Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity
<|reference_start|>Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity: 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 \href{https://baskargroup.github.io/Arboretum/}{project website} for links to our data, models, and code.<|reference_end|>
@article{yang2024arboretum:,
    title={Arboretum: A Large Multimodal Dataset Enabling AI for Biodiversity},
    author={Chih-Hsuan Yang and Benjamin Feuer and Zaki Jubery and Zi K. Deng and
  Andre Nakkab and Md Zahid Hasan and Shivani Chiranjeevi and Kelly Marshall
  and Nirmal Baishnab and Asheesh K Singh and Arti Singh and Soumik Sarkar and
  Nirav Merchant and Chinmay Hegde and Baskar Ganapathysubramanian},
    journal={arXiv preprint arXiv:2406.17720},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17720},
    primaryClass={cs.CV}
}