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- # Global Wheat 2020 dataset http://www.global-wheat.com/
- # Train command: python train.py --data GlobalWheat2020.yaml
- # Default dataset location is next to YOLOv5:
- # /parent
- # /datasets/GlobalWheat2020
- # /yolov5
-
-
- # 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, ..]
- path: ../datasets/GlobalWheat2020 # dataset root dir
- train: # train images (relative to 'path') 3422 images
- - images/arvalis_1
- - images/arvalis_2
- - images/arvalis_3
- - images/ethz_1
- - images/rres_1
- - images/inrae_1
- - images/usask_1
- val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
- - images/ethz_1
- test: # test images (optional) 1276 images
- - images/utokyo_1
- - images/utokyo_2
- - images/nau_1
- - images/uq_1
-
- # Classes
- nc: 1 # number of classes
- names: [ 'wheat_head' ] # class names
-
-
- # Download script/URL (optional) ---------------------------------------------------------------------------------------
- download: |
- from utils.general import download, Path
-
- # Download
- dir = Path(yaml['path']) # dataset root dir
- urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
- download(urls, dir=dir)
-
- # Make Directories
- for p in 'annotations', 'images', 'labels':
- (dir / p).mkdir(parents=True, exist_ok=True)
-
- # Move
- for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
- 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
- (dir / p).rename(dir / 'images' / p) # move to /images
- f = (dir / p).with_suffix('.json') # json file
- if f.exists():
- f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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