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+    {
+      "product_code": "8711000505137",
+      "product_name": "TASSIMO GEVALIA LATTE MACCHIATO KARAMELL",
+      "annotation_count": 1,
+      "corrected_count": 1,
+      "has_images": true,
+      "image_types": [
+        "front",
+        "left",
+        "main",
+        "top"
+      ]
+    },
+    {
+      "product_code": "8720608006649",
+      "product_name": "LIPTON ICETEA MANGO PULVER 50G",
+      "annotation_count": 1,
+      "corrected_count": 0,
+      "has_images": true,
+      "image_types": [
+        "back",
+        "front",
+        "main"
+      ]
+    },
+    {
+      "product_code": "8720608006663",
+      "product_name": "LIPTON ICETEA LEMON PULVER 50G",
+      "annotation_count": 1,
+      "corrected_count": 0,
+      "has_images": true,
+      "image_types": [
+        "back",
+        "front",
+        "main"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 7be5fc7f47d5db027d120b8024982df93db95b74..0584b1a8161b27114792600efc6ae52c757db402 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,97 @@
----
-license: mit
----
+---
+license: cc-by-nc-4.0
+task_categories:
+  - object-detection
+  - image-classification
+language:
+  - "no"
+tags:
+  - grocery
+  - retail
+  - shelf-detection
+  - coco
+  - yolo
+  - norgesgruppen
+size_categories:
+  - n<1K
+---
+
+# NorgesGruppen Grocery Detection Dataset
+
+Object detection dataset for Norwegian grocery store shelf images, from the NorgesGruppen Data competition.
+
+## Dataset Summary
+
+- **248 shelf images** from Norwegian grocery stores (4 sections: Egg, Frokost, Knekkebrod, Varmedrikker)
+- **22,731 bounding box annotations** in COCO format
+- **356 product categories** (IDs 0-355, where 355 = `unknown_product`)
+- **327 product reference images** with multi-angle photos (main, front, back, left, right, top, bottom)
+
+## Structure
+
+```
+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).
+
+## Converting to YOLO Format
+
+The included `scripts/coco_to_yolo.py` converts COCO annotations to YOLO format with an 85/15 train/val split:
+
+```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}/` - symlinked images
+- `yolo/labels/{train,val}/` - YOLO `.txt` label files
+- `yolo/category_names.json` - category ID to name mapping
+- `configs/dataset.yaml` - YOLOv8 dataset config (with absolute paths for your machine)
+
+## Usage with the Detection Project
+
+Clone this dataset into the detection project:
+
+```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
diff --git a/scripts/coco_to_yolo.py b/scripts/coco_to_yolo.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f1844e8e6f834eb50bcdae08bc952bf1903d246
--- /dev/null
+++ b/scripts/coco_to_yolo.py
@@ -0,0 +1,204 @@
+"""Convert COCO-format annotations to YOLO format with train/val split.
+
+Usage:
+    python scripts/coco_to_yolo.py \
+        --annotations data/raw/train/annotations.json \
+        --images data/raw/train/images \
+        --output data/yolo \
+        --val-ratio 0.15
+"""
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+
+def coco_to_yolo_bbox(
+    bbox: list[float],
+    image_width: int,
+    image_height: int,
+) -> tuple[float, float, float, float]:
+    """Convert COCO bbox [x, y, w, h] to YOLO [cx, cy, w, h] normalized."""
+    x, y, w, h = bbox
+    cx = (x + w / 2) / image_width
+    cy = (y + h / 2) / image_height
+    nw = w / image_width
+    nh = h / image_height
+
+    # Clamp to [0, 1]
+    cx = max(0.0, min(1.0, cx))
+    cy = max(0.0, min(1.0, cy))
+    nw = max(0.0, min(1.0, nw))
+    nh = max(0.0, min(1.0, nh))
+
+    return cx, cy, nw, nh
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Convert COCO to YOLO format")
+    parser.add_argument(
+        "--annotations",
+        type=str,
+        default="data/raw/train/annotations.json",
+        help="Path to COCO annotations JSON",
+    )
+    parser.add_argument(
+        "--images",
+        type=str,
+        default="data/raw/train/images",
+        help="Path to images directory",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="data/yolo",
+        help="Output directory for YOLO dataset",
+    )
+    parser.add_argument(
+        "--val-ratio",
+        type=float,
+        default=0.15,
+        help="Fraction of images for validation (default: 0.15)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for reproducible split",
+    )
