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
Primary category	fundus
Contained modalities	fundus
Tasks	grading, classification
Samples	3,662
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

Access preflight and acquisition

CLI

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

# Explicit transfer, 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 transfer

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.

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)