RIM-ONE DL

485 fundus images (313 normal, 172 glaucoma) with optic disc region of interest crops for deep learning.

At a glance

Field	Value
Short name	rimone_dl
Full name	RIM-ONE DL
First published	Unknown
Publication date precision	Unknown
Publication date evidence	Unknown
Publication date source field	Unknown
Publication date reviewed	Unknown
Primary category	fundus
Resource role	current_dataset
Dataset family	rimone_dl
Contained modalities	fundus
Tasks	classification
Primary reported quantity	485 images
Classes	2 (Normal, Glaucoma)
Splits	train, test
Size	0.5 GB
Source-stated terms	CC BY 4.0
Normalized terms	cc-by
Descriptive screening label	Standard label without an explicit NC clause; not a permission finding
Terms scope	dataset_files
Access friction	self_service_authenticated
Route backend	Kaggle
Availability	available (checked 2026-07-21)
Acquisition support	standard_platform_supported
Legacy sample-loader status	Standard loader included

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	485	images	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	kaggle.com/datasets

Counts retain their source-reported units. Additional rows can describe components, paired items, or derivative copies and are not automatically added to the primary quantity.

Access information and download

CLI

# Read-only preflight
eyehub download rimone_dl --data-dir ./data --dry-run --json

# Download, only when preflight reports supported behavior
eyehub download rimone_dl --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('rimone_dl')
print(preflight_dataset(ds, './data'))  # no download

Upstream page: kaggle.com/datasets

Source-term evidence: kaggle.com/datasets

Loader example

This entry includes a standard DatasetSample loader.

from pathlib import Path
from eyedatahub.datasets.registry import REGISTRY

data_dir = Path('~/.eyedatahub/data').expanduser()
ds = REGISTRY.get_dataset('rimone_dl')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{rimone_dl,
  title  = { RIM-ONE DL },
  note   = { Fumero et al., 'RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning', Intl. Image Analysis and Ophthalmology 2020 },
  year   = { 2020 },
  url    = { https://www.kaggle.com/datasets/orvile/rim-one-retinal-dataset-for-assessing-glaucoma },
}

Plain text

Fumero et al., 'RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning', Intl. Image Analysis and Ophthalmology 2020.

Source-stated terms

• Raw source string: CC BY 4.0
• Normalized category: cc-by
• Apparent scope: dataset_files
• Descriptive screening label: Standard label without an explicit NC clause; not a permission finding

⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.

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