{"id":13770976,"url":"https://github.com/datagym-ai/datagym-core","last_synced_at":"2026-01-17T09:52:14.886Z","repository":{"id":161099945,"uuid":"438581048","full_name":"datagym-ai/datagym-core","owner":"datagym-ai","description":"Open source annotation and labeling tool for image and video assets","archived":false,"fork":false,"pushed_at":"2023-07-21T11:19:14.000Z","size":2719,"stargazers_count":46,"open_issues_count":5,"forks_count":22,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-11-17T06:40:31.053Z","etag":null,"topics":["annotation","annotations","bounding-box","computer-vision","data-labeling","dataset","image-annotation","image-labeling","image-labeling-tool","label-images","label-videos","labeling","labeling-tool","semantic-segmentation","video-annotation","video-labeling"],"latest_commit_sha":null,"homepage":"https://www.datagym.ai","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/datagym-ai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-12-15T10:05:12.000Z","updated_at":"2024-11-02T20:38:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"32db25c1-1038-4971-80bc-64b111a68a02","html_url":"https://github.com/datagym-ai/datagym-core","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datagym-ai%2Fdatagym-core","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datagym-ai%2Fdatagym-core/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datagym-ai%2Fdatagym-core/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datagym-ai%2Fdatagym-core/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datagym-ai","download_url":"https://codeload.github.com/datagym-ai/datagym-core/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253514352,"owners_count":21920327,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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","annotations","bounding-box","computer-vision","data-labeling","dataset","image-annotation","image-labeling","image-labeling-tool","label-images","label-videos","labeling","labeling-tool","semantic-segmentation","video-annotation","video-labeling"],"created_at":"2024-08-03T17:00:45.785Z","updated_at":"2026-01-17T09:52:14.833Z","avatar_url":"https://github.com/datagym-ai.png","language":"Java","funding_links":[],"categories":["Image / video","Labeling Tools","人工智能"],"sub_categories":["Open source","Images"],"readme":"# DataGym.ai\n\nDataGym.ai is a modern, web based workbench to label images and videos.\nIt allows you to manage your projects and datasets, label data, control quality and build your own training data pipeline.\nWith DataGym.ai´s API and Python SDK you can integrate it into your toolchain.\n\n![DataGym.ai Workspace](doc/images/datagym-workspace.jpg)\n\n## :ledger: Ressources\n- Website: \u003chttps://www.datagym.ai/\u003e\n- Documentation: \u003chttps://docs.datagym.ai/documentation/\u003e\n\n## :jigsaw: Features\n\n- Organize your data into different projects with tasks\n  - Dashboard with useful statistics / overview\n  - Tasks lifecycle with states (backlog, waiting, in progress, completed, skipped, reviewed)\n  - Pagination, Filtering and Search\n  - Integrated quality control / review process\n- Organize your media within datasets\n  - Different storage types (direct upload, public url´s, aws s3 cloud storage)\n  - Supported mime types: jpeg, png, mp4\n  - Support of large high resolution images\n- Labeling features\n  - **Global classifications (image wide)**\n  - **Image annotation**\n    - Variety of geometries: point, line, bounding box, polygons\n    - Different classification types: text, checklists, option-box\n    - Supports nested geometries (child-geometries)\n  - **Video annotation: Specialized editor for video labeling**\n    - Frame-by-frame navigation\n    - Linear interpolation to track objects\n    - Adjustable playback-speed\n    - Analyze and extract video metadata (codec, framerate, duration, ...)\n  - **Image segmentation**\n    - Bitmap export\n- Feature-rich Workspace\n  - Temporary screen manipulations: contrast, brightness, saturation\n  - Hide unused geometry-groups for more clarity\n  - Shortcut support\n  - Panning and zooming, multi-select, moving, duplication\n  - Supports transformation of the same geometry type\n  - Context menu for geometries\n- Powerful **REST API** to build your own workflows\n  - Python SDK Package\n- Data exporting- and importing (json)\n  - Export your labeled data as json (works for images and videos)\n  - Import your labeled data to refine your ml model\n  - Export-/import your label configuration and use it in multiple projects\n\n\n## :dart:\tQuickstart\n\n### Running with docker-compose\nThe simplest way to run DataGym.ai locally is by using docker-compose.\n\n1. Download the `docker-compose.yml` from the projects root-directory\n  - \u003chttps://raw.githubusercontent.com/datagym-ai/datagym-core/master/docker-compose.yml\u003e\n  - `wget https://raw.githubusercontent.com/datagym-ai/datagym-core/master/docker-compose.yml`\n2. Launch container using `docker-compose up -d`\n3. Wait until the initialization is done\n4. Navigate to `localhost:8080`\n\n\n### Local development, build manually\nBuild the whole project:\n```sh\nmvn clean install \n```\n\n\n## :ballot_box:\tBuild with\n\n- Java / Spring Boot\n- Angular\n\n## :open_hands: Contributing\nWe would love to receive contributions - please review our [Contributing Guide](CONTRIBUTING.md) for all relevant details.\n\n## :scroll:\tLicense\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatagym-ai%2Fdatagym-core","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatagym-ai%2Fdatagym-core","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatagym-ai%2Fdatagym-core/lists"}