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💥Make your yolov5 dataset by using labelimg.I hope my work can help you make your yolov5 datasets more quickly.

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WangRongsheng/make-your-yolov5_dataset

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💥💥💥重大更新💥💥💥：最新版本的全面标注工具集KDAT发布！！！👉前往

数据集标注软件

更多的标注工具你可以去看：深度学习图像标注工具汇总 、十个最常用深度学习图像/视频数据标注工具 、百度一下

在这里我选择使用labelImg

数据集大小重置工具：Image_tool.exe百度网盘下载（提取码：2tmv）

数据集标注

  1. 下载labelImg
  2. 解压，直接打开labelimg.exe
  3. labelImg标注数据

标注完成后即可获得VOC格式（生成.xml文件）的数据

dataset│├─Annotations│      train_29635.xml│      train_29641.xml│      train_30090.xml│   ...│└─JPEGImages        train_29635.jpg        train_29641.jpg        train_30090.jpg        ...

yolov5数据集制作

0、数据图片重命名

对于rename.bat：

命名从01开始，设置set count=100命名从0001开始，设置set count=10000如果我们想要从038开始排序，可以将第三行代码：set count=10037

1.voc格式数据集转化yolov5格式数据集

运行voc_to_yoloV5.py【该脚本实现将voc格式的数据转化为yoloV5需要的.txt标注文件,运行该脚本，会在dataset/voc/目录下生成worktxt/目录(yolo需要的格式)】

运行代码之前，修改里面相关文件路径。

dataset│├─Annotations│      train_29635.xml│      train_29641.xml│      train_30090.xml│   ...│├─JPEGImages│       train_29635.jpg│       train_29641.jpg│       train_30090.jpg│       ...│└─worktxt       train_29635.txt       train_29641.txt       train_30090.txt    ...

2.数据集划分训练、测试、验证

运行voc_split_trainValTest.py【该脚本用于生成voc/目录下的ImageSets/..目录,分割了训练和验证集】

dataset│├─Annotations│      train_29635.xml│      train_29641.xml│      train_30090.xml│   ...│├─ImageSets│└─Main│      train.txt│      test.txt│      valid.txt│      img_train.txt│      img_test.txt│      img_valid.txt│├─JPEGImages│       train_29635.jpg│       train_29641.jpg│       train_30090.jpg│       ...│└─worktxt       train_29635.txt       train_29641.txt       train_30090.txt    ...

3.重构数据集

运行dataset_cg.py【该脚本将会生成yolov5可以训练的数据布局】

主要进行一个copy或者move的操作：

# 小数据建议：copy 大数据建议：moveforiinrange(len(img_txt_cg_train)):shutil.copy(fimg+str(img_txt_cg_train[i]),new_dataset_train)shutil.copy(flable+str(label_txt_cg_train[i]),new_dataset_trainl)forjinrange(len(img_txt_cg_test)):shutil.copy(fimg+str(img_txt_cg_test[j]),new_dataset_test)shutil.copy(flable+str(label_txt_cg_test[j]),new_dataset_testl)forqinrange(len(img_txt_cg_valid)):shutil.copy(fimg+str(img_txt_cg_valid[q]),new_dataset_valid)shutil.copy(flable+str(label_txt_cg_valid[q]),new_dataset_validl)
dataset│├─Annotations│      train_29635.xml│      train_29641.xml│      train_30090.xml│   ...│├─ImageSets│└─Main│      train.txt│      test.txt│      valid.txt│      img_train.txt│      img_test.txt│      img_valid.txt│├─data│   ├─train│   ├─test│   └─valid│├─JPEGImages│       train_29635.jpg│       train_29641.jpg│       train_30090.jpg│       ...│└─worktxt       train_29635.txt       train_29641.txt       train_30090.txt    ...

4.开始利用yolov5训练你的数据

git clone https://github.com/ultralytics/yolov5.git

5.txt格式数据回改成voc格式

修改yolo2voc.py文件中第6，7，8，10行相应内容即可得到VOC数据

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