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

LOST offers a set of out of the box annotation pipelines to instantly annotate images without programming knowledge.

Nevertheless 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.

The application is highly scalable and offers, for example, easy-to-set-up connectivity to external file systems, such as S3 Bucket or Azure Blobstorage via the user interface.

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.

• 🌎 Collaborative annotation - distribute your annotation tasks around the world
• 🚀 Out of the box annotation pipelines
  • Annotate bboxes, polygons, points or lines with the Single Image Annotation Tool (SIA)
  • Annotate whole image clusters with the Multi Image Annotation Tool (MIA)
  • Export your datasets
• 📂 Connect external file systems, such as AWS S3 bucket, MS Azure blobstorage or FTP server
• 📥 Instant annotation export allows you to access all annotations at any time
• 📈 Personal and project based annotation statistics
• 🏷️ Organize your labels with colored label trees
• 🔁 Review your annotations

• 💊 Customized annotation pipelines
  • Import and export your pipeline projects
  • Share your pipeline projects with colleagues
• 📙 Jupyter-Lab integration for easy pipeline development
• 👯 LDAP integration
• 📧 E-Mail notifications
• ☁️ Scalable design - distribute intensive computing processes across multiple machines

LOST 2 was just recently released. A lot of new features have been added and improvements have been made compared to version 1 (see Changelog). The adaptation of the documentation is currently still in progress.

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

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. Install the cryptography package in your python environment:

   pip install cryptography
   

5. Run quick_setup script:

   cd lost/docker/quick_setup/
   python3 quick_setup.py /path/to/install/lost --release 2.0.0-alpha.26
   

   If you want to use phpmyadmin, you can set it via argument

   python3 quick_setup.py /path/to/install/lost --release 2.0.0-alpha.26 --phpmyadmin
   

6. Run LOST:

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

See our Roadmap

@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

• 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.

L3bm GmbH	CVG University Jena	Hochschule Fulda