ainnotate / lost

Label Objects and Save Time (LOST) - Design your own smart Image Annotation process in a web-based environment.

LOST - Label Objects and Save Time

Demo Videos

• LOST Trailer: https://youtu.be/alocNFQmVQw
• LOST Single Image Annotation - Pipeline Demo: https://www.youtube.com/watch?v=DfDla2hAfWw
• LOST Multi Image Annotation - Pipeline Demo: https://www.youtube.com/watch?v=Q-IpGtH6Xz0

Description

LOST (Label Object and Save Time) is a flexible web-based framework for semi-automatic image annotation. It provides multiple annotation interfaces for fast image annotation.

LOST is flexible since it allows to run user defined annotation pipelines where different annotation interfaces/ tools and algorithms can be combined in one process.

It is web-based since the whole annotation process is visualized in your browser. You can quickly setup LOST with docker on your local machine or run it on a web server to make an annotation process available to your annotators around the world. LOST allows to organize label trees, to monitor the state of an annotation process and to do annotations inside the browser.

LOST was especially designed to model semi-automatic annotation pipelines to speed up the annotation process. Such a semi-automatic can be achieved by using AI generated annotation proposals that are presented to an annotator inside the annotation tool.

Getting Started

Documentation

If you feel LOST, please find our full documentation here: https://lost.readthedocs.io.

LOST QuickSetup

LOST releases are hosted on DockerHub and shipped in Containers. For a quick setup perform the following steps (these steps have been tested for Ubuntu):

1. Install docker on your machine or server: https://docs.docker.com/install/

2. Install docker-compose: https://docs.docker.com/compose/install/

3. Clone LOST:

   # Clone 1.x since it contains the current stable version
   git clone -b 1.x https://github.com/l3p-cv/lost.git
   

4. Run quick_setup script:

   cd lost/docker/quick_setup/
   # python3 quick_setup.py path/to/install/lost
   python3 quick_setup.py ~/lost
   

5. Run LOST:

   Follow instructions of the quick_setup script, printed in the command line.

!!! Experimental !!! LOST 2.x QuickSetup - use it wisely !

Lost 2.x is still in development. In the backend most of the work packages are finished, currently we are finalizing the version and expect a first official release soon. You are welcome to test the current version anyway:

1. Install docker on your machine or server: https://docs.docker.com/install/

2. Install docker-compose: https://docs.docker.com/compose/install/

3. Clone LOST:

   git clone https://github.com/l3p-cv/lost.git
   

4. Run quick_setup script:

   cd lost/docker/quick_setup/
   python3 quick_setup.py /path/to/install/lost --release 2.0.0-alpha.2
   

5. Run LOST:

   Follow instructions of the quick_setup script, printed in the command line.

Citing LOST

@article{jaeger2019lost,
    title={{LOST}: A flexible framework for semi-automatic image annotation},
    author={Jonas J\"ager and Gereon Reus and Joachim Denzler and Viviane Wolff and Klaus Fricke-Neuderth},
    year={2019},
    Journal = {arXiv preprint arXiv:1910.07486},
    eprint={1910.07486},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Find our paper on arXiv

Projects using LOST

• PlantVillage @ Pennsylvania State University is using LOST to build a tool that is positively impacting millions of lives in Kenya with the Desert Locust Crisis. See this blog article by Annalyse Kehs to get more information how LOST is utilized in the project.

If you are using LOST and like to share your project, please contact @jaeger-j.

Roadmap

See our Roadmap

Creators

People

	Github
Jonas Jäger	@jaeger-j
Gereon Reus	@gereonreus
Dennis Weiershäuser	@cartok
Tobias Kwant	@tkwant

Institutions

L3bm GmbH	CVG University Jena	Hochschule Fulda