FungiTastic: A multi-modal dataset and benchmark for image categorization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384625" target="_blank" >RIV/68407700:21230/25:00384625 - isvavai.cz</a>
Alternative codes found
RIV/49777513:23520/25:43976618
Result on the web
<a href="https://doi.org/10.1109/CVPRW67362.2025.00192" target="_blank" >https://doi.org/10.1109/CVPRW67362.2025.00192</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPRW67362.2025.00192" target="_blank" >10.1109/CVPRW67362.2025.00192</a>
Alternative languages
Result language
angličtina
Original language name
FungiTastic: A multi-modal dataset and benchmark for image categorization
Original language description
We introduce a new, challenging benchmark and a dataset, FungiTastic, based on fungal records continuously collected over a twenty-year span. The dataset is labelled and curated by experts and consists of about 350k multimodal observations of 6k fine-grained categories (species). The fungi observations include photographs and additional data, e.g., meteorological and climatic data, satellite images, and body part segmentation masks. FungiTastic is one of the few benchmarks that include a test set with DNA-sequenced ground truth of unprecedented label reliability. The benchmark is designed to support (i) standard closed-set classification, (ii) open-set classification, (iii) multi-modal classification, (iv) few-shot learning, (v) domain shift, and many more. We provide tailored baselines for many use cases, a multitude of ready-to-use pre-trained models on HuggingFace, and a framework for model training. The documentation and the baselines are available at GitHub and Kaggle.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/SS73020004" target="_blank" >SS73020004: FunDive: Monitoring and mapping fungal diversity for nature conservation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISBN
979-8-3315-9994-2
ISSN
2160-7508
e-ISSN
2160-7516
Number of pages
11
Pages from-to
2037-2047
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Nashville
Event date
Jun 11, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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