darknet培训命令不会产生任何输出,并且退出得太早(与其他CNN培训项目相比)
我已按照“如何训练(检测您的自定义对象)”的说明进行操作。
yolo-obj.cfg已相应配置。
darknet.exe已使用MSVS 2017成功编译并构建。
我在以下三个新的自定义类中:
obj.data文件:
classes= 3
train = data/train.txt
valid = data/train.txt
names = data/obj.names
backup = backup/
obj.names文件:
ring
watch
necklace
我为每个类运行yolo_mark约500张图像,生成了相应的* .txt文件。
我将所有jpg和txt文件都放在obj目录中。
train.txt文件包含* .jpg文件的路径,例如:“ data / obj / necklace 013311.jpg”
下载darknet53.conv.74文件并将其放在“ x64”目录中
以管理员身份运行命令(从虚拟机,因此没有GPU):
C:\Users\claw\Downloads\darknet-master\darknet-master\build\darknet\x64>darknet_no_gpu.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74
yolo-obj
命令行输出:
layer filters size 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 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
4 Shortcut Layer: 1
5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
8 Shortcut Layer: 5
9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
11 Shortcut Layer: 8
12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
15 Shortcut Layer: 12
16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
18 Shortcut Layer: 15
19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
21 Shortcut Layer: 18
22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
24 Shortcut Layer: 21
25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
27 Shortcut Layer: 24
28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
30 Shortcut Layer: 27
31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
33 Shortcut Layer: 30
34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
36 Shortcut Layer: 33
37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
40 Shortcut Layer: 37
41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
43 Shortcut Layer: 40
44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
46 Shortcut Layer: 43
47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
49 Shortcut Layer: 46
50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
52 Shortcut Layer: 49
53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
55 Shortcut Layer: 52
56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
58 Shortcut Layer: 55
59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
61 Shortcut Layer: 58
62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
65 Shortcut Layer: 62
66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
68 Shortcut Layer: 65
69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
71 Shortcut Layer: 68
72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
74 Shortcut Layer: 71
75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
81 conv 24 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 24 0.008 BF
82 yolo
83 route 79
84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BF
88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
93 conv 24 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 24 0.017 BF
94 yolo
95 route 91
96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BF
100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
105 conv 24 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 24 0.033 BF
106 yolo
Total BFLOPS 65.304
Loading weights from darknet53.conv.74...
seen 64
Done!
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
If error occurs - run training with flag: -dont_show
Resizing
416 x 416
Cannot load image "data/img/ring chic-criss-cross-adjustable-ad-ring.jpg"
Loaded: 1.143984 seconds
Used AVX
Region 82 Avg IOU: 0.333570, Class: 0.602019, Obj: 0.402860, No Obj: 0.528741, .5R: 0.000000, .75R: 0.000000, count: 4
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521660, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514523, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.329878, Class: 0.570290, Obj: 0.611294, No Obj: 0.528309, .5R: 0.250000, .75R: 0.000000, count: 4
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521499, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514392, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.575794, Class: 0.539979, Obj: 0.316475, No Obj: 0.528604, .5R: 0.500000, .75R: 0.500000, count: 2
Region 94 Avg IOU: 0.312451, Class: 0.125449, Obj: 0.238739, No Obj: 0.521500, .5R: 0.000000, .75R: 0.000000, count: 1
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514025, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.257590, Class: 0.547629, Obj: 0.447064, No Obj: 0.527685, .5R: 0.000000, .75R: 0.000000, count: 3
