/2ee27b62-e1d7-4341-bf2b-9d45dc4068a1.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/a62f0dd3-cf24-4f4e-a2ec-4b93ed207871.txt 
Can't open label file. (This can be normal only if you use MSCOCO): data/2a4489f6-6f7b-46f5-a937-281206307943.txt

解决方法

您必须将 yolo 注释标签文件 '.txt' 放在 'data/' 文件夹中。

data/f1a92173-262b-42d1-b64e-213cdf6afdae.jpg
data/f1a92173-262b-42d1-b64e-213cdf6afdae.txt
data/92ef9a59-d770-40b6-9ef5-2f909d6cc1c8.jpg
data/92ef9a59-d770-40b6-9ef5-2f909d6cc1c8.txt
...

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错误1：private field ‘xxx‘ is never assigned 按Altʾnter快捷键，选择第2项 参考：https://blog.csdn.net/shi_hong_fei_hei/article/details/88814070 错误2：启动时报错，不能找到主启动类 #
pip安装依赖失败
报错如下，通过源不能下载，最后警告pip需升级版本 Requirement already satisfied: pip in c:\users\ychen\appdata\local\programs\python\python310\lib\site-packages (22.0.4) Coll
maven常见错误
错误1：maven打包报错 错误还原：使用maven打包项目时报错如下 [ERROR] Failed to execute goal org.apache.maven.plugins:maven-resources-plugin:3.2.0:resources (default-resources)
cloud项目中常见错误
错误1：服务调用时报错 服务消费者模块assess通过openFeign调用服务提供者模块hires 如下为服务提供者模块hires的控制层接口 @RestController @RequestMapping("/hires") public class FeignControl
springboot常见错误
错误1：运行项目后报如下错误 解决方案 报错2：Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.8.1:compile (default-compile) on project sb 解决方案：在pom.
springmvc拦截器中使用redisTemplate报空指针异常
参考 错误原因 过滤器或拦截器在生效时，redisTemplate还没有注入 解决方案：在注入容器时就生效 @Component //项目运行时就注入Spring容器 public class RedisBean { @Resource private RedisTemplate<String
vite报错：npm ERR! command C:WINDOWSsystem32cmd.exe /d /s /c C:UsersychenAppDataLocalTempnpx-dde1c848.cmd
使用vite构建项目报错 C:\Users\ychen\work>npm init @vitejs/app @vitejs/create-app is deprecated, use npm init vite instead C:\Users\ychen\AppData\Local\npm-
VM VirtualBox中安装centos时报错：设置基础软件仓库时出错
参考1 参考2 解决方案 # 点击安装源 协议选择 http:// 路径填写 mirrors.aliyun.com/centos/8.3.2011/BaseOS/x86_64/os URL类型 软件库URL 其他路径 # 版本 7 mirrors.aliyun.com/centos/7/os/x86
kafka常见问题
报错1 [root@slave1 data_mocker]# kafka-console-consumer.sh --bootstrap-server slave1:9092 --topic topic_db [2023-12-19 18:31:12,770] WARN [Consumer clie
hive常见问题
错误1 # 重写数据 hive (edu)> insert overwrite table dwd_trade_cart_add_inc > select data.id, > data.user_id, > data.course_id, > date_format(
装载数据时报错：Failed to execute spark task, with exception 'org.apache.hadoop.hive.ql.metadata.HiveException(Failed to create spark client.)'
错误1 hive (edu)> insert into huanhuan values(1,'haoge'); Query ID = root_20240110071417_fe1517ad-3607-41f4-bdcf-d00b98ac443e Total jobs = 1
flume常见问题
报错1：执行到如下就不执行了，没有显示Successfully registered new MBean. [root@slave1 bin]# /usr/local/software/flume-1.9.0/bin/flume-ng agent -n a1 -c /usr/local/softwa
hadoop集群常见错误
虚拟及没有启动任何服务器查看jps会显示jps，如果没有显示任何东西 [root@slave2 ~]# jps 9647 Jps 解决方案 # 进入/tmp查看 [root@slave1 dfs]# cd /tmp [root@slave1 tmp]# ll 总用量 48 drwxr-xr-x. 2
hive常见错误
报错1 hive> show databases; OK Failed with exception java.io.IOException:java.lang.RuntimeException: Error in configuring object Time taken: 0.474 se
Linux常见错误
报错1 [root@localhost ~]# vim -bash: vim: 未找到命令 安装vim yum -y install vim* # 查看是否安装成功 [root@hadoop01 hadoop]# rpm -qa |grep vim vim-X11-7.4.629-8.el7_9.x
使用sqoop一直卡在：mapreduce.Job: Running job: job_1703173956074_0001
修改hadoop配置 vi /usr/local/software/hadoop-2.9.2/etc/hadoop/yarn-site.xml # 添加如下 <configuration> <property> <name>yarn.nodemanager.res
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