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* 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>
101 lines
3.4 KiB
YAML
101 lines
3.4 KiB
YAML
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
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# Example usage: python train.py --data VOC.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── VOC ← downloads here (2.8 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/VOC
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train: # train images (relative to 'path') 16551 images
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- images/train2012
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- images/train2007
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- images/val2012
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- images/val2007
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val: # val images (relative to 'path') 4952 images
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- images/test2007
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test: # test images (optional)
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- images/test2007
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# Classes
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names:
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0: aeroplane
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1: bicycle
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2: bird
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3: boat
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4: bottle
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5: bus
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6: car
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7: cat
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8: chair
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9: cow
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10: diningtable
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11: dog
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12: horse
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13: motorbike
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14: person
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15: pottedplant
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16: sheep
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17: sofa
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18: train
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19: tvmonitor
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import xml.etree.ElementTree as ET
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from tqdm import tqdm
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from utils.general import download, Path
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def convert_label(path, lb_path, year, image_id):
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def convert_box(size, box):
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dw, dh = 1. / size[0], 1. / size[1]
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x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
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return x * dw, y * dh, w * dw, h * dh
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in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
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out_file = open(lb_path, 'w')
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tree = ET.parse(in_file)
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root = tree.getroot()
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size = root.find('size')
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w = int(size.find('width').text)
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h = int(size.find('height').text)
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names = list(yaml['names'].values()) # names list
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for obj in root.iter('object'):
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cls = obj.find('name').text
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if cls in names and int(obj.find('difficult').text) != 1:
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xmlbox = obj.find('bndbox')
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bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
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cls_id = names.index(cls) # class id
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out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
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# Download
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dir = Path(yaml['path']) # dataset root dir
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url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
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urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
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f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
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f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
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download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
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# Convert
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path = dir / 'images/VOCdevkit'
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for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
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imgs_path = dir / 'images' / f'{image_set}{year}'
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lbs_path = dir / 'labels' / f'{image_set}{year}'
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imgs_path.mkdir(exist_ok=True, parents=True)
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lbs_path.mkdir(exist_ok=True, parents=True)
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with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
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image_ids = f.read().strip().split()
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for id in tqdm(image_ids, desc=f'{image_set}{year}'):
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f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
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lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
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f.rename(imgs_path / f.name) # move image
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convert_label(path, lb_path, year, id) # convert labels to YOLO format
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