You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

class_names.py 5.5 kB

2 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117
  1. # Copyright (c) OpenMMLab. All rights reserved.
  2. import mmcv
  3. def wider_face_classes():
  4. return ['face']
  5. def voc_classes():
  6. return [
  7. 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
  8. 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
  9. 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
  10. ]
  11. def imagenet_det_classes():
  12. return [
  13. 'accordion', 'airplane', 'ant', 'antelope', 'apple', 'armadillo',
  14. 'artichoke', 'axe', 'baby_bed', 'backpack', 'bagel', 'balance_beam',
  15. 'banana', 'band_aid', 'banjo', 'baseball', 'basketball', 'bathing_cap',
  16. 'beaker', 'bear', 'bee', 'bell_pepper', 'bench', 'bicycle', 'binder',
  17. 'bird', 'bookshelf', 'bow_tie', 'bow', 'bowl', 'brassiere', 'burrito',
  18. 'bus', 'butterfly', 'camel', 'can_opener', 'car', 'cart', 'cattle',
  19. 'cello', 'centipede', 'chain_saw', 'chair', 'chime', 'cocktail_shaker',
  20. 'coffee_maker', 'computer_keyboard', 'computer_mouse', 'corkscrew',
  21. 'cream', 'croquet_ball', 'crutch', 'cucumber', 'cup_or_mug', 'diaper',
  22. 'digital_clock', 'dishwasher', 'dog', 'domestic_cat', 'dragonfly',
  23. 'drum', 'dumbbell', 'electric_fan', 'elephant', 'face_powder', 'fig',
  24. 'filing_cabinet', 'flower_pot', 'flute', 'fox', 'french_horn', 'frog',
  25. 'frying_pan', 'giant_panda', 'goldfish', 'golf_ball', 'golfcart',
  26. 'guacamole', 'guitar', 'hair_dryer', 'hair_spray', 'hamburger',
  27. 'hammer', 'hamster', 'harmonica', 'harp', 'hat_with_a_wide_brim',
  28. 'head_cabbage', 'helmet', 'hippopotamus', 'horizontal_bar', 'horse',
  29. 'hotdog', 'iPod', 'isopod', 'jellyfish', 'koala_bear', 'ladle',
  30. 'ladybug', 'lamp', 'laptop', 'lemon', 'lion', 'lipstick', 'lizard',
  31. 'lobster', 'maillot', 'maraca', 'microphone', 'microwave', 'milk_can',
  32. 'miniskirt', 'monkey', 'motorcycle', 'mushroom', 'nail', 'neck_brace',
  33. 'oboe', 'orange', 'otter', 'pencil_box', 'pencil_sharpener', 'perfume',
  34. 'person', 'piano', 'pineapple', 'ping-pong_ball', 'pitcher', 'pizza',
  35. 'plastic_bag', 'plate_rack', 'pomegranate', 'popsicle', 'porcupine',
  36. 'power_drill', 'pretzel', 'printer', 'puck', 'punching_bag', 'purse',
  37. 'rabbit', 'racket', 'ray', 'red_panda', 'refrigerator',
  38. 'remote_control', 'rubber_eraser', 'rugby_ball', 'ruler',
  39. 'salt_or_pepper_shaker', 'saxophone', 'scorpion', 'screwdriver',
  40. 'seal', 'sheep', 'ski', 'skunk', 'snail', 'snake', 'snowmobile',
  41. 'snowplow', 'soap_dispenser', 'soccer_ball', 'sofa', 'spatula',
  42. 'squirrel', 'starfish', 'stethoscope', 'stove', 'strainer',
  43. 'strawberry', 'stretcher', 'sunglasses', 'swimming_trunks', 'swine',
  44. 'syringe', 'table', 'tape_player', 'tennis_ball', 'tick', 'tie',
  45. 'tiger', 'toaster', 'traffic_light', 'train', 'trombone', 'trumpet',
  46. 'turtle', 'tv_or_monitor', 'unicycle', 'vacuum', 'violin',
  47. 'volleyball', 'waffle_iron', 'washer', 'water_bottle', 'watercraft',
  48. 'whale', 'wine_bottle', 'zebra'
  49. ]
  50. def imagenet_vid_classes():
  51. return [
  52. 'airplane', 'antelope', 'bear', 'bicycle', 'bird', 'bus', 'car',
  53. 'cattle', 'dog', 'domestic_cat', 'elephant', 'fox', 'giant_panda',
  54. 'hamster', 'horse', 'lion', 'lizard', 'monkey', 'motorcycle', 'rabbit',
  55. 'red_panda', 'sheep', 'snake', 'squirrel', 'tiger', 'train', 'turtle',
  56. 'watercraft', 'whale', 'zebra'
  57. ]
  58. def coco_classes():
  59. return [
  60. 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
  61. 'truck', 'boat', 'traffic_light', 'fire_hydrant', 'stop_sign',
  62. 'parking_meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
  63. 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
  64. 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
  65. 'sports_ball', 'kite', 'baseball_bat', 'baseball_glove', 'skateboard',
  66. 'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup', 'fork',
  67. 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
  68. 'broccoli', 'carrot', 'hot_dog', 'pizza', 'donut', 'cake', 'chair',
  69. 'couch', 'potted_plant', 'bed', 'dining_table', 'toilet', 'tv',
  70. 'laptop', 'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
  71. 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
  72. 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush'
  73. ]
  74. def cityscapes_classes():
  75. return [
  76. 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
  77. 'bicycle'
  78. ]
  79. dataset_aliases = {
  80. 'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'],
  81. 'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'],
  82. 'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'],
  83. 'coco': ['coco', 'mscoco', 'ms_coco'],
  84. 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WIDERFace'],
  85. 'cityscapes': ['cityscapes']
  86. }
  87. def get_classes(dataset):
  88. """Get class names of a dataset."""
  89. alias2name = {}
  90. for name, aliases in dataset_aliases.items():
  91. for alias in aliases:
  92. alias2name[alias] = name
  93. if mmcv.is_str(dataset):
  94. if dataset in alias2name:
  95. labels = eval(alias2name[dataset] + '_classes()')
  96. else:
  97. raise ValueError(f'Unrecognized dataset: {dataset}')
  98. else:
  99. raise TypeError(f'dataset must a str, but got {type(dataset)}')
  100. return labels

No Description

Contributors (1)