用Yolov2模型训练VOC数据集的各文件理解
0.准备工作：
1. VOC格式的数据集
2. 使用darknet代码里的voc_label.py制作labels数据
3. 修改网络定义文件.cfg
这里要注意的是，数据的类别，更改类别时，在1,2,3里都需要做相应的改变
1. 修改./cfg/voc.data文件
//修改成自己的类别数目
classes= 20
//修改为你训练数据的.txt目录, 训练数据的路径
train = /home/pjreddie/data/voc/train.txt
//修改为你验证数据的.txt目录，验证数据的路径
valid = /home/pjreddie/data/voc/2007_test.txt
names = data/voc.names // voc.names修给为自己类别名称
//修改为你的模型备份目录
backup = /home/pjreddie/backup/2. 理解./cfg/yolo-voc.cfg网络定义文件
// cfg/yolo-voc.cfg
[net] # yolo的网络架构要求第一个section必须为[net]或[netowrk]，参见src/parse.c:586
# sections->size为cfg中配置的[*]数量，该cfg的size为32
batch=64
subdivisions=8
height=416 // 输入图片高
width=416 // 输入图片宽
channels=3 // 通道数
momentum=0.9 // 动量
decay=0.0005
angle=0 // 旋转角度
saturation = 1.5 // 曝光度
exposure = 1.5 // 饱和度
hue=.1 // 色调
learning_rate=0.0001 // 学习率
max_batches = 45000
policy=steps
steps=100,25000,35000
scales=10,.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
#######
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[route]
layers=-9
[reorg]
stride=2
[route]
layers=-1,-3
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=125 // 注意改为自己的数目（计算方式：(classes+5)*5）
activation=linear
[region]
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
bias_match=1
classes=20 // 注意修给为自己的类别数目
coords=4
num=5
softmax=1
jitter=.2
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6
random=0 3.训练
这里我选择的是22层的网络，batch_size=64，subdivisions=8，如果你的内存太小，运行出错的话，可以把batch_size改小一点或者subdivisions改大一点，然后运行以下指令即可开始训练：
./darknet detector train ./cfg/voc.data ./cfg/yolo-voc.cfglog如下：
nohup: ignoring input
2 layer filters size input output
3 0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
4 1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32
5 2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
6 3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64
7 4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
8 5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
9 6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
10 7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128
11 8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
12 9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
13 10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
14 11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256
15 12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
16 13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
17 14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
18 15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
19 16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
20 17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512
21 18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
22 19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
23 20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
24 21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
25 22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
26 23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
27 24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
28 25 route 16
29 26 reorg / 2 26 x 26 x 512 -> 13 x 13 x2048
30 27 route 26 24
31 28 conv 1024 3 x 3 / 1 13 x 13 x3072 -> 13 x 13 x1024
32 29 conv 125 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 125
33 30 detection
34 yolo-voc
35 Learning Rate: 0.0001, Momentum: 0.9, Decay: 0.0005
36 Loaded: 1.850000 seconds
37 Region Avg IOU: 0.358097, Class: 0.065954, Obj: 0.474037, No Obj: 0.508373, Avg Recall: 0.200000, count: 15
38 Region Avg IOU: 0.360629, Class: 0.065775, Obj: 0.466963, No Obj: 0.508891, Avg Recall: 0.178571, count: 28
39 Region Avg IOU: 0.328480, Class: 0.048047, Obj: 0.471501, No Obj: 0.509045, Avg Recall: 0.217391, count: 23
40 Region Avg IOU: 0.387740, Class: 0.060535, Obj: 0.495033, No Obj: 0.509017, Avg Recall: 0.333333, count: 12
41 Region Avg IOU: 0.284584, Class: 0.046532, Obj: 0.471151, No Obj: 0.508602, Avg Recall: 0.090909, count: 22
42 Region Avg IOU: 0.335542, Class: 0.049446, Obj: 0.520727, No Obj: 0.508899, Avg Recall: 0.150000, count: 20
43 Region Avg IOU: 0.366046, Class: 0.055863, Obj: 0.502995, No Obj: 0.509692, Avg Recall: 0.250000, count: 32
44 Region Avg IOU: 0.411998, Class: 0.050235, Obj: 0.497955, No Obj: 0.506873, Avg Recall: 0.375000, count: 16
45 1: 18.557627, 18.557627 avg, 0.000100 rate, 15.430000 seconds, 64 images
46 Loaded: 0.000000 seconds
47 Region Avg IOU: 0.394109, Class: 0.048967, Obj: 0.449230, No Obj: 0.454679, Avg Recall: 0.333333, count: 15
48 Region Avg IOU: 0.430337, Class: 0.044501, Obj: 0.475584, No Obj: 0.454697, Avg Recall: 0.250000, count: 16
49 Region Avg IOU: 0.318591, Class: 0.069988, Obj: 0.490419, No Obj: 0.454365, Avg Recall: 0.133333, count: 15
50 Region Avg IOU: 0.335521, Class: 0.060138, Obj: 0.408140, No Obj: 0.454221, Avg Recall: 0.277778, count: 18
51 Region Avg IOU: 0.360168, Class: 0.055241, Obj: 0.456031, No Obj: 0.455356, Avg Recall: 0.307692, count: 13
52 Region Avg IOU: 0.343406, Class: 0.056148, Obj: 0.439433, No Obj: 0.454594, Avg Recall: 0.187500, count: 16
53 Region Avg IOU: 0.349903, Class: 0.047826, Obj: 0.392414, No Obj: 0.454783, Avg Recall: 0.235294, count: 17
54 Region Avg IOU: 0.319748, Class: 0.059287, Obj: 0.456736, No Obj: 0.453497, Avg Recall: 0.217391, count: 23
55 2: 15.246732, 18.226538 avg, 0.000100 rate, 9.710000 seconds, 128 images
56 Loaded: 0.000000 seconds4.测试
/darknet detector test cfg/voc.data cfg/yolo-voc.cfg backup/yolo-voc_1000.weights data/images.jpg