Résumé
The third edition of the fungi recognition challenge, FungiCLEF 2024, organized within LifeCLEF, advances the field of mushroom species identification using computer vision and machine learning. Building on the Danish Fungi 2020 dataset and incorporating new data from the CzechFungi app, FungiCLEF 2024 challenges participants to recognize fungi species from images and metadata, focusing on efficient inference and minimalization of edible and poisonous species confusion. The strict limits on computational complexity ensure that the resulting solutions are practical for use in real-world settings with limited computational resources. The competition attracted seven teams, with five outperforming the provided baseline, which was based on the pre-trained EfficientNet-B1 model. This overview paper provides (i) a comprehensive description of the challenge and provided baseline method, (ii) detailed characteristics of the dataset and task specifications, (iii) an examination of the methods employed by contestants, and (iv) a discussion of the competition outcomes. The results highlight incremental advancements in fungi recognition, showcasing innovative approaches and techniques that push the limits of previous work.