{"id":13441734,"url":"https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg","last_synced_at":"2025-03-20T12:32:34.319Z","repository":{"id":39896821,"uuid":"394533498","full_name":"cnyvfang/labelGo-Yolov5AutoLabelImg","owner":"cnyvfang","description":"YOLOV5 semi-automatic annotation tool (Based on 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Detection Applications"],"sub_categories":[],"readme":"# labelGo\n\u003cp\u003eGuide Language:\u003ca href=\"https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg/blob/master/readme_zh_cn.md\"\u003e简体中文\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eA graphical Semi-automatic annotation tool based on \u003ca href=\"https://github.com/tzutalin/labelImg\"\u003elabelImg\u003c/a\u003e and \u003ca href=\"https://github.com/ultralytics/yolov5\"\u003eYOLOv5\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eSemi-automatic annotation of datasets by existing yolov5 pytorch models\u003c/p\u003e\n\n## News\n**labelGo now supports the latest version of YOLOv5, and automatic classes.txt file generation**\n\n### Demonstration of semi-automatic labeling function\n![image](https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg/blob/master/demo/demo1.gif) \n### Demonstration of converting Yolo format to VOC format with one click\n![image](https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg/blob/master/demo/demo2.gif) \n\n## Note\n\u003cp\u003eIf there is a problem, please put it forward in the issue.\u003c/p\u003e\n\u003cp\u003eThe annotation file is saved in the same location as the picture folder.\u003c/p\u003e\n\u003cp\u003eRecommended version of python: python 3.8.\u003c/p\u003e\n\u003cp\u003eRecommended for conda environments.\u003c/p\u003e\n\u003cp\u003eThe item is completely free and it is forbidden to sell the item in any way. \u003c/p\u003e\n\u003cp\u003eThis project has support for the latest version of YOLOv5, if you need to use an older version that supports YOLOv5 version5, you can find the source code in Release. \u003c/p\u003e\n\n\n## Installation and use\n\u003cp\u003e1.Fetching projects from git\u003c/p\u003e\n\n```bash\ngit clone https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg.git\n```\n\n\u003cp\u003e2.Switching the operating directory to the project directory\u003c/p\u003e\n\n```bash\ncd labelGo-Yolov5AutoLabelImg\n```\n\n\u003cp\u003e3.Installation environment\u003c/p\u003e\n\n```bash\npip install -r requirements.txt\n```\n\n\u003cp\u003e4.Launching applications\u003c/p\u003e\n\n```bash\npython labelGo.py\n```\n\n\u003cp\u003e5. Click on the \"Open directory\" button to select the folder where the images are stored\u003c/p\u003e\n\n\u003cp\u003e6. Click on the \"Auto Annotate\" button to confirm that the information is correct and then select the trained yolov5 pytorch model to complete the auto annotation\u003c/p\u003e\n\n\u003cp\u003e7. Adjust the automatic annotation results according to the actual requirements and save them\u003c/p\u003e\n\n## Acknowledgements\nThanks to [tangtang666](https://github.com/tangtang666) for submitting support for the latest version of YOLOv5\n\nThanks to [Iceprism](https://github.com/Iceprism) for fixing the bugs in the Chinese version.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcnyvfang%2FlabelGo-Yolov5AutoLabelImg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcnyvfang%2FlabelGo-Yolov5AutoLabelImg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcnyvfang%2FlabelGo-Yolov5AutoLabelImg/lists"}