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54 lines
1.9 KiB
54 lines
1.9 KiB
# Ultralytics YOLO 🚀, AGPL-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: yolo train data=GlobalWheat2020.yaml |
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# parent |
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# ├── ultralytics |
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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 ultralytics.utils.downloads import download |
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from pathlib import 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 / 'global-wheat-codalab-official' / p).rename(dir / 'images' / p) # move to /images |
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f = (dir / 'global-wheat-codalab-official' / 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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