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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["annotation-conversion","coco","computer-vision","deep-learning","image-classification","instance-segmentation","labelme","linux","machine-learning","macos","object-detection","python","semantic-segmentation","windows"],"created_at":"2024-08-03T03:02:18.379Z","updated_at":"2026-01-14T09:00:49.686Z","avatar_url":"https://github.com/fcakyon.png","language":"Python","readme":"\u003cdiv align=\"center\"\u003e\n\u003ch1\u003e\n  labelme2coco\n\u003c/h1\u003e\n\n\u003ca href=\"https://pepy.tech/project/labelme2coco\"\u003e\u003cimg src=\"https://pepy.tech/badge/labelme2coco\" alt=\"downloads\"\u003e\u003c/a\u003e\n\u003ca href=\"https://badge.fury.io/py/labelme2coco\"\u003e\u003cimg src=\"https://badge.fury.io/py/labelme2coco.svg\" alt=\"pypi version\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/fcakyon/labelme2coco/actions/workflows/ci.yml\"\u003e\u003cimg src=\"https://github.com/fcakyon/labelme2coco/workflows/CI/badge.svg\" alt=\"ci\"\u003e\u003c/a\u003e\n\u003ca href=\"https://twitter.com/fcakyon\"\u003e\u003cimg src=\"https://img.shields.io/badge/twitter-fcakyon_-blue?logo=twitter\u0026style=flat\" alt=\"fcakyon twitter\"\u003e\n\n\u003ch4\u003e\n  A lightweight package for converting your \u003ca href=\"https://github.com/wkentaro/labelme\"\u003elabelme\u003c/a\u003e annotations into COCO object detection format.\n\u003c/h4\u003e\n\n\u003ch4\u003e\n    \u003cimg width=\"700\" alt=\"teaser\" src=\"https://user-images.githubusercontent.com/34196005/148746639-9a7b9c08-2156-42ca-abae-a4e6aad095dd.gif\"\u003e\n\u003c/h4\u003e\n\u003c/div\u003e\n\n## Convert LabelMe annotations to COCO format in one step\n[labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats.\nHowever, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations.\n\nYou can use this package to convert labelme annotations to COCO format.\n\n## Getting started\n### Installation\n```\npip install -U labelme2coco\n```\n\n### Basic Usage\n\n```python\nlabelme2coco path/to/labelme/dir\n```\n\n```python\nlabelme2coco path/to/labelme/dir --train_split_rate 0.85\n```\n\n```python\nlabelme2coco path/to/labelme/dir --category_id_start 1\n```\n\n### Advanced Usage\n\n```python\n# import package\nimport labelme2coco\n\n# set directory that contains labelme annotations and image files\nlabelme_folder = \"tests/data/labelme_annot\"\n\n# set export dir\nexport_dir = \"tests/data/\"\n\n# set train split rate\ntrain_split_rate = 0.85\n\n# set category ID start value\ncategory_id_start = 1\n\n# convert labelme annotations to coco\nlabelme2coco.convert(labelme_folder, export_dir, train_split_rate, category_id_start=category_id_start)\n```\n\n```python\n# import functions\nfrom labelme2coco import get_coco_from_labelme_folder, save_json\n\n# set labelme training data directory\nlabelme_train_folder = \"tests/data/labelme_annot\"\n\n# set labelme validation data directory\nlabelme_val_folder = \"tests/data/labelme_annot\"\n\n# set path for coco json to be saved\nexport_dir = \"tests/data/\"\n\n# set category ID start value\ncategory_id_start = 1\n\n# create train coco object\ntrain_coco = get_coco_from_labelme_folder(labelme_train_folder, category_id_start=category_id_start)\n\n# export train coco json\nsave_json(train_coco.json, export_dir+\"train.json\")\n\n# create val coco object\nval_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories, category_id_start=category_id_start)\n\n# export val coco json\nsave_json(val_coco.json, export_dir+\"val.json\")\n```\n","funding_links":["https://github.com/sponsors/fcakyon"],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffcakyon%2Flabelme2coco","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffcakyon%2Flabelme2coco","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffcakyon%2Flabelme2coco/lists"}