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---
license: mit
size_categories:
- 10K<n<100K
task_categories:
- image-classification
pretty_name: BackHome19K
tags:
- seashells
- marine-biology
- biodiversity
- pacific
- caribbean
- image-dataset
- mollusks
library_name: datasets
---
# BackHome19K
[Paper](https://huggingface.co/papers/2501.04873)
## Dataset Description
BackHome19K is the first comprehensive seashell image dataset specifically created for ecosystem-level classification—distinguishing between shells from the Pacific and Caribbean coasts—rather than traditional taxonomic identification.
The dataset includes 19,058 high-quality images of 516 marine mollusk species, collected with the support of the School of Biology at the University of Costa Rica. It features a wide range of gastropods and bivalves, from families such as Cypraeidae, Cassidae, Ostreidae, Veneridae, and others.
This collection addresses the challenge of visually similar species across different ecosystems and supports research in geographic origin inference, morphological pattern analysis, and marine biodiversity.
## Dataset Structure
The dataset follows the folder structure below:
```plaintext
BackHome19K/
├── train/
│ ├── Pacifico/
│ │ ├── species_1.jpg
│ │ ├── species_2.jpg
│ │ └── ...
│ └── Caribe/
│ ├── species_1.jpg
│ ├── species_2.jpg
│ └── ...
├── val/
│ ├── Pacifico/
│ │ ├── species_1.jpg
│ │ └── ...
│ └── Caribe/
│ ├── species_1.jpg
│ └── ...
└── test/
├── Pacifico/
│ ├── species_1.jpg
│ └── ...
└── Caribe/
├── species_1.jpg
└── ...
```
## Dataset Details
- Total number of images: [19,058]
- Number of families: [84]
- Number of species: [516]
- Geographic Regions: Caribbean and Pacific from Costa Rica
- Image sources: Publicly available images from various online sources
- Image Format: JPEG/JPG/PNG
- Partitioning: The dataset is split into train, val, and test folders for supervised learning tasks.
- List of families:
| Family | Family |
|-------------------------|-------------------------|
| Acmaeidae | Acteonidae |
| Architectonicida | Architectonicidae |
| Arcidae | Areneidae |
| Batillariidae | Buccinidae |
| Bullidae | Bursidae |
| Calliostomatidae | Calyptraeidae |
| Cardiidae | Carditidae |
| Cassidae | Cerithiidae |
| Chamidae | Chamidae_Pseudochama |
| Charoniidae | Columbellidae |
| Conidae | Corbulidae |
| Crassatellidae | Cymatiidae |
| Cypraeidae | Cyrenidae |
| Cyrenoididae | Cystiscidae |
| Donacidae | Dreissenidae |
| Ellobiidae | Fasciolariidae |
| Ficidae | Fissurellidae |
| Glycymerididae | Gryphaeidae |
| Harpidae | Hipponicidae |
| Isognomonidae | Janthinidae |
| Limidae | Littorinidae |
| Lottiidae | Lucinidae |
| Mactridae | Margaritidae |
| Melongenidae | Mitridae |
| Modulidae | Muricidae |
| Mytilidae | Nassariidae |
| Naticidae | Neritidae |
| Noetiidae | Olividae |
| Ostreidae | Ovulidae |
| Pectinidae | Personidae |
| Pinnidae | Pisaniidae |
| Planaxidae | Plicatulidae |
| Psammobiidae | Pteriidae |
| Ranellidae | Semelidae |
| Siphonariidae | Solecurtidae |
| Spondylidae | Strombidae |
| Tegulidae | Tellinidae |
| Terebridae | Tonnidae |
| Triviidae | Trochidae |
| Turbinellidae | Turbinidae |
| Turritellidae | Ungulinidae |
| Veneridae | Volutidae |
## Download the Dataset
You can easily download and load the dataset using the `datasets` library:
### 1. Install the Datasets library
```bash
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("BackHome19K", split="train")
#Replace "train" with "val" or "test" to access the other splits.
```
## Licensing and Usage
- License: MIT License
- Usage:
This dataset is intended for educational and research purposes only.
Commercial use of the dataset requires prior verification of the original image sources.
Respect the copyright and intellectual property rights associated with the collected images.
### Acknowledgments
We thank the School of Biology at the University of Costa Rica for their guidance in species selection and validation. We also acknowledge the marine biologists who contributed to expert taxonomic verification and the collection of reference images.
We are deeply grateful to all contributors who have made marine species documentation publicly available, and to the creators of open-source tools that made this project possible.
This dataset was created to promote awareness of marine biodiversity and to support scientific research and conservation efforts. We hope it helps researchers, educators, and enthusiasts understand and protect our oceans’ ecosystems.
## Citation
If you use this dataset in your research, please cite it as follows:
```bibtex
@misc{valverde2025homecomputervisionsolution,
title={Back Home: A Computer Vision Solution to Seashell Identification for Ecological Restoration},
author={Alexander Valverde and Luis Solano and André Montoya},
year={2025},
eprint={2501.04873},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.04873},
}
``` |