BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity
<|reference_start|>BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity: As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, this paper presents the BIOSCAN-5M Insect dataset to the machine learning community and establish several benchmark tasks. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, and geographical information. We propose three benchmark experiments to demonstrate the impact of the multi-modal data types on the classification and clustering accuracy. First, we pretrain a masked language model on the DNA barcode sequences of the BIOSCAN-5M dataset, and demonstrate the impact of using this large reference library on species- and genus-level classification performance. Second, we propose a zero-shot transfer learning task applied to images and DNA barcodes to cluster feature embeddings obtained from self-supervised learning, to investigate whether meaningful clusters can be derived from these representation embeddings. Third, we benchmark multi-modality by performing contrastive learning on DNA barcodes, image data, and taxonomic information. This yields a general shared embedding space enabling taxonomic classification using multiple types of information and modalities. The code repository of the BIOSCAN-5M Insect dataset is available at https://github.com/zahrag/BIOSCAN-5M.<|reference_end|>
@article{gharaee2024bioscan-5m:,
    title={BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity},
    author={Zahra Gharaee, Scott C. Lowe, ZeMing Gong, Pablo Millan Arias,
  Nicholas Pellegrino, Austin T. Wang, Joakim Bruslund Haurum, Iuliia
  Zarubiieva, Lila Kari, Dirk Steinke, Graham W. Taylor, Paul Fieguth, Angel X.
  Chang},
    journal={arXiv preprint arXiv:2406.12723},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12723},
    primaryClass={cs.LG}
}