mlsanityData to 7 Oct 2026

FungiTastic: A multi-modal dataset and benchmark for image categorization

arXiv 2408.13632 ↗First posted 24 Aug 2024cs.CVcited by 7

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Abstract (arXiv)

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 https://huggingface.co/collections/BVRA/fungitastic-66a227ce0520be533dc6403b, and a framework for model training. The documentation and the baselines are available at https://github.com/BohemianVRA/FungiTastic/ and https://www.kaggle.com/datasets/picekl/fungitastic.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 39 of its 73 references that are in the map. First posted on arXiv 24 Aug 2024.

Builds on

39 of 73 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025Transfer Learning and Mixup for Fine-Grained Few-Shot Fungi Classificationcited by 1
  2. 2025Robust Context-Aware Object Recognitioncited by 1
  3. 2026Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baselinecited by 1
  4. 2024Bringing the Context Back into Object Recognition, Robustlycited by 0
  5. 2025Improving Fungi Prototype Representations for Few-Shot Classificationcited by 0
  6. 2025Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctorscited by 0
  7. 2026Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learningcited by 0