datagym-ai / datagym-core

Open source annotation and labeling tool for image and video assets

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.

📒 Ressources

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

🧩 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

🎯 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

• https://raw.githubusercontent.com/datagym-ai/datagym-core/master/docker-compose.yml
• wget https://raw.githubusercontent.com/datagym-ai/datagym-core/master/docker-compose.yml

2. Launch container using docker-compose up -d
3. Wait until the initialization is done
4. Navigate to localhost:8080

Local development, build manually

Build the whole project:

mvn clean install 

🗳️ Build with

• Java / Spring Boot
• Angular

👐 Contributing

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

📜 License

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