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SnakeCLEF 2022


Tentative Schedule

  • Jan 2022: registration opens for all LifeCLEF challenges
  • 13 Feb 2022: training data release
  • 13 May 2022: deadline for submission of runs by participants
  • 20 May 2022: release of processed results by the task organizers
  • 27 May 2022: deadline for submission of working note papers by participants [CEUR-WS proceedings]
  • June 2022: notification of acceptance of working note papers [CEUR-WS proceedings]
  • July 2022: camera ready working note papers by participants and organizers
  • 5-9 Sept 2022: CLEF 2022 Università di Bologna


Building an automatic and robust image-based system for snake species identification is an important goal for biodiversity, conservation, and global health. With recent estimates of 81,410-137,880 deaths and up to three times as many victims of amputations, permanent disability and disfigurement (globally each year) caused by venomous snakebite, such a system has the potential to improve eco-epidemiological data and treatment outcomes (e.g. based on the specific use of antivenoms). This applies especially in remote geographic areas and developing countries, where automatic snake species identification has the greatest potential to save lives.

The difficulty of snake species identification — from both a human and a machine perspective — lies in the high intra-class and low inter-class variance in appearance, which may depend on geographic location, colour morph, sex, or age. At the same time, many species are visually similar to other species (e.g. mimicry).

Our knowledge of which snake species occur in which countries is incomplete, and it is common that most or all images of a given snake species might originate from a small handful of countries or even a single country. Furthermore, many snake species resemble species found on other continents, with which they are entirely allopatric. Knowing the geographic origin of an unidentified snake can narrow down the possible correct identifications considerably. In no location on Earth do more than 126 of the approximately 3,900 snake species co-occur. Thus, regularization to all countries is a critical component of any snake identification method.


For this year challenge, we prepared a dataset based on 187,129 snake observations with 318,532 photographs belonging to 1,572 snake species and observed in 208 countries. The data were gathered from the online biodiversity platform — iNaturalist.

The provided dataset has a heavy long-tailed class distribution, where the most frequent species (Natrix natrix) is represented by 6,472 images and the least frequent species by just 5 samples.


Task description

Given the set of snake observations — multiple photographs of the same individual — and corresponding geographic locality information, the goal of the task is to return for each observation a species id.

How to participate ?

1. Subscribe to CLEF (LifeCLEF - SnakeCLEF task) by filling this form.
2. Go to the Kaggle SnakeCLEF2022 challenge page


LifeCLEF 2022 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world.

The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (
Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.


    Machine Learning
  • Lukas Picek, Dept. of Cybernetics, FAV, University of West Bohemia, Czechia,
  • Marek Hruz, Dept. of Cybernetics, FAV, University of West Bohemia, Czechia,
  • Herpatology
  • Andrew Durso, Department of Biological Sciences, Florida Gulf Coast University, Fort Myers, USA
  • Clinical Expert
  • Isabelle Bolon, Institute of Global Health, Department of Community Health and Medicine, University of Geneva, Switzerland



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