Papers › BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity

BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity

18 Jun 2024arXiv:2406.12723archive 2025-07-28

Zahra Gharaee, Scott C. Lowe, ZeMing Gong, Pablo Millan Arias, Nicholas Pellegrino, Austin T. Wang, Joakim Bruslund Haurum, Iuliia Zarubiieva, Lila Kari, Dirk Steinke, Graham W. Taylor, Paul Fieguth, Angel X. Chang

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, geographical, and size 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/bioscan-ml/BIOSCAN-5M.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2406.12723")

Code

Syntology Ran 5 of 8 code samples harvested from 2 repositories linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 4 ran with no contract checked.

By repository: official repository: 7 samples from 1 repository, 4 ran; named in the paper: 1 sample from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

bioscan-ml/dataset officialmentioned on GitHubpytorchMIT report
bioscan-ml/bioscan-5m mentioned in papermentioned on GitHub report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

8 samples harvested; 5 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
4ran
3unverified

Licence: 1 of the 8 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

explode_metasplit bioscan-ml/dataset/bioscan_dataset/bioscan1m.py official repository ran MIT (permissive) · dc506d4a13eab0d4 · report
explode_metasplit bioscan-ml/dataset/bioscan_dataset/bioscan5m.py official repository ran MIT (permissive) · 5f08ce8fdd4591c3 · report
explode_metasplit bioscan-ml/dataset/bioscan_dataset/canadian_invertebrates.py official repository ran MIT (permissive) · 4c5c26438d0a1ed3 · report
get_image_path bioscan-ml/dataset/bioscan_dataset/bioscan5m.py official repository ran MIT (permissive) · 05399c3e5a171397 · report
load_bioscan1m_metadata bioscan-ml/dataset/bioscan_dataset/bioscan1m.py official repository unverified MIT (permissive) · 4e89e4f5066040d8 · report
load_bioscan5m_metadata bioscan-ml/dataset/bioscan_dataset/bioscan5m.py official repository unverified MIT (permissive) · c9f937458e5a98eb · report
load_canadian_invertebrates_metadata bioscan-ml/dataset/bioscan_dataset/canadian_invertebrates.py official repository unverified MIT (permissive) · 02bef57d188bc054 · report
read_id_mapping bioscan-ml/bioscan-5m/BIOSCAN_DATASET/bioscan_datadownload.py named in the paper ran · our draft was wrong licence not identified · pointer only · dba2948de282f831 · report

Tasks

Contrastive LearningLanguage ModellingSelf-Supervised LearningTransfer LearningZero-Shot Learning

Datasets

Introduced by this paper, per the archive.

BIOSCAN-5M

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Contrastive Learning

1 archive method tag without a method page not shown.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections