title: Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
summary: We introduce Arboretum, the largest publicly accessible dataset designed to
  advance AI for biodiversity applications. This dataset, curated from the
  iNaturalist community science platform and vetted by domain experts to ensure
  accuracy, includes 134.6 million images, surpassing existing datasets in scale
  by an order of magnitude. The dataset encompasses image-language paired data
  for a diverse set of species from birds (Aves), spiders/ticks/mites
  (Arachnida), insects (Insecta), plants (Plantae), fungus/mushrooms (Fungi),
  snails (Mollusca), and snakes/lizards (Reptilia), making it a valuable resource
  for multimodal vision-language AI models for biodiversity assessment and
  agriculture research. Each image is annotated with scientific names, taxonomic
  details, and common names, enhancing the robustness of AI model training.
    We showcase the value of Arboretum by releasing a suite of CLIP models
  trained using a subset of 40 million captioned images. We introduce several new
  benchmarks for rigorous assessment, report accuracy for zero-shot learning, and
  evaluations across life stages, rare species, confounding species, and various
  levels of the taxonomic hierarchy.
    We anticipate that Arboretum will spur the development of AI models that can
  enable a variety of digital tools ranging from pest control strategies, crop
  monitoring, and worldwide biodiversity assessment and environmental
  conservation. These advancements are critical for ensuring food security,
  preserving ecosystems, and mitigating the impacts of climate change. Arboretum
  is publicly available, easily accessible, and ready for immediate use.
    Please see the https://baskargroup.github.io/Arboretum/{project
  website} for links to our data, models, and code.
paper_authors._id: 667c91f07f75474515f7f3f3
paper_authors.name: Chih-Hsuan Yang
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paper_authors.name: Benjamin Feuer
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paper_authors.fullname: Benjamin Feuer
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paper_authors.user: penfever
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paper_authors.name: Zaki Jubery
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paper_authors.name: Zi K. Deng
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paper_authors.name: Andre Nakkab
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paper_authors.name: Md Zahid Hasan
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paper_authors.name: Shivani Chiranjeevi
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paper_authors.name: Kelly Marshall
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paper_authors.fullname: Kelly Marshall
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paper_authors.user: ke-lly
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paper_authors.name: Nirmal Baishnab
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paper_authors.name: Asheesh K Singh
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paper_authors.name: Arti Singh
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paper_authors.name: Soumik Sarkar
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paper_authors.name: Nirav Merchant
paper_authors._id: 667c91f07f75474515f7f400
paper_authors.name: Chinmay Hegde
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Chinmay Hegde
paper_authors.type: user
paper_authors.user: chegde
paper_authors._id: 667c91f07f75474515f7f401
paper_authors.name: Baskar Ganapathysubramanian
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paper_authors.fullname: Baskar Group
paper_authors.type: user
paper_authors.user: BGLab
paper_title: Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
paper_summary: We introduce Arboretum, the largest publicly accessible dataset designed to
  advance AI for biodiversity applications. This dataset, curated from the
  iNaturalist community science platform and vetted by domain experts to ensure
  accuracy, includes 134.6 million images, surpassing existing datasets in scale
  by an order of magnitude. The dataset encompasses image-language paired data
  for a diverse set of species from birds (Aves), spiders/ticks/mites
  (Arachnida), insects (Insecta), plants (Plantae), fungus/mushrooms (Fungi),
  snails (Mollusca), and snakes/lizards (Reptilia), making it a valuable resource
  for multimodal vision-language AI models for biodiversity assessment and
  agriculture research. Each image is annotated with scientific names, taxonomic
  details, and common names, enhancing the robustness of AI model training.
    We showcase the value of Arboretum by releasing a suite of CLIP models
  trained using a subset of 40 million captioned images. We introduce several new
  benchmarks for rigorous assessment, report accuracy for zero-shot learning, and
  evaluations across life stages, rare species, confounding species, and various
  levels of the taxonomic hierarchy.
    We anticipate that Arboretum will spur the development of AI models that can
  enable a variety of digital tools ranging from pest control strategies, crop
  monitoring, and worldwide biodiversity assessment and environmental
  conservation. These advancements are critical for ensuring food security,
  preserving ecosystems, and mitigating the impacts of climate change. Arboretum
  is publicly available, easily accessible, and ready for immediate use.
    Please see the https://baskargroup.github.io/Arboretum/{project
  website} for links to our data, models, and code.
paper_ai_keywords: CLIP models zero-shot learning multimodal vision-language AI models