Datasetpublic

valiantlynxz/norwegian-grocery

NorgesGruppen Grocery Detection Dataset Object detection dataset for Norwegian grocery store shelf images, from the NorgesGruppen Data competition. Quick Start from datasets import load_dataset # COCO format (default) - raw competition annotations ds = load_dataset("valiantlynxz/norwegian-grocery", trust_remote_code=True) # YOLO format - auto-converted on load, no scripts needed ds = load_dataset("valiantlynxz/norwegian-grocery", name="yolo"… See the full description on the dataset page: https://huggingface.co/datasets/valiantlynxz/norwegian-grocery.

sourceHugging Facecc-by-nc-4.0updated 6mo agoView on Hugging Face

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Dataset Card

NorgesGruppen Grocery Detection Dataset

Object detection dataset for Norwegian grocery store shelf images, from the NorgesGruppen Data competition.

Quick Start

python

from datasets import load_dataset

# COCO format (default) - raw competition annotations
ds = load_dataset("valiantlynxz/norwegian-grocery", trust_remote_code=True)

# YOLO format - auto-converted on load, no scripts needed
ds = load_dataset("valiantlynxz/norwegian-grocery", name="yolo", trust_remote_code=True)

# Product reference images - multi-angle shots per product
ds = load_dataset("valiantlynxz/norwegian-grocery", name="products", trust_remote_code=True)

Configurations

Config	Default	Description	Splits
`coco`	Yes	Raw COCO annotations with full metadata	train, validation
`yolo`		YOLO normalized `[cx, cy, w, h]` (converted on-the-fly)	train, validation
`products`		Product reference images with metadata	train

COCO Config

Each example contains a shelf image and all its COCO-format annotations:

python

ds = load_dataset("valiantlynxz/norwegian-grocery", trust_remote_code=True)
example = ds["train"][0]

example["image"]          # PIL Image
example["image_id"]       # int
example["width"]          # image width in pixels
example["height"]         # image height in pixels
example["annotations"]    # JSON string of COCO annotation list
# Each annotation: {"id", "category_id", "bbox": [x, y, w, h], "area", "product_code", ...}

YOLO Config

Same images, but bounding boxes are auto-converted to YOLO normalized center format:

python

ds = load_dataset("valiantlynxz/norwegian-grocery", name="yolo", trust_remote_code=True)
example = ds["train"][0]

example["image"]      # PIL Image
example["image_id"]   # int
example["labels"]     # [int, ...] - category IDs
example["bboxes"]     # [[cx, cy, w, h], ...] - normalized to [0, 1]

Products Config

Reference images for 327 products, organized by product code:

python

ds = load_dataset("valiantlynxz/norwegian-grocery", name="products", trust_remote_code=True)
example = ds["train"][0]

example["image"]            # PIL Image (one angle)
example["product_code"]     # barcode string
example["product_name"]     # e.g. "EVERGOOD CLASSIC KOKMALT 250G"
example["image_type"]       # "main", "front", "back", "left", "right", "top", "bottom"
example["annotation_count"] # how often this product appears in shelf images

Dataset Summary

—248 shelf images from Norwegian grocery stores (Egg, Frokost, Knekkebrod, Varmedrikker)
—22,731 bounding box annotations in COCO format
—356 product categories (IDs 0-355, where 355 = unknown_product)
—327 products with multi-angle reference photos (1,582 images total)
—Train/val split: 211 / 37 images (85/15, seed=42)

Structure

norwegian_grocery.py         # Custom loading script (auto-discovered by HF)
train/
  annotations.json           # COCO-format annotations
  images/                    # 248 shelf images (.jpg/.jpeg)
NM_NGD_product_images/
  metadata.json              # Product names, barcodes, annotation counts
  {barcode}/                 # Product reference images per barcode
scripts/
  coco_to_yolo.py            # Convert COCO to YOLO format with train/val split

Annotation Format

COCO format (annotations.json):

json

{
  "images": [{"id": 1, "file_name": "img_00001.jpg", "width": 2000, "height": 1500}],
  "categories": [{"id": 0, "name": "VESTLANDSLEFSA TØRRE 10STK 360G", "supercategory": "product"}],
  "annotations": [{
    "id": 1, "image_id": 1, "category_id": 42,
    "bbox": [141, 49, 169, 152],
    "area": 25688, "iscrowd": 0,
    "product_code": "8445291513365",
    "product_name": "NESCAFE VANILLA LATTE 136G NESTLE",
    "corrected": true
  }]
}

bbox is [x, y, width, height] in pixels (COCO format).

For Local YOLO Training

If you need on-disk YOLO files for ultralytics, the repo includes scripts/coco_to_yolo.py:

bash

python scripts/coco_to_yolo.py \
    --annotations train/annotations.json \
    --images train/images \
    --output yolo \
    --val-ratio 0.15

This creates yolo/images/{train,val}/, yolo/labels/{train,val}/, and configs/dataset.yaml.

Usage with the Detection Project in https://github.com/kuben-labs/nmai

bash

cd detection/
make data    # clones from HuggingFace
make yolo    # generates YOLO format + dataset.yaml

Statistics

—Images: 481-5712px wide, 399-4624px tall
—Average: ~92 annotations per image
—Most frequent category: unknown_product (355) with 422 annotations
—Product reference images: 1,582 total across 327 products