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  ## Description
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  [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.
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  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.
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  ### BioTrove-Balanced
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- For balanced species distribution across the 7 categories, we curated `BioTrove-Balanced`. Each category includes up to 500 species, with 50 images per species.
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  ### BioTrove-Unseen
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- 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.
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  ### BioTrove-LifeStages
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- 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.
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  ## Dataset Information
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  ## Description
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+ **See the [BioTrove-Train](https://huggingface.co/BGLab/BioTrove-Train) dataset card on HuggingFace to access the samller `BioTrove-Train` dataset (40M)**
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  [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.
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  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.
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  ### BioTrove-Balanced
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+ For balanced species distribution across the 7 categories, we curated `BioTrove-Balanced`. Each category includes up to 500 species, with 50 images per species, totaling of `~112K` image samples.
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  ### BioTrove-Unseen
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+ 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, totaling of `~11.9K` image samples.
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  ### BioTrove-LifeStages
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+ 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 Feb 1, 2024 to May 20, 2024 to ensure no overlap with the training dataset. For each species, life stage filters (egg, larva, pupa, or adult) were applied.
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  ## Dataset Information
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