|
- # COCO 2017 dataset http://cocodataset.org
- # Download command: bash yolov5/data/get_coco2017.sh
- # Train command: python train.py --data ./data/coco.yaml
- # Dataset should be placed next to yolov5 folder:
- # /parent_folder
- # /coco
- # /yolov5
-
-
- # train and val datasets (image directory or *.txt file with image paths)
- train: ../coco/train2017.txt # 118k images
- val: ../coco/val2017.txt # 5k images
- test: ../coco/test-dev2017.txt # 20k images for submission to https://competitions.codalab.org/competitions/20794
-
- # number of classes
- nc: 80
-
- # class names
- 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']
-
- # Print classes
- # with open('data/coco.yaml') as f:
- # d = yaml.load(f, Loader=yaml.FullLoader) # dict
- # for i, x in enumerate(d['names']):
- # print(i, x)
|
