ILSVRC2012数据集处理

素颜马尾好姑娘i 2024-02-17 11:02 65阅读 0赞

#### ILSVRC2012数据集处理 ####

*  解压图像
 *  处理标签和图像
 *  VOC转YOLO
 *   *  转voc格式
     *  VOC2YOLO
     *  最后修改COCO128.yaml文件

## 解压图像 ##

先解压

tar -xvf ILSVRC2012_img_train.tar

![在这里插入图片描述][4afebc1d7d9647eba5dbccc775c64451.jpeg_pic_center]

解压之后其实还是1000个tar压缩包（对应1000个类别），需要再次解压，解压脚本unzip.sh如下（PS：可能需要自己改一下目录 dir ）：  
（这里也可以用解压软件一键解压）  
[批量解压脚本][Link 1]

dir=./train 
    for x in `ls $dir/*tar` do     
      filename=`basename $x .tar`     
      mkdir $dir/$filename     
      tar -xvf $x -C $dir/$filename 
    done 
    rm *.tar

执行 sh 脚本

chmod 777 unzip.sh
    ./ unzip.sh

报错:./unzip.sh: line 3: syntax error near unexpected token `filename=`basename $x .tar\`’

直接使用指令`ls *.tar | xargs -n1 tar xvf`

将当前文件夹下的tar解压[linux批量解压方法][linux]

或者：

[python批量解压zip文件][python_zip]

import os
    import shutil
    import zipfile
     
    # 首先引入需要的工具包
    # shutil为后期移动文件所需，可以忽略此项 
    # 路径改这里！
    #parent_path = r'输入路径，会解压该路径下的所有zip压缩文件'
    parent_path = r'E:\py\python3.7\test\test99\zip' 
    # 文件类型选择
    # 可以自行更改压缩文件类型，需要引入其它工具包，如tarfile等
    # 这里是因为在自己的windows上，zip比较常见，其他类型请自行更改
    file_flag = '.zip'   #修改需解压的格式 例如：.rar
    # 删除已解压的zip文件
    # 不建议初次使用，在确定程序无误后可以添加使用
    def del_old_zip(file_path):
        os.remove(file_path)  
    # 解压
    def decompress(file_path, root):
        # 开始
        # zipfile打开zip文件
        z = zipfile.ZipFile(f'{
          file_path}', 'r') 
        # 解压
        z.extractall(path=f"{
          root}")    # path为解压路径，解包后位于该路径下 
        # 判断是否需要重复解包
        for names in z.namelist():
            if names.endswith(file_flag):
                z.close()
                return 1 
        # 结束
        z.close() 
        return 0
         
    # 因为我在使用过程中发现有些zip解包后会混在一起
    # 在平时大家手动解压时可能也会遇到提示是否覆盖的问题
    # 下面的两个函数解决这一问题 
    # 开始要先创建一个大文件夹  与压缩包名字相同
    # 避免后期混乱和麻烦
    def start_dir_make(root, dirname):
        os.chdir(root)
        os.mkdir(dirname)
        return os.path.join(root, dirname) 
    # 去除多余文件夹
    def rem_dir_extra(root, father_dir_name):
        # 递归要注意信息的正常处理  搞不好上一个调用已经改变了东西  而下面的调用还是使用之前的数据 
        try: 
           # 判断文件夹重名  开始
            for item in os.listdir(os.path.join(root, father_dir_name)): 
                # 第一步判断是不是一个文件夹，如果不是则跳过本次循环
                if not os.path.isdir(os.path.join(root, father_dir_name, item)):
                    continue 
                # 判断是否要脱掉一层目录结构
                # 文件夹名字要相同，且子目录中只有单独的一个文件夹
                if item == father_dir_name and len(
                        os.listdir(os.path.join(root, father_dir_name))) == 1: 
                    # 改变工作目录
                    os.chdir(root)                 
                    # 将无用文件夹重命名，因为直接移动会有重名错误
                    os.rename(father_dir_name, father_dir_name + '-old')                 
                    # 移动文件后删除空文件夹
                    shutil.move(os.path.join(root, father_dir_name + '-old', item), os.path.join(root))
                    os.rmdir(os.path.join(root, father_dir_name + '-old')) 
                    # 将去掉一层目录结构后的文件夹继续作为父本递归处理下去
                    # 这里要注意，上面已经发生过数据的改动，所以下面递归传参一定要正确！
                    rem_dir_extra(root, item) 
                else: 
                    # 处理那些不满足上面条件的文件夹
                    rem_dir_extra(os.path.join(root, father_dir_name), item) 
        except Exception as e: 
            # 打印错误信息
            print("清除文件夹出错" + str(e)) 
    # 入口
    if __name__ == '__main__': 
        flag = 1 
        while flag: 
            #  循环遍历文件夹
            for root, dirs, files in os.walk(parent_path): 
                # 读取文件名
                for name in files:
                    if name.endswith(file_flag): 
                        # 创建文件夹
                        new_ws = start_dir_make(root, name.replace(file_flag, '')) 
                        # zip文件地址
                        zip_path = os.path.join(root, name) 
                        # 解压
                        flag = decompress(zip_path, new_ws) 
                        # 删除解压后的文件
                        # 有点危险
                        # 但不删除又可能会重复运行
                         # 一定要备份或先测试，不然可能会凉，自己选择修改
                        del_old_zip(zip_path) 
                        # 去掉多余的文件结构
                        rem_dir_extra(root, name.replace(file_flag, '')) 
                        print(f'{
          root}\\{
          name}'.join(['文件：', '\n解压完成\n']))
      
        # 由于解压可能解了好几次 所以可能会有已经解压好的父级目录重名无法处理 这里要再处理一次
        rem_dir_extra(os.path.split(parent_path)[0], os.path.split(parent_path)[1])  
        print("解压完成啦，记得检查有没有zip格式之外的呀!\n\n其他格式需要自己改一下了")

查看文件夹下文件数量

ls -l|grep "^-" |wc -l

统计文件名中包含某个名称（n15075141\_）的文件的文件总数

find -name "n15075141_*" | wc -l

![在这里插入图片描述][8d53a4271b024bbaa7fa68be95cf8ab4.png]

--------------------

这里我使用这官方提供的脚本文件

#!/bin/bash
    #
    # script to extract ImageNet dataset
    # ILSVRC2012_img_train.tar (about 138 GB)
    # ILSVRC2012_img_val.tar (about 6.3 GB)
    # make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
    #
    #  Adapted from:
    #  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
    #  https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4
    # 
    #  imagenet/train/
    #  ├── n01440764
    #  │   ├── n01440764_10026.JPEG
    #  │   ├── n01440764_10027.JPEG
    #  │   ├── ......
    #  ├── ......
    #  imagenet/val/
    #  ├── n01440764
    #  │   ├── ILSVRC2012_val_00000293.JPEG
    #  │   ├── ILSVRC2012_val_00002138.JPEG
    #  │   ├── ......
    #  ├── ......
    #
    #
    # Make imagnet directory
    #
    mkdir imagenet
    #
    # Extract the training data:
    #
    # Create train directory; move .tar file; change directory
    mkdir imagenet/train && mv ILSVRC2012_img_train.tar imagenet/train/ && cd imagenet/train
    # Extract training set; remove compressed file
    tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
    #
    # At this stage imagenet/train will contain 1000 compressed .tar files, one for each category
    #
    # For each .tar file: 
    #   1. create directory with same name as .tar file
    #   2. extract and copy contents of .tar file into directory
    #   3. remove .tar file
    find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
    #
    # This results in a training directory like so:
    #
    #  imagenet/train/
    #  ├── n01440764
    #  │   ├── n01440764_10026.JPEG
    #  │   ├── n01440764_10027.JPEG
    #  │   ├── ......
    #  ├── ......
    #
    # Change back to original directory
    cd ../..
    #
    # Extract the validation data and move images to subfolders:
    #
    # Create validation directory; move .tar file; change directory; extract validation .tar; remove compressed file
    mkdir imagenet/val && mv ILSVRC2012_img_val.tar imagenet/val/ && cd imagenet/val && tar -xvf ILSVRC2012_img_val.tar && rm -f ILSVRC2012_img_val.tar
    # get script from soumith and run; this script creates all class directories and moves images into corresponding directories
    wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
    #
    # This results in a validation directory like so:
    #
    #  imagenet/val/
    #  ├── n01440764
    #  │   ├── ILSVRC2012_val_00000293.JPEG
    #  │   ├── ILSVRC2012_val_00002138.JPEG
    #  │   ├── ......
    #  ├── ......
    #
    #
    # Check total files after extract
    #
    #  $ find train/ -name "*.JPEG" | wc -l
    #  1281167
    #  $ find val/ -name "*.JPEG" | wc -l
    #  50000
    #

[参考文章][Link 2]

具体类别如下[1000类信息][1000]

## 处理标签和图像 ##

因为标签很少，图像较多，只有部分图像有标签对应，所以需要删除多余的图像。

[数据集目录结构如下][Link 3]

先查看一下类别信息  
在ILSVRC2012\_devkit\_t12中的meta.mat文件中存放着类型信息(1000)类  
![在这里插入图片描述][4ffd6d501b62474dace0cdcf65f068c8.png]

[matlab配置][matlab]

![在这里插入图片描述][d0ddde60576f42f99ff5bfd24531883d.png]

[Ubuntu下通过命令打开图片][Ubuntu]

xdg-open n02098286_4811.JPEG

在一个文件夹下就是一类的

![在这里插入图片描述][262cec8631fd493d877590180930f6d6.png]

![在这里插入图片描述][e0bb761e35b5409caff36ce6414a6fac.png]

将标签全部复制到ann下

[python遍历文件夹下的所有子文件夹，并将指定的文件复制到指定目录][python]

# coding: utf-8
    from PIL import Image, ImageDraw, ImageFont
    import os
    import shutil
    import cv2 as cv
    import numpy as np
    import json
    
    
    #批量复制
    # 为了防止重名覆盖问题，先使用改名软件将文件名全部改了
    
    
    def batchCopy(src,des,suffix):
        print("封装代码")
        for root, dirs, files in os.walk(src):
            print(root)
            all_files = os.listdir(root)
            filtered_files = [file for file in all_files if file.endswith(suffix)]
            # 遍历原文件夹中的文件
            if (len(filtered_files)):
                for i in range(len(files)):
                    full_file_name = root+"/"+files[i]
                    print("要被复制的全文件路径全名:", full_file_name)
                    shutil.copy(full_file_name, des)  # shutil.copy函数放入原文件的路径文件全名  然后放入目标文件夹
    
    
    if __name__ == "__main__":
        src = "/home/ubuntu/data/ilsvrc2012/ILSVRC2012_img_train_new/imagenet/val"  # 原文件夹路径
        des = "/home/ubuntu/data/ilsvrc2012/imageval"  # 目标文件夹路径
        suffix = ".JPEG"
        batchCopy(src,des,suffix)

![在这里插入图片描述][d57983bd8ece437bbda20d54f95f57f6.png]

删除两文件夹多出的文件，两文件夹文件后缀名不同，单个文件夹有多个后缀名文件  
[参考代码][Link 4]  
就是图片比标签多，将多余的图片删除，使得图片和标签数量一一对应

![在这里插入图片描述][f588fe574d2143f5b6b508746dd99acd.png]  
![在这里插入图片描述][6c42c852d9cb4c6eacede934100ac88c.png]

# encoding:utf-8
    import os
    import os.path
    
    # 删除pic_dir中比txt_dir多出的文件
    pic_dir = 'D:\\dataset\\total'  # 你的图片文件夹路径
    txt_dir = 'D:\\dataset\\ann'  # 你的标签文件夹路径
    
    
    def txt(rootdir):
        a = []
        for parent, dirnames, filenames in os.walk(rootdir):
            for filenames in filenames:
                filenames = filenames.split('.')[0]  # 获取无后缀名的文件名称
                a.append(filenames)
        return a
    
    
    def pic(rootdir):
        b = []
        for parent, dirnames, filenames in os.walk(rootdir):
            for filenames in filenames:
                filenames = filenames.split('.')[0]  # 获取无后缀名的文件名称
                b.append(filenames)
        return b
    
    
    if __name__ == '__main__':
        txt_set = txt(txt_dir)
        txt_set = set(txt_set)  # 删除txt_set中的重复
        pic_set = pic(pic_dir)
        pic_set = set(pic_set)  # 删除pic_set中的重复
        # comp=txt_set-pic_set
        comp = pic_set - txt_set  # 图片比标注多时，进行做差
        print("ok")
        print(len(comp))
    
        for item in comp:
            # 删去这些无标注的图片
            for root, dirs, files in os.walk(pic_dir):
                for file in files:
                    filename = file.split('.')[0]  # 获取无后缀名的文件名称
                    if item == filename:
                        os.remove(pic_dir + '/' + file)
            # file = pic_dir + '/' + item + '.png' #删除pic_dir中多出的文件
            # if os.path.exists(file):
            #     os.remove(file)
            #     print(file)

> 这种方法删除很慢，特别是有70W张图片待删除

[高效删除多余图片方法][Link 5]

效果：  
![在这里插入图片描述][db6e7194a6ef4447810aa4e1586bad1d.png]  
统计待删除txt文件的行数

file_path = '/home/ubuntu/code/yolov8-pytorch-master/myDel.txt'  # 替换成你的文本文件路径
     
    with open(file_path, 'r') as file:
        lines = file.readlines()
     
    num_lines = len(lines)
    print("文本文件的行数:", num_lines)

待删除图像723295张（半分钟就找出来了）

后缀转换后：  
![在这里插入图片描述][74441902149346a88a1240e3df55a7f3.png]

## VOC转YOLO ##

Imagenet提供了标签是xml的，就和voc是一样的

首先获取类别信息文件(voc\_classes.txt)

