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662 lines
12 KiB
662 lines
12 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
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# Example usage: yolo train data=open-images-v7.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── open-images-v7 ← downloads here (561 GB)
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# 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, ..]
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path: ../datasets/open-images-v7 # dataset root dir
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train: images/train # train images (relative to 'path') 1743042 images
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val: images/val # val images (relative to 'path') 41620 images
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test: # test images (optional)
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# Classes
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names:
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0: Accordion
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1: Adhesive tape
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2: Aircraft
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3: Airplane
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4: Alarm clock
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5: Alpaca
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6: Ambulance
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7: Animal
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8: Ant
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9: Antelope
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10: Apple
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11: Armadillo
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12: Artichoke
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13: Auto part
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14: Axe
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15: Backpack
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16: Bagel
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17: Baked goods
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18: Balance beam
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19: Ball
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20: Balloon
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21: Banana
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22: Band-aid
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23: Banjo
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24: Barge
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25: Barrel
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26: Baseball bat
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27: Baseball glove
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28: Bat (Animal)
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29: Bathroom accessory
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30: Bathroom cabinet
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31: Bathtub
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32: Beaker
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33: Bear
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34: Bed
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35: Bee
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36: Beehive
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37: Beer
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38: Beetle
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39: Bell pepper
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40: Belt
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41: Bench
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42: Bicycle
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43: Bicycle helmet
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44: Bicycle wheel
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45: Bidet
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46: Billboard
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47: Billiard table
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48: Binoculars
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49: Bird
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50: Blender
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51: Blue jay
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52: Boat
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53: Bomb
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54: Book
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55: Bookcase
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56: Boot
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57: Bottle
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58: Bottle opener
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59: Bow and arrow
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60: Bowl
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61: Bowling equipment
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62: Box
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63: Boy
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64: Brassiere
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65: Bread
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66: Briefcase
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67: Broccoli
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68: Bronze sculpture
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69: Brown bear
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70: Building
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71: Bull
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72: Burrito
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73: Bus
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74: Bust
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75: Butterfly
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76: Cabbage
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77: Cabinetry
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78: Cake
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79: Cake stand
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80: Calculator
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81: Camel
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82: Camera
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83: Can opener
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84: Canary
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85: Candle
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86: Candy
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87: Cannon
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88: Canoe
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89: Cantaloupe
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90: Car
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91: Carnivore
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92: Carrot
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93: Cart
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94: Cassette deck
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95: Castle
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96: Cat
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97: Cat furniture
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98: Caterpillar
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99: Cattle
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100: Ceiling fan
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101: Cello
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102: Centipede
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103: Chainsaw
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104: Chair
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105: Cheese
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106: Cheetah
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107: Chest of drawers
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108: Chicken
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109: Chime
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110: Chisel
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111: Chopsticks
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112: Christmas tree
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113: Clock
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114: Closet
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115: Clothing
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116: Coat
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117: Cocktail
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118: Cocktail shaker
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119: Coconut
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120: Coffee
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121: Coffee cup
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122: Coffee table
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123: Coffeemaker
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124: Coin
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125: Common fig
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126: Common sunflower
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127: Computer keyboard
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128: Computer monitor
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129: Computer mouse
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130: Container
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131: Convenience store
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132: Cookie
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133: Cooking spray
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134: Corded phone
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135: Cosmetics
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136: Couch
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137: Countertop
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138: Cowboy hat
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139: Crab
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140: Cream
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141: Cricket ball
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142: Crocodile
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143: Croissant
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144: Crown
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145: Crutch
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146: Cucumber
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147: Cupboard
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148: Curtain
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149: Cutting board
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150: Dagger
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151: Dairy Product
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152: Deer
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153: Desk
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154: Dessert
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155: Diaper
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156: Dice
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157: Digital clock
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158: Dinosaur
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159: Dishwasher
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160: Dog
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161: Dog bed
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162: Doll
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163: Dolphin
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164: Door
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165: Door handle
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166: Doughnut
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167: Dragonfly
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168: Drawer
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169: Dress
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170: Drill (Tool)
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171: Drink
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172: Drinking straw
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173: Drum
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174: Duck
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175: Dumbbell
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176: Eagle
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177: Earrings
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178: Egg (Food)
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179: Elephant
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180: Envelope
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181: Eraser
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182: Face powder
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183: Facial tissue holder
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184: Falcon
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185: Fashion accessory
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186: Fast food
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187: Fax
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188: Fedora
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189: Filing cabinet
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190: Fire hydrant
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191: Fireplace
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192: Fish
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193: Flag
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194: Flashlight
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195: Flower
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196: Flowerpot
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197: Flute
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198: Flying disc
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199: Food
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200: Food processor
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201: Football
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202: Football helmet
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203: Footwear
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204: Fork
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205: Fountain
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206: Fox
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207: French fries
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208: French horn
