Datasetpublic

TsinghuaCorals/reefnet_species_images

ReefNet Species Images Full-resolution coral reef images with species-level annotations from the ReefNet dataset. Dataset Description This dataset contains 51,080 images from 54 CoralNet sources with 365,030 annotation patches across 245 species. Each annotation in metadata.parquet specifies a patch center (Row, Column) within a full-resolution image. Crop a 224×224 patch centered at these coordinates for classification tasks. Splits The curated… See the full description on the dataset page: https://huggingface.co/datasets/TsinghuaCorals/reefnet_species_images.

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Dataset Card

ReefNet Species Images

Full-resolution coral reef images with species-level annotations from the ReefNet dataset.

Dataset Description

This dataset contains 51,080 images from 54 CoralNet sources with 365,030 annotation patches across 245 species.

Each annotation in metadata.parquet specifies a patch center (Row, Column) within a full-resolution image. Crop a 224×224 patch centered at these coordinates for classification tasks.

Splits

The curated 92-class subset used for training has the following splits:

Split	Patches	Description
train	48,313	Training set (max 1000/class)
image_val	32,792	Image-level validation (10%)
image_test	33,090	Image-level test (10%)
source_val	8,074	Source-level holdout (WAPA Interns)
null	485,522	Not in curated split (other species/annotations)

Metadata Columns

Column	Description
`file_name`	Relative path: `{source}/{image_name}`
`split`	Split assignment (null if not in curated set)
`in_curated_92class`	True if in the 92-class training split
`label_taxonomy`	Full taxonomy string
`label_coralnet`	Original CoralNet label
`source`	CoralNet source name
`Row`, `Column`	Patch center coordinates
`Ecoregion`, `Province`, `Realm`	Geographic classification
`Latitude`, `Longitude`	GPS coordinates

Citation

bibtex

@article{reefnet2024,
  title={ReefNet: A Large-Scale Coral Reef Annotation Dataset},
  year={2024},
  note={https://huggingface.co/datasets/ReefNet/ReefNet-1.0}
}