0 n01440764 tench 鱼
    1 n01443537 goldfish 鱼
    2 n01484850 great_white_shark 鱼
    3 n01491361 tiger_shark 鱼
    4 n01494475 hammerhead 鱼
    5 n01496331 electric_ray 鱼
    6 n01498041 stingray 鱼
    7 n01514668 cock 鸡
    8 n01514859 hen 鸡
    9 n01518878 ostrich 鸵鸟
    10 n01530575 brambling 鸟
    11 n01531178 goldfinch 鸟
    12 n01532829 house_finch 鸟
    13 n01534433 junco 鸟
    14 n01537544 indigo_bunting 鸟
    15 n01558993 robin 鸟
    16 n01560419 bulbul 鸟
    17 n01580077 jay 鸟
    18 n01582220 magpie 鸟
    19 n01592084 chickadee 鸟
    20 n01601694 water_ouzel 鸟
    21 n01608432 kite 鸟
    22 n01614925 bald_eagle 鹰
    23 n01616318 vulture 鹰
    24 n01622779 great_grey_owl 猫头鹰
    25 n01629819 European_fire_salamander 壁虎
    26 n01630670 common_newt 壁虎
    27 n01631663 eft 壁虎
    28 n01632458 spotted_salamander 壁虎
    29 n01632777 axolotl 壁虎
    30 n01641577 bullfrog 蛤蟆
    31 n01644373 tree_frog 青蛙
    32 n01644900 tailed_frog 青蛙
    33 n01664065 loggerhead 龟
    34 n01665541 leatherback_turtle 龟
    35 n01667114 mud_turtle 龟
    36 n01667778 terrapin 龟
    37 n01669191 box_turtle 龟
    38 n01675722 banded_gecko 蜥蜴
    39 n01677366 common_iguana 蜥蜴
    40 n01682714 American_chameleon 蜥蜴
    41 n01685808 whiptail 蜥蜴
    42 n01687978 agama 蜥蜴
    43 n01688243 frilled_lizard 蜥蜴
    44 n01689811 alligator_lizard 蜥蜴
    45 n01692333 Gila_monster 蜥蜴
    46 n01693334 green_lizard 蜥蜴
    47 n01694178 African_chameleon 蜥蜴
    48 n01695060 Komodo_dragon 蜥蜴
    49 n01697457 African_crocodile 鳄鱼
    50 n01698640 American_alligator 鳄鱼
    51 n01704323 triceratops 恐龙
    52 n01728572 thunder_snake 蛇
    53 n01728920 ringneck_snake 蛇
    54 n01729322 hognose_snake 蛇
    55 n01729977 green_snake 蛇
    56 n01734418 king_snake 蛇
    57 n01735189 garter_snake 蛇
    58 n01737021 water_snake 蛇
    59 n01739381 vine_snake 蛇
    60 n01740131 night_snake 蛇
    61 n01742172 boa_constrictor 蛇
    62 n01744401 rock_python 蛇
    63 n01748264 Indian_cobra 蛇
    64 n01749939 green_mamba 蛇
    65 n01751748 sea_snake 蛇
    66 n01753488 horned_viper 蛇
    67 n01755581 diamondback 蛇
    68 n01756291 sidewinder 蛇
    69 n01768244 trilobite 化石
    70 n01770081 harvestman 蜘蛛
    71 n01770393 scorpion 蝎子
    72 n01773157 black_and_gold_garden_spider 蜘蛛
    73 n01773549 barn_spider 蜘蛛
    74 n01773797 garden_spider 蜘蛛
    75 n01774384 black_widow 蜘蛛
    76 n01774750 tarantula 蜘蛛
    77 n01775062 wolf_spider 蜘蛛
    78 n01776313 tick 蜘蛛
    79 n01784675 centipede 蜈蚣
    80 n01795545 black_grouse 鸟
    81 n01796340 ptarmigan 鸟
    82 n01797886 ruffed_grouse 鸟
    83 n01798484 prairie_chicken 鸟
    84 n01806143 peacock 孔雀
    85 n01806567 quail 鸟
    86 n01807496 partridge 鸟
    87 n01817953 African_grey 鸟
    88 n01818515 macaw 鸟
    89 n01819313 sulphur-crested_cockatoo 鸟
    90 n01820546 lorikeet 鸟
    91 n01824575 coucal 鸟
    92 n01828970 bee_eater 鸟
    93 n01829413 hornbill 鸟
    94 n01833805 hummingbird 鸟
    95 n01843065 jacamar 鸟
    96 n01843383 toucan 鸟
    97 n01847000 drake 鸭子
    98 n01855032 red-breasted_merganser 鹅
    99 n01855672 goose 鹅
    100 n01860187 black_swan 鹅
    101 n01871265 tusker 大象
    102 n01872401 echidna 刺猬
    103 n01873310 platypus 鸭嘴兽
    104 n01877812 wallaby 袋鼠
    105 n01882714 koala 考拉
    106 n01883070 wombat 土拨鼠
    107 n01910747 jellyfish 水母
    108 n01914609 sea_anemone 珊瑚
    109 n01917289 brain_coral 珊瑚
    110 n01924916 flatworm 海洋生物
    111 n01930112 nematode 海蛇
    112 n01943899 conch 海螺
    113 n01944390 snail 蜗牛
    114 n01945685 slug 蜗牛
    115 n01950731 sea_slug 海洋生物
    116 n01955084 chiton 海洋生物
    117 n01968897 chambered_nautilus 海螺
    118 n01978287 Dungeness_crab 螃蟹
    119 n01978455 rock_crab 螃蟹
    120 n01980166 fiddler_crab 螃蟹
    121 n01981276 king_crab 螃蟹
    122 n01983481 American_lobster 龙虾
    123 n01984695 spiny_lobster 龙虾
    124 n01985128 crayfish 龙虾
    125 n01986214 hermit_crab 寄居蟹
    126 n01990800 isopod 海洋生物
    127 n02002556 white_stork 鸟
    128 n02002724 black_stork 鸟
    129 n02006656 spoonbill 鸟
    130 n02007558 flamingo 鸟
    131 n02009229 little_blue_heron 鸟
    132 n02009912 American_egret 鸟
    133 n02011460 bittern 鸟
    134 n02012849 crane 鸟
    135 n02013706 limpkin 鸟
    136 n02017213 European_gallinule 鸟
    137 n02018207 American_coot 鸟
    138 n02018795 bustard 鸟
    139 n02025239 ruddy_turnstone 鸟
    140 n02027492 red-backed_sandpiper 鸟
    141 n02028035 redshank 鸟
    142 n02033041 dowitcher 鸟
    143 n02037110 oystercatcher 鸟
    144 n02051845 pelican 鸟
    145 n02056570 king_penguin 企鹅
    146 n02058221 albatross 鸟
    147 n02066245 grey_whale 鲸鱼
    148 n02071294 killer_whale 鲸鱼
    149 n02074367 dugong 海象
    150 n02077923 sea_lion 海狮
    151 n02085620 Chihuahua 狗
    152 n02085782 Japanese_spaniel 狗
    153 n02085936 Maltese_dog 狗
    154 n02086079 Pekinese 狗
    155 n02086240 Shih-Tzu 狗
    156 n02086646 Blenheim_spaniel 狗
    157 n02086910 papillon 狗
    158 n02087046 toy_terrier 狗
    159 n02087394 Rhodesian_ridgeback 狗
    160 n02088094 Afghan_hound 狗
    161 n02088238 basset 狗
    162 n02088364 beagle 狗
    163 n02088466 bloodhound 狗
    164 n02088632 bluetick 狗
    165 n02089078 black-and-tan_coonhound 狗
    166 n02089867 Walker_hound 狗
    167 n02089973 English_foxhound 狗
    168 n02090379 redbone 狗
    169 n02090622 borzoi 狗
    170 n02090721 Irish_wolfhound 狗
    171 n02091032 Italian_greyhound 狗
    172 n02091134 whippet 狗
    173 n02091244 Ibizan_hound 狗
    174 n02091467 Norwegian_elkhound 狗
    175 n02091635 otterhound 狗
    176 n02091831 Saluki 狗
    177 n02092002 Scottish_deerhound 狗
    178 n02092339 Weimaraner 狗
    179 n02093256 Staffordshire_bullterrier 狗
    180 n02093428 American_Staffordshire_terrier 狗
    181 n02093647 Bedlington_terrier 狗
    182 n02093754 Border_terrier 狗
    183 n02093859 Kerry_blue_terrier 狗
    184 n02093991 Irish_terrier 狗
    185 n02094114 Norfolk_terrier 狗
    186 n02094258 Norwich_terrier 狗
    187 n02094433 Yorkshire_terrier 狗
    188 n02095314 wire-haired_fox_terrier 狗
    189 n02095570 Lakeland_terrier 狗
    190 n02095889 Sealyham_terrier 狗
    191 n02096051 Airedale 狗
    192 n02096177 cairn 狗
    193 n02096294 Australian_terrier 狗
    194 n02096437 Dandie_Dinmont 狗
    195 n02096585 Boston_bull 狗
    196 n02097047 miniature_schnauzer 狗
    197 n02097130 giant_schnauzer 狗
    198 n02097209 standard_schnauzer 狗
    199 n02097298 Scotch_terrier 狗
    200 n02097474 Tibetan_terrier 狗
    201 n02097658 silky_terrier 狗
    202 n02098105 soft-coated_wheaten_terrier 狗
    203 n02098286 West_Highland_white_terrier 狗
    204 n02098413 Lhasa 狗
    205 n02099267 flat-coated_retriever 狗
    206 n02099429 curly-coated_retriever 狗
    207 n02099601 golden_retriever 狗
    208 n02099712 Labrador_retriever 狗
    209 n02099849 Chesapeake_Bay_retriever 狗
    210 n02100236 German_short-haired_pointer 狗
    211 n02100583 vizsla 狗
    212 n02100735 English_setter 狗
    213 n02100877 Irish_setter 狗
    214 n02101006 Gordon_setter 狗
    215 n02101388 Brittany_spaniel 狗
    216 n02101556 clumber 狗
    217 n02102040 English_springer 狗
    218 n02102177 Welsh_springer_spaniel 狗
    219 n02102318 cocker_spaniel 狗
    220 n02102480 Sussex_spaniel 狗
    221 n02102973 Irish_water_spaniel 狗
    222 n02104029 kuvasz 狗
    223 n02104365 schipperke 狗
    224 n02105056 groenendael 狗
    225 n02105162 malinois 狗
    226 n02105251 briard 狗
    227 n02105412 kelpie 狗
    228 n02105505 komondor 狗
    229 n02105641 Old_English_sheepdog 狗
    230 n02105855 Shetland_sheepdog 狗
    231 n02106030 collie 狗
    232 n02106166 Border_collie 狗
    233 n02106382 Bouvier_des_Flandres 狗
    234 n02106550 Rottweiler 狗
    235 n02106662 German_shepherd 狗
    236 n02107142 Doberman 狗
    237 n02107312 miniature_pinscher 狗
    238 n02107574 Greater_Swiss_Mountain_dog 狗
    239 n02107683 Bernese_mountain_dog 狗
    240 n02107908 Appenzeller 狗
    241 n02108000 EntleBucher 狗
    242 n02108089 boxer 狗
    243 n02108422 bull_mastiff 狗
    244 n02108551 Tibetan_mastiff 狗
    245 n02108915 French_bulldog 狗
    246 n02109047 Great_Dane 狗
    247 n02109525 Saint_Bernard 狗
    248 n02109961 Eskimo_dog 狗
    249 n02110063 malamute 狗
    250 n02110185 Siberian_husky 狗
    251 n02110341 dalmatian 狗
    252 n02110627 affenpinscher 狗
    253 n02110806 basenji 狗
    254 n02110958 pug 狗
    255 n02111129 Leonberg 狗
    256 n02111277 Newfoundland 狗
    257 n02111500 Great_Pyrenees 狗
    258 n02111889 Samoyed 狗
    259 n02112018 Pomeranian 狗
    260 n02112137 chow 狗
    261 n02112350 keeshond 狗
    262 n02112706 Brabancon_griffon 狗
    263 n02113023 Pembroke 狗
    264 n02113186 Cardigan 狗
    265 n02113624 toy_poodle 狗
    266 n02113712 miniature_poodle 狗
    267 n02113799 standard_poodle 狗
    268 n02113978 Mexican_hairless 狗
    269 n02114367 timber_wolf 狼
    270 n02114548 white_wolf 狼
    271 n02114712 red_wolf 狼
    272 n02114855 coyote 狼
    273 n02115641 dingo 狼
    274 n02115913 dhole 狼
    275 n02116738 African_hunting_dog 狼
    276 n02117135 hyena 狼
    277 n02119022 red_fox 狐狸
    278 n02119789 kit_fox 狐狸
    279 n02120079 Arctic_fox 狐狸
    280 n02120505 grey_fox 狐狸
    281 n02123045 tabby 猫
    282 n02123159 tiger_cat 猫
    283 n02123394 Persian_cat 猫
    284 n02123597 Siamese_cat 猫
    285 n02124075 Egyptian_cat 猫
    286 n02125311 cougar 猫
    287 n02127052 lynx 猫
    288 n02128385 leopard 豹
    289 n02128757 snow_leopard 豹
    290 n02128925 jaguar 豹
    291 n02129165 lion 狮子
    292 n02129604 tiger 老虎
    293 n02130308 cheetah 豹
    294 n02132136 brown_bear 熊
    295 n02133161 American_black_bear 熊
    296 n02134084 ice_bear 熊
    297 n02134418 sloth_bear 熊
    298 n02137549 mongoose 猫鼬
    299 n02138441 meerkat 猫鼬
    300 n02165105 tiger_beetle 昆虫
    301 n02165456 ladybug 昆虫
    302 n02167151 ground_beetle 昆虫
    303 n02168699 long-horned_beetle 昆虫
    304 n02169497 leaf_beetle 昆虫
    305 n02172182 dung_beetle 昆虫
    306 n02174001 rhinoceros_beetle 昆虫
    307 n02177972 weevil 昆虫
    308 n02190166 fly 昆虫
    309 n02206856 bee 昆虫
    310 n02219486 ant 昆虫
    311 n02226429 grasshopper 昆虫
    312 n02229544 cricket 昆虫
    313 n02231487 walking_stick 昆虫
    314 n02233338 cockroach 昆虫
    315 n02236044 mantis 昆虫
    316 n02256656 cicada 昆虫
    317 n02259212 leafhopper 昆虫
    318 n02264363 lacewing 昆虫
    319 n02268443 dragonfly 蜻蜓
    320 n02268853 damselfly 蜻蜓
    321 n02276258 admiral 蝴蝶
    322 n02277742 ringlet 蝴蝶
    323 n02279972 monarch 蝴蝶
    324 n02280649 cabbage_butterfly 蝴蝶
    325 n02281406 sulphur_butterfly 蝴蝶
    326 n02281787 lycaenid 蝴蝶
    327 n02317335 starfish 海星
    328 n02319095 sea_urchin 海胆
    329 n02321529 sea_cucumber 海洋生物
    330 n02325366 wood_rabbit 兔子
    331 n02326432 hare 兔子
    332 n02328150 Angora 兔子
    333 n02342885 hamster 鼠
    334 n02346627 porcupine 鼠
    335 n02356798 fox_squirrel 松鼠
    336 n02361337 marmot 鼠
    337 n02363005 beaver 鼠
    338 n02364673 guinea_pig 鼠
    339 n02389026 sorrel 马
    340 n02391049 zebra 斑马
    341 n02395406 hog 猪
    342 n02396427 wild_boar 猪
    343 n02397096 warthog 猪
    344 n02398521 hippopotamus 河马
    345 n02403003 ox 牛
    346 n02408429 water_buffalo 牛
    347 n02410509 bison 牛
    348 n02412080 ram 羊
    349 n02415577 bighorn 羊
    350 n02417914 ibex 羊
    351 n02422106 hartebeest 羊
    352 n02422699 impala 羊
    353 n02423022 gazelle 羊
    354 n02437312 Arabian_camel 骆驼
    355 n02437616 llama 羊驼
    356 n02441942 weasel 狸
    357 n02442845 mink 狸
    358 n02443114 polecat 狸
    359 n02443484 black-footed_ferret 狸
    360 n02444819 otter 狸
    361 n02445715 skunk 狸
    362 n02447366 badger 狸
    363 n02454379 armadillo 穿山甲
    364 n02457408 three-toed_sloth 树懒
    365 n02480495 orangutan 狒狒
    366 n02480855 gorilla 猩猩
    367 n02481823 chimpanzee 猴子
    368 n02483362 gibbon 猴子
    369 n02483708 siamang 猴子
    370 n02484975 guenon 猴子
    371 n02486261 patas 猴子
    372 n02486410 baboon 猴子
    373 n02487347 macaque 猴子
    374 n02488291 langur 猴子
    375 n02488702 colobus 猴子
    376 n02489166 proboscis_monkey 猴子
    377 n02490219 marmoset 猴子
    378 n02492035 capuchin 猴子
    379 n02492660 howler_monkey 猴子
    380 n02493509 titi 猴子
    381 n02493793 spider_monkey 猴子
    382 n02494079 squirrel_monkey 猴子
    383 n02497673 Madagascar_cat 猴子
    384 n02500267 indri 猴子
    385 n02504013 Indian_elephant 大象
    386 n02504458 African_elephant 大象
    387 n02509815 lesser_panda 浣熊
    388 n02510455 giant_panda 熊猫
    389 n02514041 barracouta 鱼
    390 n02526121 eel 鱼
    391 n02536864 coho 鱼
    392 n02606052 rock_beauty 鱼
    393 n02607072 anemone_fish 鱼
    394 n02640242 sturgeon 鱼
    395 n02641379 gar 鱼
    396 n02643566 lionfish 鱼
    397 n02655020 puffer 鱼
    398 n02666196 abacus 算盘
    399 n02667093 abaya 穆斯林
    400 n02669723 academic_gown 学士服
    401 n02672831 accordion 手风琴
    402 n02676566 acoustic_guitar 吉他
    403 n02687172 aircraft_carrier 航空母舰
    404 n02690373 airliner 飞机
    405 n02692877 airship 飞艇
    406 n02699494 altar 教堂
    407 n02701002 ambulance 救护车
    408 n02704792 amphibian 水陆两用车
    409 n02708093 analog_clock 钟
    410 n02727426 apiary 箱子
    411 n02730930 apron 围裙
    412 n02747177 ashcan 垃圾箱
    413 n02749479 assault_rifle 枪
    414 n02769748 backpack 背包
    415 n02776631 bakery 面包柜
    416 n02777292 balance_beam 体操
    417 n02782093 balloon 热气球
    418 n02783161 ballpoint 钢笔
    419 n02786058 Band_Aid 邦迪
    420 n02787622 banjo 乐器
    421 n02788148 bannister 楼梯
    422 n02790996 barbell 杠铃
    423 n02791124 barber_chair 座椅
    424 n02791270 barbershop 理发
    425 n02793495 barn 木屋
    426 n02794156 barometer 表
    427 n02795169 barrel 酒桶
    428 n02797295 barrow 手推车
    429 n02799071 baseball 棒球
    430 n02802426 basketball 篮球
    431 n02804414 bassinet 婴儿
    432 n02804610 bassoon 乐器
    433 n02807133 bathing_cap 游泳
    434 n02808304 bath_towel 婴儿毛巾
    435 n02808440 bathtub 浴缸
    436 n02814533 beach_wagon 轿车
    437 n02814860 beacon 灯塔
    438 n02815834 beaker 烧杯
    439 n02817516 bearskin 守卫
    440 n02823428 beer_bottle 啤酒
    441 n02823750 beer_glass 啤酒
    442 n02825657 bell_cote 建筑
    443 n02834397 bib 围兜
    444 n02835271 bicycle-built-for-two 双人自行车
    445 n02837789 bikini 比基尼
    446 n02840245 binder 笔记本
    447 n02841315 binoculars 望远镜
    448 n02843684 birdhouse 信箱
    449 n02859443 boathouse 小屋
    450 n02860847 bobsled 雪橇
    451 n02865351 bolo_tie 项链
    452 n02869837 bonnet 帽子
    453 n02870880 bookcase 书柜
    454 n02871525 bookshop 书店
    455 n02877765 bottlecap 瓶盖
    456 n02879718 bow 弓箭
    457 n02883205 bow_tie 领结
    458 n02892201 brass 墓碑
    459 n02892767 brassiere 胸罩
    460 n02894605 breakwater 海岸
    461 n02895154 breastplate 盔甲
    462 n02906734 broom 扫帚
    463 n02909870 bucket 水桶
    464 n02910353 buckle 皮带
    465 n02916936 bulletproof_vest 防弹背心
    466 n02917067 bullet_train 火车
    467 n02927161 butcher_shop 肉铺
    468 n02930766 cab 出租车
    469 n02939185 caldron 锅
    470 n02948072 candle 蜡烛
    471 n02950826 cannon 炮
    472 n02951358 canoe 艇
    473 n02951585 can_opener 订书机
    474 n02963159 cardigan 毛衣
    475 n02965783 car_mirror 反光镜
    476 n02966193 carousel 旋转木马
    477 n02966687 carpenter’s_kit 工具箱
    478 n02971356 carton 盒子
    479 n02974003 car_wheel 轮胎
    480 n02977058 cash_machine 取款机
    481 n02978881 cassette 磁带
    482 n02979186 cassette_player 磁带
    483 n02980441 castle 城堡
    484 n02981792 catamaran 帆船
    485 n02988304 CD_player cd播放器
    486 n02992211 cello 大提琴
    487 n02992529 cellular_telephone 手机
    488 n02999410 chain 铁链
    489 n03000134 chainlink_fence 铁丝网
    490 n03000247 chain_mail 铁丝网
    491 n03000684 chain_saw 电锯
    492 n03014705 chest 木箱
    493 n03016953 chiffonier 木柜
    494 n03017168 chime 锣鼓
    495 n03018349 china_cabinet 柜子
