Researchers at the California Institute of Technology (CalTech) and Cornell University's Cornell Tech engineering campus have created the iNaturalist Challenge, a competition to produce the best machine-learning algorithm for identifying the world's plant and animal species.
From April to July, anyone who wanted to compete in the challenge was given access to a database of 650,000 images featuring more than 5,000 species in categories that included, among others, protozoans, fungi, plants, arachnids, and mammals. Participants used that database to develop algorithms for automatic species identification. When the competition closed in July, the organizers had received 32 entries from teams and individuals.
The winning algorithm was able to correctly identify species in a test database of 100,000 photos 80% of the time when given one guess for each photo, says CalTech researcher Grant Van Horn. He notes when the algorithm was given five guesses for every photo, the accuracy increased to 95%.
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