Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth Hosted By Microsoft AI for Earth
Can you predict which animals are present in camera trap images? Leverage millions of images of animals on the Serengeti to build a classifier that distinguishes between gazelles, lions, and more!
No. of Entries
ValAn_picekl1st Place Team
Leverage millions of images of animals on the Serengeti to build a classifier that distinguishes between gazelles, lions, and more!
In this competition, participants will predict the presence and species of wildlife in new camera trap data from the Snapshot Serengeti project, which boasts over 6 million images.
Camera traps are motion-triggered systems for passively collecting animal behavior data with minimal disturbance to their natural tendencies. Camera traps are an invaluable tool in conservation research, but the sheer amount of data they generate presents a huge barrier to using them effectively. This is where AI can help!
There are two immediate challenges where efforts like this competition are needed:
- Camera traps can't automatically label the animals they observe, creating an immense (and sometimes prohibitive) burden on humans to determine where and what wildlife are present.
- Even when automated animal tagging models are available, the models that do exist don't generalize well across time and locations, severely limiting their usefulness with new data.
To address these opportunities, we're challenging data scientists, researchers, and developers from around the world to build the best algorithms for wildlife detection.
The competition is designed with a few objectives in mind:
- Innovation: Participants use state-of-the-art approaches in computer vision and AI and get live feedback on how well their solutions perform
- Generalization: This competition is designed to reward the best generalizable solutions. The private test data used to determine the winners will come entirely from the latest, unreleased season of data from the Snapshot Serengeti project (season 11). For more information on the competition timeline and evaluation, see the problem description.
- Execution: Models are trained locally and submitted to execute inference in the cloud - read on!
- Openness: All prize-winning models are released under an open source license for anyone to use and learn from
This is a brand new kind of DrivenData challenge! Previous models trained for camera trap images have often failed to generalize well. In this competition, we want to reward the models that generalize best to new images, so you won’t interact directly with the test set. Rather than submitting your predicted labels for a test set you have, you'll package up everything needed to do inference and send it to us. We'll execute that code on Azure in a Docker container that has access to the test set images. By leveraging Microsoft Azure's cloud computing platform and Docker containers, we're moving our competition infrastructure one step closer to translating participants’ innovation into impact.
We can't wait to run what you come up with!
Competition End Date:
Jan. 22, 2020, midnight UTC
Microsoft employees are not eligible for prizes.
Prize generously supplied by our friends at Microsoft AI for Earth.
Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
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Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Public dissemination of entries
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Open licensing of winners
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the provided Winning Model Documentation Template in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
External data is not allowed unless otherwise noted explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
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Selection of winners
This Competition is a challenge of skill and the final results are determined by evaluating a combination of quantitative and qualitative factors, as more fully described on the Competition Website. For that portion of the Competition evaluated quantitatively, the results will be determined solely by leaderboard ranking on the private leaderboard based on an objective statistical evaluation metric published on the Competition Website prior to commencement of the Competition (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the objective statistical evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data. For that portion of the Competition evaluated qualitatively, the results will be determined by a panel of judges, as more fully described on the Competition Website. As more fully described on the Competition Website, the Competition may have a quantitative winner, a qualitative winner, a combined quantitative and qualitative winner, or all three.
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Data use and code sharing
DATA. "Data" means the Data or Datasets linked from the Competition Website for the purpose of use by Participants in the Competition. For the avoidance of doubt, Data is deemed for the purpose of these Competition Rules to include any prototype or executable code provided to Participants by DrivenData or via the Competition Website. Participants must use the Data only as permitted by these Competition Rules and any associated data use rules specified on the Competition Website.
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