    496 n03026506 Christmas_stocking 袜子
    497 n03028079 church 教堂
    498 n03032252 cinema 剧院
    499 n03041632 cleaver 刀
    500 n03042490 cliff_dwelling 堡垒
    501 n03045698 cloak 斗篷
    502 n03047690 clog 鞋子
    503 n03062245 cocktail_shaker 瓶子
    504 n03063599 coffee_mug 杯子
    505 n03063689 coffeepot 壶
    506 n03065424 coil 螺旋
    507 n03075370 combination_lock 锁
    508 n03085013 computer_keyboard 键盘
    509 n03089624 confectionery 糖果
    510 n03095699 container_ship 船
    511 n03100240 convertible 轿车
    512 n03109150 corkscrew 开瓶器
    513 n03110669 cornet 号（乐器）
    514 n03124043 cowboy_boot 靴子
    515 n03124170 cowboy_hat 帽子
    516 n03125729 cradle 婴儿床
    517 n03126707 crane 起重机
    518 n03127747 crash_helmet 头盔
    519 n03127925 crate 木箱
    520 n03131574 crib 婴儿床
    521 n03133878 Crock_Pot 电饭锅
    522 n03134739 croquet_ball 推球
    523 n03141823 crutch 拐杖
    524 n03146219 cuirass 盔甲
    525 n03160309 dam 水库
    526 n03179701 desk 办公桌
    527 n03180011 desktop_computer 电脑
    528 n03187595 dial_telephone 电话机
    529 n03188531 diaper 尿布
    530 n03196217 digital_clock 闹钟
    531 n03197337 digital_watch 手表
    532 n03201208 dining_table 餐桌
    533 n03207743 dishrag 垫子
    534 n03207941 dishwasher 洗碗柜
    535 n03208938 disk_brake 车轮
    536 n03216828 dock 港口
    537 n03218198 dogsled 雪橇
    538 n03220513 dome 穹顶
    539 n03223299 doormat 地毯
    540 n03240683 drilling_platform 油田
    541 n03249569 drum 鼓
    542 n03250847 drumstick 鼓棒
    543 n03255030 dumbbell 哑铃
    544 n03259280 Dutch_oven 锅
    545 n03271574 electric_fan 风扇
    546 n03272010 electric_guitar 电吉他
    547 n03272562 electric_locomotive 火车
    548 n03290653 entertainment_center 电视机
    549 n03291819 envelope 信
    550 n03297495 espresso_maker 咖啡机
    551 n03314780 face_powder 化妆品
    552 n03325584 feather_boa 绒毛
    553 n03337140 file 柜子
    554 n03344393 fireboat 喷泉
    555 n03345487 fire_engine 消防车
    556 n03347037 fire_screen 壁炉
    557 n03355925 flagpole 旗杆
    558 n03372029 flute 笛子
    559 n03376595 folding_chair 座椅
    560 n03379051 football_helmet 橄榄球
    561 n03384352 forklift 叉车
    562 n03388043 fountain 喷泉
    563 n03388183 fountain_pen 钢笔
    564 n03388549 four-poster 床
    565 n03393912 freight_car 火车
    566 n03394916 French_horn 圆号
    567 n03400231 frying_pan 平底锅
    568 n03404251 fur_coat 裘皮
    569 n03417042 garbage_truck 卡车
    570 n03424325 gasmask 面具
    571 n03425413 gas_pump 加油
    572 n03443371 goblet 酒杯
    573 n03444034 go-kart 卡丁车
    574 n03445777 golf_ball 高尔夫
    575 n03445924 golfcart 高尔夫车
    576 n03447447 gondola 小船
    577 n03447721 gong 锣鼓
    578 n03450230 gown 婚纱
    579 n03452741 grand_piano 钢琴
    580 n03457902 greenhouse 大棚
    581 n03459775 grille 轿车
    582 n03461385 grocery_store 菜场
    583 n03467068 guillotine 断头台
    584 n03476684 hair_slide 发饰
    585 n03476991 hair_spray 发蜡
    586 n03478589 half_track 坦克
    587 n03481172 hammer 榔头
    588 n03482405 hamper 竹筒
    589 n03483316 hand_blower 吹风机
    590 n03485407 hand-held_computer pos机
    591 n03485794 handkerchief 手帕
    592 n03492542 hard_disc 硬盘
    593 n03494278 harmonica 口风琴
    594 n03495258 harp 竖琴
    595 n03496892 harvester 起重机
    596 n03498962 hatchet 斧头
    597 n03527444 holster 手枪
    598 n03529860 home_theater 电视机
    599 n03530642 honeycomb 蜂巢
    600 n03532672 hook 钩子
    601 n03534580 hoopskirt 裙子
    602 n03535780 horizontal_bar 体操
    603 n03538406 horse_cart 马车
    604 n03544143 hourglass 沙漏
    605 n03584254 iPod 音乐播放器
    606 n03584829 iron 电熨斗
    607 n03590841 jack-o’-lantern 南瓜灯
    608 n03594734 jean 牛仔裤
    609 n03594945 jeep 吉普车
    610 n03595614 jersey T恤
    611 n03598930 jigsaw_puzzle 拼图
    612 n03599486 jinrikisha 黄包车
    613 n03602883 joystick 操纵杆
    614 n03617480 kimono 和服
    615 n03623198 knee_pad 护具
    616 n03627232 knot 绳结
    617 n03630383 lab_coat 医生
    618 n03633091 ladle 勺子
    619 n03637318 lampshade 灯
    620 n03642806 laptop 笔记本电脑
    621 n03649909 lawn_mower 割草机
    622 n03657121 lens_cap 镜头盖
    623 n03658185 letter_opener 小刀
    624 n03661043 library 图书馆
    625 n03662601 lifeboat 救生船
    626 n03666591 lighter 打火机
    627 n03670208 limousine 加长车
    628 n03673027 liner 轮船
    629 n03676483 lipstick 口红
    630 n03680355 Loafer 鞋子
    631 n03690938 lotion 护肤品
    632 n03691459 loudspeaker 音响
    633 n03692522 loupe 放大镜
    634 n03697007 lumbermill 原木
    635 n03706229 magnetic_compass 指南针
    636 n03709823 mailbag 包
    637 n03710193 mailbox 邮箱
    638 n03710637 maillot 泳衣
    639 n03710721 maillot 泳衣
    640 n03717622 manhole_cover 窨井盖
    641 n03720891 maraca 手摇铃
    642 n03721384 marimba 木琴
    643 n03724870 mask 面具
    644 n03729826 matchstick 火柴
    645 n03733131 maypole 绳子
    646 n03733281 maze 迷宫
    647 n03733805 measuring_cup 烧杯
    648 n03742115 medicine_chest 冰箱
    649 n03743016 megalith 石柱
    650 n03759954 microphone 话筒
    651 n03761084 microwave 微波炉
    652 n03763968 military_uniform 军人
    653 n03764736 milk_can 水壶
    654 n03769881 minibus 小客车
    655 n03770439 miniskirt 短裙
    656 n03770679 minivan 面包车
    657 n03773504 missile 导弹
    658 n03775071 mitten 手套
    659 n03775546 mixing_bowl 碗
    660 n03776460 mobile_home 房车
    661 n03777568 Model_T 老爷车
    662 n03777754 modem 路由器
    663 n03781244 monastery 建筑
    664 n03782006 monitor 显示器
    665 n03785016 moped 摩托车
    666 n03786901 mortar 砚
    667 n03787032 mortarboard 学士帽
    668 n03788195 mosque 建筑
    669 n03788365 mosquito_net 蚊帐
    670 n03791053 motor_scooter 助动车
    671 n03792782 mountain_bike 自行车
    672 n03792972 mountain_tent 帐篷
    673 n03793489 mouse 键盘鼠标
    674 n03794056 mousetrap 捕鼠夹
    675 n03796401 moving_van 货车
    676 n03803284 muzzle 狗嘴套
    677 n03804744 nail 钉子
    678 n03814639 neck_brace 颈托
    679 n03814906 necklace 项链
    680 n03825788 nipple 奶瓶
    681 n03832673 notebook 笔记本电脑
    682 n03837869 obelisk 建筑
    683 n03838899 oboe 黑管
    684 n03840681 ocarina 埙
    685 n03841143 odometer 仪表盘
    686 n03843555 oil_filter 机油滤清器
    687 n03854065 organ 管风琴
    688 n03857828 oscilloscope 示波器
    689 n03866082 overskirt 礼服
    690 n03868242 oxcart 牛车
    691 n03868863 oxygen_mask 呼吸器
    692 n03871628 packet 零食
    693 n03873416 paddle 划桨
    694 n03874293 paddlewheel 水轮
    695 n03874599 padlock 锁
    696 n03876231 paintbrush 刷子
    697 n03877472 pajama 睡衣
    698 n03877845 palace 建筑
    699 n03884397 panpipe 乐器
    700 n03887697 paper_towel 纸巾
    701 n03888257 parachute 降落伞
    702 n03888605 parallel_bars 体操
    703 n03891251 park_bench 长椅
    704 n03891332 parking_meter 停车缴费器
    705 n03895866 passenger_car 火车
    706 n03899768 patio 院子
    707 n03902125 pay-phone 公用电话
    708 n03903868 pedestal 柱子
    709 n03908618 pencil_box 文具袋
    710 n03908714 pencil_sharpener 卷笔刀
    711 n03916031 perfume 香水
    712 n03920288 Petri_dish 培养皿
    713 n03924679 photocopier 打印机
    714 n03929660 pick 吉他拨片
    715 n03929855 pickelhaube 头盔
    716 n03930313 picket_fence 栅栏
    717 n03930630 pickup 轿车
    718 n03933933 pier 桥
    719 n03935335 piggy_bank 储蓄罐
    720 n03937543 pill_bottle 药丸
    721 n03938244 pillow 枕头
    722 n03942813 ping-pong_ball 乒乓球
    723 n03944341 pinwheel 风车
    724 n03947888 pirate 帆船
    725 n03950228 pitcher 茶壶
    726 n03954731 plane 刨子
    727 n03956157 planetarium 建筑
    728 n03958227 plastic_bag 塑料袋
    729 n03961711 plate_rack 碗架
    730 n03967562 plow 推土机
    731 n03970156 plunger 搋子
    732 n03976467 Polaroid_camera 相机
    733 n03976657 pole 杆子
    734 n03977966 police_van 警车
    735 n03980874 poncho 披风
    736 n03982430 pool_table 桌球
    737 n03983396 pop_bottle 瓶子
    738 n03991062 pot 盆栽
    739 n03992509 potter’s_wheel 陶艺
    740 n03995372 power_drill 钻机
    741 n03998194 prayer_rug 毯子
    742 n04004767 printer 打印机
    743 n04005630 prison 监狱
    744 n04008634 projectile 导弹
    745 n04009552 projector 投影仪
    746 n04019541 puck 冰球
    747 n04023962 punching_bag 拳击
    748 n04026417 purse 手提袋
    749 n04033901 quill 羽毛笔
    750 n04033995 quilt 床
    751 n04037443 racer 赛车
    752 n04039381 racket 网球
    753 n04040759 radiator 加热器
    754 n04041544 radio 收音机
    755 n04044716 radio_telescope 卫星接收器
    756 n04049303 rain_barrel 酒桶
    757 n04065272 recreational_vehicle 房车
    758 n04067472 reel 鱼竿
    759 n04069434 reflex_camera 相机
    760 n04070727 refrigerator 冰箱
    761 n04074963 remote_control 遥控器
    762 n04081281 restaurant 餐厅
    763 n04086273 revolver 手枪
    764 n04090263 rifle 狙击枪
    765 n04099969 rocking_chair 摇椅
    766 n04111531 rotisserie 烤箱
    767 n04116512 rubber_eraser 橡皮
    768 n04118538 rugby_ball 橄榄球
    769 n04118776 rule 尺
    770 n04120489 running_shoe 运动鞋
    771 n04125021 safe 保险箱
    772 n04127249 safety_pin 回形针
    773 n04131690 saltshaker 调料瓶
    774 n04133789 sandal 拖鞋
    775 n04136333 sarong 长裙
    776 n04141076 sax 萨克斯
    777 n04141327 scabbard 剑
    778 n04141975 scale 秤
    779 n04146614 school_bus 校车
    780 n04147183 schooner 帆船
    781 n04149813 scoreboard 计分板
    782 n04152593 screen 显示器
    783 n04153751 screw 螺丝
    784 n04154565 screwdriver 螺丝刀
    785 n04162706 seat_belt 安全带
    786 n04179913 sewing_machine 缝纫机
    787 n04192698 shield 盾牌
    788 n04200800 shoe_shop 鞋店
    789 n04201297 shoji 榻榻米
    790 n04204238 shopping_basket 购物篮
    791 n04204347 shopping_cart 购物车
    792 n04208210 shovel 铲子
    793 n04209133 shower_cap 浴帽
    794 n04209239 shower_curtain 浴帘
    795 n04228054 ski 滑雪
    796 n04229816 ski_mask 面罩
    797 n04235860 sleeping_bag 睡袋
    798 n04238763 slide_rule 游标卡尺
    799 n04239074 sliding_door 移门
    800 n04243546 slot 老虎机
    801 n04251144 snorkel 游泳眼镜
    802 n04252077 snowmobile 滑雪车
    803 n04252225 snowplow 铲雪车
    804 n04254120 soap_dispenser 洗手液
    805 n04254680 soccer_ball 足球
    806 n04254777 sock 袜子
    807 n04258138 solar_dish 太阳能板
    808 n04259630 sombrero 帽子
    809 n04263257 soup_bowl 碗
    810 n04264628 space_bar 键盘
    811 n04265275 space_heater 电热器
    812 n04266014 space_shuttle 航天飞船
    813 n04270147 spatula 锅铲
    814 n04273569 speedboat 快艇
    815 n04275548 spider_web 蜘蛛网
    816 n04277352 spindle 毛线
    817 n04285008 sports_car 运动型轿车
    818 n04286575 spotlight 探照灯
    819 n04296562 stage 乐队
    820 n04310018 steam_locomotive 蒸汽机车
    821 n04311004 steel_arch_bridge 桥
    822 n04311174 steel_drum 鼓
    823 n04317175 stethoscope 听诊器
    824 n04325704 stole 担架
    825 n04326547 stone_wall 石堆
    826 n04328186 stopwatch 秒表
    827 n04330267 stove 火炉
    828 n04332243 strainer 滤网
    829 n04335435 streetcar 公交车
    830 n04336792 stretcher 担架
    831 n04344873 studio_couch 沙发
    832 n04346328 stupa 皇宫
    833 n04347754 submarine 轮船
    834 n04350905 suit 西装
    835 n04355338 sundial 日晷
    836 n04355933 sunglass 墨镜
    837 n04356056 sunglasses 墨镜
    838 n04357314 sunscreen 防晒霜
    839 n04366367 suspension_bridge 桥
    840 n04367480 swab 拖把
    841 n04370456 sweatshirt 连帽衫
    842 n04371430 swimming_trunks 沙滩裤
    843 n04371774 swing 秋千
    844 n04372370 switch 开关
    845 n04376876 syringe 针筒
    846 n04380533 table_lamp 台灯
    847 n04389033 tank 坦克
    848 n04392985 tape_player 磁带播放器
    849 n04398044 teapot 茶壶
    850 n04399382 teddy 毛绒玩具
    851 n04404412 television 电视机
    852 n04409515 tennis_ball 网球
    853 n04417672 thatch 草屋
    854 n04418357 theater_curtain 幕布
    855 n04423845 thimble 指套
    856 n04428191 thresher 装甲车
    857 n04429376 throne 皇位
    858 n04435653 tile_roof 瓦片
    859 n04442312 toaster 面包机
    860 n04443257 tobacco_shop 烟酒店
    861 n04447861 toilet_seat 马桶
    862 n04456115 torch 火炬
    863 n04458633 totem_pole 图腾
    864 n04461696 tow_truck 大卡车
    865 n04462240 toyshop 玩具店
    866 n04465501 tractor 拖拉机
    867 n04467665 trailer_truck 大货车
    868 n04476259 tray 碟子
    869 n04479046 trench_coat 风衣
    870 n04482393 tricycle 儿童自行车
    871 n04483307 trimaran 船
    872 n04485082 tripod 三脚架
    873 n04486054 triumphal_arch 拱门
    874 n04487081 trolleybus 巴士
    875 n04487394 trombone 长号
    876 n04493381 tub 浴缸
    877 n04501370 turnstile 闸机
    878 n04505470 typewriter_keyboard 打字机
    879 n04507155 umbrella 伞
    880 n04509417 unicycle 独轮车
    881 n04515003 upright 钢琴
    882 n04517823 vacuum 吸尘器
    883 n04522168 vase 花瓶
    884 n04523525 vault 拱廊
    885 n04525038 velvet 珊瑚绒
    886 n04525305 vending_machine 自动贩卖机
    887 n04532106 vestment 教皇袍
    888 n04532670 viaduct 桥
    889 n04536866 violin 小提琴
    890 n04540053 volleyball 排球
    891 n04542943 waffle_iron 煎饼锅
    892 n04548280 wall_clock 挂钟
    893 n04548362 wallet 钱夹
    894 n04550184 wardrobe 柜子
    895 n04552348 warplane 飞机
    896 n04553703 washbasin 台盆
    897 n04554684 washer 洗衣机
    898 n04557648 water_bottle 水瓶
    899 n04560804 water_jug 水壶
    900 n04562935 water_tower 煤气包
    901 n04579145 whiskey_jug 水壶
    902 n04579432 whistle 哨子
    903 n04584207 wig 头发
    904 n04589890 window_screen 窗户
    905 n04590129 window_shade 百叶窗
    906 n04591157 Windsor_tie 领带
    907 n04591713 wine_bottle 葡萄酒
    908 n04592741 wing 飞机
    909 n04596742 wok 炒锅
    910 n04597913 wooden_spoon 勺子
    911 n04599235 wool 围巾
    912 n04604644 worm_fence 栅栏
    913 n04606251 wreck 沉船
    914 n04612504 yawl 帆船
    915 n04613696 yurt 蒙古包
    916 n06359193 web_site 网页
    917 n06596364 comic_book 海报
    918 n06785654 crossword_puzzle 填字游戏
    919 n06794110 street_sign 交通标志
    920 n06874185 traffic_light 交通灯
    921 n07248320 book_jacket 书
    922 n07565083 menu 菜单
    923 n07579787 plate 菜
    924 n07583066 guacamole 菜
    925 n07584110 consomme 菜
    926 n07590611 hot_pot 菜
    927 n07613480 trifle 蛋糕
    928 n07614500 ice_cream 冰激凌
    929 n07615774 ice_lolly 棒冰
    930 n07684084 French_loaf 面包
    931 n07693725 bagel 甜甜圈
    932 n07695742 pretzel 面包
    933 n07697313 cheeseburger 汉堡
    934 n07697537 hotdog 热狗
    935 n07711569 mashed_potato 焗饭
    936 n07714571 head_cabbage 蔬菜
    937 n07714990 broccoli 西蓝花
    938 n07715103 cauliflower 花椰菜
    939 n07716358 zucchini 蔬菜
    940 n07716906 spaghetti_squash 金瓜
    941 n07717410 acorn_squash 南瓜
    942 n07717556 butternut_squash 南瓜
    943 n07718472 cucumber 黄瓜
    944 n07718747 artichoke 蔬菜
    945 n07720875 bell_pepper 青椒
    946 n07730033 cardoon 花
    947 n07734744 mushroom 蘑菇
    948 n07742313 Granny_Smith 苹果
    949 n07745940 strawberry 草莓
    950 n07747607 orange 橙子
    951 n07749582 lemon 柠檬
    952 n07753113 fig 水果
    953 n07753275 pineapple 菠萝
    954 n07753592 banana 香蕉
    955 n07754684 jackfruit 榴莲
    956 n07760859 custard_apple 水果
    957 n07768694 pomegranate 石榴
    958 n07802026 hay 草垛
    959 n07831146 carbonara 意大利面
    960 n07836838 chocolate_sauce 甜品
    961 n07860988 dough 面团
    962 n07871810 meat_loaf 肉酱
    963 n07873807 pizza 披萨
    964 n07875152 potpie 派
    965 n07880968 burrito 肉卷
    966 n07892512 red_wine 红酒
    967 n07920052 espresso 咖啡
    968 n07930864 cup 茶
    969 n07932039 eggnog 饮料杯
    970 n09193705 alp 雪山
    971 n09229709 bubble 泡泡
    972 n09246464 cliff 悬崖
    973 n09256479 coral_reef 珊瑚
    974 n09288635 geyser 温泉
    975 n09332890 lakeside 风景
    976 n09399592 promontory 小岛
    977 n09421951 sandbar 沙滩
    978 n09428293 seashore 海滩
    979 n09468604 valley 瀑布
    980 n09472597 volcano 火山
    981 n09835506 ballplayer 棒球
    982 n10148035 groom 婚礼
    983 n10565667 scuba_diver 潜水
    984 n11879895 rapeseed 油菜花
    985 n11939491 daisy 菊花
    986 n12057211 yellow_lady’s_slipper 植物
    987 n12144580 corn 玉米
    988 n12267677 acorn 松果
    989 n12620546 hip 植物
    990 n12768682 buckeye 栗子
    991 n12985857 coral_fungus 菌菇
    992 n12998815 agaric 菌菇
    993 n13037406 gyromitra 菌菇
    994 n13040303 stinkhorn 菌菇
    995 n13044778 earthstar 菌菇
    996 n13052670 hen-of-the-woods 菌菇
    997 n13054560 bolete 菌菇
    998 n13133613 ear 玉米
    999 n15075141 toilet_tissue 卷筒纸

