Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem
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The main goal of the new LifeCLEF challenge, FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem, was to provide an evaluation ground for end-to-end fungi species recognition in an open class set scenario. An AI-based fungi species recognition system deployed in the Atlas of Danish Fungi helps mycologists to collect valuable data and allows users to learn about fungi species identification. Advances in fungi recognition from images and metadata will allow continuous improvement of the system deployed in this citizen science project. The training set is based on the Danish Fungi 2020 dataset and contains 295,938 photographs of 1,604 species. For testing, we provided a collection of 59,420 expert-approved observations collected in 2021. The test set includes 1,165 species from the training set and 1,969 unknown species, leading to an open-set recognition problem. This paper provides (i) a description of the challenge task and datasets, (ii) a summary of the evaluation methodology, (iii) a review of the systems submitted by the participating teams, and (iv) a discussion of the challenge results.
|Journal||CEUR Workshop Proceedings|
|Number of pages||12|
|Publication status||Published - 2022|
|Event||2022 Conference and Labs of the Evaluation Forum, CLEF 2022 - Bologna, Italy|
Duration: 5 Sep 2022 → 8 Sep 2022
|Conference||2022 Conference and Labs of the Evaluation Forum, CLEF 2022|
|Period||05/09/2022 → 08/09/2022|
© 2022 Copyright for this paper by its authors.
- classification, computer vision, fine grained visual categorization, fungi, FungiCLEF, LifeCLEF, machine learning, metadata, open-set recognition, species identification