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", 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:

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:

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:

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):

{
  "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:

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

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