metadata

license: cc-by-3.0
language:
  - en
size_categories:
  - 1M<n<10M
pretty_name: BIOSCAN-5M
tags:
  - Multimodal_dataset
  - Large_dcale_dataset
  - Image
  - DNA_barcode
  - Taxonomy
  - Biodiversity
  - LLMs
  - BERT
  - Clustering
  - Multimodal_retrieval_learning
  - Zero_shot_transfer_learning
  - Geo_location
  - Specimen_size
  - Insect
  - Species
maintainers:
  - https://huggingface.co/Gharaee
author:
  name: Zahra Gharaee
  github: https://github.com/zahrag
  hf: https://huggingface.co/Gharaee
dataset_loader_script: dataset.py
dataset_split_names:
  - pretarin
  - train
  - validation
  - test
  - val_unseen
  - test_unseen
  - key_unseen
  - other_heldout

BIOSCAN-5M

Overview

As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. 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, geographical information, and specimen size.

Citation

If you make use of the BIOSCAN-5M dataset and/or its code repository, please cite the following paper:

cite as:

@inproceedings{gharaee2024bioscan5m,
    title={{BIOSCAN-5M}: A Multimodal Dataset for Insect Biodiversity},
    booktitle={Advances in Neural Information Processing Systems},
    author={Zahra Gharaee and Scott C. Lowe and ZeMing Gong and Pablo Millan Arias
        and Nicholas Pellegrino and Austin T. Wang and Joakim Bruslund Haurum
        and Iuliia Zarubiieva and Lila Kari and Dirk Steinke and Graham W. Taylor
        and Paul Fieguth and Angel X. Chang
    },
    editor={A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
    pages={36285--36313},
    publisher={Curran Associates, Inc.},
    year={2024},
    volume={37},
    url={https://proceedings.neurips.cc/paper_files/paper/2024/file/3fdbb472813041c9ecef04c20c2b1e5a-Paper-Datasets_and_Benchmarks_Track.pdf},
}

Large-Scale Foundation Model Training for Biodiversity Research

Dataset partitions

Partition	Example Splits	Description
Closed-world	train, val, test	Samples with known species names for supervised classification.
Open-world	key_unseen, val_unseen, test_unseen	Placeholder species names but known genera, enabling generalization to unseen species.
Novelty Detection	other_heldout	Unknown species and genus, suitable for open-set detection.
Pretraining	pretrain	Unlabeled data for self-/semi-supervised learning at scale.

Supported tasks

Task	Description
DNA-based Taxonomic Classification	Predict taxonomic labels from raw DNA barcode sequences.
Zero-Shot Transfer Learning	Evaluate whether unlabeled models can semantically cluster data—across modalities like image and DNA—using learned representations.
Multimodal Retrieval Learning	Retrieve matching specimens across modalities (e.g., image ↔ DNA ↔ text) via shared embeddings.

Dataset features via metadata fields

Field Group	Field(s)	Description
Image	`image`	RGB JPEG image of an individual insect specimen.
Indexing	`processid`, `sampleid`	Unique identifiers from BOLD and the collector.
Taxonomy	`phylum`, `class`, `order`, `family`, `subfamily`, `genus`, `species`	Hierarchical taxonomic classification.
Genetics	`dna_bin`, `dna_barcode`	Barcode Index Number and DNA sequence.
Geography	`country`, `province_state`, `coord-lat`, `coord-lon`	Collection location and geographic coordinates.
Specimen Size	`image_measurement_value`, `area_fraction`, `scale_factor`	Image-based size measures and normalization factors.
Splits & Storage	`split`, `chunk`	Data partition (e.g., train/test) and storage subdirectory.

Usage

First, download the dataset.py script to your project directory by running the following command:

wget -P /path/to/your/project_directory https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M/resolve/main/dataset.py

Once you've downloaded the script, you can use the datasets library to load the dataset. For example:

from datasets import load_dataset

ds = load_dataset("dataset.py", name="cropped_256_eval", split="validation", trust_remote_code=True)

ℹ️ Note: The CSV metadata and image package associated with the selected configuration will be automatically downloaded and extracted to ~/.cache/huggingface/datasets/downloads/extracted/ .

📑 Configurations

Each configuration loads specimen images along with associated metadata fields:

`name`	Available `split` values
`cropped_256`, `original_256`	`pretrain`, `train`, `validation`, `test`, `val_unseen`, `test_unseen`, `key_unseen`, `other_heldout`
`cropped_256_pretrain`, `original_256_pretrain`	`pretrain`
`cropped_256_train`, `original_256_train`	`train`
`cropped_256_eval`, `original_256_eval`	`validation`, `test`, `val_unseen`, `test_unseen`, `key_unseen`, `other_heldout`

ℹ️ Note: If you do not specify the split when loading the dataset, all available splits will be loaded as a dictionary.

Sample Usage

First, download the usage_demo_bioscan5m.py script to your project directory by running the following command:

wget -P /path/to/your/project_directory https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M/resolve/main/usage_demo_bioscan5m.py

This script demonstrates how to load and visualize samples from the BIOSCAN-5M dataset.

To run the script, execute:

python usage_demo_bioscan5m.py

This will display 10 dataset samples, each showing the organism image on the right, and the corresponding metadata fields on the left, including taxonomic, geographic, genetic, and size-related information.

Dataset Access

To clone this dataset repository, use the following command:

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M

📦 Resources and Access

📄 Paper: arXiv
🌐 Website: BIOSCAN-5M Project Page
💻 GitHub: bioscan-ml/BIOSCAN-5M
📁 Downloads: