DataGym.ai

DataGym.ai is a modern, web based workbench to label images and videos. It allows you to manage your projects and datasets, label data, control quality and build your own training data pipeline. With DataGym.ai´s API and Python SDK you can integrate it into your toolchain.

:ledger: Ressources

• Website: https://www.datagym.ai/
• Documentation: https://docs.datagym.ai/documentation/

:jigsaw: Features

• Organize your data into different projects with tasks
• Dashboard with useful statistics / overview
• Tasks lifecycle with states (backlog, waiting, in progress, completed, skipped, reviewed)
• Pagination, Filtering and Search
• Integrated quality control / review process
• Organize your media within datasets
• Different storage types (direct upload, public url´s, aws s3 cloud storage)
• Supported mime types: jpeg, png, mp4
• Support of large high resolution images
• Labeling features
• Global classifications (image wide)
• Image annotation
  • Variety of geometries: point, line, bounding box, polygons
  • Different classification types: text, checklists, option-box
  • Supports nested geometries (child-geometries)
• Video annotation: Specialized editor for video labeling
  • Frame-by-frame navigation
  • Linear interpolation to track objects
  • Adjustable playback-speed
  • Analyze and extract video metadata (codec, framerate, duration, ...)
• Image segmentation
  • Bitmap export
• Feature-rich Workspace
• Temporary screen manipulations: contrast, brightness, saturation
• Hide unused geometry-groups for more clarity
• Shortcut support
• Panning and zooming, multi-select, moving, duplication
• Supports transformation of the same geometry type
• Context menu for geometries
• Powerful REST API to build your own workflows
• Python SDK Package
• Data exporting- and importing (json)
• Export your labeled data as json (works for images and videos)
• Import your labeled data to refine your ml model
• Export-/import your label configuration and use it in multiple projects

:dart: Quickstart

Running with docker-compose

The simplest way to run DataGym.ai locally is by using docker-compose.

1. Download the docker-compose.yml from the projects root-directory
2. https://raw.githubusercontent.com/datagym-ai/datagym-core/master/docker-compose.yml
3. wget https://raw.githubusercontent.com/datagym-ai/datagym-core/master/docker-compose.yml
4. Launch container using docker-compose up -d
5. Wait until the initialization is done
6. Navigate to localhost:8080

Local development, build manually

Build the whole project:

mvn clean install 

:ballot_box: Build with

• Java / Spring Boot
• Angular

:open_hands: Contributing

We would love to receive contributions - please review our Contributing Guide for all relevant details.

:scroll: License

This project is licensed under the MIT License - see the LICENSE file for details