1.yolov5数据的要求

查看yolov5的配置文件coco.yaml和coco128.yaml，yolov5数据集可以有两种形式，

         一种像coco128所示，在images和labels文件夹下，建立train、test、val三个文件夹，存放训练集、测试集、验证集的标签和数据，然后再复制coco128.yaml前面路径部分，改为自己的路径，其中，训练集、测试集、验证集均写相应的文件夹路径，再将类别数量、名称改为自己数据集的名称和数量。download部分完全没用，不要复制

        另一种就是将训练集、测试集、验证集图片路径直接写入txt文件中，复制coco.yaml，路径修改为自己的txt文件、种类数量、名称都要改为自己的数据 

2.VOC数据集

        VOC数据集如图所示：Annotations_original是数据集没有的，是我调整数据后加的，JPEGImages下存放的是图片，Annotations存放标签，ImageSets下的main文件夹存放train/test/val文件标签

 3.VOC数据集调整为coco格式

1.JPEGImages文件夹名称需要改为images（手动修改）

2.需要存放训练集、测试集、验证集图片路径的txt文件

3.labels需要由xml文件改为yolo格式

运行下面的代码即可：

import os
import random
import xml.etree.ElementTree as ET
 
import numpy as np
 
# --------------------------------------------------------------------------------------------------------------------------------#
#   annotation_mode用于指定该文件运行时计算的内容
#   annotation_mode为0代表整个标签处理过程，训练集、测试集、验证集图片路径的txt文件以及标签xml文件改为yolo格式
#   annotation_mode为1代表获得训练集、测试集、验证集图片路径的txt文件
#   annotation_mode为2代表标签xml文件改为yolo格式
# --------------------------------------------------------------------------------------------------------------------------------#
annotation_mode = 0
# -------------------------------------------------------------------#
#   必须要修改，用于标签的目标信息
#   与训练和预测所用的classes_path一致即可
#   如果生成的2007_train.txt里面没有目标信息
#   那么就是因为classes没有设定正确
#   仅在annotation_mode为0和2的时候有效
# -------------------------------------------------------------------#
classes_path = '../Detection/classes.txt'
# --------------------------------------------------------------------------------------------------------------------------------#
#   trainval_percent用于指定(训练集+验证集)与测试集的比例，默认情况下 (训练集+验证集):测试集 = 9:1
#   train_percent用于指定(训练集+验证集)中训练集与验证集的比例，默认情况下 训练集:验证集 = 9:1
#   仅在annotation_mode为0和1的时候有效
# --------------------------------------------------------------------------------------------------------------------------------#
trainval_percent = 0.8
train_percent = 0.8
# -------------------------------------------------------#
#   指向VOC数据集所在的文件夹
#   默认指向根目录下的VOC数据集
# -------------------------------------------------------#
VOCdevkit_path = '../Detection'
 
 
def get_classes(classes_path):
    with open(classes_path, encoding='utf-8') as f:
        class_names = f.readlines()
    class_names = [c.strip() for c in class_names]
    return class_names, len(class_names)
 
 
classes, _ = get_classes(classes_path)
# -------------------------------------------------------#
#   统计目标数量
# -------------------------------------------------------#
photo_nums = np.zeros(len(VOCdevkit_sets))
nums = np.zeros(len(classes))
 
 
def convert_label(path, image_id):
    def convert_box(size, box):
        dw, dh = 1. / size[0], 1. / size[1]
        x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
        return x * dw, y * dh, w * dw, h * dh
 
    in_file = open(os.path.join(path,f'VOC2007/Annotations/{image_id}.xml'),encoding='utf-8')
    out_file = open(os.path.join(path, f'VOC2007/labels/{image_id}.txt'), 'w',encoding='utf-8')
    tree = ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)
 
    for obj in root.iter('object'):
        cls = obj.find('name').text
        if cls in classes and not int(obj.find('difficult').text) == 1:
            xmlbox = obj.find('bndbox')
            bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
            cls_id = classes.index(cls)  # class id
            out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
 
 
if __name__ == "__main__":
    random.seed(42)
    if " " in os.path.abspath(VOCdevkit_path):
        raise ValueError("数据集存放的文件夹路径与图片名称中不可以存在空格，否则会影响正常的模型训练，请注意修改。")
 
    if annotation_mode == 0 or annotation_mode == 1:
        print("Generate train/test/val txt")
        xmlfilepath = os.path.join(VOCdevkit_path, 'VOC2007/Annotations')
        saveBasePath = os.path.join(VOCdevkit_path, 'VOC2007')
        temp_xml = os.listdir(xmlfilepath)
        total_xml = []
        for xml in temp_xml:
            if xml.endswith(".xml"):
                total_xml.append(xml)
 
        num = len(total_xml)
        list = list(range(num))
        tv = int(num * trainval_percent)
        tr = int(tv * train_percent)
        trainval = random.sample(list, tv)
        train = random.sample(trainval, tr)
        random.shuffle(list)
 
        print("train and val size", tv)
        print("train size", tr)
        ftrainval = open(os.path.join(saveBasePath, 'trainval.txt'), 'w',encoding='utf-8')
        ftest = open(os.path.join(saveBasePath, 'test.txt'), 'w',encoding='utf-8')
        ftrain = open(os.path.join(saveBasePath, 'train.txt'), 'w',encoding='utf-8')
        fval = open(os.path.join(saveBasePath, 'val.txt'), 'w',encoding='utf-8')
 
        for i in list:
            name = total_xml[i][:-4]
            if i in trainval:
                ftrainval.write('%s/VOC2007/images/%s.jpg\n'% (os.path.abspath(VOCdevkit_path), name))
                if i in train:
                    ftrain.write('%s/VOC2007/images/%s.jpg\n'% (os.path.abspath(VOCdevkit_path), name))
                else:
                    fval.write('%s/VOC2007/images/%s.jpg\n'% (os.path.abspath(VOCdevkit_path), name))
            else:
                ftest.write('%s/VOC2007/images/%s.jpg\n'% (os.path.abspath(VOCdevkit_path), name))
 
        ftrainval.close()
        ftrain.close()
        fval.close()
        ftest.close()
        print("Generate train/test/val.txt done.")
 
    if annotation_mode == 0 or annotation_mode == 2:
        print("Generate coco labels")
        if not os.path.exists(os.path.join(VOCdevkit_path,'VOC2007/labels/')):
            os.makedirs(os.path.join(VOCdevkit_path,'VOC2007/labels/'))
        image_ids = os.listdir(os.path.join(VOCdevkit_path, 'VOC2007/Annotations'))
        for image_id in image_ids:
            convert_label(VOCdevkit_path, image_id[:-4])
        print("Generate coco labels done")

最终得到的文件情况：

最后按照第一点说的，复制 配置文件并修改可以了

 4.训练参数的修改

data指向自己的yaml文件，训练权重、epochs等按照自己的情况修改