The Bio-Image Indexing and Graphical Labelling Environment (BIIGLE) is a web service for the efficient and rapid annotation of still images and videos. Read the paper or take a look at the manual.
BIIGLE is available at biigle.de.
This is the production setup of a BIIGLE instance. You can fork this repository to customize your own production instance.
Head over to the admin documentation for installation instructions and more.
If you have a question or request that is not a bug report or feature request, please start a new discussion about it.
Contributions to BIIGLE are always welcome. Check out biigle/core to get started.
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master: The default setup of a BIIGLE instance. -
gpu: A setup of BIIGLE with GPU computing resources (see the docs). -
arm32v6: A setup for ARM 32bit platforms (e.g. Raspberry Pi). -
arm64v8: A setup for ARM 64bit platforms.
Reference publications that you should cite if you use BIIGLE for one of your studies.
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BIIGLE 2.0 Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). Biigle 2.0-browsing and annotating large marine image collections. Frontiers in Marine Science, 4, 83. doi:
10.3389/fmars.2017.00083 -
Observations From Four Years of BIIGLE 2.0 Zurowietz, M., & Nattkemper, T. W. (2021). Current Trends and Future Directions of Large Scale Image and Video Annotation: Observations From Four Years of BIIGLE 2.0. Frontiers in Marine Science, 8, 760036. doi:
10.3389/fmars.2021.760036 -
Laser Point Detection Schoening, T., Kuhn, T., Bergmann, M., & Nattkemper, T. W. (2015). DELPHI—fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections. Frontiers in Marine Science, 2, 20. doi:
10.3389/fmars.2015.00020 -
MAIA Zurowietz, M., Langenkämper, D., Hosking, B., Ruhl, H. A., & Nattkemper, T. W. (2018). MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration. PloS one, 13(11), e0207498. doi:
10.1371/journal.pone.0207498 -
UnKnoT Zurowietz, M., & Nattkemper, T. W. (2020). Unsupervised Knowledge Transfer for Object Detection in Marine Environmental Monitoring and Exploration. IEEE Access, 8, 143558-143568. doi:
10.1109/ACCESS.2020.3014441 -
Video Object Tracking Lukezic, A., Vojir, T., ˇCehovin Zajc, L., Matas, J., & Kristan, M. (2017). Discriminative correlation filter with channel and spatial reliability. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6309-6318). doi:
10.1109/CVPR.2017.515
If you discover a security vulnerability within BIIGLE, please send an email to the BIIGLE team via info@biigle.de. All security vulnerabilities will be promptly addressed.