参考下方链接进行学习，感谢大大。

Faster-RCNN跑自己的数据集（个人记录过程）FPN学习_faster rcnn训练自己的数据集-CSDN博客

1.标签制作

需要使用xml格式的标签文件，如果是yolo格式的，需要转成xml文件，代码如下：

from xml.dom.minidom import Document
import os
import cv2


# def makexml(txtPath, xmlPath, picPath):  # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径
def makexml(picPath, txtPath, xmlPath):  # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径
    """此函数用于将yolo格式txt标注文件转换为voc格式xml标注文件
    """
    dic = {'0': "fish",  # 创建字典用来对类型进行转换
           '1': "jellyfish",  # 此处的字典要与自己的classes.txt文件中的类对应，且顺序要一致
           '2': "penguin",
           '3': "puffin",
           '4': "shark",
           '5': "starfish",
           '6': "stingray",
           }
    files = os.listdir(txtPath)
    for i, name in enumerate(files):
        xmlBuilder = Document()
        annotation = xmlBuilder.createElement("annotation")  # 创建annotation标签
        xmlBuilder.appendChild(annotation)
        txtFile = open(txtPath + name)
        txtList = txtFile.readlines()
        aa=picPath + name[0:-4] + ".jpg"
        img = cv2.imread(picPath + name[0:-4] + ".jpg")
        Pheight, Pwidth, Pdepth = img.shape

        folder = xmlBuilder.createElement("folder")  # folder标签
        foldercontent = xmlBuilder.createTextNode("driving_annotation_dataset")
        folder.appendChild(foldercontent)
        annotation.appendChild(folder)  # folder标签结束

        filename = xmlBuilder.createElement("filename")  # filename标签
        filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")
        filename.appendChild(filenamecontent)
        annotation.appendChild(filename)  # filename标签结束

        size = xmlBuilder.createElement("size")  # size标签
        width = xmlBuilder.createElement("width")  # size子标签width
        widthcontent = xmlBuilder.createTextNode(str(Pwidth))
        width.appendChild(widthcontent)
        size.appendChild(width)  # size子标签width结束

        height = xmlBuilder.createElement("height")  # size子标签height
        heightcontent = xmlBuilder.createTextNode(str(Pheight))
        height.appendChild(heightcontent)
        size.appendChild(height)  # size子标签height结束

        depth = xmlBuilder.createElement("depth")  # size子标签depth
        depthcontent = xmlBuilder.createTextNode(str(Pdepth))
        depth.appendChild(depthcontent)
        size.appendChild(depth)  # size子标签depth结束

        annotation.appendChild(size)  # size标签结束

        for j in txtList:
            oneline = j.strip().split(" ")
            object = xmlBuilder.createElement("object")  # object 标签
            picname = xmlBuilder.createElement("name")  # name标签
            namecontent = xmlBuilder.createTextNode(dic[oneline[0]])
            picname.appendChild(namecontent)
            object.appendChild(picname)  # name标签结束

            pose = xmlBuilder.createElement("pose")  # pose标签
            posecontent = xmlBuilder.createTextNode("Unspecified")
            pose.appendChild(posecontent)
            object.appendChild(pose)  # pose标签结束

            truncated = xmlBuilder.createElement("truncated")  # truncated标签
            truncatedContent = xmlBuilder.createTextNode("0")
            truncated.appendChild(truncatedContent)
            object.appendChild(truncated)  # truncated标签结束

            difficult = xmlBuilder.createElement("difficult")  # difficult标签
            difficultcontent = xmlBuilder.createTextNode("0")
            difficult.appendChild(difficultcontent)
            object.appendChild(difficult)  # difficult标签结束

            bndbox = xmlBuilder.createElement("bndbox")  # bndbox标签
            xmin = xmlBuilder.createElement("xmin")  # xmin标签
            mathData = int(((float(oneline[1])) * Pwidth + 1) - (float(oneline[3])) * 0.5 * Pwidth)
            xminContent = xmlBuilder.createTextNode(str(mathData))
            xmin.appendChild(xminContent)
            bndbox.appendChild(xmin)  # xmin标签结束

