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ultralytics/cfg/datasets/SKU-110K.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
+# Example usage: yolo train data=SKU-110K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── SKU-110K ← downloads here (13.6 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+names:
+  0: object
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from pathlib import Path
+
+  import numpy as np
+  import polars as pl
+
+  from ultralytics.utils import TQDM
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywh
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  parent = Path(dir.parent)  # download dir
+  urls = ["http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz"]
+  download(urls, dir=parent)
+
+  # Rename directories
+  if dir.exists():
+      shutil.rmtree(dir)
+  (parent / "SKU110K_fixed").rename(dir)  # rename dir
+  (dir / "labels").mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = "image", "x1", "y1", "x2", "y2", "class", "image_width", "image_height"  # column names
+  for d in "annotations_train.csv", "annotations_val.csv", "annotations_test.csv":
+      x = pl.read_csv(dir / "annotations" / d, has_header=False, new_columns=names, infer_schema_length=None).to_numpy()  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix(".txt").__str__().replace("annotations_", ""), "w", encoding="utf-8") as f:
+          f.writelines(f"./images/{s}\n" for s in unique_images)
+      for im in TQDM(unique_images, desc=f"Converting {dir / d}"):
+          cls = 0  # single-class dataset
+          with open((dir / "labels" / im).with_suffix(".txt"), "a", encoding="utf-8") as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label