Hakuna Ma-data: Identify Wildlife on the Serengeti with 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! Competition hosted by Microsoft AI for Earth. #climate
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
This was a brand new kind of DrivenData competition! Using novel infrastructure to execute code submissions in the cloud and evaluate them on a large holdout set never provided to participants, this challenge pushed the boundary of innovation and generalization for wildlife conservation.
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.
There are two immediate challenges where efforts like this competition are needed. 1) Camera traps can't automatically label the animals they observe, creating an immense burden on humans to determine where and what wildlife are present. 2) 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.
Microsoft and DrivenData put together a machine learning competition to build the best computer vision models for tagging species from a new trove of camera trap imagery. To power more accurate and generalizable models, the challenge featured more data from the Snapshot Serengeti project and a more realistic holdout set for training and testing models. Then, to move the solutions one step closer to applied impact, participants had to package everything needed to do inference and submit for containerized execution on Azure.
The winning algorithm beat out more than 500 other containerized entries. On the full test set, this model identified blank images with 97% accuracy (blanks represents ~70% of all images), and predicted the correct species in non-blank image sequences with 86% accuracy (i.e. the species with the highest predicted probability was indeed the one in the image).
This and all the other prize-wining solutions were shared openly for continued learning and development. For more check out the links below!
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
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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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OPEN-SOURCE CODE. A Submission will be ineligible to win a prize if it was developed using code containing or depending on software licensed under an open source license:
- other than an Open Source Initiative-approved license (see http://opensource.org/licenses); or
- an open source license that prohibits commercial use.
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