使用脚本将数字编号和末尾中文删除

import codecs
    import os
    
    path = 'D:/code/yolov8-pytorch-master/voc_classes.txt'  # 标签文件train路径
    newFile = 'D:/code/yolov8-pytorch-master/voc_classesNew.txt'
    # 读取路径下的txt文件
    t = codecs.open(path, mode='r', encoding='utf-8')
    line = t.readline()  # 以行的形式进行读取文件
    list1 = []    # 保存了信息列表
    while line:
        a = line.split()
        list1.append(a)
        line = t.readline()
    t.close()
    
    lt = open(newFile, "w")
    newlist1 = list1
    
    for num in range(0, len(newlist1)):
        del newlist1[num][0]
        del newlist1[num][2]
    
    for num in range(0, len(newlist1)):
        lt.writelines(' '.join(newlist1[num]) + '\n')  # 每个元素以空格间隔，一行元素写完并换行
    lt.close()
    print(" 修改完成")

![在这里插入图片描述][6ef085be1c3a4445a6b1ebbe7fa0d3ee.png]

转换的时候，列表存在编码问题  
![在这里插入图片描述][23135675223247d486e893dee2819961.png]  
删掉修改就行

> 处理思路为;  
> 先转成voc格式  
> 在将voc格式转yolo格式

### 转voc格式 ###

[转将xml数据转换为标准的VOC格式][xml_VOC]

voc\_annotation.py里面有一些参数需要设置。  
分别是annotation\_mode、classes\_path、trainval\_percent、train\_percent、VOCdevkit\_path，第一次训练可以仅修改classes\_path

import os
    import random
    import xml.etree.ElementTree as ET
    
    import numpy as np
    
    from utils.utils import get_classes
    
    #--------------------------------------------------------------------------------------------------------------------------------#
    #   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，包括获得VOCdevkit/VOC2007/ImageSets里面的txt以及训练用的2007_train.txt、2007_val.txt
    #   annotation_mode为1代表获得VOCdevkit/VOC2007/ImageSets里面的txt
    #   annotation_mode为2代表获得训练用的2007_train.txt、2007_val.txt
    #--------------------------------------------------------------------------------------------------------------------------------#
    annotation_mode     = 0
    #-------------------------------------------------------------------#
    #   必须要修改，用于生成2007_train.txt、2007_val.txt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        = 'model_data/voc_classesNew.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    = 0.9
    train_percent       = 0.9
    #-------------------------------------------------------#
    #   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  = 'VOCdevkit'
    
