A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
Keywords: Insect biodiversity, Image classification, Class-imbalance distribution, Fine-grained classification, Taxonomic classification, DNA barcode sequences, Barcode Index Number (BIN)
TL;DR: The paper presents the BIOSCAN-1M Insect Dataset, a million-image collection of hand-labelled insect images with genetic data, aimed to train computer vision models for taxonomic assessment and foster machine learning applications in biodiversity.
Abstract: In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-1M 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 genetic-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. The code repository of the BIOSCAN-1M-Insect dataset is available at https://github.com/zahrag/BIOSCAN-1M
Supplementary Material: pdf
Submission Number: 583