MALPOLON让生态学家轻松用深度学习建模物种分布。
MALPOLON: A Framework for Deep Species Distribution Modeling
- 基于PyTorch的模块化框架，支持自定义与预设模型训练。
- 提供多GPU加速、并行计算与基准模型，提升效率与可比性。
- 适合无深度学习背景的生态研究者快速上手使用。
本文介绍了一种名为MALPOLON的深度物种分布建模（deep-SDM）框架。该框架采用Python编写，基于PyTorch构建，旨在帮助仅具备基础编程能力的建模生态学家（如生态学家）轻松训练和推理深度物种分布模型，并促进方法共享。高级用户可通过重写现有类实现特定实验，同时利用一键式示例在自定义或提供的原始与预处理数据集上进行多分类任务的神经网络训练。框架已开源至GitHub与PyPi，配备详尽文档与多种应用场景示例。MALPOLON支持简单安装、基于YAML的配置、并行计算、多GPU利用、基线与基础模型用于基准测试，以及全面教程与文档，致力于提升生态学家与研究人员在可访问性与性能扩展性方面的体验。
原文摘要 · Abstract (English)
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.
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