Datasets:
Tasks:
Zero-Shot Classification
Formats:
parquet
Languages:
English
Size:
100M - 1B
Tags:
biodiverstiy
cryptic species
fine-grained image recognition
vision-language
multimodal dataset
License:
Update README.md
Browse files
README.md
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Rich research-graded metadata annotations extending scientific, multicultural, and multilingual species terminology, hierarchical taxonomy, spatiotemporal context, and cryptic group species, further challenge the multimodal AI biodiversity research.
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## New Benchmark Datasets
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We created
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### CrypticBio-Commom
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We curate a cryptic species subset of a common species from Arachnida, Aves, Insecta, Plantae, Fungi, Mollusca, and Reptilia. We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species.
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Rich research-graded metadata annotations extending scientific, multicultural, and multilingual species terminology, hierarchical taxonomy, spatiotemporal context, and cryptic group species, further challenge the multimodal AI biodiversity research.
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## New Benchmark Datasets
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We created four new benchmark datasets for fine-grained image classification.
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### CrypticBio-Commom
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We curate a cryptic species subset of a common species from Arachnida, Aves, Insecta, Plantae, Fungi, Mollusca, and Reptilia. We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species.
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