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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17720 |
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| _version_ | 1866910802013847552 |
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| author | Yang, Chih-Hsuan Feuer, Benjamin Jubery, Zaki Deng, Zi K. Nakkab, Andre Hasan, Md Zahid Chiranjeevi, Shivani Marshall, Kelly Baishnab, Nirmal Singh, Asheesh K Singh, Arti Sarkar, Soumik Merchant, Nirav Hegde, Chinmay Ganapathysubramanian, Baskar |
| author_facet | Yang, Chih-Hsuan Feuer, Benjamin Jubery, Zaki Deng, Zi K. Nakkab, Andre Hasan, Md Zahid Chiranjeevi, Shivani Marshall, Kelly Baishnab, Nirmal Singh, Asheesh K Singh, Arti Sarkar, Soumik Merchant, Nirav Hegde, Chinmay Ganapathysubramanian, Baskar |
| contents | We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia ("animals"), Fungi ("fungi"), and Plantae ("plants"), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems.
We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves ("birds"), Arachnida ("spiders/ticks/mites"), Insecta ("insects"), Plantae ("plants"), Fungi ("fungi"), Mollusca ("snails"), and Reptilia ("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels.
We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17720 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity Yang, Chih-Hsuan Feuer, Benjamin Jubery, Zaki Deng, Zi K. Nakkab, Andre Hasan, Md Zahid Chiranjeevi, Shivani Marshall, Kelly Baishnab, Nirmal Singh, Asheesh K Singh, Arti Sarkar, Soumik Merchant, Nirav Hegde, Chinmay Ganapathysubramanian, Baskar Computer Vision and Pattern Recognition We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia ("animals"), Fungi ("fungi"), and Plantae ("plants"), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems. We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves ("birds"), Arachnida ("spiders/ticks/mites"), Insecta ("insects"), Plantae ("plants"), Fungi ("fungi"), Mollusca ("snails"), and Reptilia ("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels. We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use. |
| title | BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.17720 |