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SnakeCLEF 2020

SnakeCLEF

Motivation

Developing a robust system for identifying species of snakes from photographs is an important goal in biodiversity and global health. With over half a million victims of death and disability from venomous snakebite annually, understanding the global distribution of the >3700 species of snakes and differentiating species from images (particularly images of low quality) will significantly improve epidemiology data and treatment outcomes. The goals and usage of image-based snake identification are complementary with those of other challenges: classifying snake species in images, predicting the list of species that are the most likely to be observed at a given location, and eventually developing automated tools that can facilitate integration of changing taxonomies and new discoveries.

Data collection

Images of about 100 snake species from all around the world (between 300 and 150,000 images per species) will be aggregated from different data sources (including the iNaturalist platform). This will extend the dataset used in a previous Snake challenge \footnote{\url{}} hosted on the AICrowd platform. The distribution of the number of images between the classes is highly imbalanced.

Task description

Given the set of images and corresponding geographic locality information, the goal of the task will be to return for each image a ranked list of species sorted according to the likelihood that they are in the image and might have been observed at that location.

How to participate ?

to be completed soon

Reward

The winner of each of the four LifeCLEF 2020 challenges will be offered a cloud credit grants of 5k USD as part of Microsoft's AI for earth program.
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