Start Date: February 29 End Date:  24 May Competition URL: HuggingFace

Motivation

Creating a robust system to identify snake species from photos is crucial for biodiversity and global health, given the significant impact of venomous snakebites. With over half a million annual deaths and disabilities, understanding the global distribution of 4,000+ snake species through image differentiation enhances epidemiology and treatment outcomes. Despite machines showing accuracy in predictions, especially with long-tailed distributions and 1800 species, challenges persist in neglected regions. The next step involves testing in specific tropical and subtropical countries while considering species' medical importance for more reliable machine predictions.

Snake species identification, challenging for both humans and machines, is hindered by high intra-class and low inter-class variance, influenced by factors like location, color, sex, and age. Visual similarities and mimicry further complicate identification. Incomplete knowledge of species distribution by country and images originating from limited locations adds complexity. Many snake species resemble those from different continents, emphasizing the importance of knowing the geographic origin for accurate identification. Regularization across all countries is vital, considering that no location hosts more than 126 of the 4,000 snake species.

Task Description

The SnakeCLEF challenge aims to be a major benchmark for observation-based snake species identification. The goal of the task is to create a classification model that returns a ranked list of predicted species for each set of images and location (i.e., snake observation) and minimize the danger to human life and the waste of antivenom if a bite from the snake in the image were treated as coming from the top-ranked prediction.

The classification model must fit limits for memory footprint and a prediction time limit.