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Datasets:
V4ldeLund
/
SMK-image-text

Tasks:
Image-to-Text
Feature Extraction
Modalities:
Image
Tabular
Text
Formats:
parquet
Sub-tasks:
image-captioning
Languages:
Danish
English
Size:
10K - 100K
Libraries:
Datasets
Dask
Polars
License:
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metadata
dataset_info:
  splits:
    - name: train
      num_examples: 39353
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
language:
  - da
  - en
pretty_name: SMK Image-Text (Danish/English)
size_categories:
  - 10K<n<100K
license: other
task_categories:
  - image-to-text
  - feature-extraction
task_ids:
  - image-captioning

Dataset Card for “SMK Image-Text (Danish/English)”

Dataset Description

  • Source: Statens Museum for Kunst (SMK) collection API.
  • Records: 39,353 objects with paired images and bilingual metadata.
  • Storage: 2 Parquet shards on the Hub (data/train-*.parquet), ~3 GB total each.
  • Languages: Danish (da) and English (en) fields where available.

Summary

Each row corresponds to an SMK collection object. It contains:

  • Raw image bytes (image_bytes) plus thumbnails and basic image stats (width/height/size, entropy, contrast, etc.).
  • Object metadata in Danish and English: titles, object names, artists/creators, production dates, techniques, materials, inscriptions, labels, documentation references.
  • Rights information: public_domain flag and rights text per object.

Dataset Structure

Key Fields

  • Images
    • image_bytes (binary): full-resolution image bytes. Cast to datasets.Image() to decode.
    • image_thumbnail (string URL), image_width, image_height, image_size, image_orientation, image_cropped, colors, suggested_bg_color, entropy, contrast, brightness, saturation, colortemp.
  • Identity
    • object_number, id, object_url, frontend_url, responsible_department.
  • Dating & dimensions
    • acquisition_date, acquisition_date_precision, production_date_en/da (list with fields start, end, start_prec, end_prec, period), dimensions (list with fieldsvalue, unit, part, type, notes, precision).
  • Titles & names
    • titles_en/da (list with fields language, title, type, notes, translation).
    • object_names_en/da (list with fields name, classification_notes).
  • Creators
    • artist_en/da (list of strings).
    • production_en/da (list with fields creator, creator_forename, creator_surname, creator_gender, creator_nationality, creator_role, creator_history, creator_lref, creator_qualifier, craftsman, birth/death dates, notes).
  • Techniques & materials
    • techniques_en/da, materials_en/da, medium_en/da.
  • Context & documentation
    • labels_en/da (list with fields date, source, text, type).
    • inscriptions_en/da (list with fields content, description, language, type, date, notes).
    • documentation_en/da (list with fields author, title, shelfmark, page_reference, year_of_publication, notes).
    • content_description_en/da, production_dates_notes_en/da.
  • Rights
    • public_domain (bool) and rights (string) per object.

Usage 

from datasets import load_dataset, Image

ds = load_dataset("V4ldeLund/SMK-image-text", split="train")

# Decode image bytes into PIL images
ds = ds.cast_column("image_bytes", Image())
sample = ds[0]
sample["image_bytes"].show()

# Example: English title and production info
print(sample["titles_en"], sample["production_en"])

Intended Uses

  • Image captioning, image text retrieval, metadata completion, museum collection exploration, multilingual vision language modelling.

Maintainer / Contact

  • Maintainer: Vladimir Salnikov — v4ldesalnikov@gmail.com
  • Issues & questions: Please open a discussion on the dataset’s Hugging Face page: https://huggingface.co/datasets/V4ldeLund/SMK-image-text