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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>
75 lines
2.7 KiB
YAML
75 lines
2.7 KiB
YAML
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
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# Example usage: python train.py --data Argoverse.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── Argoverse ← downloads here (31.3 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/Argoverse # dataset root dir
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train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
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val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
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test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: bus
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5: truck
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6: traffic_light
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7: stop_sign
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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import json
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from tqdm import tqdm
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from utils.general import download, Path
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def argoverse2yolo(set):
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labels = {}
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a = json.load(open(set, "rb"))
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for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
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img_id = annot['image_id']
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img_name = a['images'][img_id]['name']
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img_label_name = f'{img_name[:-3]}txt'
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cls = annot['category_id'] # instance class id
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x_center, y_center, width, height = annot['bbox']
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x_center = (x_center + width / 2) / 1920.0 # offset and scale
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y_center = (y_center + height / 2) / 1200.0 # offset and scale
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width /= 1920.0 # scale
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height /= 1200.0 # scale
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img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
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if not img_dir.exists():
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img_dir.mkdir(parents=True, exist_ok=True)
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k = str(img_dir / img_label_name)
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if k not in labels:
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labels[k] = []
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labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
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for k in labels:
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with open(k, "w") as f:
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f.writelines(labels[k])
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
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download(urls, dir=dir, delete=False)
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# Convert
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annotations_dir = 'Argoverse-HD/annotations/'
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(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
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for d in "train.json", "val.json":
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argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
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