| 123456789101112131415161718192021222324252627282930313233343536373839404142434445 |
- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- # COCO 2017 dataset http://cocodataset.org by Microsoft
- # Example usage: python train.py --data coco.yaml
- # parent
- # ├── yolov5
- # └── datasets
- # └── coco ← downloads here (20.1 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/coco # dataset root dir
- train: train2017.txt # train images (relative to 'path') 118287 images
- val: val2017.txt # val images (relative to 'path') 5000 images
- test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
- # Classes
- nc: 80 # number of classes
- names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
- 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
- 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
- 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
- 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
- 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
- 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
- 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
- 'hair drier', 'toothbrush'] # class names
- # Download script/URL (optional)
- download: |
- from utils.general import download, Path
- # Download labels
- segments = False # segment or box labels
- dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
- urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
- download(urls, dir=dir.parent)
- # Download data
- urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
- 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
- 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
- download(urls, dir=dir / 'images', threads=3)
|
