OCTDL: OCT Deep Learning Dataset

2,000+ OCT images labeled for 7 conditions: AMD, DME, ERM, NO (normal), RAO, RVO, VID.

At a glance

Field	Value
Short name	octdl
Full name	OCTDL: OCT Deep Learning Dataset
First published	2023-12-13
Publication date precision	day
Publication date evidence	data.mendeley.com/datasets
Publication date source field	citation_publication_date (version 1)
Publication date reviewed	2026-09-11
Primary category	oct
Resource role	current_dataset
Dataset family	octdl
Contained modalities	oct
Tasks	classification
Primary reported quantity	2,000 images
Classes	7 (AMD, DME, ERM, NO, RAO, RVO, VID)
Splits	all
Size	0.8 GB
Source-stated terms	CC BY 4.0
Normalized terms	cc-by
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	Mendeley Data
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	2,000	images	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	data.mendeley.com/datasets

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.

• 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)

Access information and download

CLI

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

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

Python

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

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

Upstream page: data.mendeley.com/datasets

Source-term evidence: data.mendeley.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('octdl')
samples = ds.load(data_dir, split='all')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{octdl,
  title  = { OCTDL: OCT Deep Learning Dataset },
  note   = { Kulyabin et al., 'OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods', Scientific Data 2024 },
  year   = { 2024 },
  url    = { https://data.mendeley.com/datasets/sncdhf53xc/4 },
}

Plain text

Kulyabin et al., 'OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods', Scientific Data 2024.

Source-stated terms

• Raw source string: CC BY 4.0
• Normalized category: cc-by
• 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.

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• kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 images, cc-by)
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• mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 images, cc-by)
• mmrdr: MMRDR: Multi-Modal Retinal Diabetic Retinopathy Dataset (24,460 images, cc-by)