[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-8790":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":17,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":37,"readmeContent":38,"aiSummary":39,"trendingCount":16,"starSnapshotCount":16,"syncStatus":40,"lastSyncTime":41,"discoverSource":42},8790,"coco-annotator","jsbroks\u002Fcoco-annotator","jsbroks",":pencil2: Web-based image segmentation tool for object detection, localization, and keypoints","",null,"Vue",2276,479,43,222,0,1,6,61.14,"MIT License",false,"master",true,[25,26,5,27,28,29,30,31,32,33,34,35,36],"annotate-images","coco","coco-format","computer-vision","datasets","deep-learning","detection","image-annotation","image-labeling","image-segmentation","label","machine-learning","2026-06-12 04:00:41","\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Fi.imgur.com\u002FAA7IdbQ.png\">\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"#features\">Features\u003C\u002Fa> •\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fjsbroks\u002Fcoco-annotator\u002Fwiki\">Wiki\u003C\u002Fa> •\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fjsbroks\u002Fcoco-annotator\u002Fwiki\u002FGetting-Started\">Getting Started\u003C\u002Fa> •\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fjsbroks\u002Fcoco-annotator\u002Fissues\">Issues\u003C\u002Fa> •\n  \u003Ca href=\"#license\">License\u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"\u002Fjsbroks\u002Fcoco-annotator\u002Fstargazers\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fjsbroks\u002Fcoco-annotator.svg\">\n  \u003C\u002Fa>\n  \u003Ca href=\"\u002Fjsbroks\u002Fcoco-annotator\u002Fissues\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues\u002Fjsbroks\u002Fcoco-annotator.svg\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Ftldrlegal.com\u002Flicense\u002Fmit-license\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmashape\u002Fapistatus.svg\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Flgtm.com\u002Fprojects\u002Fg\u002Fjsbroks\u002Fcoco-annotator\u002Fcontext:javascript\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Flgtm\u002Fgrade\u002Fjavascript\u002Fg\u002Fjsbroks\u002Fcoco-annotator.svg?label=code%20quality\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fannotator.justinbrooks.ca\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdemo-online-green.svg\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Ftravis-ci.org\u002Fjsbroks\u002Fcoco-annotator\">\n    \u003Cimg src=\"https:\u002F\u002Ftravis-ci.org\u002Fjsbroks\u002Fcoco-annotator.svg?branch=master\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fhub.docker.com\u002Fr\u002Fjsbroks\u002Fcoco-annotator\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Fjsbroks\u002Fcoco-annotator.svg\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\nCOCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, labeling objects with disconnected visible parts, efficiently storing and export annotations in the well-known [COCO format](http:\u002F\u002Fcocodataset.org\u002F#format-data). The annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets.\n\n\n\u003Cbr \u002F>\n\n\u003Cp align=\"center\">Join our growing \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002F4zP5Qkj\">discord community\u003C\u002Fa> of ML practitioner\u003C\u002Fp>\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002F4zP5Qkj\">\n    \u003Cimg src=\"https:\u002F\u002Fdiscord.com\u002Fassets\u002Fe4923594e694a21542a489471ecffa50.svg\" width=\"120\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cbr \u002F>\n\n\u003Cp align=\"center\">\u003Ca href=\"http:\u002F\u002Fwww.youtube.com\u002Fwatch?feature=player_embedded&v=OMJRcjnMMok\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fimg.youtube.com\u002Fvi\u002FOMJRcjnMMok\u002Fmaxresdefault.jpg\" \nalt=\"Image annotations using COCO Annotator\" width=\"600\" \u002F>\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp align=\"center\">\u003Ci>Checkout the video for a basic guide on installing and using COCO Annotator.