TL;DR: ~10–11k retail-scene photos (UK/US) for evaluation, research, and benchmarking. Faces are blurred. Provenance embedded (checksums + EXIF/IPTC/XMP). Evaluation-only license.

Trademarks. All retailer names, store marks, and logos referenced in this dataset are the property of their respective owners. Kanops is not affiliated with, endorsed by, or sponsored by any retailer. Images are provided for evaluation and research under the included license.

Intended use. Retail scene understanding (shelf detection, planogram research, seasonal merchandising analysis). Not for brand endorsement or marketing.

Access (gated)

This dataset is gated on Hugging Face. Request access on the repo page. By requesting and using the data you agree to the Kanops Evaluation License (see LICENSE at the root).

What's included

Images under train/:
- train/2014/<Retailer>/*.jpg
- train/FullStores/<Retailer>/**/*
- train/Halloween2024/<Retailer>/**/*
Control files at the root:
- MANIFEST.csv
- metadata.csv
- checksums.sha256 — SHA-256 for all images in this release
- LICENSE
- README.md — this file

Images live only under train/. All control files sit at the repository root.

How to load

Hugging Face Datasets (ImageFolder)

from datasets import load_dataset

 ds = load_dataset(
      "imagefolder",
      data_dir="hf://datasets/dresserman/kanops-open-retail-imagery/train",
      split="train",
  )

img = ds[0]["image"]  # PIL.Image

Read metadata

import pandas as pd
meta = pd.read_csv("hf://datasets/dresserman/kanops-open-retail-imagery/metadata.csv")
meta.head()

Starter Notebooks

Explore the dataset with our Kaggle notebooks: - Exploratory Data Analysis - OCR & Product Recognition - Retailer Classification CNN

Data schema (minimum)

metadata.csv columns (you may see additional fields):

file_name — path relative to dataset root (e.g., train/2014/Aldi/IMG_1234.jpeg)
bytes — file size
width, height — image dimensions (if available)
sha256 — content hash (provenance/integrity)
collection — one of {2014, FullStores, Halloween2024}

If present:

retailer — inferred from path
year — inferred from path

Intended use

Evaluation/benchmarking of shelf/fixture detection, retrieval, layout analysis, merchandising.
Research and prototyping under evaluation terms.

Not permitted: redistribution of images/derivatives; public model-weight redistribution; production or commercial training without a separate commercial license. See LICENSE.

Privacy & provenance

Faces blurred via automated pass + manual review.
Rights/usage embedded in EXIF/IPTC/XMP.
checksums.sha256 provides per-file SHA-256 for integrity.
For takedown or corrections, contact hello@kanops.ai with file_name and SHA-256.

Versioning

v0 — initial release; faces redacted; metadata + checksums included.
Future versions (v1, v2, …) will be published immutably.

Full Dataset

This is a ~10k sample. The full Kanops archive contains:

1.135M+ images spanning 2009–2025
20+ UK retailers with extensive coverage
Seasonal collections: 325,000+ seasonal event images

For commercial licensing: hello@kanops.ai

Citation

APA Dresser, S., & Dresser, K. (2025). Kanops — Open Access · Imagery (Retail Scenes v0) [Dataset]. Kanops.ai / Grocery Insight. https://doi.org/10.5281/zenodo.18071325

BibTeX

@dataset{dresser_kanops_2025,
    title   = {Kanops Open Retail Imagery (v0)},
  author  = {Dresser, Steve and Dresser, Katie},
  year    = {2025},
  doi     = {10.5281/zenodo.18071325},
  url     = {https://doi.org/10.5281/zenodo.18071325},
  note    = {Evaluation-only license. Faces blurred.}
}

Contact

Questions, broader licensing, or takedowns: hello@kanops.ai / www.kanops.ai

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