SMDG-19: Standardized Multi-channel Glaucoma Benchmark
~12,000 fundus images aggregated from 19 public glaucoma datasets with standardized disc/cup/vessel channels and unified labels. CC0 — fully public domain.
At a glance
| Field | Value |
|---|---|
| Short name | smdg |
| Full name | SMDG-19: Standardized Multi-channel Glaucoma Benchmark |
| Primary category | fundus |
| Contained modalities | fundus |
| Tasks | classification, segmentation |
| Samples | 12,449 |
| Classes | 3 (non_glaucoma, glaucoma, suspect) |
| Splits | train, val, test |
| Size | 5.0 GB |
| Source-stated terms | CC0 1.0 (Public Domain) |
| Normalized terms | cc0 |
| 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 | Metadata and access only |
Notes
Aggregator — overlaps with several EyeDataHub-indexed glaucoma datasets. Useful for unified multi-source training.
Access preflight and acquisition
- CLI
- Python
# Read-only preflight
eyehub download smdg --data-dir ./data --dry-run --json
# Explicit transfer, only when preflight reports supported behavior
eyehub download smdg --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('smdg')
print(preflight_dataset(ds, './data')) # no transfer
Upstream page: kaggle.com/datasets
Source-term evidence: kaggle.com/datasets
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
- Plain text
@misc{smdg,
title = { SMDG-19: Standardized Multi-channel Glaucoma Benchmark },
note = { Kiefer, 'Standardized Multi-channel Dataset for Glaucoma (SMDG-19)', Kaggle 2022. Breakdown: 7,499 non-glaucoma + 4,817 glaucoma + 133 suspect },
year = { 2022 },
url = { https://www.kaggle.com/datasets/deathtrooper/multichannel-glaucoma-benchmark-dataset },
}
Kiefer, 'Standardized Multi-channel Dataset for Glaucoma (SMDG-19)', Kaggle 2022. Breakdown: 7,499 non-glaucoma + 4,817 glaucoma + 133 suspect.
Source-stated terms
- Raw source string: CC0 1.0 (Public Domain)
- Normalized category:
cc0 - 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)