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209: Frog
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210: Fruit
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211: Frying pan
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212: Furniture
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213: Garden Asparagus
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214: Gas stove
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215: Giraffe
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216: Girl
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217: Glasses
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218: Glove
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219: Goat
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220: Goggles
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221: Goldfish
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222: Golf ball
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223: Golf cart
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224: Gondola
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225: Goose
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226: Grape
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227: Grapefruit
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228: Grinder
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229: Guacamole
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230: Guitar
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231: Hair dryer
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232: Hair spray
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233: Hamburger
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234: Hammer
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235: Hamster
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236: Hand dryer
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237: Handbag
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238: Handgun
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239: Harbor seal
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240: Harmonica
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241: Harp
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242: Harpsichord
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243: Hat
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244: Headphones
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245: Heater
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246: Hedgehog
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247: Helicopter
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248: Helmet
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249: High heels
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250: Hiking equipment
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251: Hippopotamus
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252: Home appliance
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253: Honeycomb
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254: Horizontal bar
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255: Horse
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256: Hot dog
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257: House
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258: Houseplant
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259: Human arm
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260: Human beard
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261: Human body
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262: Human ear
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263: Human eye
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264: Human face
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265: Human foot
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266: Human hair
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267: Human hand
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268: Human head
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269: Human leg
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270: Human mouth
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271: Human nose
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272: Humidifier
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273: Ice cream
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274: Indoor rower
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275: Infant bed
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276: Insect
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277: Invertebrate
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278: Ipod
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279: Isopod
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280: Jacket
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281: Jacuzzi
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282: Jaguar (Animal)
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283: Jeans
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284: Jellyfish
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285: Jet ski
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286: Jug
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287: Juice
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288: Kangaroo
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289: Kettle
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290: Kitchen & dining room table
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291: Kitchen appliance
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292: Kitchen knife
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293: Kitchen utensil
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294: Kitchenware
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295: Kite
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296: Knife
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297: Koala
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298: Ladder
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299: Ladle
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300: Ladybug
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301: Lamp
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302: Land vehicle
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303: Lantern
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304: Laptop
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305: Lavender (Plant)
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306: Lemon
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307: Leopard
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308: Light bulb
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309: Light switch
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310: Lighthouse
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311: Lily
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312: Limousine
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313: Lion
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314: Lipstick
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315: Lizard
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316: Lobster
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317: Loveseat
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318: Luggage and bags
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319: Lynx
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320: Magpie
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321: Mammal
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322: Man
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323: Mango
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324: Maple
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325: Maracas
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326: Marine invertebrates
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327: Marine mammal
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328: Measuring cup
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329: Mechanical fan
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330: Medical equipment
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331: Microphone
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332: Microwave oven
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333: Milk
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334: Miniskirt
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335: Mirror
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336: Missile
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337: Mixer
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338: Mixing bowl
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339: Mobile phone
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340: Monkey
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341: Moths and butterflies
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342: Motorcycle
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343: Mouse
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344: Muffin
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345: Mug
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346: Mule
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347: Mushroom
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348: Musical instrument
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349: Musical keyboard
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350: Nail (Construction)
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351: Necklace
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352: Nightstand
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353: Oboe
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354: Office building
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355: Office supplies
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356: Orange
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357: Organ (Musical Instrument)
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358: Ostrich
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359: Otter
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360: Oven
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361: Owl
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362: Oyster
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363: Paddle
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364: Palm tree
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365: Pancake
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366: Panda
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367: Paper cutter
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368: Paper towel
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369: Parachute
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370: Parking meter
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371: Parrot
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372: Pasta
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373: Pastry
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374: Peach
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375: Pear
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376: Pen
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377: Pencil case
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378: Pencil sharpener
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379: Penguin
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380: Perfume
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381: Person
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382: Personal care
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383: Personal flotation device
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384: Piano
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385: Picnic basket
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386: Picture frame
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387: Pig
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388: Pillow
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389: Pineapple
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390: Pitcher (Container)
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391: Pizza
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392: Pizza cutter
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393: Plant
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394: Plastic bag
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395: Plate
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396: Platter
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397: Plumbing fixture
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398: Polar bear
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399: Pomegranate
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400: Popcorn
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401: Porch
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402: Porcupine
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403: Poster
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404: Potato
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405: Power plugs and sockets
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406: Pressure cooker
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407: Pretzel
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408: Printer
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409: Pumpkin
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410: Punching bag
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411: Rabbit
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412: Raccoon
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413: Racket
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414: Radish
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415: Ratchet (Device)
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416: Raven
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417: Rays and skates
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418: Red panda
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419: Refrigerator
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420: Remote control
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421: Reptile
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422: Rhinoceros
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423: Rifle
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424: Ring binder
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425: Rocket
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426: Roller skates
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427: Rose
