Loading
Round 1: Completed Round 2: Completed Round 3: Completed Round 4: 32 days left

Snake Species Identification Challenge

Classify images of snake species from around the world

1 Travel Grants
1 Authorship/Co-Authorship

 

Note: Are you a LifeCLEF 2020 Participant? If so, please read specific details for your participation to this challenge in the section "SnakeCLEF 2020" at the end of this page. If you are not a LifeCLEF2020 Participant, do not worry about this, just read the rest of the information and enjoy the challenge! 

 

Starter Kit : https://github.com/AIcrowd/snake-species-identification-challenge-starter-kit

Snakebite is the most deadly neglected tropical disease (NTD), being responsible for a dramatic humanitarian crisis in global health

Snakebite causes over 100,000 human deaths and 400,000 victims of disability and disfigurement globally every year. It affects poor and rural communities in developing countries, which host the highest venomous snake diversity and the highest burden of snakebite due to limited medical expertise and access to antivenoms

Antivenoms can be life‐saving when correctly administered but this often depends on the correct taxonomic identification (i.e. family, genus, species) of the biting snake. Snake identification is also important for improving our understanding of snake diversity and distribution in a given area (i.e. snake ecology) as well as the impact of different snakes on human populations (e.g. snakebite epidemiology). But snake identification is challenging due to:

  • their high diversity
  • the incomplete or misleading information provided by snakebite victims
  • the lack of knowledge or resources in herpetology that healthcare professionals have

In this challenge we want to explore how Machine Learning can help with snake identification, in order to potentially reduce erroneous and delayed healthcare actions and improve snakebite eco-epidemiological data.

image4_challenge_dataset.png

Species richness of reptiles worldwide

Task

In this challenge you will be provided with a dataset of RGB images of snakes, and their corresponding species (class) and geographic location (continent, country). The goal is to train a classification model.

The difficulty of the challenge relies on the dataset characteristics, as there might be a high intraclass variance for certain classes and a low interclass variance among others, as shown in the examples from the Datasets section. Also, the distribution of images between class is not equal for all classes: the class with the most images has 14,369 while the class with the fewest images has 17.

In round 3, you had 85 classes and 187,720 images. For round 4, we would like to make the barrier to entry much lower and demonstrate that the approach works well on 783 classes and 245,185 images. The idea is to renew the challenge every 4 months in order to get closer to our final goal, which is to build an algorithm which best predicts which antivenon should be given (if any) when given a specific image.

image5_challenge_dataset.png

Number of images per species in the dataset

Datasets

Snakes are extremely diverse, and snake biologists continue to document & describe snake diversity, with an average of 30 new species described per year since the year 2000. Although most people probably think of snakes as a single “kind” of animal, humans are as evolutionarily close to whales as pythons are to rattlesnakes, so snakes in fact are very diverse! Taxonomically speaking, snakes are classified into 24 families, containing 528 genera and 3,709 species.

image1_challenge_dataset.png

You can download the datasets in the Datasets Section. You are provided with a Train.tar.gz, file composed of 245,185 RGB images of varying size, split into 783 species.

Several aspects of snake morphology make this challenge more challenging:

Some species have patterns that vary depending on their age

Some species have patterns that vary depending on their location

Two species might look very similar, with one being venomous and the other not

The first iteration of the data set contains few such species, but we will add in more later.

picture2_challenge_datasets.png
picture3_challenge_datasets.png

Prizes

Note: To be eligible for these prizes, participants to the challenge will have to release their code under an open-source license of their choice

Timeline

This is the very first benchmarking challenge, meaning that it has no end date, but it will be updated every 3-4 months. Here is our next deadline:

July 6, 2020
Round 4 submission deadline

Note: See specific deadline for SnakeCLEF participants in the next section

SnakeCLEF 2020

This information concerns only LifeCLEF2020 participants.

Please read the details on LifeCLEF2020 here. Note that the deadline for submission of your runs is the 5th June 2020. We will announce the winner of SnakeCLEF 2020 by choosing the participant with the highest score among all registered participants.  

Please read the specific rules for participation and complete your short profile here

 

image_challenge_dataset.png

A puff adder, one of the most dangerous snakes in Africa

Contact us

If you have any problems or comments, please contact us via the discussion forum or via email:

Rafael Ruiz de Castaneda: rafael.ruizdecastaneda@unige.ch

Isabelle Bolon: isabelle.bolon@unige.ch

Lukas Picek: lukaspicek@gmail.com

Andrew Durso: amdurso@gmail.com

 

Participants

Latest Submissions

saviola777 submitted
CKAB2020 failed
CKAB2020 failed
saviola777 graded
saviola777 graded