48 lines
1.6 KiB
YAML
48 lines
1.6 KiB
YAML
# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train command: python train.py --data GlobalWheat2020.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /datasets/GlobalWheat2020
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# /yolov5
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: # 3422 images
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- ../../data/exitPPS/train/images/
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val: # 748 images (WARNING: train set contains ethz_1)
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- ../../data/exitPPS/val/images/
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test: # 1276 images
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- ../../data/exitPPS/val/images/
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# number of classes
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nc: 9
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# class names
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names: [ 'ex','dEx','qV','wF','oth','bdg','rsh','fam','st']
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# download command/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('../datasets/GlobalWheat2020') # dataset directory
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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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