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 |
| Primary category | fundus |
| Contained modalities | fundus |
| Tasks | classification |
| Samples | 485 |
| 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 |
Access preflight and acquisition
- CLI
- Python
# Read-only preflight
eyehub download rimone_dl --data-dir ./data --dry-run --json
# Explicit transfer, only when preflight reports supported behavior
eyehub download rimone_dl --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('rimone_dl')
print(preflight_dataset(ds, './data')) # no transfer
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
- Plain text
@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 },
}
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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