Darknet：训练自定义对象后未创建权重

时间：2018-08-07 08:20:58

标签： python darknet

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？

3 个答案:

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

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