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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail | |
| # 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: ../datasets/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 pandas as pd | |
| from tqdm import tqdm | |
| from ultralytics.yolo.utils.downloads import download | |
| from ultralytics.yolo.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 = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations | |
| images, unique_images = x[:, 0], np.unique(x[:, 0]) | |
| with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') 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') 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 | |