Open Sunmingyang1987 opened 4 years ago
Hello, I have the same problem.
Have you solved your problem?Can you tell me the reason if it has been solved?Thank you very much!
Or incorrect dataset/train.txt/image-path/image format Or incorrect OpenCV installation
Thank you for your prompt reply! I checked my trian.txt/image-path/image format.I didn't find anything wrong.
This is my train.txt file.
/home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_5199.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_395.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_30486.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_3189.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_319.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_11957.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_26795.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_4582.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_11644.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_17632.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_2335.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_7118.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_4656.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_13292.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_9769.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_23736.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_19618.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_26359.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_29523.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_9648.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_8656.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_29220.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_4486.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_6066.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_24192.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_12176.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_17765.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_5288.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_30316.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_20015.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_5812.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_12674.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_14445.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_10542.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_1001.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_392.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_25343.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_1092.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_23885.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_1640.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_9082.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_29503.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_3488.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_19933.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_14709.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_11663.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_236.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_503.txt
Here is my obj.data file.
classes= 21 train = /home/xuwt/xu/yolov4/darknet/data/train.txt valid = /home/xuwt/xu/yolov4/darknet/data/test.txt names = data/obj.names backup = data/backup
Here is my Makefile file.
GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1 AVX=0 OPENMP=0 LIBSO=0
I run the training command"./darknet detector train data/obj.data cfg/yolo-obj.cfg yolov4.conv.137". The following information appears.
CUDA-version: 10000 (10020), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 2
CUDNN_HALF=1
OpenCV version: 3.2.0
yolo-obj
0 : compute_capability = 750, cudnn_half = 1, GPU: TITAN RTX
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 3 -> 608 x 608 x 32 0.639 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 78 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 78 0.231 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 -> 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 78 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 78 0.115 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 -> 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 78 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 78 0.058 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 127.543
avg_outputs = 1051829
Allocate additional workspace_size = 237.17 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
If error occurs - run training with flag: -dont_show
Resizing, random_coef = 1.40
896 x 896 Create 6 permanent cpu-threads OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv
Error in load_data_detection() - OpenCV
I can't find anything wrong.Can you give me a hand @AlexeyAB .Thank you very much for your kind help and your excellent work!!
Attach one of these image to zip-archive RARP4_frame_4662.zip
Is it 3-channels RGB? Yes, my picture is RGB.
Try to run detection by using default yolov4.cfg/weights model, do you see this error? I try to run detection by using default yolov4.cfg/weights model as you say.I got the same error as above
Show content of files bad.list and bad_label.list Here is my bad.list file. bad.zip
I have 40152 training pictures. But the bad. list only contains 28804 lines .txt.
My image is in. JPG format. There are only 896 lines of JPG paths in the bad.
The remaining 2280 lines are the path of the PNG image.This struck me as odd.The PNG image's name and path format looks like this: data/labels/100_3.png.
The bad_label.list file was not found.
I'm sorry to bother you @AlexeyAB again.Your work is of great help to my study. Thank you very much!!
Hello @AlexeyAB
The data set I used is SARAS_ESAD. It only contains.jpg files and.txt files.The names of the two files correspond. Each class has five Numbers, the first represents the class, the last four Numbers represent the coordinates.
Here is my TXT .The above file has only one class, and the following file has two classes.
8 0.455208 0.897685 0.193750 0.202778
14 0.517708 0.442130 0.411458 0.7250008 0.641406 0.853704 0.228646 0.292593
Try to run detection by using default yolov4.cfg/weights model, do you see this error? I try to run detection by using default yolov4.cfg/weights model as you say.I got the same error as above
I don't get any error when try to detect on your image.
So the issue is in your JPG-library or OpenCV installation.
What OS do you use?
Try to install JPG-library
sudo apt-get install libjpeg-dev
Try to re-install OpenCV
sudo apt-get install opencv-dev
The problem is that the python version of OpenCV is 4.2, and the OpenCV version of the system is 3.2. Is that the problem?
Try to install JPG-library I have checked the system and installed JPG-library.
Try to re-install OpenCV Like the first question I answered.Should the two OpenCV versions be consistent?
Thank you for your patience in answering my question @AlexeyAB .Have a good day!
The problem is that the python version of OpenCV is 4.2, and the OpenCV version of the system is 3.2. Is that the problem?
I don't know. May be yes.
Hello @AlexeyAB . I have trained my data set with your yolov3 code, and the experiment can proceed normally.I also set the OpenCV in the makefile to 1, which means that my OpenCV is ok, right?
Hello@AlexeyAB I started training by using the command line:
./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show
, and one error occured, as shown below, "OpenCV exception: load_image_mat_cv OpenCV exception: load_image_mat_cv try to allocate additional workspace_size = 52.43 MB CUDA allocate done! Loaded: 0.000032 seconds Segmentation fault (core dumped)". I used a very small datasets, 35 images in total. Could you please give me some advise? Thanks a lot.