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  1. # COCO 2017 dataset http://cocodataset.org - first 128 training images
  2. # Train command: python train.py --data coco128.yaml
  3. # Default dataset location is next to /yolov5:
  4. # /parent_folder
  5. # /coco128
  6. # /yolov5
  7. # download command/URL (optional)
  8. download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
  9. # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
  10. train: ../coco128/images/train2017/ # 128 images
  11. val: ../coco128/images/train2017/ # 128 images
  12. # number of classes
  13. nc: 80
  14. # class names
  15. names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
  16. 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
  17. 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
  18. 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
  19. 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
  20. 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
  21. 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
  22. 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
  23. 'hair drier', 'toothbrush']