55 lines
1.8 KiB
YAML
55 lines
1.8 KiB
YAML
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
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# Example usage: python train.py --data GlobalWheat2020.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── GlobalWheat2020 ← downloads here (7.0 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/GlobalWheat2020 # dataset root dir
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train: # train images (relative to 'path') 3422 images
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- images/arvalis_1
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- images/arvalis_2
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- images/arvalis_3
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- images/ethz_1
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- images/rres_1
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- images/inrae_1
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- images/usask_1
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val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
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- images/ethz_1
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test: # test images (optional) 1276 images
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- images/utokyo_1
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- images/utokyo_2
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- images/nau_1
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- images/uq_1
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# Classes
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names:
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0: wheat_head
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from utils.general import download, Path
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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download(urls, dir=dir)
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# Make Directories
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for p in 'annotations', 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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(dir / p).rename(dir / 'images' / p) # move to /images
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f = (dir / p).with_suffix('.json') # json file
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if f.exists():
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f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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