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https://github.com/datagym-ai/datagym-core

Open source annotation and labeling tool for image and video assets
https://github.com/datagym-ai/datagym-core

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

Last synced: 26 days ago
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Open source annotation and labeling tool for image and video assets

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README 

        
# 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.



![DataGym.ai Workspace](doc/images/datagym-workspace.jpg)



## :ledger: Ressources

- Website: 

- 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

  - 

  - `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:

```sh

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](CONTRIBUTING.md) for all relevant details.



## :scroll:	License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details