TITLE: A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset 

ABSTRACT: In an effort to catalog insect biodiversity, we propose a new large dataset
of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is
taxonomically classified by an expert, and also has associated genetic
information including raw nucleotide barcode sequences and assigned barcode
index numbers, which are genetically-based proxies for species classification.
This paper presents a curated million-image dataset, primarily to train
computer-vision models capable of providing image-based taxonomic assessment,
however, the dataset also presents compelling characteristics, the study of
which would be of interest to the broader machine learning community. Driven by
the biological nature inherent to the dataset, a characteristic long-tailed
class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is
a hierarchical classification scheme, presenting a highly fine-grained
classification problem at lower levels. Beyond spurring interest in
biodiversity research within the machine learning community, progress on
creating an image-based taxonomic classifier will also further the ultimate
goal of all BIOSCAN research: to lay the foundation for a comprehensive survey
of global biodiversity. This paper introduces the dataset and explores the
classification task through the implementation and analysis of a baseline
classifier.
In an effort to catalog insect biodiversity, we propose a new large dataset
of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is
taxonomically classified by an expert, and also has associated genetic
information including raw nucleotide barcode sequences and assigned barcode
index numbers, which are genetically-based proxies for species classification.
This paper presents a curated million-image dataset, primarily to train
computer-vision models capable of providing image-based taxonomic assessment,
however, the dataset also presents compelling characteristics, the study of
which would be of interest to the broader machine learning community. Driven by
the biological nature inherent to the dataset, a characteristic long-tailed
class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is
a hierarchical classification scheme, presenting a highly fine-grained
classification problem at lower levels. Beyond spurring interest in
biodiversity research within the machine learning community, progress on
creating an image-based taxonomic classifier will also further the ultimate
goal of all BIOSCAN research: to lay the foundation for a comprehensive survey
of global biodiversity. This paper introduces the dataset and explores the
classification task through the implementation and analysis of a baseline
classifier.
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
http://arxiv.org/abs/2307.10455v1