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# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
# Train command: python train.py --data SKU-110K.yaml
# Default dataset location is next to YOLOv5:
# /parent_folder
# /datasets/SKU-110K
# /yolov5
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../datasets/SKU-110K/train.txt # 8219 images
val: ../datasets/SKU-110K/val.txt # 588 images
test: ../datasets/SKU-110K/test.txt # 2936 images
# number of classes
nc: 1
# class names
names: [ 'object' ]
# download command/URL (optional) --------------------------------------------------------------------------------------
download: |
import shutil
from tqdm import tqdm
from utils.general import np, pd, Path, download, xyxy2xywh
# Download
datasets = Path('../datasets') # download directory
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
download(urls, dir=datasets, delete=False)
# Rename directories
dir = (datasets / 'SKU-110K')
if dir.exists():
shutil.rmtree(dir)
(datasets / '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
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