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BIOSCAN-5M

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1M<n<10M
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Multimodal_dataset
Large_dcale_dataset
Image
DNA_barcode
Taxonomy
Biodiversity
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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

Author: zahrag

Dataset Card for BIOSCAN-5M

image/png

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.

Large-Scale Foundation Model Training

BIOSCAN-5M is partitioned to support both closed-world and open-world biodiversity learning:

  • Closed-world (train, val, test): Samples with known species names for supervised classification.

  • Open-world (key_unseen, val_unseen, test_unseen): Placeholder species 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- or semi-supervised learning at scale.

Supported Tasks

  • Task I: DNA-based Taxonomic Classification Predict taxonomic labels from raw DNA barcode sequences.

  • Task II: Zero-Shot Transfer Learning Evaluate whether unlabeled models can organize data into semantically meaningful clusters—across modalities like image and DNA—using learned representations.

  • Task III: Multimodal Retrieval Learning Retrieve matching specimens across modalities (e.g., image ↔ DNA ↔ Text) using shared embeddings.

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},
}

Dataset Features

Each configuration loads specimen images along with associated metadata fields:

  • image
    RGB JPEG images of individual insect specimens.

  • Metadata fields

    • Indexing fields
      • processid: A unique number assigned by BOLD (International Barcode of Life Consortium).
      • sampleid: A unique identifier given by the collector.
    • Taxonomic labels
      • phylum, class, order, family, subfamily, genus, species: Hierarchical taxonomic classification
    • Genetic information
      • dna_bin: Barcode Index Number
      • dna_barcode: DNA barcode sequence
    • Geographic data
      • country, province_state: Collection location
      • coord-lat, coord-lon: Latitude and longitude coordinates
    • Specimen size data
      • image_measurement_value: Measured image property (e.g., pixel area)
      • area_fraction: Fraction of the image occupied by the specimen
      • scale_factor: Relative size normalization factor
    • Split and localization
      • split: Data partition label (e.g., train, test, val)
      • chunk: Subdirectory label for scalable storage

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

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.

image

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

Dataset Sources

image/png