# 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