Summary of Bioscan-5m: a Multimodal Dataset For Insect Biodiversity, by Zahra Gharaee et al.
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity
by 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
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Populations and Evolution (q-bio.PE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed BIOSCAN-5M Insect dataset, a comprehensive collection of multi-modal information for over 5 million insect specimens, is introduced to the machine learning community. The dataset significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical, and size information. Three benchmark experiments are proposed to demonstrate the impact of the multi-modal data types on classification and clustering accuracy. These include pretraining a masked language model on DNA barcode sequences, zero-shot transfer learning for clustering feature embeddings, and contrastive learning for taxonomic classification using multiple modalities. The code repository is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The BIOSCAN-5M Insect dataset is a big collection of information about over 5 million insects! Scientists want to use machine learning to understand and protect insect biodiversity. To help with that, they created the BIOSCAN-5M dataset, which has lots of different types of data about each insect, like pictures, DNA code, and more. They’re testing how well different machine learning models can work with this kind of data. |
Keywords
» Artificial intelligence » Classification » Clustering » Machine learning » Masked language model » Multi modal » Pretraining » Transfer learning » Zero shot
