HRF: High-Resolution Fundus Image Database

45 high-resolution fundus images (healthy/DR/glaucoma) with manual vessel segmentation.

At a glance

Field	Value
Short name	hrf
Full name	HRF: High-Resolution Fundus Image Database
Primary category	fundus
Contained modalities	fundus
Tasks	segmentation
Samples	45
Classes	Not reported (Not reported)
Splits	all
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	anonymous_direct
Route backend	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	guided_instructions_only
Legacy sample-loader status	Standard loader included

Notes

Requires manual download from FAU Erlangen-Nuremberg website.

Access preflight and acquisition

CLI

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

# Explicit transfer, only when preflight reports supported behavior
eyehub download hrf --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('hrf')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: www5.cs.fau.de/research

Source-term evidence: www5.cs.fau.de/research

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('hrf')
samples = ds.load(data_dir, split='all')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{hrf,
  title  = { HRF: High-Resolution Fundus Image Database },
  note   = { Budai et al., 'Robust vessel segmentation in fundus images', Intl Journal of Biomedical Imaging 2013 },
  year   = { 2013 },
  url    = { https://www5.cs.fau.de/research/data/fundus-images/ },
}

Plain text

Budai et al., 'Robust vessel segmentation in fundus images', Intl Journal of Biomedical Imaging 2013.

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.

Related datasets with shared modalities

• airogs: AIROGS: AI for Robust Glaucoma Screening (113,893 records, cc-by-nc-nd)
• eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records, unknown)
• justraigs: JustRAIGS: Just Referral AI Glaucoma Screening Dataset (101,442 records, cc-by-nc-nd)
• eyepacs: EyePACS — Diabetic Retinopathy Detection (Kaggle 2015) (88,702 records, research-only)
• multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records, mit)
• angioreport: AngioReport Fundus Angiography Report Dataset (55,361 records, unknown)
• ffa_ir: FFA-IR Medical Report Dataset (47,247 records, unknown)
• bidr: BiDR: Diabetic Retinopathy Diagnosis Dataset (35,126 records, unknown)