    VOCdevkit_sets  = [('2007', 'train'), ('2007', 'val')]
    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_annotation(year, image_id, list_file):
        in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml'%(year, image_id)), encoding='utf-8')
        tree=ET.parse(in_file)
        root = tree.getroot()
    
        for obj in root.iter('object'):
            difficult = 0 
            if obj.find('difficult')!=None:
                difficult = obj.find('difficult').text
            cls = obj.find('name').text
            if cls not in classes or int(difficult)==1:
                continue
            cls_id = classes.index(cls)
            xmlbox = obj.find('bndbox')
            b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
            list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
            
            nums[classes.index(cls)] = nums[classes.index(cls)] + 1
            
    if __name__ == "__main__":
        random.seed(0)
        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 txt in ImageSets.")
            xmlfilepath     = os.path.join(VOCdevkit_path, 'VOC2007/Annotations')
            saveBasePath    = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main')
            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    = range(num)  
            tv      = int(num*trainval_percent)  
            tr      = int(tv*train_percent)  
            trainval= random.sample(list,tv)  
            train   = random.sample(trainval,tr)  
            
            print("train and val size",tv)
            print("train size",tr)
            ftrainval   = open(os.path.join(saveBasePath,'trainval.txt'), 'w')  
            ftest       = open(os.path.join(saveBasePath,'test.txt'), 'w')  
            ftrain      = open(os.path.join(saveBasePath,'train.txt'), 'w')  
            fval        = open(os.path.join(saveBasePath,'val.txt'), 'w')  
            
            for i in list:  
                name=total_xml[i][:-4]+'\n'  
                if i in trainval:  
                    ftrainval.write(name)  
                    if i in train:  
                        ftrain.write(name)  
                    else:  
                        fval.write(name)  
                else:  
                    ftest.write(name)  
            
            ftrainval.close()  
            ftrain.close()  
            fval.close()  
            ftest.close()
            print("Generate txt in ImageSets done.")
    
        if annotation_mode == 0 or annotation_mode == 2:
            print("Generate 2007_train.txt and 2007_val.txt for train.")
            type_index = 0
            for year, image_set in VOCdevkit_sets:
                image_ids = open(os.path.join(VOCdevkit_path, 'VOC%s/ImageSets/Main/%s.txt'%(year, image_set)), encoding='utf-8').read().strip().split()
                list_file = open('%s_%s.txt'%(year, image_set), 'w', encoding='utf-8')
                for image_id in image_ids:
                    list_file.write('%s/VOC%s/JPEGImages/%s.jpg'%(os.path.abspath(VOCdevkit_path), year, image_id))
    
                    convert_annotation(year, image_id, list_file)
                    list_file.write('\n')
                photo_nums[type_index] = len(image_ids)
                type_index += 1
                list_file.close()
            print("Generate 2007_train.txt and 2007_val.txt for train done.")
            
            def printTable(List1, List2):
                for i in range(len(List1[0])):
                    print("|", end=' ')
                    for j in range(len(List1)):
                        print(List1[j][i].rjust(int(List2[j])), end=' ')
                        print("|", end=' ')
                    print()
    
            str_nums = [str(int(x)) for x in nums]
            tableData = [
                classes, str_nums
            ]
            colWidths = [0]*len(tableData)
            len1 = 0
            for i in range(len(tableData)):
                for j in range(len(tableData[i])):
                    if len(tableData[i][j]) > colWidths[i]:
                        colWidths[i] = len(tableData[i][j])
            printTable(tableData, colWidths)
    
            if photo_nums[0] <= 500:
                print("训练集数量小于500，属于较小的数据量，请注意设置较大的训练世代（Epoch）以满足足够的梯度下降次数（Step）。")
    
            if np.sum(nums) == 0:
                print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
                print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
                print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
                print("（重要的事情说三遍）。")

![在这里插入图片描述][c82629b89a8843a495d76897973ccb1e.png]

> 需要注意的是，xml中的类别是文件名，为了识别匹配到类别信息，类别txt文件需要对应

![在这里插入图片描述][0640a5da32644726b9d5bc27af2c8d10.png]  
类别文件保留名称  
![在这里插入图片描述][e5248d1dea224b1aa77b9f96f09beaa1.png]  
这时再执行voc\_annotation.py  
![在这里插入图片描述][45cf1eeaca9c4446bb34ffe49e19d8ea.png]  
看得出类别已经识别了

> 只不过后续还得将名称和类别做一次转换

![在这里插入图片描述][ed5ec5786bff4c61a41a1f532e56d9f6.png]

### VOC2YOLO ###

1.  将xml文件转化为txt文件，xml文件包含了对应的GT框以及图片长宽大小等信息，通过对其解析，并进行归一化最终读到txt文件中
2.  同时生成train、val和test数据集中图片的绝对路径，用于索引到图片位置

获取纯类别信息的txt

import codecs
    import os
    
    path = 'D:/code/yolov8-pytorch-master/voc_classesNew.txt'  # 标签文件train路径
    newFile = 'D:/code/yolov8-pytorch-master/voc_classesNew2.txt'
    # 读取路径下的txt文件
    t = codecs.open(path, mode='r', encoding='utf-8')
    line = t.readline()  # 以行的形式进行读取文件
    list1 = []    # 保存了信息列表
    while line:
        a = line.split()
        list1.append(a)
        line = t.readline()
    t.close()
    
    lt = open(newFile, "w")
    newlist1 = list1
    
    for num in range(0, len(newlist1)):
        del newlist1[num][0]
    
    for num in range(0, len(newlist1)):
        lt.writelines(' '.join(newlist1[num]) + '\n')  # 每个元素以空格间隔，一行元素写完并换行
    lt.close()
    print(" 修改完成")

![在这里插入图片描述][00a6903ebf6e4606940cb07c196fe646.png]

> 在上述类别信息txt中存在个别编码错误，需要改一下编码

查看一下VOC中数据格式  
![在这里插入图片描述][6684fd02237c4dfda9e9da73a758c4da.png]

> 最后一个999是类别编号(最后一行)

voc2txt代码

import xml.etree.ElementTree as ET
    import pickle
    import os
    from os import listdir, getcwd
    from os.path import join
    import random
    from shutil import copyfile
    
    # 根据自己的数据标签修改
    classes=["tumor"]
    
    
    def clear_hidden_files(path):
        dir_list = os.listdir(path)
        for i in dir_list:
            abspath = os.path.join(os.path.abspath(path), i)
            if os.path.isfile(abspath):
                if i.startswith("._"):
                    os.remove(abspath)
            else:
                clear_hidden_files(abspath)
    
    def convert(size, box):
        dw = 1./size[0]
        dh = 1./size[1]
        x = (box[0] + box[1])/2.0
        y = (box[2] + box[3])/2.0
        w = box[1] - box[0]
        h = box[3] - box[2]
        x = x*dw
        w = w*dw
        y = y*dh
        h = h*dh
        return (x,y,w,h)
    
    def convert_annotation(image_id):
        in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
        out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %image_id, 'w')
        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'):
            difficult = obj.find('difficult').text
            cls = obj.find('name').text
            if cls not in classes or int(difficult) == 1:
                continue
            cls_id = classes.index(cls)
            xmlbox = obj.find('bndbox')
            b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
            bb = convert((w,h), b)
            out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
        in_file.close()
        out_file.close()
    
    wd = os.getcwd()
    wd = os.getcwd()
    data_base_dir = os.path.join(wd, "VOCdevkit/")
    if not os.path.isdir(data_base_dir):
        os.mkdir(data_base_dir)
    work_sapce_dir = os.path.join(data_base_dir, "VOC2007/")
    if not os.path.isdir(work_sapce_dir):
        os.mkdir(work_sapce_dir)
    annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
    if not os.path.isdir(annotation_dir):
            os.mkdir(annotation_dir)
    clear_hidden_files(annotation_dir)
    image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
    if not os.path.isdir(image_dir):
            os.mkdir(image_dir)
    clear_hidden_files(image_dir)
    yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
    if not os.path.isdir(yolo_labels_dir):
            os.mkdir(yolo_labels_dir)
    clear_hidden_files(yolo_labels_dir)
    yolov5_images_dir = os.path.join(data_base_dir, "images/")
    if not os.path.isdir(yolov5_images_dir):
            os.mkdir(yolov5_images_dir)
    clear_hidden_files(yolov5_images_dir)
    yolov5_labels_dir = os.path.join(data_base_dir, "labels/")
    if not os.path.isdir(yolov5_labels_dir):
            os.mkdir(yolov5_labels_dir)
    clear_hidden_files(yolov5_labels_dir)
    yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")
    if not os.path.isdir(yolov5_images_train_dir):
            os.mkdir(yolov5_images_train_dir)
    clear_hidden_files(yolov5_images_train_dir)
    yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/")
    if not os.path.isdir(yolov5_images_test_dir):
            os.mkdir(yolov5_images_test_dir)
    clear_hidden_files(yolov5_images_test_dir)
    yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")
    if not os.path.isdir(yolov5_labels_train_dir):
            os.mkdir(yolov5_labels_train_dir)
    clear_hidden_files(yolov5_labels_train_dir)
    yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/")
    if not os.path.isdir(yolov5_labels_test_dir):
            os.mkdir(yolov5_labels_test_dir)
    clear_hidden_files(yolov5_labels_test_dir)
    
    train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w')
    test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w')
    train_file.close()
    test_file.close()
    train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a')
    test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a')
    list_imgs = os.listdir(image_dir) # list image files
    probo = random.randint(1, 100)
    print("Probobility: %d" % probo)
    for i in range(0,len(list_imgs)):
        path = os.path.join(image_dir,list_imgs[i])
        if os.path.isfile(path):
            image_path = image_dir + list_imgs[i]
            voc_path = list_imgs[i]
            (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
            (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
            annotation_name = nameWithoutExtention + '.xml'
            annotation_path = os.path.join(annotation_dir, annotation_name)
            label_name = nameWithoutExtention + '.txt'
            label_path = os.path.join(yolo_labels_dir, label_name)
        probo = random.randint(1, 100)
        print("Probobility: %d" % probo)
        if(probo < 80): # train dataset
            if os.path.exists(annotation_path):
                train_file.write(image_path + '\n')
                convert_annotation(nameWithoutExtention) # convert label
                copyfile(image_path, yolov5_images_train_dir + voc_path)
                copyfile(label_path, yolov5_labels_train_dir + label_name)
        else: # test dataset
            if os.path.exists(annotation_path):
                test_file.write(image_path + '\n')
                convert_annotation(nameWithoutExtention) # convert label
                copyfile(image_path, yolov5_images_test_dir + voc_path)
                copyfile(label_path, yolov5_labels_test_dir + label_name)
    train_file.close()
    test_file.close()

> 关键就在于如何将1000类的信息写到代码里面的列表中去。。。。

实现代码

import codecs
    import os
    
    path = 'D:/code/yolov8-pytorch-master/voc_classes.txt'  # 类别文件路径
    newpath = 'D:/code/yolov8-pytorch-master/voc_classList.txt'  # 生成类别列表
    
    #   ["n1507514100","n150751410","n15075142","n15075141"]  DEMO
    
    
    # 使用with open打开文件，这样不用调用close()方法来关闭文件了
    with open(path, 'r') as f:
        # readlines()方法，读取文件中所有行，每一行作为一个字符串存入在列表中，并且换行符用\n来表示。
        content = f.readlines()
        #print(content)
    
    separator = '\\'
    newcontent = []
    for i in range(len(content)):
        newcontent =newcontent+[content[i].replace('\n','')]
        #print(content[i].split('\\',1)[0])
        #print(type(content[i].split('\\',1)[0]))
    print(newcontent)

[python读写文件详解，将数据写入一个文件或读一个文件写入另一个文件中][python 1]

[在 Python 中如何删除指定字符之前或之后的所有内容][Python]

[python去除字符串最后的换行符‘\\n’][python_n]

![在这里插入图片描述][60ce8d6ccc944d36985c047d5df395cc.png]

得到voc2coco代码

import xml.etree.ElementTree as ET
    import pickle
    import os
    from os import listdir, getcwd
    from os.path import join
    import random
    from shutil import copyfile
    
