Overview of FungiCLEF 2025: Few-Shot Classification With Rare Fungi Species

Abstract

FungiCLEF 2025, the 4th edition of the FungiCLEF challenge, was organized as part of the LifeCLEF and the FGVC workshops. This year’s edition targeted few-shot classification of rare fungi species. Participants were tasked with identifying species from multimodal observations, including images, structured metadata, and environmental data. The data was collected through citizen science and underwent expert-based labeling. Building upon the FungiTastic dataset, FungiCLEF 2025 emphasized real-world constraints such as limited training samples, high intra-class variability, fine-grained inter-class similarities, and distribution shift. The competition attracted 74 teams, with the leading submissions demonstrating significant gains over the provided baselines, showcasing the potential of pretrained vision transformers, contrastive learning, and ensemble techniques. This overview summarizes the challenge setup, dataset, baselines, participant strategies, and key findings, and outlines directions for future work. The winning team achieved a top-5 accuracy of 78.9%, outperforming baselines by over 52%. © 2025 Copyright for this paper by its authors.