+    args = parser.parse_args()
+
+    annotations_path = Path(args.annotations)
+    images_dir = Path(args.images)
+    output_dir = Path(args.output)
+
+    # Load COCO annotations
+    print(f"Loading annotations from {annotations_path}")
+    with open(annotations_path) as f:
+        coco_data = json.load(f)
+
+    images = coco_data["images"]
+    annotations = coco_data["annotations"]
+    categories = coco_data["categories"]
+
+    print(f"  Images: {len(images)}")
+    print(f"  Annotations: {len(annotations)}")
+    print(f"  Categories: {len(categories)}")
+
+    # Build lookup: image_id -> image info
+    image_lookup = {img["id"]: img for img in images}
+
+    # Build lookup: image_id -> list of annotations
+    image_annotations: dict[int, list[dict]] = {}
+    for ann in annotations:
+        image_id = ann["image_id"]
+        if image_id not in image_annotations:
+            image_annotations[image_id] = []
+        image_annotations[image_id].append(ann)
+
+    # Train/val split
+    random.seed(args.seed)
+    image_ids = sorted([img["id"] for img in images])
+    random.shuffle(image_ids)
+    val_count = max(1, int(len(image_ids) * args.val_ratio))
+    val_ids = set(image_ids[:val_count])
+    train_ids = set(image_ids[val_count:])
+
+    print(f"  Train images: {len(train_ids)}")
+    print(f"  Val images: {len(val_ids)}")
+
+    # Create output directories
+    for split in ["train", "val"]:
+        (output_dir / "images" / split).mkdir(parents=True, exist_ok=True)
+        (output_dir / "labels" / split).mkdir(parents=True, exist_ok=True)
+
+    # Process each image
+    skipped = 0
+    total_labels = 0
+    for image_id in image_ids:
+        img_info = image_lookup[image_id]
+        file_name = img_info["file_name"]
+        width = img_info["width"]
+        height = img_info["height"]
+
+        # Determine split
+        split = "val" if image_id in val_ids else "train"
+
+        # Find source image
+        src_image = images_dir / file_name
+        if not src_image.exists():
+            print(f"  WARNING: Image not found: {src_image}")
+            skipped += 1
+            continue
+
+        # Create symlink to image (saves disk space)
+        dst_image = output_dir / "images" / split / file_name
+        if dst_image.is_symlink() or dst_image.exists():
+            dst_image.unlink()  # Remove stale/broken symlinks
+        dst_image.symlink_to(src_image.resolve())
+
+        # Write YOLO label file
+        label_name = Path(file_name).stem + ".txt"
+        label_path = output_dir / "labels" / split / label_name
+
+        anns = image_annotations.get(image_id, [])
+        lines = []
+        for ann in anns:
+            category_id = ann["category_id"]
+            bbox = ann["bbox"]
+
+            # Skip zero-area boxes
+            if bbox[2] <= 0 or bbox[3] <= 0:
+                continue
+
+            cx, cy, nw, nh = coco_to_yolo_bbox(bbox, width, height)
+
+            # Skip degenerate boxes
+            if nw <= 0 or nh <= 0:
+                continue
+
+            lines.append(f"{category_id} {cx:.6f} {cy:.6f} {nw:.6f} {nh:.6f}")
+
+        with open(label_path, "w") as f:
+            f.write("\n".join(lines))
+            if lines:
+                f.write("\n")
+
+        total_labels += len(lines)
+
+    print(f"\nDone! Output: {output_dir}")
+    print(f"  Total label entries: {total_labels}")
+    print(f"  Skipped images: {skipped}")
+
+    # Save category names for reference
+    names_path = output_dir / "category_names.json"
+    cat_names = {cat["id"]: cat["name"] for cat in categories}
+    with open(names_path, "w") as f:
+        json.dump(cat_names, f, indent=2, ensure_ascii=False)
+    print(f"  Category names saved to: {names_path}")
+
+    # Generate dataset.yaml with absolute path for this machine
+    configs_dir = Path("configs")
+    configs_dir.mkdir(exist_ok=True)
+    yaml_path = configs_dir / "dataset.yaml"
+
+    yaml_lines = []
+    yaml_lines.append(f"path: {output_dir.resolve()}")
+    yaml_lines.append("train: images/train")
+    yaml_lines.append("val: images/val")
+    yaml_lines.append("")
+    yaml_lines.append(f"nc: {len(categories)}")
+    yaml_lines.append("")
+    yaml_lines.append("names:")
+    for cat in sorted(categories, key=lambda c: c["id"]):
+        name = cat["name"].replace("'", "''")
+        yaml_lines.append(f"  {cat['id']}: '{name}'")
+