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428: Rugby ball
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429: Ruler
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430: Salad
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431: Salt and pepper shakers
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432: Sandal
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433: Sandwich
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434: Saucer
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435: Saxophone
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436: Scale
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437: Scarf
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438: Scissors
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439: Scoreboard
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440: Scorpion
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441: Screwdriver
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442: Sculpture
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443: Sea lion
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444: Sea turtle
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445: Seafood
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446: Seahorse
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447: Seat belt
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448: Segway
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449: Serving tray
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450: Sewing machine
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451: Shark
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452: Sheep
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453: Shelf
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454: Shellfish
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455: Shirt
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456: Shorts
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457: Shotgun
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458: Shower
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459: Shrimp
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460: Sink
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461: Skateboard
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462: Ski
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463: Skirt
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464: Skull
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465: Skunk
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466: Skyscraper
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467: Slow cooker
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468: Snack
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469: Snail
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470: Snake
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471: Snowboard
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472: Snowman
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473: Snowmobile
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474: Snowplow
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475: Soap dispenser
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476: Sock
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477: Sofa bed
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478: Sombrero
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479: Sparrow
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480: Spatula
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481: Spice rack
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482: Spider
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483: Spoon
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484: Sports equipment
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485: Sports uniform
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486: Squash (Plant)
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487: Squid
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488: Squirrel
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489: Stairs
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490: Stapler
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491: Starfish
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492: Stationary bicycle
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493: Stethoscope
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494: Stool
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495: Stop sign
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496: Strawberry
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497: Street light
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498: Stretcher
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499: Studio couch
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500: Submarine
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501: Submarine sandwich
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502: Suit
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503: Suitcase
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504: Sun hat
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505: Sunglasses
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506: Surfboard
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507: Sushi
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508: Swan
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509: Swim cap
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510: Swimming pool
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511: Swimwear
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512: Sword
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513: Syringe
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514: Table
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515: Table tennis racket
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516: Tablet computer
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517: Tableware
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518: Taco
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519: Tank
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520: Tap
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521: Tart
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522: Taxi
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523: Tea
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524: Teapot
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525: Teddy bear
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526: Telephone
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527: Television
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528: Tennis ball
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529: Tennis racket
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530: Tent
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531: Tiara
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532: Tick
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533: Tie
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534: Tiger
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535: Tin can
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536: Tire
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537: Toaster
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538: Toilet
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539: Toilet paper
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540: Tomato
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541: Tool
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542: Toothbrush
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543: Torch
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544: Tortoise
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545: Towel
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546: Tower
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547: Toy
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548: Traffic light
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549: Traffic sign
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550: Train
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551: Training bench
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552: Treadmill
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553: Tree
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554: Tree house
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555: Tripod
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556: Trombone
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557: Trousers
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558: Truck
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559: Trumpet
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560: Turkey
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561: Turtle
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562: Umbrella
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563: Unicycle
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564: Van
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565: Vase
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566: Vegetable
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567: Vehicle
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568: Vehicle registration plate
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569: Violin
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570: Volleyball (Ball)
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571: Waffle
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572: Waffle iron
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573: Wall clock
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574: Wardrobe
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575: Washing machine
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576: Waste container
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577: Watch
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578: Watercraft
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579: Watermelon
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580: Weapon
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581: Whale
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582: Wheel
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583: Wheelchair
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584: Whisk
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585: Whiteboard
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586: Willow
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587: Window
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588: Window blind
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589: Wine
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590: Wine glass
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591: Wine rack
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592: Winter melon
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593: Wok
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594: Woman
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595: Wood-burning stove
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596: Woodpecker
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597: Worm
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598: Wrench
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599: Zebra
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600: Zucchini
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
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from ultralytics.utils.checks import check_requirements, check_version
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check_requirements('fiftyone')
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if is_ubuntu() and check_version(get_ubuntu_version(), '>=22.04'):
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# Ubuntu>=22.04 patch https://github.com/voxel51/fiftyone/issues/2961#issuecomment-1666519347
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check_requirements('fiftyone-db-ubuntu2204')
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import fiftyone as fo
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import fiftyone.zoo as foz
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import warnings
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name = 'open-images-v7'
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fraction = 1.0 # fraction of full dataset to use
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LOGGER.warning('WARNING ⚠️ Open Images V7 dataset requires at least **561 GB of free space. Starting download...')
|
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for split in 'train', 'validation': # 1743042 train, 41620 val images
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train = split == 'train'
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# Load Open Images dataset
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dataset = foz.load_zoo_dataset(name,
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split=split,
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label_types=['detections'],
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dataset_dir=Path(SETTINGS['datasets_dir']) / 'fiftyone' / name,
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max_samples=round((1743042 if train else 41620) * fraction))
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|
|
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# Define classes
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if train:
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|
classes = dataset.default_classes # all classes
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# classes = dataset.distinct('ground_truth.detections.label') # only observed classes
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|
|
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# Export to YOLO format
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|
with warnings.catch_warnings():
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|
warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
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|
dataset.export(export_dir=str(Path(SETTINGS['datasets_dir']) / name),
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|
dataset_type=fo.types.YOLOv5Dataset,
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|
label_field='ground_truth',
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|
split='val' if split == 'validation' else split,
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|
classes=classes,
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|
overwrite=train)
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