Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning
<|reference_start|>Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning: FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species, with a focus on identifying poisonous species. This task is challenging due to the size and class imbalance of the dataset, subtle inter-class variations, and significant intra-class variability amongst samples. In this paper, we document our approach in tackling this challenge through the use of ensemble classifier heads on pre-computed image embeddings. Our team (DS@GT) demonstrate that state-of-the-art self-supervised vision models can be utilized as robust feature extractors for downstream application of computer vision tasks without the need for task-specific fine-tuning on the vision backbone. Our approach achieved the best Track 3 score (0.345), accuracy (78.4%) and macro-F1 (0.577) on the private test set in post competition evaluation. Our code is available at https://github.com/dsgt-kaggle-clef/fungiclef-2024.<|reference_end|>
@article{chiu2024fine-grained,
    title={Fine-Grained Classification for Poisonous Fungi Identification with
  Transfer Learning},
    author={Christopher Chiu, Maximilian Heil, Teresa Kim, Anthony Miyaguchi},
    journal={arXiv preprint arXiv:2407.07492},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07492},
    primaryClass={cs.CV cs.LG}
}