无法打开标签文件。(这只有在使用MSCOCO时才是正常的) Yolo
无法打开标签文件。(这只有在使用MSCOCO时才是正常的) Yolo
提问于 2021-02-19 00:20:17
在Yolo的训练过程中，我遇到了这个错误。
.data文件具有
classes = 1
train = data/train.txt
valid = data/valid.txt
names = classes.names
backup = backuptrain.txt已经被放置在"data“文件夹中，那里有完整的图像集。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文件已适当修改，用于运行训练的命令为：
!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来运行模型。
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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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, wt = 0, wn = 0, 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), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, 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), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, 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回答 1
Stack Overflow用户
发布于 2021-06-02 14:47:32
您必须将带有yolo注释的标签文件'.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
...页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/66263909
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