Résumé
This paper describes a deep-SDM framework, MALPOLON. Written in Python and
built upon the PyTorch library, this framework aims to facilitate training and
inferences of deep species distribution models (deep-SDM) and sharing for users
with only general Python language skills (e.g., modeling ecologists) who are
interested in testing deep learning approaches to build new SDMs. More advanced
users can also benefit from the framework's modularity to run more specific
experiments by overriding existing classes while taking advantage of
press-button examples to train neural networks on multiple classification tasks
using custom or provided raw and pre-processed datasets. The framework is
open-sourced on GitHub and PyPi along with extensive documentation and examples
of use in various scenarios. MALPOLON offers straightforward installation,
YAML-based configuration, parallel computing, multi-GPU utilization, baseline
and foundational models for benchmarking, and extensive
tutorials/documentation, aiming to enhance accessibility and performance
scalability for ecologists and researchers.