README.md

---
license: cc
task_categories:
- zero-shot-classification
language:
- en
tags:
- biodiverstiy
- fine-grained image recognition
- vision-language
- multimodal embedding space
pretty_name: A Large Multimodal Dataset for Visually Confusing Biodiversity
size_categories:
- 100M<n<1B
---

# CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity

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## Description

[CrypticBio](https://georgianagmanolache.github.io/crypticbio/) comprises metadata including species scientific and multicultural vernacular terminology, image URL, taxonomic hierarchy, spatiotemporal context, and corresponding visually confusing species group. Visually confusing or cryptic species are groups of two or more taxa that are nearly indistinguishable based on visual characteristics alone. 

## CrypticBio Dataset
We present CrypticBio, the largest publicly available multimodal dataset of visually confusing species groups, specifically curated to support the development of AI models in the context of biodiversity identification applications.
Visually confusing or cryptic species are groups of two or more taxa that are nearly indistinguishable based on visual characteristics alone. Curated from real-world trends in species misidentification among community annotators of iNaturalist, CrypticBio contains 66K cryptic species groups spanning 64K species, represented in 170 million annotated images. 
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.

## 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.

### CrypticBio-Commom
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.

### CrypticBio-CommonUnseen
To assess zero-shot performance on common species from CrypticBio-Common not encountered during training of state-of-the-art models, we specifically curate a subset spanning data from 01-09-2024 to 01-04-2025.  We randomly select 100 samples from each species in a cryptic group where there are more than 150 observation per species.

### CrypticBio-Endagered
We propose a cryptic species subset of endangered species according to global IUCN Red List. We randomly select 30 samples from Arachnida, Fungi, Insecta, Mollusca, and Reptilia taxa and corresponding cryptic group, filtering out taxa where there are less than 150 observation.

### CrypticBio-Invasive
We also propose a cryptic species subset of invasive alien species (IAS) according to global the Global Invasive Species Database (GISD). IAS are a significant concern for biodiversity as their records appear to be exponentially rising across the Earth. Thus, identifying invasive species in cryptic groups is essential in biodiversity. We randomly select 100 samples from each invasive species cryptic group, filtering out taxa where there are less than 150 observation. 

## Dataset Information

### Directory
```plaintext
main/
├── crypticbio.parquet/
│   ├── part_0.csv
│   ├── part_0.parquet
│   ├── part_1.parquet
│   ├── .
│   ├── .
│   ├── .
│   └── part_626.parquet
├── CrypticBio-benchmark/
│   ├── CrypticBio-Common.csv
│   ├── CrypticBio-CommonUnseen.csv
│   ├── CrypticBio-Endangered.csv
│   └── CrypticBio-Invasive.csv
├──README.md
└──.gitignore
```

### Acknowledgements

The data and the code are publicly available at [georgianagmanolache.github.io/crypticbio](https://georgianagmanolache.github.io/crypticbio/)