2024-06-09 04:29:29 +08:00
|
|
|
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
2021-12-19 22:19:04 +08:00
|
|
|
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
|
2021-07-28 05:23:41 +08:00
|
|
|
# Example usage: python train.py --data SKU-110K.yaml
|
2021-07-26 20:23:43 +08:00
|
|
|
# parent
|
|
|
|
|
# ├── yolov5
|
|
|
|
|
# └── datasets
|
2022-04-05 21:14:54 +08:00
|
|
|
# └── SKU-110K ← downloads here (13.6 GB)
|
2021-05-14 23:05:26 +08:00
|
|
|
|
2021-06-25 07:25:03 +08:00
|
|
|
# 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, ..]
|
2024-01-19 00:53:34 +08:00
|
|
|
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
|
2021-05-14 23:05:26 +08:00
|
|
|
|
2021-06-25 07:25:03 +08:00
|
|
|
# Classes
|
2022-08-17 23:52:53 +08:00
|
|
|
names:
|
|
|
|
|
0: object
|
2021-05-14 23:05:26 +08:00
|
|
|
|
2021-06-25 07:25:03 +08:00
|
|
|
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
2021-05-14 23:05:26 +08:00
|
|
|
download: |
|
|
|
|
|
import shutil
|
2022-04-27 06:00:01 +08:00
|
|
|
from tqdm import tqdm
|
2021-05-14 23:05:26 +08:00
|
|
|
from utils.general import np, pd, Path, download, xyxy2xywh
|
|
|
|
|
|
2022-03-23 08:19:37 +08:00
|
|
|
|
2021-05-14 23:05:26 +08:00
|
|
|
# Download
|
2021-06-25 07:25:03 +08:00
|
|
|
dir = Path(yaml['path']) # dataset root dir
|
|
|
|
|
parent = Path(dir.parent) # download dir
|
2021-05-14 23:05:26 +08:00
|
|
|
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
|
2021-06-25 07:25:03 +08:00
|
|
|
download(urls, dir=parent, delete=False)
|
2021-05-14 23:05:26 +08:00
|
|
|
|
|
|
|
|
# Rename directories
|
|
|
|
|
if dir.exists():
|
|
|
|
|
shutil.rmtree(dir)
|
2021-06-25 07:25:03 +08:00
|
|
|
(parent / 'SKU110K_fixed').rename(dir) # rename dir
|
2021-05-14 23:05:26 +08:00
|
|
|
(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
|
