  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.