    # 根据自己的数据标签修改
    classes=['n01440764', 'n01443537', 'n01484850', 'n01491361', 'n01494475', 'n01496331', 'n01498041', 'n01514668', 'n01514859', 'n01518878', 'n01530575', 'n01531178', 'n01532829', 'n01534433', 'n01537544', 'n01558993', 'n01560419', 'n01580077', 'n01582220', 'n01592084', 'n01601694', 'n01608432', 'n01614925', 'n01616318', 'n01622779', 'n01629819', 'n01630670', 'n01631663', 'n01632458', 'n01632777', 'n01641577', 'n01644373', 'n01644900', 'n01664065', 'n01665541', 'n01667114', 'n01667778', 'n01669191', 'n01675722', 'n01677366', 'n01682714', 'n01685808', 'n01687978', 'n01688243', 'n01689811', 'n01692333', 'n01693334', 'n01694178', 'n01695060', 'n01697457', 'n01698640', 'n01704323', 'n01728572', 'n01728920', 'n01729322', 'n01729977', 'n01734418', 'n01735189', 'n01737021', 'n01739381', 'n01740131', 'n01742172', 'n01744401', 'n01748264', 'n01749939', 'n01751748', 'n01753488', 'n01755581', 'n01756291', 'n01768244', 'n01770081', 'n01770393', 'n01773157', 'n01773549', 'n01773797', 'n01774384', 'n01774750', 'n01775062', 'n01776313', 'n01784675', 'n01795545', 'n01796340', 'n01797886', 'n01798484', 'n01806143', 'n01806567', 'n01807496', 'n01817953', 'n01818515', 'n01819313', 'n01820546', 'n01824575', 'n01828970', 'n01829413', 'n01833805', 'n01843065', 'n01843383', 'n01847000', 'n01855032', 'n01855672', 'n01860187', 'n01871265', 'n01872401', 'n01873310', 'n01877812', 'n01882714', 'n01883070', 'n01910747', 'n01914609', 'n01917289', 'n01924916', 'n01930112', 'n01943899', 'n01944390', 'n01945685', 'n01950731', 'n01955084', 'n01968897', 'n01978287', 'n01978455', 'n01980166', 'n01981276', 'n01983481', 'n01984695', 'n01985128', 'n01986214', 'n01990800', 'n02002556', 'n02002724', 'n02006656', 'n02007558', 'n02009229', 'n02009912', 'n02011460', 'n02012849', 'n02013706', 'n02017213', 'n02018207', 'n02018795', 'n02025239', 'n02027492', 'n02028035', 'n02033041', 'n02037110', 'n02051845', 'n02056570', 'n02058221', 'n02066245', 'n02071294', 'n02074367', 'n02077923', 'n02085620', 'n02085782', 'n02085936', 'n02086079', 'n02086240', 'n02086646', 'n02086910', 'n02087046', 'n02087394', 'n02088094', 'n02088238', 'n02088364', 'n02088466', 'n02088632', 'n02089078', 'n02089867', 'n02089973', 'n02090379', 'n02090622', 'n02090721', 'n02091032', 'n02091134', 'n02091244', 'n02091467', 'n02091635', 'n02091831', 'n02092002', 'n02092339', 'n02093256', 'n02093428', 'n02093647', 'n02093754', 'n02093859', 'n02093991', 'n02094114', 'n02094258', 'n02094433', 'n02095314', 'n02095570', 'n02095889', 'n02096051', 'n02096177', 'n02096294', 'n02096437', 'n02096585', 'n02097047', 'n02097130', 'n02097209', 'n02097298', 'n02097474', 'n02097658', 'n02098105', 'n02098286', 'n02098413', 'n02099267', 'n02099429', 'n02099601', 'n02099712', 'n02099849', 'n02100236', 'n02100583', 'n02100735', 'n02100877', 'n02101006', 'n02101388', 'n02101556', 'n02102040', 'n02102177', 'n02102318', 'n02102480', 'n02102973', 'n02104029', 'n02104365', 'n02105056', 'n02105162', 'n02105251', 'n02105412', 'n02105505', 'n02105641', 'n02105855', 'n02106030', 'n02106166', 'n02106382', 'n02106550', 'n02106662', 'n02107142', 'n02107312', 'n02107574', 'n02107683', 'n02107908', 'n02108000', 'n02108089', 'n02108422', 'n02108551', 'n02108915', 'n02109047', 'n02109525', 'n02109961', 'n02110063', 'n02110185', 'n02110341', 'n02110627', 'n02110806', 'n02110958', 'n02111129', 'n02111277', 'n02111500', 'n02111889', 'n02112018', 'n02112137', 'n02112350', 'n02112706', 'n02113023', 'n02113186', 'n02113624', 'n02113712', 'n02113799', 'n02113978', 'n02114367', 'n02114548', 'n02114712', 'n02114855', 'n02115641', 'n02115913', 'n02116738', 'n02117135', 'n02119022', 'n02119789', 'n02120079', 'n02120505', 'n02123045', 'n02123159', 'n02123394', 'n02123597', 'n02124075', 'n02125311', 'n02127052', 'n02128385', 'n02128757', 'n02128925', 'n02129165', 'n02129604', 'n02130308', 'n02132136', 'n02133161', 'n02134084', 'n02134418', 'n02137549', 'n02138441', 'n02165105', 'n02165456', 'n02167151', 'n02168699', 'n02169497', 'n02172182', 'n02174001', 'n02177972', 'n02190166', 'n02206856', 'n02219486', 'n02226429', 'n02229544', 'n02231487', 'n02233338', 'n02236044', 'n02256656', 'n02259212', 'n02264363', 'n02268443', 'n02268853', 'n02276258', 'n02277742', 'n02279972', 'n02280649', 'n02281406', 'n02281787', 'n02317335', 'n02319095', 'n02321529', 'n02325366', 'n02326432', 'n02328150', 'n02342885', 'n02346627', 'n02356798', 'n02361337', 'n02363005', 'n02364673', 'n02389026', 'n02391049', 'n02395406', 'n02396427', 'n02397096', 'n02398521', 'n02403003', 'n02408429', 'n02410509', 'n02412080', 'n02415577', 'n02417914', 'n02422106', 'n02422699', 'n02423022', 'n02437312', 'n02437616', 'n02441942', 'n02442845', 'n02443114', 'n02443484', 'n02444819', 'n02445715', 'n02447366', 'n02454379', 'n02457408', 'n02480495', 'n02480855', 'n02481823', 'n02483362', 'n02483708', 'n02484975', 'n02486261', 'n02486410', 'n02487347', 'n02488291', 'n02488702', 'n02489166', 'n02490219', 'n02492035', 'n02492660', 'n02493509', 'n02493793', 'n02494079', 'n02497673', 'n02500267', 'n02504013', 'n02504458', 'n02509815', 'n02510455', 'n02514041', 'n02526121', 'n02536864', 'n02606052', 'n02607072', 'n02640242', 'n02641379', 'n02643566', 'n02655020', 'n02666196', 'n02667093', 'n02669723', 'n02672831', 'n02676566', 'n02687172', 'n02690373', 'n02692877', 'n02699494', 'n02701002', 'n02704792', 'n02708093', 'n02727426', 'n02730930', 'n02747177', 'n02749479', 'n02769748', 'n02776631', 'n02777292', 'n02782093', 'n02783161', 'n02786058', 'n02787622', 'n02788148', 'n02790996', 'n02791124', 'n02791270', 'n02793495', 'n02794156', 'n02795169', 'n02797295', 'n02799071', 'n02802426', 'n02804414', 'n02804610', 'n02807133', 'n02808304', 'n02808440', 'n02814533', 'n02814860', 'n02815834', 'n02817516', 'n02823428', 'n02823750', 'n02825657', 'n02834397', 'n02835271', 'n02837789', 'n02840245', 'n02841315', 'n02843684', 'n02859443', 'n02860847', 'n02865351', 'n02869837', 'n02870880', 'n02871525', 'n02877765', 'n02879718', 'n02883205', 'n02892201', 'n02892767', 'n02894605', 'n02895154', 'n02906734', 'n02909870', 'n02910353', 'n02916936', 'n02917067', 'n02927161', 'n02930766', 'n02939185', 'n02948072', 'n02950826', 'n02951358', 'n02951585', 'n02963159', 'n02965783', 'n02966193', 'n02966687', 'n02971356', 'n02974003', 'n02977058', 'n02978881', 'n02979186', 'n02980441', 'n02981792', 'n02988304', 'n02992211', 'n02992529', 'n02999410', 'n03000134', 'n03000247', 'n03000684', 'n03014705', 'n03016953', 'n03017168', 'n03018349', 'n03026506', 'n03028079', 'n03032252', 'n03041632', 'n03042490', 'n03045698', 'n03047690', 'n03062245', 'n03063599', 'n03063689', 'n03065424', 'n03075370', 'n03085013', 'n03089624', 'n03095699', 'n03100240', 'n03109150', 'n03110669', 'n03124043', 'n03124170', 'n03125729', 'n03126707', 'n03127747', 'n03127925', 'n03131574', 'n03133878', 'n03134739', 'n03141823', 'n03146219', 'n03160309', 'n03179701', 'n03180011', 'n03187595', 'n03188531', 'n03196217', 'n03197337', 'n03201208', 'n03207743', 'n03207941', 'n03208938', 'n03216828', 'n03218198', 'n03220513', 'n03223299', 'n03240683', 'n03249569', 'n03250847', 'n03255030', 'n03259280', 'n03271574', 'n03272010', 'n03272562', 'n03290653', 'n03291819', 'n03297495', 'n03314780', 'n03325584', 'n03337140', 'n03344393', 'n03345487', 'n03347037', 'n03355925', 'n03372029', 'n03376595', 'n03379051', 'n03384352', 'n03388043', 'n03388183', 'n03388549', 'n03393912', 'n03394916', 'n03400231', 'n03404251', 'n03417042', 'n03424325', 'n03425413', 'n03443371', 'n03444034', 'n03445777', 'n03445924', 'n03447447', 'n03447721', 'n03450230', 'n03452741', 'n03457902', 'n03459775', 'n03461385', 'n03467068', 'n03476684', 'n03476991', 'n03478589', 'n03481172', 'n03482405', 'n03483316', 'n03485407', 'n03485794', 'n03492542', 'n03494278', 'n03495258', 'n03496892', 'n03498962', 'n03527444', 'n03529860', 'n03530642', 'n03532672', 'n03534580', 'n03535780', 'n03538406', 'n03544143', 'n03584254', 'n03584829', 'n03590841', 'n03594734', 'n03594945', 'n03595614', 'n03598930', 'n03599486', 'n03602883', 'n03617480', 'n03623198', 'n03627232', 'n03630383', 'n03633091', 'n03637318', 'n03642806', 'n03649909', 'n03657121', 'n03658185', 'n03661043', 'n03662601', 'n03666591', 'n03670208', 'n03673027', 'n03676483', 'n03680355', 'n03690938', 'n03691459', 'n03692522', 'n03697007', 'n03706229', 'n03709823', 'n03710193', 'n03710637', 'n03710721', 'n03717622', 'n03720891', 'n03721384', 'n03724870', 'n03729826', 'n03733131', 'n03733281', 'n03733805', 'n03742115', 'n03743016', 'n03759954', 'n03761084', 'n03763968', 'n03764736', 'n03769881', 'n03770439', 'n03770679', 'n03773504', 'n03775071', 'n03775546', 'n03776460', 'n03777568', 'n03777754', 'n03781244', 'n03782006', 'n03785016', 'n03786901', 'n03787032', 'n03788195', 'n03788365', 'n03791053', 'n03792782', 'n03792972', 'n03793489', 'n03794056', 'n03796401', 'n03803284', 'n03804744', 'n03814639', 'n03814906', 'n03825788', 'n03832673', 'n03837869', 'n03838899', 'n03840681', 'n03841143', 'n03843555', 'n03854065', 'n03857828', 'n03866082', 'n03868242', 'n03868863', 'n03871628', 'n03873416', 'n03874293', 'n03874599', 'n03876231', 'n03877472', 'n03877845', 'n03884397', 'n03887697', 'n03888257', 'n03888605', 'n03891251', 'n03891332', 'n03895866', 'n03899768', 'n03902125', 'n03903868', 'n03908618', 'n03908714', 'n03916031', 'n03920288', 'n03924679', 'n03929660', 'n03929855', 'n03930313', 'n03930630', 'n03933933', 'n03935335', 'n03937543', 'n03938244', 'n03942813', 'n03944341', 'n03947888', 'n03950228', 'n03954731', 'n03956157', 'n03958227', 'n03961711', 'n03967562', 'n03970156', 'n03976467', 'n03976657', 'n03977966', 'n03980874', 'n03982430', 'n03983396', 'n03991062', 'n03992509', 'n03995372', 'n03998194', 'n04004767', 'n04005630', 'n04008634', 'n04009552', 'n04019541', 'n04023962', 'n04026417', 'n04033901', 'n04033995', 'n04037443', 'n04039381', 'n04040759', 'n04041544', 'n04044716', 'n04049303', 'n04065272', 'n04067472', 'n04069434', 'n04070727', 'n04074963', 'n04081281', 'n04086273', 'n04090263', 'n04099969', 'n04111531', 'n04116512', 'n04118538', 'n04118776', 'n04120489', 'n04125021', 'n04127249', 'n04131690', 'n04133789', 'n04136333', 'n04141076', 'n04141327', 'n04141975', 'n04146614', 'n04147183', 'n04149813', 'n04152593', 'n04153751', 'n04154565', 'n04162706', 'n04179913', 'n04192698', 'n04200800', 'n04201297', 'n04204238', 'n04204347', 'n04208210', 'n04209133', 'n04209239', 'n04228054', 'n04229816', 'n04235860', 'n04238763', 'n04239074', 'n04243546', 'n04251144', 'n04252077', 'n04252225', 'n04254120', 'n04254680', 'n04254777', 'n04258138', 'n04259630', 'n04263257', 'n04264628', 'n04265275', 'n04266014', 'n04270147', 'n04273569', 'n04275548', 'n04277352', 'n04285008', 'n04286575', 'n04296562', 'n04310018', 'n04311004', 'n04311174', 'n04317175', 'n04325704', 'n04326547', 'n04328186', 'n04330267', 'n04332243', 'n04335435', 'n04336792', 'n04344873', 'n04346328', 'n04347754', 'n04350905', 'n04355338', 'n04355933', 'n04356056', 'n04357314', 'n04366367', 'n04367480', 'n04370456', 'n04371430', 'n04371774', 'n04372370', 'n04376876', 'n04380533', 'n04389033', 'n04392985', 'n04398044', 'n04399382', 'n04404412', 'n04409515', 'n04417672', 'n04418357', 'n04423845', 'n04428191', 'n04429376', 'n04435653', 'n04442312', 'n04443257', 'n04447861', 'n04456115', 'n04458633', 'n04461696', 'n04462240', 'n04465501', 'n04467665', 'n04476259', 'n04479046', 'n04482393', 'n04483307', 'n04485082', 'n04486054', 'n04487081', 'n04487394', 'n04493381', 'n04501370', 'n04505470', 'n04507155', 'n04509417', 'n04515003', 'n04517823', 'n04522168', 'n04523525', 'n04525038', 'n04525305', 'n04532106', 'n04532670', 'n04536866', 'n04540053', 'n04542943', 'n04548280', 'n04548362', 'n04550184', 'n04552348', 'n04553703', 'n04554684', 'n04557648', 'n04560804', 'n04562935', 'n04579145', 'n04579432', 'n04584207', 'n04589890', 'n04590129', 'n04591157', 'n04591713', 'n04592741', 'n04596742', 'n04597913', 'n04599235', 'n04604644', 'n04606251', 'n04612504', 'n04613696', 'n06359193', 'n06596364', 'n06785654', 'n06794110', 'n06874185', 'n07248320', 'n07565083', 'n07579787', 'n07583066', 'n07584110', 'n07590611', 'n07613480', 'n07614500', 'n07615774', 'n07684084', 'n07693725', 'n07695742', 'n07697313', 'n07697537', 'n07711569', 'n07714571', 'n07714990', 'n07715103', 'n07716358', 'n07716906', 'n07717410', 'n07717556', 'n07718472', 'n07718747', 'n07720875', 'n07730033', 'n07734744', 'n07742313', 'n07745940', 'n07747607', 'n07749582', 'n07753113', 'n07753275', 'n07753592', 'n07754684', 'n07760859', 'n07768694', 'n07802026', 'n07831146', 'n07836838', 'n07860988', 'n07871810', 'n07873807', 'n07875152', 'n07880968', 'n07892512', 'n07920052', 'n07930864', 'n07932039', 'n09193705', 'n09229709', 'n09246464', 'n09256479', 'n09288635', 'n09332890', 'n09399592', 'n09421951', 'n09428293', 'n09468604', 'n09472597', 'n09835506', 'n10148035', 'n10565667', 'n11879895', 'n11939491', 'n12057211', 'n12144580', 'n12267677', 'n12620546', 'n12768682', 'n12985857', 'n12998815', 'n13037406', 'n13040303', 'n13044778', 'n13052670', 'n13054560', 'n13133613', 'n15075141']
    
    
    def clear_hidden_files(path):
        dir_list = os.listdir(path)
        for i in dir_list:
            abspath = os.path.join(os.path.abspath(path), i)
            if os.path.isfile(abspath):
                if i.startswith("._"):
                    os.remove(abspath)
            else:
                clear_hidden_files(abspath)
    
    def convert(size, box):
        dw = 1./size[0]
        dh = 1./size[1]
        x = (box[0] + box[1])/2.0
        y = (box[2] + box[3])/2.0
        w = box[1] - box[0]
        h = box[3] - box[2]
        x = x*dw
        w = w*dw
        y = y*dh
        h = h*dh
        return (x,y,w,h)
    
    def convert_annotation(image_id):
        in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' %image_id)
        out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' %image_id, 'w')
        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'):
            difficult = obj.find('difficult').text
            cls = obj.find('name').text
            #在这里完成标签中类别转换
            # if cls =='n15075141':
            #     cls = 'toilet_tissue'
            if cls not in classes or int(difficult) == 1:
                continue
            cls_id = classes.index(cls)
            xmlbox = obj.find('bndbox')
            b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
            bb = convert((w,h), b)
            out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
        in_file.close()
        out_file.close()
    
