Skip to content
Label Objects and Save Time (LOST) - Design your own smart Image Annotation process in a web-based environment.
Python JavaScript HTML Other
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github/ISSUE_TEMPLATE Update issue templates Jan 23, 2019
backend/lost Adjusted lost version in init May 15, 2020
docker Merge branch '1.0' into 1.1 May 8, 2020
docs Adjusted quick_setup instructions for gpu-worker in docker compose May 15, 2020
frontend/lost Fixed sia canvas no possible labels bug Apr 6, 2020
.gitignore ignore settings.js Jul 31, 2019
.gitlab-ci.yml adapted docker in docker version to new nodejs version Feb 27, 2020
CHANGELOG.md Adjusted release date in Changelog May 15, 2020
LICENSE cleanup Oct 4, 2019
PULL_REQUEST_TEMPLATE.md Create PULL_REQUEST_TEMPLATE.md Jan 28, 2019
README.md Added link to paper in readme Oct 17, 2019
package-lock.json import sia from npm Feb 27, 2020

README.md

pipeline status Documentation Status Gitter

LOST - Label Objects and Save Time

LOST Features

Demo Videos

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:

    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
    python3 quick_setup.py ~/lost
    
  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

Contact

We've created a chat room for you. Feel free to contact us directly: https://gitter.im/l3p-cv/lost

Creators

People

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

Institutions

L3P UG CVG University Jena Hochschule Fulda
L3P UG CVG Uni Jena Hochschule Fulda
You can’t perform that action at this time.