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LOST - Label Objects and Save Time

LOST Features

Demo Videos


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


If you feel LOST, please find our full documentation here:

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:

  2. Install docker-compose:

  3. Clone LOST:

    # Clone 1.x since it contains the current stable version
    git clone -b 1.x
  4. Run quick_setup script:

    cd lost/docker/quick_setup/
    # python3 path/to/install/lost
    python3 ~/lost
  5. Run LOST:

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

Citing LOST

    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},
    Journal = {arXiv preprint arXiv:1910.07486},

Find our paper on arXiv

Projects using LOST

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


See our Roadmap



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


L3bm GmbH CVG University Jena Hochschule Fulda
L3bm GmbH CVG Uni Jena Hochschule Fulda