Glenn Jocher
34cf749958
* Update LICENSE to AGPL-3.0 This pull request updates the license of the YOLOv5 project from GNU General Public License v3.0 (GPL-3.0) to GNU Affero General Public License v3.0 (AGPL-3.0). We at Ultralytics have decided to make this change in order to better protect our intellectual property and ensure that any modifications made to the YOLOv5 source code will be shared back with the community when used over a network. AGPL-3.0 is very similar to GPL-3.0, but with an additional clause to address the use of software over a network. This change ensures that if someone modifies YOLOv5 and provides it as a service over a network (e.g., through a web application or API), they must also make the source code of their modified version available to users of the service. This update includes the following changes: - Replace the `LICENSE` file with the AGPL-3.0 license text - Update the license reference in the `README.md` file - Update the license headers in source code files We believe that this change will promote a more collaborative environment and help drive further innovation within the YOLOv5 community. Please review the changes and let us know if you have any questions or concerns. Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update headers to AGPL-3.0 --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
54 lines
2.3 KiB
YAML
54 lines
2.3 KiB
YAML
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
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# Example usage: python train.py --data SKU-110K.yaml
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# parent
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# ├── yolov5
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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: ../datasets/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 tqdm import tqdm
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from utils.general import np, pd, Path, download, 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, delete=False)
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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') 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') 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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