\u003C\u002Fi>\u003C\u002Fp>\n\n\u003Cbr \u002F>\n\n\u003Cp align=\"center\">\u003Cimg width=\"600\" src=\"https:\u002F\u002Fi.imgur.com\u002Fm4RmjCp.gif\">\u003C\u002Fp>\n\u003Cp align=\"center\">\u003Ci>Note: This video is from v0.1.0 and many new features have been added.\u003C\u002Fi>\u003C\u002Fp>\n\n\n\u003Cbr>\n\n\u003Cp align=\"center\">If you enjoy my work please consider supporting me\u003C\u002Fp>\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.patreon.com\u002Fjsbroks\">\n    \u003Cimg src=\"https:\u002F\u002Fc5.patreon.com\u002Fexternal\u002Flogo\u002Fbecome_a_patron_button@2x.png\" width=\"120\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\u003Cbr>\n\n# Features\n\nSeveral annotation tools are currently available, with most applications as a desktop installation. Once installed, users can manually define regions in an image and creating a textual description. Generally, objects can be marked by a bounding box, either directly, through a masking tool, or by marking points to define the containing area. _COCO Annotator_ allows users to annotate images using free-form curves or polygons and provides many additional features were other annotations tool fall short.\n\n- Directly export to COCO format\n- Segmentation of objects\n- Ability to add key points\n- Useful API endpoints to analyze data\n- Import datasets already annotated in COCO format\n- Annotate disconnect objects as a single instance\n- Labeling image segments with any number of labels simultaneously\n- Allow custom metadata for each instance or object\n- Advanced selection tools such as, [DEXTR](https:\u002F\u002Fgithub.com\u002Fjsbroks\u002Fdextr-keras), [MaskRCNN](https:\u002F\u002Fgithub.com\u002Fmatterport\u002FMask_RCNN) and Magic Wand\n- Annotate images with semi-trained models\n- Generate datasets using google images\n- User authentication system\n\nFor examples and more information check out the [wiki](https:\u002F\u002Fgithub.com\u002Fjsbroks\u002Fcoco-annotator\u002Fwiki).\n\n# Demo\n\n| Login Information      |\n| ---------------------- |\n| **Username:** admin    |\n| **Password:** password |\n\nhttps:\u002F\u002Fannotator.justinbrooks.ca\u002F\n\n# Backers\n\nIf you enjoy the development of coco-annotator or are looking for an enterprise annotation tool, consider checking out DataTorch.\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fdatatorch.io\">\n    \u003Cimg src=\"https:\u002F\u002Fi.imgur.com\u002FsOQ1s5F.png\" width=\"250\" \u002F>\n  \u003C\u002Fa>\n  \u003Cp align=\"center\">\n    https:\u002F\u002Fdatatorch.io · \u003Ca href=\"mailto:support@datatorch.io\">support@datatorch.io\u003C\u002Fa> · \u003Ci>Next generation of coco-annotator\u003C\u002Fi>\n   \u003C\u002Fp>\n\u003C\u002Fp>\n\n# Built With\n\nThanks to all these wonderful libaries\u002Fframeworks:\n\n### Backend\n\n- [Flask](http:\u002F\u002Fflask.pocoo.org\u002F) - Python web microframework\n- [MongoDB](https:\u002F\u002Fwww.mongodb.com\u002F) - Cross-platform document-oriented database\n- [MongoEngine](http:\u002F\u002Fmongoengine.org\u002F) - Python object data mapper for MongoDB\n\n### Frontend\n\n- [Vue](https:\u002F\u002Fvuejs.org\u002F) - JavaScript framework for building user interfaces\n- [Axios](https:\u002F\u002Fgithub.com\u002Faxios\u002Faxios) - Promise based HTTP client\n- [PaperJS](http:\u002F\u002Fpaperjs.org\u002F) - HTML canvas vector graphics library\n- [Bootstrap](https:\u002F\u002Fgetbootstrap.com\u002F) - Frontend component library\n\n# License\n\n[MIT](https:\u002F\u002Ftldrlegal.com\u002Flicense\u002Fmit-license)\n\n# Citation\n\n```\n  @MISC{cocoannotator,\n    author = {Justin Brooks},\n    title = {{COCO Annotator}},\n    howpublished = \"\\url{https:\u002F\u002Fgithub.com\u002Fjsbroks\u002Fcoco-annotator\u002F}\",\n    year = {2019},\n  }\n```\n","COCO Annotator 是一个基于Web的图像标注工具，专为图像定位和目标检测创建训练数据而设计。其核心功能包括图像分割标注、对象实例跟踪以及对具有不连续可见部分的对象进行标注，并支持以广泛使用的COCO格式高效存储和导出标注结果。该工具提供了一个直观且可自定义的用户界面，内置多种工具以帮助生成精确的数据集。适用于需要高质量图像标注数据的计算机视觉项目，如深度学习模型训练前的数据准备阶段。",2,"2026-06-11 03:19:47","top_language"]