BIOSCAN-1M Insect Dataset
Creators
- 1. University of Waterloo
- 2. Simon Fraser University
- 3. University of Guelph & Vector Institute for AI
- 4. Aalborg University & Pioneer Centre for AI
- 5. Vector Institute for AI & Dalhousie University
- 6. Centre for Biodiversity Genomics & University of Guelph
- 7. University of Guelph
- 8. Simon Fraser University & Alberta Machine Intelligence Institute (Amii)
Contributors
Hosting institution:
Description
Overview
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 genetically-based proxies for species classification. This dataset 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.
Technical info
Additional BIOSCAN-1M Insect dataset-related packages are accessible through the GoogleDrive folder including:
- BIOSCAN_1M_original_images: The raw images of the dataset.
- BIOSCAN_1M_cropped_images: Images after cropping with our cropping tool introduced in BIOSCAN-1M.
Files
cropped_256.zip
Files
(44.2 GB)
| Name | Size | Download all |
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md5:ad17eef57cd9f690153e344a4fd42af4
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2.1 GB | Download |
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md5:dec3bb23870a35e2e13bc17a5809c901
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1.2 GB | Download |
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md5:3a73931ba37cf2aff7a9c715c089198f
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7.4 GB | Download |
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md5:fe1175815742db14f7372d505345284a
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7.2 GB | Preview Download |
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md5:9729fc1c49d84e7f1bfc6f5a0916d72b
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26.4 GB | Preview Download |
Additional details
Identifiers
Related works
- Is supplemented by
- Dataset: 10.5281/zenodo.11973457 (DOI)
Software
- Repository URL
- https://github.com/zahrag/BIOSCAN-1M
- Programming language
- Python
- Development Status
- Active
References
- @inproceedings{gharaee2023step, title={A Step Towards Worldwide Biodiversity Assessment: The {BIOSCAN-1M} Insect Dataset}, booktitle={Advances in Neural Information Processing Systems}, author={Gharaee, Z. and Gong, Z. and Pellegrino, N. and Zarubiieva, I. and Haurum, J. B. and Lowe, S. C. and McKeown, J. T. A. and Ho, C. Y. and McLeod, J. and Wei, Y. C. and Agda, J. and Ratnasingham, S. and Steinke, D. and Chang, A. X. and Taylor, G. W. and Fieguth, P.}, editor={A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine}, pages={43593--43619}, publisher={Curran Associates, Inc.}, year={2023}, volume={36}, url={https://proceedings.neurips.cc/paper_files/paper/2023/file/87dbbdc3a685a97ad28489a1d57c45c1-Paper-Datasets_and_Benchmarks.pdf}, }