    wd = os.getcwd()
    wd = os.getcwd()
    data_base_dir = os.path.join(wd, "VOCdevkit/")
    if not os.path.isdir(data_base_dir):
        os.mkdir(data_base_dir)
    work_sapce_dir = os.path.join(data_base_dir, "VOC2007/")
    if not os.path.isdir(work_sapce_dir):
        os.mkdir(work_sapce_dir)
    annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
    if not os.path.isdir(annotation_dir):
            os.mkdir(annotation_dir)
    clear_hidden_files(annotation_dir)
    image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
    if not os.path.isdir(image_dir):
            os.mkdir(image_dir)
    clear_hidden_files(image_dir)
    yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
    if not os.path.isdir(yolo_labels_dir):
            os.mkdir(yolo_labels_dir)
    clear_hidden_files(yolo_labels_dir)
    yolov5_images_dir = os.path.join(data_base_dir, "images/")
    if not os.path.isdir(yolov5_images_dir):
            os.mkdir(yolov5_images_dir)
    clear_hidden_files(yolov5_images_dir)
    yolov5_labels_dir = os.path.join(data_base_dir, "labels/")
    if not os.path.isdir(yolov5_labels_dir):
            os.mkdir(yolov5_labels_dir)
    clear_hidden_files(yolov5_labels_dir)
    yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/")
    if not os.path.isdir(yolov5_images_train_dir):
            os.mkdir(yolov5_images_train_dir)
    clear_hidden_files(yolov5_images_train_dir)
    yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/")
    if not os.path.isdir(yolov5_images_test_dir):
            os.mkdir(yolov5_images_test_dir)
    clear_hidden_files(yolov5_images_test_dir)
    yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/")
    if not os.path.isdir(yolov5_labels_train_dir):
            os.mkdir(yolov5_labels_train_dir)
    clear_hidden_files(yolov5_labels_train_dir)
    yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/")
    if not os.path.isdir(yolov5_labels_test_dir):
            os.mkdir(yolov5_labels_test_dir)
    clear_hidden_files(yolov5_labels_test_dir)
    
    train_file = open(os.path.join(wd, "yolov8_train.txt"), 'w')
    test_file = open(os.path.join(wd, "yolov8_val.txt"), 'w')
    train_file.close()
    test_file.close()
    train_file = open(os.path.join(wd, "yolov8_train.txt"), 'a')
    test_file = open(os.path.join(wd, "yolov8_val.txt"), 'a')
    list_imgs = os.listdir(image_dir) # list image files
    probo = random.randint(1, 100)
    print("Probobility: %d" % probo)
    for i in range(0,len(list_imgs)):
        path = os.path.join(image_dir,list_imgs[i])
        if os.path.isfile(path):
            image_path = image_dir + list_imgs[i]
            voc_path = list_imgs[i]
            (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
            (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
            annotation_name = nameWithoutExtention + '.xml'
            annotation_path = os.path.join(annotation_dir, annotation_name)
            label_name = nameWithoutExtention + '.txt'
            label_path = os.path.join(yolo_labels_dir, label_name)
        probo = random.randint(1, 100)
        print("Probobility: %d" % probo)
        if(probo < 80): # train dataset
            if os.path.exists(annotation_path):
                train_file.write(image_path + '\n')
                convert_annotation(nameWithoutExtention) # convert label
                copyfile(image_path, yolov5_images_train_dir + voc_path)
                copyfile(label_path, yolov5_labels_train_dir + label_name)
        else: # test dataset
            if os.path.exists(annotation_path):
                test_file.write(image_path + '\n')
                convert_annotation(nameWithoutExtention) # convert label
                copyfile(image_path, yolov5_images_test_dir + voc_path)
                copyfile(label_path, yolov5_labels_test_dir + label_name)
    train_file.close()
    test_file.close()

![在这里插入图片描述][99ab803a9c57496cb29d22dce896b47c.png]

这样转换后，类别信息就对啦！

### 最后修改COCO128.yaml文件 ###

同样也是需要修改类别信息  
![在这里插入图片描述][28e12f32a67348999f0f4a48eb10de90.png]

将1000类插入进去

代码如下

#读取所有的行数，并按列输出
    SaveList = []  # 存档列表
    # 读取文本内容到列表
    
    filepath = r'D:\code\yolov8-pytorch-master\voc_classes3.txt'
    with open(filepath, "r", encoding='utf-8') as file:
        for line in file:
            line = line.strip('\n')  # 删除换行符
            SaveList.append(line)
        file.close()
    print(SaveList)
    #写入存档到文件
    writhpath = r'D:\code\yolov8-pytorch-master\voc_classes4.txt'
    #(按列写入)
    # with open(writhpath,"w",encoding='utf-8') as file:
    #     for i,j,z in zip(SaveList[0].split(), SaveList[1].split(), SaveList[2].split()):#此处修改为你自己的行数SaveList[1]，或者添加行
    #                 file.write(str(i)+'   '+str(j)+'   '+str(z)+'\n')
    #     file.close()
    #按行写入
    with open(writhpath,"w+",encoding='utf-8') as file2:
        i = 0     #类别起始编号
        for line in SaveList:
            file2.writelines(str(i) + ': '+line+'\n')
            i = i+1
        file2.close()

[日常工具搬运——python逐行写入txt文件][python_txt]

[python按行读取txt文件任意行内容，并按列写入另一个txt文件中（修改行数后直接使用）][python_txt_txt]

结果如下：

0: tench
    1: goldfish
    2: great_white_shark
    3: tiger_shark
    4: hammerhead
    5: electric_ray
    6: stingray
    7: cock
    8: hen
    9: ostrich
    10: brambling
    11: goldfinch
    12: house_finch
    13: junco
    14: indigo_bunting
    15: robin
    16: bulbul
    17: jay
    18: magpie
    19: chickadee
    20: water_ouzel
    21: kite
    22: bald_eagle
    23: vulture
    24: great_grey_owl
    25: European_fire_salamander
    26: common_newt
    27: eft
    28: spotted_salamander
    29: axolotl
    30: bullfrog
    31: tree_frog
    32: tailed_frog
    33: loggerhead
    34: leatherback_turtle
    35: mud_turtle
    36: terrapin
    37: box_turtle
    38: banded_gecko
    39: common_iguana
    40: American_chameleon
    41: whiptail
    42: agama
    43: frilled_lizard
    44: alligator_lizard
    45: Gila_monster
    46: green_lizard
    47: African_chameleon
    48: Komodo_dragon
    49: African_crocodile
    50: American_alligator
    51: triceratops
    52: thunder_snake
    53: ringneck_snake
    54: hognose_snake
    55: green_snake
    56: king_snake
    57: garter_snake
    58: water_snake
    59: vine_snake
    60: night_snake
    61: boa_constrictor
    62: rock_python
    63: Indian_cobra
    64: green_mamba
    65: sea_snake
    66: horned_viper
    67: diamondback
    68: sidewinder
    69: trilobite
    70: harvestman
    71: scorpion
    72: black_and_gold_garden_spider
    73: barn_spider
    74: garden_spider
    75: black_widow
    76: tarantula
    77: wolf_spider
    78: tick
    79: centipede
    80: black_grouse
    81: ptarmigan
    82: ruffed_grouse
    83: prairie_chicken
    84: peacock
    85: quail
    86: partridge
    87: African_grey
    88: macaw
    89: sulphur-crested_cockatoo
    90: lorikeet
    91: coucal
    92: bee_eater
    93: hornbill
    94: hummingbird
    95: jacamar
    96: toucan
    97: drake
    98: red-breasted_merganser
    99: goose
    100: black_swan
    101: tusker
    102: echidna
    103: platypus
    104: wallaby
    105: koala
    106: wombat
    107: jellyfish
    108: sea_anemone
    109: brain_coral
    110: flatworm
    111: nematode
    112: conch
    113: snail
    114: slug
    115: sea_slug
    116: chiton
    117: chambered_nautilus
    118: Dungeness_crab
    119: rock_crab
    120: fiddler_crab
    121: king_crab
    122: American_lobster
    123: spiny_lobster
    124: crayfish
    125: hermit_crab
    126: isopod
    127: white_stork
    128: black_stork
    129: spoonbill
    130: flamingo
    131: little_blue_heron
    132: American_egret
    133: bittern
    134: crane
    135: limpkin
    136: European_gallinule
    137: American_coot
    138: bustard
    139: ruddy_turnstone
    140: red-backed_sandpiper
    141: redshank
    142: dowitcher
    143: oystercatcher
    144: pelican
    145: king_penguin
    146: albatross
    147: grey_whale
    148: killer_whale
    149: dugong
    150: sea_lion
    151: Chihuahua
    152: Japanese_spaniel
    153: Maltese_dog
    154: Pekinese
    155: Shih-Tzu
    156: Blenheim_spaniel
    157: papillon
    158: toy_terrier
    159: Rhodesian_ridgeback
    160: Afghan_hound
    161: basset
    162: beagle
    163: bloodhound
    164: bluetick
    165: black-and-tan_coonhound
    166: Walker_hound
    167: English_foxhound
    168: redbone
    169: borzoi
    170: Irish_wolfhound
    171: Italian_greyhound
    172: whippet
    173: Ibizan_hound
    174: Norwegian_elkhound
    175: otterhound
    176: Saluki
    177: Scottish_deerhound
    178: Weimaraner
    179: Staffordshire_bullterrier
    180: American_Staffordshire_terrier
    181: Bedlington_terrier
    182: Border_terrier
    183: Kerry_blue_terrier
    184: Irish_terrier
    185: Norfolk_terrier
    186: Norwich_terrier
    187: Yorkshire_terrier
    188: wire-haired_fox_terrier
    189: Lakeland_terrier
    190: Sealyham_terrier
    191: Airedale
    192: cairn
    193: Australian_terrier
    194: Dandie_Dinmont
    195: Boston_bull
    196: miniature_schnauzer
    197: giant_schnauzer
    198: standard_schnauzer
    199: Scotch_terrier
    200: Tibetan_terrier
    201: silky_terrier
    202: soft-coated_wheaten_terrier
    203: West_Highland_white_terrier
    204: Lhasa
    205: flat-coated_retriever
    206: curly-coated_retriever
    207: golden_retriever
    208: Labrador_retriever
    209: Chesapeake_Bay_retriever
    210: German_short-haired_pointer
    211: vizsla
    212: English_setter
    213: Irish_setter
    214: Gordon_setter
    215: Brittany_spaniel
    216: clumber
    217: English_springer
    218: Welsh_springer_spaniel
    219: cocker_spaniel
    220: Sussex_spaniel
    221: Irish_water_spaniel
    222: kuvasz
    223: schipperke
    224: groenendael
    225: malinois
    226: briard
    227: kelpie
    228: komondor
    229: Old_English_sheepdog
    230: Shetland_sheepdog
    231: collie
    232: Border_collie
    233: Bouvier_des_Flandres
    234: Rottweiler
    235: German_shepherd
    236: Doberman
    237: miniature_pinscher
    238: Greater_Swiss_Mountain_dog
    239: Bernese_mountain_dog
    240: Appenzeller
    241: EntleBucher
    242: boxer
    243: bull_mastiff
    244: Tibetan_mastiff
    245: French_bulldog
    246: Great_Dane
    247: Saint_Bernard
    248: Eskimo_dog
    249: malamute
    250: Siberian_husky
    251: dalmatian
    252: affenpinscher
    253: basenji
    254: pug
    255: Leonberg
    256: Newfoundland
    257: Great_Pyrenees
    258: Samoyed
    259: Pomeranian
    260: chow
    261: keeshond
    262: Brabancon_griffon
    263: Pembroke
    264: Cardigan
    265: toy_poodle
    266: miniature_poodle
    267: standard_poodle
    268: Mexican_hairless
    269: timber_wolf
    270: white_wolf
    271: red_wolf
    272: coyote
    273: dingo
    274: dhole
    275: African_hunting_dog
    276: hyena
    277: red_fox
    278: kit_fox
    279: Arctic_fox
    280: grey_fox
    281: tabby
    282: tiger_cat
    283: Persian_cat
    284: Siamese_cat
    285: Egyptian_cat
    286: cougar
    287: lynx
    288: leopard
    289: snow_leopard
    290: jaguar
    291: lion
    292: tiger
    293: cheetah
    294: brown_bear
    295: American_black_bear
    296: ice_bear
    297: sloth_bear
    298: mongoose
    299: meerkat
    300: tiger_beetle
    301: ladybug
    302: ground_beetle
    303: long-horned_beetle
    304: leaf_beetle
    305: dung_beetle
    306: rhinoceros_beetle
    307: weevil
    308: fly
    309: bee
    310: ant
    311: grasshopper
    312: cricket
    313: walking_stick
    314: cockroach
    315: mantis
    316: cicada
    317: leafhopper
    318: lacewing
    319: dragonfly
    320: damselfly
    321: admiral
    322: ringlet
    323: monarch
    324: cabbage_butterfly
    325: sulphur_butterfly
    326: lycaenid
    327: starfish
    328: sea_urchin
    329: sea_cucumber
    330: wood_rabbit
    331: hare
    332: Angora
    333: hamster
    334: porcupine
    335: fox_squirrel
    336: marmot
    337: beaver
    338: guinea_pig
    339: sorrel
    340: zebra
    341: hog
    342: wild_boar
    343: warthog
    344: hippopotamus
    345: ox
    346: water_buffalo
    347: bison
    348: ram
    349: bighorn
    350: ibex
    351: hartebeest
    352: impala
    353: gazelle
    354: Arabian_camel
    355: llama
    356: weasel
    357: mink
    358: polecat
    359: black-footed_ferret
    360: otter
    361: skunk
    362: badger
    363: armadillo
    364: three-toed_sloth
    365: orangutan
    366: gorilla
    367: chimpanzee
    368: gibbon
    369: siamang
    370: guenon
    371: patas
    372: baboon
    373: macaque
    374: langur
    375: colobus
    376: proboscis_monkey
    377: marmoset
    378: capuchin
    379: howler_monkey
    380: titi
    381: spider_monkey
    382: squirrel_monkey
    383: Madagascar_cat
    384: indri
    385: Indian_elephant
    386: African_elephant
    387: lesser_panda
    388: giant_panda
    389: barracouta
    390: eel
    391: coho
    392: rock_beauty
    393: anemone_fish
    394: sturgeon
    395: gar
    396: lionfish
    397: puffer
    398: abacus
    399: abaya
    400: academic_gown
    401: accordion
    402: acoustic_guitar
    403: aircraft_carrier
    404: airliner
    405: airship
    406: altar
    407: ambulance
    408: amphibian
    409: analog_clock
    410: apiary
    411: apron
    412: ashcan
    413: assault_rifle
    414: backpack
    415: bakery
    416: balance_beam
    417: balloon
    418: ballpoint
    419: Band_Aid
    420: banjo
    421: bannister
    422: barbell
    423: barber_chair
    424: barbershop
    425: barn
    426: barometer
    427: barrel
    428: barrow
    429: baseball
    430: basketball
    431: bassinet
    432: bassoon
    433: bathing_cap
    434: bath_towel
    435: bathtub
    436: beach_wagon
    437: beacon
    438: beaker
    439: bearskin
    440: beer_bottle
    441: beer_glass
    442: bell_cote
    443: bib
    444: bicycle-built-for-two
    445: bikini
    446: binder
    447: binoculars
    448: birdhouse
    449: boathouse
    450: bobsled
    451: bolo_tie
    452: bonnet
    453: bookcase
    454: bookshop
    455: bottlecap
    456: bow
    457: bow_tie
    458: brass
    459: brassiere
    460: breakwater
    461: breastplate
    462: broom
    463: bucket
    464: buckle
    465: bulletproof_vest
    466: bullet_train
    467: butcher_shop
    468: cab
    469: caldron
    470: candle
    471: cannon
    472: canoe
    473: can_opener
    474: cardigan
    475: car_mirror
    476: carousel
    477: carpenter’s_kit
    478: carton
    479: car_wheel
    480: cash_machine
    481: cassette
    482: cassette_player
    483: castle
    484: catamaran
    485: CD_player
    486: cello
    487: cellular_telephone
    488: chain
    489: chainlink_fence
    490: chain_mail
    491: chain_saw
    492: chest
    493: chiffonier
    494: chime
    495: china_cabinet
    496: Christmas_stocking
    497: church
    498: cinema
    499: cleaver
    500: cliff_dwelling
    501: cloak
    502: clog
    503: cocktail_shaker
    504: coffee_mug
    505: coffeepot
    506: coil
    507: combination_lock
    508: computer_keyboard
    509: confectionery
    510: container_ship
    511: convertible
    512: corkscrew
    513: cornet
    514: cowboy_boot
    515: cowboy_hat
    516: cradle
    517: crane
    518: crash_helmet
    519: crate
    520: crib
    521: Crock_Pot
    522: croquet_ball
    523: crutch
    524: cuirass
    525: dam
    526: desk
    527: desktop_computer
    528: dial_telephone
    529: diaper
    530: digital_clock
    531: digital_watch
    532: dining_table
    533: dishrag
    534: dishwasher
    535: disk_brake
    536: dock
    537: dogsled
    538: dome
    539: doormat
    540: drilling_platform
    541: drum
    542: drumstick
    543: dumbbell
    544: Dutch_oven
    545: electric_fan
    546: electric_guitar
    547: electric_locomotive
    548: entertainment_center
    549: envelope
    550: espresso_maker
    551: face_powder
    552: feather_boa
    553: file
    554: fireboat
    555: fire_engine
    556: fire_screen
    557: flagpole
    558: flute
    559: folding_chair
    560: football_helmet
    561: forklift
    562: fountain
    563: fountain_pen
    564: four-poster
    565: freight_car
    566: French_horn
    567: frying_pan
    568: fur_coat
    569: garbage_truck
    570: gasmask
    571: gas_pump
    572: goblet
    573: go-kart
    574: golf_ball
    575: golfcart
    576: gondola
    577: gong
    578: gown
    579: grand_piano
    580: greenhouse
    581: grille
    582: grocery_store
    583: guillotine
    584: hair_slide
    585: hair_spray
    586: half_track
    587: hammer
    588: hamper
    589: hand_blower
    590: hand-held_computer
    591: handkerchief
    592: hard_disc
    593: harmonica
    594: harp
    595: harvester
    596: hatchet
    597: holster
    598: home_theater
    599: honeycomb
    600: hook
    601: hoopskirt
    602: horizontal_bar
    603: horse_cart
    604: hourglass
    605: iPod
    606: iron
    607: jack-o’-lantern
    608: jean
    609: jeep
    610: jersey
    611: jigsaw_puzzle
    612: jinrikisha
    613: joystick
    614: kimono
    615: knee_pad
    616: knot
    617: lab_coat
    618: ladle
    619: lampshade
    620: laptop
    621: lawn_mower
    622: lens_cap
    623: letter_opener
    624: library
    625: lifeboat
    626: lighter
    627: limousine
    628: liner
    629: lipstick
    630: Loafer
    631: lotion
    632: loudspeaker
    633: loupe
    634: lumbermill
    635: magnetic_compass
    636: mailbag
    637: mailbox
    638: maillot
    639: maillot
    640: manhole_cover
    641: maraca
    642: marimba
    643: mask
    644: matchstick
    645: maypole
    646: maze
    647: measuring_cup
    648: medicine_chest
    649: megalith
    650: microphone
    651: microwave
    652: military_uniform
    653: milk_can
    654: minibus
    655: miniskirt
    656: minivan
    657: missile
    658: mitten
    659: mixing_bowl
    660: mobile_home
    661: Model_T
    662: modem
    663: monastery
    664: monitor
    665: moped
    666: mortar
    667: mortarboard
    668: mosque
    669: mosquito_net
    670: motor_scooter
    671: mountain_bike
    672: mountain_tent
    673: mouse
    674: mousetrap
    675: moving_van
    676: muzzle
    677: nail
    678: neck_brace
    679: necklace
    680: nipple
    681: notebook
    682: obelisk
    683: oboe
    684: ocarina
    685: odometer
    686: oil_filter
    687: organ
    688: oscilloscope
    689: overskirt
    690: oxcart
    691: oxygen_mask
    692: packet
    693: paddle
    694: paddlewheel
    695: padlock
    696: paintbrush
    697: pajama
    698: palace
    699: panpipe
    700: paper_towel
    701: parachute
    702: parallel_bars
    703: park_bench
    704: parking_meter
    705: passenger_car
    706: patio
    707: pay-phone
    708: pedestal
    709: pencil_box
    710: pencil_sharpener
    711: perfume
    712: Petri_dish
    713: photocopier
    714: pick
    715: pickelhaube
    716: picket_fence
    717: pickup
    718: pier
    719: piggy_bank
    720: pill_bottle
    721: pillow
    722: ping-pong_ball
    723: pinwheel
    724: pirate
    725: pitcher
    726: plane
    727: planetarium
    728: plastic_bag
    729: plate_rack
    730: plow
    731: plunger
    732: Polaroid_camera
    733: pole
    734: police_van
    735: poncho
    736: pool_table
    737: pop_bottle
    738: pot
    739: potter’s_wheel
    740: power_drill
    741: prayer_rug
    742: printer
    743: prison
    744: projectile
    745: projector
    746: puck
    747: punching_bag
    748: purse
    749: quill
    750: quilt
    751: racer
    752: racket
    753: radiator
    754: radio
    755: radio_telescope
    756: rain_barrel
    757: recreational_vehicle
    758: reel
    759: reflex_camera
    760: refrigerator
    761: remote_control
    762: restaurant
    763: revolver
    764: rifle
    765: rocking_chair
    766: rotisserie
    767: rubber_eraser
    768: rugby_ball
    769: rule
    770: running_shoe
    771: safe
    772: safety_pin
    773: saltshaker
    774: sandal
    775: sarong
    776: sax
    777: scabbard
    778: scale
    779: school_bus
    780: schooner
    781: scoreboard
    782: screen
    783: screw
    784: screwdriver
    785: seat_belt
    786: sewing_machine
    787: shield
    788: shoe_shop
    789: shoji
    790: shopping_basket
    791: shopping_cart
    792: shovel
    793: shower_cap
    794: shower_curtain
    795: ski
    796: ski_mask
    797: sleeping_bag
    798: slide_rule
    799: sliding_door
    800: slot
    801: snorkel
    802: snowmobile
    803: snowplow
    804: soap_dispenser
    805: soccer_ball
    806: sock
    807: solar_dish
    808: sombrero
    809: soup_bowl
    810: space_bar
    811: space_heater
    812: space_shuttle
    813: spatula
    814: speedboat
    815: spider_web
    816: spindle
    817: sports_car
    818: spotlight
    819: stage
    820: steam_locomotive
    821: steel_arch_bridge
    822: steel_drum
    823: stethoscope
    824: stole
    825: stone_wall
    826: stopwatch
    827: stove
    828: strainer
    829: streetcar
    830: stretcher
    831: studio_couch
    832: stupa
    833: submarine
    834: suit
    835: sundial
    836: sunglass
    837: sunglasses
    838: sunscreen
    839: suspension_bridge
    840: swab
    841: sweatshirt
    842: swimming_trunks
    843: swing
    844: switch
    845: syringe
    846: table_lamp
    847: tank
    848: tape_player
    849: teapot
    850: teddy
    851: television
    852: tennis_ball
    853: thatch
    854: theater_curtain
    855: thimble
    856: thresher
    857: throne
    858: tile_roof
    859: toaster
    860: tobacco_shop
    861: toilet_seat
    862: torch
    863: totem_pole
    864: tow_truck
    865: toyshop
    866: tractor
    867: trailer_truck
    868: tray
    869: trench_coat
    870: tricycle
    871: trimaran
    872: tripod
    873: triumphal_arch
    874: trolleybus
    875: trombone
    876: tub
    877: turnstile
    878: typewriter_keyboard
    879: umbrella
    880: unicycle
    881: upright
    882: vacuum
    883: vase
    884: vault
    885: velvet
    886: vending_machine
    887: vestment
    888: viaduct
    889: violin
    890: volleyball
    891: waffle_iron
    892: wall_clock
    893: wallet
    894: wardrobe
    895: warplane
    896: washbasin
    897: washer
    898: water_bottle
    899: water_jug
    900: water_tower
    901: whiskey_jug
    902: whistle
    903: wig
    904: window_screen
    905: window_shade
    906: Windsor_tie
    907: wine_bottle
    908: wing
    909: wok
    910: wooden_spoon
    911: wool
    912: worm_fence
    913: wreck
    914: yawl
    915: yurt
    916: web_site
    917: comic_book
    918: crossword_puzzle
    919: street_sign
    920: traffic_light
    921: book_jacket
    922: menu
    923: plate
    924: guacamole
    925: consomme
    926: hot_pot
    927: trifle
    928: ice_cream
    929: ice_lolly
    930: French_loaf
    931: bagel
    932: pretzel
    933: cheeseburger
    934: hotdog
    935: mashed_potato
    936: head_cabbage
    937: broccoli
    938: cauliflower
    939: zucchini
    940: spaghetti_squash
    941: acorn_squash
    942: butternut_squash
    943: cucumber
    944: artichoke
    945: bell_pepper
    946: cardoon
    947: mushroom
    948: Granny_Smith
    949: strawberry
    950: orange
    951: lemon
    952: fig
    953: pineapple
    954: banana
    955: jackfruit
    956: custard_apple
    957: pomegranate
    958: hay
    959: carbonara
    960: chocolate_sauce
    961: dough
    962: meat_loaf
    963: pizza
    964: potpie
    965: burrito
    966: red_wine
    967: espresso
    968: cup
    969: eggnog
    970: alp
    971: bubble
    972: cliff
    973: coral_reef
    974: geyser
    975: lakeside
    976: promontory
    977: sandbar
    978: seashore
    979: valley
    980: volcano
    981: ballplayer
    982: groom
    983: scuba_diver
    984: rapeseed
    985: daisy
    986: yellow_lady’s_slipper
    987: corn
    988: acorn
    989: hip
    990: buckeye
    991: coral_fungus
    992: agaric
    993: gyromitra
    994: stinkhorn
    995: earthstar
    996: hen-of-the-woods
    997: bolete
    998: ear
    999: toilet_tissue

整合到coco128.yaml中（前面需要空两格）

完成

[数据集介绍][Link 6]  
[数据集介绍][Link 7]

[4afebc1d7d9647eba5dbccc775c64451.jpeg_pic_center]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/ff6dabcafdd240d1a5e84e12819f1477.png
[Link 1]: https://blog.csdn.net/qq_45281807/article/details/123339198
[linux]: https://blog.csdn.net/Quincuntial/article/details/82761939
[python_zip]: https://blog.csdn.net/qq_45365214/article/details/123914055
[8d53a4271b024bbaa7fa68be95cf8ab4.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/103c11318df3441281e90860e2d89fce.png
[Link 2]: https://blog.csdn.net/weixin_43490422/article/details/123718182
[1000]: https://blog.csdn.net/weiyichenlun/article/details/100134317
[Link 3]: https://blog.csdn.net/qq_36665989/article/details/119947229
[4ffd6d501b62474dace0cdcf65f068c8.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/fe076cebc9e045c59b26519730407b95.png
[matlab]: https://blog.csdn.net/qq_41701723/article/details/134662166
[d0ddde60576f42f99ff5bfd24531883d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/84c20580be83426aacc00bd47ac0a783.png
[Ubuntu]: https://blog.csdn.net/gaishi_hero/article/details/81209994
[262cec8631fd493d877590180930f6d6.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/da1a10489e2a438f8281311e29c0d0eb.png
[e0bb761e35b5409caff36ce6414a6fac.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/a857bbaf5ef94dbd91a8d4c6135c8599.png
[python]: https://blog.csdn.net/qq_41701723/article/details/132576456
[d57983bd8ece437bbda20d54f95f57f6.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/c0bf21dc77184ed0a707873c0f811071.png
[Link 4]: https://blog.csdn.net/balabala_333/article/details/126977284
[f588fe574d2143f5b6b508746dd99acd.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/ca2e746eabfb426483fa82fe46e30627.png
[6c42c852d9cb4c6eacede934100ac88c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/6adf4eb707f34aa489db10fcc4d42cbb.png
[Link 5]: https://blog.csdn.net/qq_41701723/article/details/134691848
[db6e7194a6ef4447810aa4e1586bad1d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/a9f4dbf7a86146c48bb20679d60534d9.png
[74441902149346a88a1240e3df55a7f3.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/2e8cfe3903c34c869eddeb0b8eef6a16.png
[6ef085be1c3a4445a6b1ebbe7fa0d3ee.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/ec79d186a5254132a36e049c0a92518c.png
[23135675223247d486e893dee2819961.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/ac919057be1f49b5b2185b5620380d47.png
[xml_VOC]: https://blog.csdn.net/weixin_44791964/article/details/129978504
[c82629b89a8843a495d76897973ccb1e.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/8d1707d511c5459fba2c31bc042fa942.png
[0640a5da32644726b9d5bc27af2c8d10.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/cb77836245e9493d836cf10d7872f706.png
[e5248d1dea224b1aa77b9f96f09beaa1.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/72252289784241e9a8480506676fbd02.png
[45cf1eeaca9c4446bb34ffe49e19d8ea.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/a84b4ebbc3e344a790891a56fe67c3ec.png
[ed5ec5786bff4c61a41a1f532e56d9f6.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/c573a9597ba84d09be6503b33d69fa5e.png
[00a6903ebf6e4606940cb07c196fe646.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/10ea639c9274423093f4e356163d6237.png
[6684fd02237c4dfda9e9da73a758c4da.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/1412ee574a30438cb005cf361399369d.png
[python 1]: https://blog.csdn.net/BaoITcore/article/details/127225147
[Python]: https://blog.csdn.net/fengqianlang/article/details/129675068
[python_n]: https://blog.csdn.net/Vertira/article/details/124140685
[60ce8d6ccc944d36985c047d5df395cc.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/c759e2da95a54709bdf31d29af1fadcf.png
[99ab803a9c57496cb29d22dce896b47c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/405f18b1c82b4222b93abfa68215bc28.png
[28e12f32a67348999f0f4a48eb10de90.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/02/06/1079777844a048969a16639d2612f389.png
[python_txt]: https://blog.csdn.net/qq_37730871/article/details/116896097
[python_txt_txt]: https://blog.csdn.net/qq_40379132/article/details/125047398
[Link 6]: https://www.iotword.com/2117.html
[Link 7]: https://www.xjx100.cn/news/443229.html?action=onClick