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521665, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.515411, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.297573, Class: 0.436722, Obj: 0.389306, No Obj: 0.528302, .5R: 0.500000, .75R: 0.000000, count: 4
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521452, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.513978, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.191856, Class: 0.645887, Obj: 0.364560, No Obj: 0.528137, .5R: 0.000000, .75R: 0.000000, count: 5
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521575, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514143, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.475039, Class: 0.419801, Obj: 0.578539, No Obj: 0.527876, .5R: 0.500000, .75R: 0.500000, count: 2
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521085, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514371, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.264798, Class: 0.416162, Obj: 0.462117, No Obj: 0.527412, .5R: 0.000000, .75R: 0.000000, count: 5
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521446, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514205, .5R: -nan(ind), .75R: -nan(ind), count: 0
1: 1003.093994, 1003.093994 avg loss, 0.000000 rate, 1056.320056 seconds, 64 images
Loaded: 0.000000 seconds
Cannot load image "data/img/necklace 570239071_2906.jpg"
Cannot load image "data/img/necklace 570239072_2906.jpg"
Cannot load image "data/img/necklace 10019367_no_place_like_roam_necklace_green_main.jpg"
Region 82 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.527527, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521694, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514430, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.213376, Class: 0.587271, Obj: 0.565966, No Obj: 0.528763, .5R: 0.000000, .75R: 0.000000, count: 5
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.522077, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.515318, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.314485, Class: 0.501796, Obj: 0.458959, No Obj: 0.528414, .5R: 0.000000, .75R: 0.000000, count: 2
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521397, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514781, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.278535, Class: 0.518696, Obj: 0.510300, No Obj: 0.528529, .5R: 0.000000, .75R: 0.000000, count: 5
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521170, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514448, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.270750, Class: 0.498121, Obj: 0.530221, No Obj: 0.528569, .5R: 0.000000, .75R: 0.000000, count: 2
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521003, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.513312, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.235287, Class: 0.480098, Obj: 0.517194, No Obj: 0.527906, .5R: 0.000000, .75R: 0.000000, count: 4
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521571, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.513103, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.368155, Class: 0.552764, Obj: 0.482865, No Obj: 0.528044, .5R: 0.200000, .75R: 0.000000, count: 5
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.521782, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.514365, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 82 Avg IOU: 0.393099, Class: 0.568679, Obj: 0.534074, No Obj: 0.528130, .5R: 0.000000, .75R: 0.000000, count: 2
Region 94 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.522459, .5R: -nan(ind), .75R: -nan(ind), count: 0
Region 106 Avg IOU: -nan(ind), Class: -nan(ind), Obj: -nan(ind), No Obj: 0.515186, .5R: -nan(ind), .75R: -nan(ind), count: 0
2: 1002.576904, 1003.042297 avg loss, 0.000000 rate, 1043.121191 seconds, 128 images
Loaded: 0.000000 seconds
此后,我检查了备份目录,其中仅创建了* .tmp文件(0 kb)
没有创建weigth文件...
我做错了什么?
答案 0 :(得分:0)
默认情况下,每100次迭代记录一次权重。您需要等待很长的时间来训练YOLO(尤其是没有GPU),然后才能推断出自己的体重。
答案 1 :(得分:0)
我认为您的训练集配置不正确。 您的大多数结果都是-nan(ind)
您的train.txt可能有问题。
1:1003.093994、1003.093994平均损失,0.000000速率,1056.320056秒,64张图像 ^这是迭代编号。默认情况下,darknet会在100次迭代后将权重写入备份文件夹。 如果要在此之前获得重量,请在src中打开detector.c文件并修改
newElem
第204行,例如我为我所做的,如果您希望在第一次迭代中获得权重,则将数字设为1(而不是3) 然后重新构建。
请尝试在较小的网络(如yolo-voc)上进行训练,如本教程所述:https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/ 使用基于yolov3.cfg的网络(获得相同的输出)时,即使我也遇到问题
答案 2 :(得分:0)
问题是对象txt文件(由yolomarkup创建)几乎全部为空。我添加了3个新对象;项链,戒指,手表,每个对象大约有500张jpg图像,我在yolomarkup.exe中使用过。对于许多标记了图像的图像,相应的txt文件为空!我完全放弃了对那些物体的培训