LAG: Large-scale Attention-based Glaucoma Database

11,760 fundus images with glaucoma classification labels and ophthalmologist-derived attention maps. Largest public glaucoma dataset with attention ground truth.

At a glance

Field	Value
Short name	lag
Full name	LAG: Large-scale Attention-based Glaucoma Database
Primary category	fundus
Contained modalities	fundus
Tasks	classification, segmentation
Samples	11,760
Classes	2 (normal, glaucoma)
Splits	train, test
Size	3.0 GB
Source-stated terms	Research only (no redistribution; password by email)
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	author_contact
Route backend	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	manual_access_blocked
Legacy sample-loader status	Metadata and access only

Notes

Dropbox link gated by password — email [email protected] to request access. Cannot be auto-downloaded.

Access preflight and acquisition

CLI

# This route requires upstream human action; no transfer starts.
eyehub download lag --data-dir ./data --dry-run --json
# Follow the official instructions shown by preflight.

Python

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

ds = REGISTRY.get_dataset('lag')
print(preflight_dataset(ds, './data'))  # returns manual_access_blocked

Upstream page: github.com/smilell

Source-term evidence: github.com/smilell

Loader status

This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.

Citation

BibTeX

@misc{lag,
  title  = { LAG: Large-scale Attention-based Glaucoma Database },
  note   = { Li et al., 'Attention Based Glaucoma Detection: A Large-scale Database and CNN Model', CVPR 2019 },
  year   = { 2019 },
  url    = { https://github.com/smilell/AG-CNN },
}

Plain text

Li et al., 'Attention Based Glaucoma Detection: A Large-scale Database and CNN Model', CVPR 2019.

Source-stated terms

• Raw source string: Research only (no redistribution; password by email)
• Normalized category: research-only
• Apparent scope: dataset_files
• Descriptive screening label: Research or challenge restriction recorded; check source

⚠️ 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

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• 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)