To participate, you have to agree to the following rules:
- Submission is in the form of docker containers, as we can’t allow access to the test images during the competition. You will be given a template docker container to use for this.
- Participants are requested to publish a (brief) description of their method and results on ArXiv (or similar pre-print platforms) that is linked to their final submission. There is no page limit for that description, but it has to include a conclusive description of the approach of the participating team.
- Training data is licensed as CC BY, i.e. everyone (also non-participants of the challenge) are free to use the training data set in their respective work, given attribution in the publication.
- For participation in the challenge, data is restricted to the provided training dataset. However, data augmentation on this set is allowed.
- Algorithms must be made publicly available as e.g. GitHub repository with a permissive open source license (Apache Licence 2.0, MIT Licence, GNU GPLv3, GNU AGPLv3, Mozilla Public Licence 2.0, Boost Software Licence 1.0, The Unlicence)
- Researchers belonging to the institutes of the organizers are not allowed to participate to avoid potential conflict of interest.
- The provided baselines will hold their respective positions, but without eligibility for prize money.
- Resubmissions of the provided baseline models are not eligible for prizes.
- Participants may publish papers including their official performance on the challenge data set, given proper reference of the challenge and the dataset. There is no embargo time in that regard. Please use the following references:
- We aim to publish a summary of the challenge in a peer-reviewed journal (further information). The first and last author of the submitted arxiv paper will qualify as authors in the summary paper. Participating teams are free to publish their own results in a separate publication after coordination (for selected submissions of summary paper) to avoid siginficant overlap with the summary paper.