---VOC2012
--------Annotations
---------------xml0
---------------xml1
--------JPEGImages
---------------img0
---------------img1
--------pascal_voc_classes.txt

102 0.682813 0.415278 0.237500 0.502778
102 0.914844 0.396528 0.168750 0.451389

第一位 label，为图片中物体的类别
后面四位为图片中物体的位置，（xcenter, ycenter, h, w）即目标物体中心位置的相对坐标和相对高宽
上图中存在两个目标

python center/xml_yolo.py

["aeroplane","bicycle", "bird","boat","bottle","bus","car","cat","chair","cow","diningtable","dog","horse","motorbike","person","pottedplant","sheep","sofa","train", "tvmonitor"]

---VOC2012
--------Annotations
--------JPEGImages
--------pascal_voc_classes.json
---yolodata
--------images
--------labels

python center/get_train_val.py

---VOC2012
--------Annotations
--------JPEGImages
--------pascal_voc_classes.json
---yolodata
--------images
--------labels
---traindata
--------images
----------------train
----------------val
--------labels
----------------train
----------------val

dataset             # 数据集
------traindata     # 训练数据集
inference           # 输入输出接口
------inputs        # 输入数据
------outputs       # 输出数据
config              # 配置文件
------score.yaml    # 训练配置文件
------yolov5l.yaml  # 模型配置文件
models              # 模型代码
runs	            # 日志文件
utils               # 代码文件
weights             # 模型保存路径，last.pt，best.pt
train.py            # 训练代码
detect.py           # 测试代码

# train and val datasets (image directory)
train: ./datasets/traindata/images/train/
val: ./datasets/traindata/images/val/
# number of classes
nc: 2
# class names
names: ['苹果','香蕉']

nc: 2 # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32
backbone:
  # [from, number, module, args]
  [[-1, 1, Focus, [64, 3]],  # 1-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 2-P2/4
   [-1, 3, Bottleneck, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 4-P3/8
   [-1, 9, BottleneckCSP, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 6-P4/16
   [-1, 9, BottleneckCSP, [512]],
   [-1, 1, Conv, [1024, 3, 2]], # 8-P5/32
   [-1, 1, SPP, [1024, [5, 9, 13]]],
   [-1, 6, BottleneckCSP, [1024]],  # 10
  ]
head:
  [[-1, 3, BottleneckCSP, [1024, False]],  # 11
   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]],  # 12 (P5/32-large)
   [-2, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 1, Conv, [512, 1, 1]],
   [-1, 3, BottleneckCSP, [512, False]],
   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]],  # 17 (P4/16-medium)
   [-2, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 3, BottleneckCSP, [256, False]],
   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]],  # 22 (P3/8-small)
   [[], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

epoch:         控制训练迭代的次数
batch_size     输入迭代的图片数量
cfg:           配置网络模型路径
data:          训练配置文件路径
weights:       载入模型，进行断点继续训练

 python train.py

source：        需要检测的images/videos路径
out：		保存结果的路径
weights：       训练得到的模型权重文件的路径

 python detect.py

 python app.py