| # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
| # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan | |
| # Example usage: python train.py --data GlobalWheat2020.yaml | |
| # parent | |
| # ├── yolov5 | |
| # └── datasets | |
| # └── GlobalWheat2020 ← downloads here (7.0 GB) | |
| # 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 | |
| names: | |
| 0: wheat_head | |
| # 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 | |