APTOS 2019 Blindness Detection

3662 fundus images from Aravind Eye Hospital, graded 0-4 for diabetic retinopathy severity.

At a glance

Field	Value
Short name	aptos2019
Full name	APTOS 2019 Blindness Detection
First published	2019-06-27
Publication date precision	day
Publication date evidence	kaggle.com/competitions
Publication date source field	Kaggle competition Overview: Start
Publication date reviewed	2026-09-11
Primary category	fundus
Resource role	current_dataset
Dataset family	aptos2019
Contained modalities	fundus
Tasks	grading, classification
Primary reported quantity	3,662 images
Classes	5 (No DR, Mild, Moderate, Severe, Proliferative DR)
Splits	train
Size	9.0 GB
Source-stated terms	Competition rules apply
Normalized terms	unknown
Descriptive screening label	Unknown or unclear; do not assume permission
Terms scope	challenge_participation
Access friction	self_service_clickthrough
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	3,662	images	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	kaggle.com/c

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.

• aptos_arcade_onh_masks is derived from this record: The deposit supplies vascular-arcade and optic-nerve-head masks for APTOS 2019 images. (evidence)
• mm_retinal_reason is derived from this record: The version-pinned official dataset card lists this record among the CFP or OCT sources used to construct MM-Retinal-Reason. (evidence)
• x_pcr is derived from this record: Source labels in the version-pinned public X-PCR deposit identify this catalog record as upstream material. (evidence)

Access information and download

CLI

# Read-only preflight
eyehub download aptos2019 --data-dir ./data --dry-run --json

# Download, only when preflight reports supported behavior
eyehub download aptos2019 --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('aptos2019')
print(preflight_dataset(ds, './data'))  # no download

Upstream page: kaggle.com/c

Source-term evidence: kaggle.com/c

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('aptos2019')
samples = ds.load(data_dir, split='train')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{aptos2019,
  title  = { APTOS 2019 Blindness Detection },
  note   = { Karthik et al., APTOS 2019 Blindness Detection. Kaggle competition, 2019 },
  year   = { 2019 },
  url    = { https://www.kaggle.com/c/aptos2019-blindness-detection },
}

Plain text

Karthik et al., APTOS 2019 Blindness Detection. Kaggle competition, 2019.

Source-stated terms

• Raw source string: Competition rules apply
• Normalized category: unknown
• Apparent scope: challenge_participation
• Descriptive screening label: Unknown or unclear; do not assume permission

⚠️ 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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