59 lines
2.5 KiB
YAML
59 lines
2.5 KiB
YAML
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
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# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
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# Example usage: yolo train data=SKU-110K.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── SKU-110K ← downloads here (13.6 GB)
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# 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, ..]
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path: SKU-110K # dataset root dir
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train: train.txt # train images (relative to 'path') 8219 images
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val: val.txt # val images (relative to 'path') 588 images
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test: test.txt # test images (optional) 2936 images
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# Classes
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names:
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0: object
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import shutil
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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from ultralytics.utils.downloads import download
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from ultralytics.utils.ops import xyxy2xywh
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# Download
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dir = Path(yaml["path"]) # dataset root dir
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parent = Path(dir.parent) # download dir
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urls = ["http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz"]
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download(urls, dir=parent)
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# Rename directories
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if dir.exists():
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shutil.rmtree(dir)
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(parent / "SKU110K_fixed").rename(dir) # rename dir
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(dir / "labels").mkdir(parents=True, exist_ok=True) # create labels dir
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# Convert labels
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names = "image", "x1", "y1", "x2", "y2", "class", "image_width", "image_height" # column names
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for d in "annotations_train.csv", "annotations_val.csv", "annotations_test.csv":
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x = pd.read_csv(dir / "annotations" / d, names=names).values # annotations
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images, unique_images = x[:, 0], np.unique(x[:, 0])
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with open((dir / d).with_suffix(".txt").__str__().replace("annotations_", ""), "w", encoding="utf-8") as f:
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f.writelines(f"./images/{s}\n" for s in unique_images)
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for im in tqdm(unique_images, desc=f"Converting {dir / d}"):
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cls = 0 # single-class dataset
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with open((dir / "labels" / im).with_suffix(".txt"), "a", encoding="utf-8") as f:
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for r in x[images == im]:
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w, h = r[6], r[7] # image width, height
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xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
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f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
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