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-access-imagery/train",
    split="train",
)
img = ds[0]["image"]  # PIL.Image
~~~

### Read metadata
~~~python
import pandas as pd
meta = pd.read_csv("hf://datasets/dresserman/kanops-open-access-imagery/metadata.csv")
meta.head()
~~~

---

## 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 [email protected] with `file_name` and SHA-256.

---

## Versioning

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

---

## Citation

**APA**  
Dresser, S., & Dresser, K. (2025). *Kanops — Open Access · Imagery (Retail Scenes v0)* [Dataset]. Grocery Insight (Kanops). https://huggingface.co/datasets/dresserman/kanops-open-access-imagery

**BibTeX**
```bibtex
@dataset{dresser_kanops_retail_scenes_v0_2025,
  title   = {Kanops — Open Access · Imagery (Retail Scenes v0)},
  author  = {Dresser, Steve and Dresser, Katie},
  year    = {2025},
  howpublished = {\url{https://huggingface.co/datasets/dresserman/kanops-open-access-imagery}},
  note    = {Evaluation-only license. Faces blurred.}
}
~~~

---

## Contact

Questions, broader licensing, or takedowns: [email protected] / www.groceryinsight.com

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