- Pre-trained models are allowed in the competition (see Guidelines below).
- Acquiring additional image or video data for the species in this competition is not allowed, either is using older versions of the dataset.
- Participants are allowed to collect additional annotations (e.g. bounding boxes, keypoints) on the provided training and validation sets. Teams should specify that they collected additional annotations when submitting final results.
- This competition does not count towards Kaggle ranking points or medals.
- We ask that you respect the spirit of the competition and do not cheat. Hand-labeling is forbidden.
One account per participant
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
No private sharing outside teams
Privately sharing code or data outside of teams is not permitted.
       It's okay to share code if made available to all participants on the forums.
Guidelines
The general rule is that participants should only use the provided training and validation images to train a model to classify the test images. We do not want participants crawling the web in search of additional data. Pretrained models may be used to construct the algorithms (e.g. ImageNet pretrained models). Please specify any and all external data used for training when uploading results.
Terms of Use
By downloading this dataset you agree to the following terms:
You will use the data only for non-commercial research and educational purposes.
You will NOT distribute the above images.
The Danish Svampe Atlas makes no representations or warranties regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose.
You accept full responsibility for your use of the data and shall defend and indemnify the Danish Svampe Atlas, including its employees, officers and agents, against any and all claims arising from your use of the data, including but not limited to your use of any copies of copyrighted images that you may create from the data.
Team Mergers
Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
Team Limits
There is no maximum team size.
Submission Limits
You may submit a maximum of 5 entries per day.
You may select up to 2 final submissions for judging.
Competition Timeline
Merger Deadline:
June 4, 2018
Entry Deadline:
June 4, 2018
End Date:
June 11, 2018 11:59 PM UTC
COMPETITION-SPECIFIC TERMS
Competition Sponsors
Competition Name (the 'Competition'): FGVCx Fungi Classification Challenge at FGVC5

Competition Sponsors: FGVC5 Workshop and Svampe Atlas

Competition Acknowledgements: Brigit Schroeder, Yin Cui

Competition Website: https://www.kaggle.com/c/fungi-challenge-fgvc-2018 and https://sites.google.com/corp/view/fgvc5/home

Competitions are open to residents of the United States and worldwide, except if you are a resident of Crimea, Cuba, Iran, Syria, North Korea, Sudan, or are subject to U.S. export controls or sanctions, then you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions.
ENTRY IN THIS COMPETITION CONSTITUTES YOUR ACCEPTANCE OF THESE OFFICIAL COMPETITION RULES.
The Competition named above is a skills-based competition to promote and further the field of data science. You must register via the Competition Website to enter. Your competition submissions (“Submissions”) must conform to the requirements set forth on the Competition Website. Your Submissions will be scored based on the evaluation metric described on the Competition Website. Subject to compliance with the Competition Rules, Prizes described on the Competition Website, if any, will be awarded to participants with the best scores, based on the merits of the data science models submitted.