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  1. # COCO 2017 dataset http://cocodataset.org
  2. # Download command: bash yolov5/data/get_coco2017.sh
  3. # Train command: python train.py --data ./data/coco.yaml
  4. # Dataset should be placed next to yolov5 folder:
  5. # /parent_folder
  6. # /coco
  7. # /yolov5
  8. # train and val datasets (image directory or *.txt file with image paths)
  9. train: ../coco/train2017.txt # 118k images
  10. val: ../coco/val2017.txt # 5k images
  11. test: ../coco/test-dev2017.txt # 20k images for submission to https://competitions.codalab.org/competitions/20794
  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']
  24. # Print classes
  25. # with open('data/coco.yaml') as f:
  26. # d = yaml.load(f, Loader=yaml.FullLoader) # dict
  27. # for i, x in enumerate(d['names']):
  28. # print(i, x)