FungiTastic: A multi-modal dataset and benchmark for image categorization
<|reference_start|>FungiTastic: A multi-modal dataset and benchmark for image categorization: We introduce a new, highly challenging benchmark and a dataset -- FungiTastic -- based on data continuously collected over a twenty-year span. The dataset originates in fungal records labeled and curated by experts. It consists of about 350k multi-modal observations that include more than 650k photographs from 5k fine-grained categories and diverse accompanying information, e.g., acquisition metadata, satellite images, and body part segmentation. FungiTastic is the only benchmark that includes a test set with partially DNA-sequenced ground truth of unprecedented label reliability. The benchmark is designed to support (i) standard close-set classification, (ii) open-set classification, (iii) multi-modal classification, (iv) few-shot learning, (v) domain shift, and many more. We provide baseline methods tailored for almost all the use-cases. We provide a multitude of ready-to-use pre-trained models on HuggingFace and a framework for model training. A comprehensive documentation describing the dataset features and the baselines are available at https://bohemianvra.github.io/FungiTastic/ and https://www.kaggle.com/datasets/picekl/fungitastic.<|reference_end|>
@article{picek2024fungitastic:,
    title={FungiTastic: A multi-modal dataset and benchmark for image
  categorization},
    author={Lukas Picek, Klara Janouskova, Milan Sulc, Jiri Matas},
    journal={arXiv preprint arXiv:2408.13632},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13632},
    primaryClass={cs.CV}
}