            ymin = xmlBuilder.createElement("ymin")  # ymin标签
            mathData = int(((float(oneline[2])) * Pheight + 1) - (float(oneline[4])) * 0.5 * Pheight)
            yminContent = xmlBuilder.createTextNode(str(mathData))
            ymin.appendChild(yminContent)
            bndbox.appendChild(ymin)  # ymin标签结束

            xmax = xmlBuilder.createElement("xmax")  # xmax标签
            mathData = int(((float(oneline[1])) * Pwidth + 1) + (float(oneline[3])) * 0.5 * Pwidth)
            xmaxContent = xmlBuilder.createTextNode(str(mathData))
            xmax.appendChild(xmaxContent)
            bndbox.appendChild(xmax)  # xmax标签结束

            ymax = xmlBuilder.createElement("ymax")  # ymax标签
            mathData = int(((float(oneline[2])) * Pheight + 1) + (float(oneline[4])) * 0.5 * Pheight)
            ymaxContent = xmlBuilder.createTextNode(str(mathData))
            ymax.appendChild(ymaxContent)
            bndbox.appendChild(ymax)  # ymax标签结束

            object.appendChild(bndbox)  # bndbox标签结束

            annotation.appendChild(object)  # object标签结束

        f = open(xmlPath + name[0:-4] + ".xml", 'w')
        xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')
        f.close()


if __name__ == "__main__":
    picPath = "E:/aReference/deep-learning-for-image-processing-master/deep-learning-for-image-processing-master/dataset/images/valid/"  # 图片所在文件夹路径，后面的/一定要带上
    txtPath = "E:/aReference/deep-learning-for-image-processing-master/deep-learning-for-image-processing-master/dataset/labels/valid/"  # txt所在文件夹路径，后面的/一定要带上
    xmlPath = "E:/aReference/deep-learning-for-image-processing-master/deep-learning-for-image-processing-master/dataset/Annotations/valid/"  # xml文件保存路径，后面的/一定要带上
    makexml(picPath, txtPath, xmlPath)

上述代码需要修改的地方就是目标类别和相关文件地址，如下图所示。

2.数据集存放位置和划分数据集

所有图像放到JPEGImages中，xml标签文件放到Annotations中，运行如下代码，生成Main文件夹中的train.txt和val.txt，切记文件存放格式和文件夹名字一定要完全一样！！！

在代码中需要修改files_path、val_rate、train_f和eval_f。

import os
import random


def main():
    random.seed(0)  # 设置随机种子，保证随机结果可复现

    files_path = r"E:\aReference\deep-learning-for-image-processing-master\deep-learning-for-image-processing-master\dataset\Annotations"
    assert os.path.exists(files_path), "path: '{}' does not exist.".format(files_path)

    val_rate = 0.2

    files_name = sorted([file.split(".xml")[0] for file in os.listdir(files_path)])
    files_num = len(files_name)
    val_index = random.sample(range(0, files_num), k=int(files_num*val_rate))
    train_files = []
    val_files = []
    for index, file_name in enumerate(files_name):
        if index in val_index:
            val_files.append(file_name)
        else:
            train_files.append(file_name)

    try:
        train_f = open(r"E:\aReference\deep-learning-for-image-processing-master\deep-learning-for-image-processing-master\pytorch_object_detection\faster_rcnn\VOCdevkit\VOC2012\ImageSets\Main\train.txt", "w")
        eval_f = open(r"E:\aReference\deep-learning-for-image-processing-master\deep-learning-for-image-processing-master\pytorch_object_detection\faster_rcnn\VOCdevkit\VOC2012\ImageSets\Main\val.txt", "w")
        train_f.write("\n".join(train_files))
        eval_f.write("\n".join(val_files))
    except FileExistsError as e:
        print(e)
        exit(1)


if __name__ == '__main__':
    main()

生成的Main文件夹中的两个txt文件内容是图片的名字去掉.jpg后缀，如下图：

3.修改训练参数

我选择了 train_res50_fpn.py这个文件进行训练，设置超参数如下：

batch_size设置为1或2（根据个人硬件性能调整），设置的太大的话会出现如下问题：

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 108.00 MiB (GPU 0; 4.00 GiB total capacity; 1.11 GiB already allocated; 523.75 MiB free; 1.19 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

设置完直接训练就好