We’re releasing Kanops. Open Access · Imagery (Retail Scenes v0): a curated set of retail in store photographs (multi-retailer, multiple years, seasonal “Halloween 2024”), intended for tasks like shelf/fixture detection, planogram reasoning, and merchandising classification alongside many other use cases, such as spatial awareness and detection and other use cases we haven’t thought of.
Our first dataset attempt!
Part of a 1m strong image dataset in totality.
- Size: ~10.8k images (v0)
- Format: folder-per-retailer/category; MANIFEST.csv, metadata.csv, checksums.sha256
- Privacy: all identifiable faces blurred; EXIF/IPTC owner/terms embedded
- License: evaluation-only (no redistribution of images or model weights derived exclusively from this data)
- Access: gated on HF (quick request form)
Hugging Face: https://huggingface.co/datasets/dresserman/kanops-open-access-imagery
(quiick load after access granted)
# pip install datasets
from datasets import load_dataset
ds = load_dataset(“imagefolder”, data_dir=”hf://datasets/dresserman/kanops-open-access-imagery/train”)
print(len(ds[“train”]))
Contact: HF Discussions on the dataset card or DM u/malctucker
submitted by /u/malctucker
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