CC BY 4.0Wolf, StefanStefanWolfThelen, Philipp HenryPhilipp HenryThelenBeyerer, JürgenJürgenBeyerer2024-08-222024-08-222024https://publica.fraunhofer.de/handle/publica/474000https://doi.org/10.24406/publica-358810.24406/publica-3588The FungiCLEF 2024 challenge aims to foster research in the field of application-oriented fine-grained open-set classification. Particularly, it sets the challenge to optimize fungi species classification while recognizing unknown species with the evaluation of multiple metrics targeting the problems of actual use-cases, e.g., the risk of a highly detrimental confusion of a poisonous species for an edible species. To develop a well-performing approach, we focus on reducing this particular risk by introducing multiple improvements. The major improvements are a poisonous reranking which prevents predicting an edible species while a significant chance of the sample being poisonous exists and a genus loss which provides additional training information improving the regularization of the feature space. The advancements provide a large improvement in terms of poisonous confusion but also in terms of overall classification accuracy. With this approach, we achieved the 1st place in the challenge’s main metric. Code is available at https://huggingface.co/stefanwolf/fungi2024.enFungi classificationOpen-set classificationFungiCLEFEntropyconference paper