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# YOLOv5 π by Ultralytics, GPL-3.0 license | |
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail | |
# Example usage: python train.py --data SKU-110K.yaml | |
# parent | |
# βββ yolov5 | |
# βββ datasets | |
# βββ SKU-110K β downloads here | |
# 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 | |
nc: 1 # number of classes | |
names: ['object'] # class names | |
# Download script/URL (optional) --------------------------------------------------------------------------------------- | |
download: | | |
import shutil | |
from tqdm import tqdm | |
from utils.general import np, pd, Path, download, 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, delete=False) | |
# 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 | |