+    with open(yaml_path, "w") as f:
+        f.write("\n".join(yaml_lines) + "\n")
+    print(f"  Dataset config saved to: {yaml_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/train/annotations.json b/train/annotations.json
new file mode 100644
index 0000000000000000000000000000000000000000..dacde1b67946fe23e28ab283beaf8ff2c8639a2a
--- /dev/null
+++ b/train/annotations.json
@@ -0,0 +1 @@
+{"images": [{"id": 1, "file_name": "img_00001.jpg", "width": 2000, "height": 1500}, {"id": 2, "file_name": "img_00002.jpg", "width": 2016, "height": 1512}, {"id": 4, "file_name": "img_00004.jpg", "width": 3200, "height": 1440}, {"id": 5, "file_name": "img_00005.jpg", "width": 4032, "height": 3024}, {"id": 6, "file_name": "img_00006.jpg", "width": 2728, "height": 2070}, {"id": 7, "file_name": "img_00007.jpg", "width": 3024, "height": 4032}, {"id": 9, "file_name": "img_00009.jpg", "width": 3024, "height": 4032}, {"id": 10, "file_name": "img_00010.jpg", "width": 2604, "height": 4624}, {"id": 11, "file_name": "img_00011.jpg", "width": 2604, "height": 4624}, {"id": 12, "file_name": "img_00012.jpg", "width": 4000, "height": 3000}, {"id": 13, "file_name": "img_00013.jpg", "width": 4000, "height": 3000}, {"id": 14, "file_name": "img_00014.jpg", "width": 2978, "height": 1938}, {"id": 15, "file_name": "img_00015.jpg", "width": 3024, "height": 3024}, {"id": 17, "file_name": "img_00017.jpg", "width": 2000, "height": 1500}, {"id": 18, "file_name": "img_00018.jpg", "width": 3618, "height": 2473}, {"id": 19, "file_name": "img_00019.jpg", "width": 3060, "height": 4080}, {"id": 20, "file_name": "img_00020.jpg", "width": 3060, "height": 4080}, {"id": 21, "file_name": "img_00021.jpeg", "width": 1200, "height": 1600}, {"id": 22, "file_name": "img_00022.jpeg", "width": 1200, "height": 1600}, {"id": 23, "file_name": "img_00023.jpeg", "width": 4032, "height": 3024}, {"id": 25, "file_name": "img_00025.jpeg", "width": 3024, "height": 4032}, {"id": 26, "file_name": "img_00026.jpeg", "width": 1153, "height": 1560}, {"id": 27, "file_name": "img_00027.jpeg", "width": 1170, "height": 1560}, {"id": 29, "file_name": "img_00029.jpeg", "width": 4032, "height": 3024}, {"id": 31, "file_name": "img_00031.jpg", "width": 4000, "height": 3000}, {"id": 32, "file_name": "img_00032.jpeg", "width": 1513, "height": 1196}, {"id": 33, "file_name": "img_00033.jpeg", "width": 4032, "height": 3024}, {"id": 34, "file_name": "img_00034.jpg", "width": 4032, "height": 3024}, {"id": 35, "file_name": "img_00035.jpg", "width": 3024, "height": 4032}, {"id": 36, "file_name": "img_00036.jpg", "width": 4032, "height": 3024}, {"id": 37, "file_name": "img_00037.jpg", "width": 2220, "height": 2246}, {"id": 38, "file_name": "img_00038.jpeg", "width": 3024, "height": 4032}, {"id": 44, "file_name": "img_00044.jpg", "width": 2160, "height": 3840}, {"id": 46, "file_name": "img_00046.jpeg", "width": 1080, "height": 1440}, {"id": 47, "file_name": "img_00047.jpg", "width": 3024, "height": 4032}, {"id": 48, "file_name": "img_00048.jpeg", "width": 720, "height": 1280}, {"id": 50, "file_name": "img_00050.jpg", "width": 4032, "height": 3024}, {"id": 54, "file_name": "img_00054.jpeg", "width": 1908, "height": 971}, {"id": 55, "file_name": "img_00055.jpeg", "width": 2016, "height": 1512}, {"id": 56, "file_name": "img_00056.jpeg", "width": 924, "height": 2000}, {"id": 58, "file_name": "img_00058.jpeg", "width": 1080, "height": 1440}, {"id": 59, "file_name": "img_00059.jpg", "width": 1440, "height": 1920}, {"id": 60, "file_name": "img_00060.jpg", "width": 1353, "height": 1504}, {"id": 61, "file_name": "img_00061.jpg", "width": 1431, "height": 1351}, {"id": 62, "file_name": "img_00062.jpg", "width": 1350, "height": 1230}, {"id": 64, "file_name": "img_00064.jpg", "width": 1440, "height": 1920}, {"id": 66, "file_name": "img_00066.jpg", "width": 1810, "height": 976}, {"id": 71, "file_name": "img_00071.jpg", "width": 3441, "height": 3116}, {"id": 72, "file_name": "img_00072.jpg", "width": 2621, "height": 2761}, {"id": 73, "file_name": "img_00073.jpg", "width": 1996, "height": 2268}, {"id": 74, "file_name": "img_00074.jpg", "width": 2988, "height": 2363}, {"id": 75, "file_name": "img_00075.jpg", "width": 1216, "height": 995}, {"id": 76, "file_name": "img_00076.jpg", "width": 1074, "height": 1273}, {"id": 77, "file_name": "img_00077.jpg", "width": 2116, "height": 1329}, {"id": 78, "file_name": 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