--- License: cc0-1.0 language: - en pretty_name: BioTrove task_categories: - image-classification - zero-shot-classification tags: - biology - image - animals - species - taxonomy - rare species - endangered species - evolutionary biology - balanced - CV - multimodal - CLIP - knowledge-guided size_categories: 100M

## Description [BioTrove](https://baskargroup.github.io/BioTrove/) comprises well-processed metadata with full taxa information and URLs pointing to image files. The metadata can be used to filter specific categories, visualize data distribution, and manage imbalance effectively. We provide a collection of software tools that enable users to easily download, access, and manipulate the dataset. ## BioTrove Dataset `BioTrove` comprises over `161.9M` images across several taxonomic groups- including Reptilia (reptiles), Plantae (plants), Mollusca (mollusks), Mammalia (mammals), Insecta (insects), Fungi (fungi), Aves (birds), Arachnida (arachnids), Animalia (animals), Amphibia (amphibians), and Actinopterygii (ray-finned fish). These taxonomic groups were chosen to represent the span of species — outside of charismatic megafauna. The images in BioTrove span `366.6K`species. Overall, this dataset nearly matches the state-of-the-art curated dataset (TREEOFLIFE-10M) in terms of species diversity, while comfortably exceeding it in terms of scale by a factor of nearly 16.2 times. ## New Benchmark Datasets We created three new benchmark datasets for fine-grained image classification. In addition, we provide a new benchmark dataset for species recognition across various developmental Life-stages. ### BioTrove-Balanced For balanced species distribution across the 7 categories, we curated `BioTrove-Balanced`. Each category includes up to 500 species, with 50 images per species. ### BioTrove-Unseen To provide a robust benchmark for evaluating the generalization capability of models on unseen species, we curated `BioTrove-Unseen`. The test dataset was constructed by identifying species with fewer than 30 instances in BioTrove, ensuring that the dataset contains species that were unseen by BioTrove-CLIP. Each species contained 10 images. ### BioTrove-LifeStages To assess the model’s ability to recognize species across various developmental stages, we curated `BioTrove-LifeStages`. This dataset has 20 labels in total and focuses on insects, since these species often exhibit significant visual differences across their lifespan. BioTrove-LifeStages contains five insect species and utilized the observation export feature on the iNaturalist platform to collect data from 2/1/2024 to 5/20/2024 to ensure no overlap with the training dataset. For each species, life stage filters (egg, larva, pupa, or adult) were applied. ## Dataset Information - **Full Taxa Information**: Detailed metadata, including taxonomic hierarchy and image URLs. - **Comprehensive Metadata**: Enables filtering, visualization, and effective management of data imbalance. - **Software Tools**: Collection of tools for easy dataset access, download, and manipulation. - **Balanced Species Distribution**: Up to 500 species per category with 50 images per species. - **Unseen Species Benchmark**: Includes species with fewer than 30 instances to evaluate generalization capability. - **Life Stages Dataset**: Focuses on insects across various developmental stages. ## BioTrove-CLIP Models **See the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace to download the trained model checkpoints** We released three trained model checkpoints in the [BioTrove-CLIP](https://huggingface.co/BGLab/BioTrove-CLIP) model card on HuggingFace. These CLIP-style models were trained on [BioTrove-40M](https://baskargroup.github.io/BioTrove/) for the following configurations: - **BioTrove-CLIP-O:** Trained a ViT-B/16 backbone initialized from the [OpenCLIP's](https://github.com/mlfoundations/open_clip) checkpoint. The training was conducted for 40 epochs. - **BioTrove-CLIP-B:** Trained a ViT-B/16 backbone initialized from the [BioCLIP's](https://github.com/Imageomics/BioCLIP) checkpoint. The training was conducted for 8 epochs. - **BioTrove-CLIP-M:** Trained a ViT-L/14 backbone initialized from the [MetaCLIP's](https://github.com/facebookresearch/MetaCLIP) checkpoint. The training was conducted for 12 epochs. ## Usage **To start using the BioTrove dataset, follow the instructions provided in the [GitHub](https://github.com/baskargroup/BioTrove). Model checkpoints are shared in the [model_ckpt](#directory) directory.** **Metadata files are included in the [Directory](#directory). Please download the metadata from the [Directory](#directory)** and pre-process the data using the [arbor_process](https://pypi.org/project/arbor-process/) PyPI library. The instructions to use the library can be found in [here](https://github.com/baskargroup/BioTrove/blob/main/Arbor-preprocess/README_arbor_process.md). The Readme file contains the detailed description of data preparation steps. ### Directory ```plaintext main/ ├── BioTrove/ │ ├── chunk_0.csv │ ├── chunk_0.parquet │ ├── chunk_1.parquet │ ├── . │ ├── . │ ├── . │ └── chunk_2691.parquet ├── BioTrove-benchmark/ │ ├── BioTrove-Balanced.csv │ ├── BioTrove-Balanced.parquet │ ├── BioTrove-Lifestages.csv │ ├── BioTrove-Lifestages.parquet │ ├── BioTrove-Unseen.csv │ └──BioTrove-Unseen.parquet ├──README.md └──.gitignore ``` ### Acknowledgements This work was supported by the AI Research Institutes program supported by the NSF and USDA-NIFA under [AI Institute: for Resilient Agriculture](https://aiira.iastate.edu/), Award No. 2021-67021-35329. This was also partly supported by the NSF under CPS Frontier grant CNS-1954556. Also, we gratefully acknowledge the support of NYU IT [High Performance Computing](https://www.nyu.edu/life/information-technology/research-computing-services/high-performance-computing.html) resources, services, and staff expertise.

Citation

If you find this dataset useful in your research, please consider citing our paper:

@misc{yang2024BioTrovelargemultimodaldataset,
        title={BioTrove: A Large Multimodal Dataset Enabling AI for Biodiversity}, 
        author={Chih-Hsuan Yang, Benjamin Feuer, Zaki Jubery, Zi K. Deng, Andre Nakkab,
           Md Zahid Hasan, Shivani Chiranjeevi, Kelly Marshall, Nirmal Baishnab, Asheesh K Singh,
            Arti Singh, Soumik Sarkar, Nirav Merchant, Chinmay Hegde, Baskar Ganapathysubramanian},
        year={2024},
        eprint={2406.17720},
        archivePrefix={arXiv},
        primaryClass={cs.CV},
        url={https://arxiv.org/abs/2406.17720}, 
  }

--- For more details and access to the dataset, please visit the [Project Page](https://baskargroup.github.io/BioTrove/).