ODIR-2019: Ocular Disease Intelligent Recognition
8000 patients (16000 fundus images, left + right eye) with 8 disease labels for multi-label ocular disease classification.
At a glance
| Field | Value |
|---|---|
| Short name | odir2019 |
| Full name | ODIR-2019: Ocular Disease Intelligent Recognition |
| 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 | odir2019 |
| Contained modalities | fundus |
| Tasks | multilabel, classification |
| Primary reported quantity | 8,000 participants |
| Classes | 8 (Normal, Diabetes, Glaucoma, Cataract, AMD, Hypertension, Myopia, Other) |
| Splits | train, test |
| Size | 3.5 GB |
| Source-stated terms | CC BY-SA 4.0 |
| Normalized terms | cc-by-sa |
| 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 | 8,000 | participants | Primary quantity reported in the reviewed catalog source | legacy_catalog_field | kaggle.com/datasets |
| Additional | 16,000 | images | Left- and right-eye fundus images | official_source_description | odir2019.grand-challenge.org |
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.
Documented relationships
These links record source-supported lineage or overlap, not merely similar modality tags.
- amdnet23 is
derived fromthis record: AMDNet23 compiles preprocessed images from ODIR, RFMiD, HRF, ARIA, DR_200, and Fundus Dataset. (evidence) - aod is
derived fromthis record: The AOD deposit describes an augmented and preprocessed ODIR-5K resource. (evidence) - multieye is
derived fromthis record: The MultiEYE paper names this record as one of the public fundus or OCT sources assembled for the benchmark. (evidence) - smdg is
derived fromthis record: The official SMDG source table lists this catalog record among the 19 standardized source domains. (evidence) - x_pcr is
derived fromthis record: Source labels in the version-pinned public X-PCR deposit identify this catalog record as upstream material. (evidence)
Access information and download
- CLI
- Python
# Read-only preflight
eyehub download odir2019 --data-dir ./data --dry-run --json
# Download, only when preflight reports supported behavior
eyehub download odir2019 --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('odir2019')
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('odir2019')
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{odir2019,
title = { ODIR-2019: Ocular Disease Intelligent Recognition },
note = { Li et al., 'An Annotation-Free Restoration Network for Cataractous Fundus Images', arXiv 2021 },
year = { 2021 },
url = { https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k },
}
Li et al., 'An Annotation-Free Restoration Network for Cataractous Fundus Images', arXiv 2021.
Source-stated terms
- Raw source string: CC BY-SA 4.0
- Normalized category:
cc-by-sa - 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.
Similar resources by shared modality
- airogs: AIROGS: AI for Robust Glaucoma Screening (113,893 images,
cc-by-nc-nd) - multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (103,959 images,
mit) - eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 question answer pairs,
unknown) - justraigs: JustRAIGS: Just Referral AI Glaucoma Screening Dataset (101,442 images,
cc-by-nc-nd) - eyepacs: EyePACS — Diabetic Retinopathy Detection (Kaggle 2015) (88,702 images,
research-only) - angioreport: AngioReport Fundus Angiography Report Dataset (55,361 images,
unknown) - ffa_ir: FFA-IR Medical Report Dataset (47,247 images,
unknown) - mfiddr: MFIDDR: Multi-Field Imaging Dataset for Diabetic Retinopathy (34,452 images,
mit)