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。
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