GlobalWheat2020.yaml
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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_folder
# /datasets/GlobalWheat2020
# /yolov5
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: # 3422 images
- ../datasets/GlobalWheat2020/images/arvalis_1
- ../datasets/GlobalWheat2020/images/arvalis_2
- ../datasets/GlobalWheat2020/images/arvalis_3
- ../datasets/GlobalWheat2020/images/ethz_1
- ../datasets/GlobalWheat2020/images/rres_1
- ../datasets/GlobalWheat2020/images/inrae_1
- ../datasets/GlobalWheat2020/images/usask_1
val: # 748 images (WARNING: train set contains ethz_1)
- ../datasets/GlobalWheat2020/images/ethz_1
test: # 1276 images
- ../datasets/GlobalWheat2020/images/utokyo_1
- ../datasets/GlobalWheat2020/images/utokyo_2
- ../datasets/GlobalWheat2020/images/nau_1
- ../datasets/GlobalWheat2020/images/uq_1
# number of classes
nc: 1
# class names
names: [ 'wheat_head' ]
# download command/URL (optional) --------------------------------------------------------------------------------------
download: |
from utils.general import download, Path
# Download
dir = Path('../datasets/GlobalWheat2020') # dataset directory
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