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444 lines
9.0 KiB
444 lines
9.0 KiB
2 years ago
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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2 years ago
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# Objects365 dataset https://www.objects365.org/ by Megvii
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2 years ago
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# Example usage: yolo train data=Objects365.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
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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/Objects365 # dataset root dir
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train: images/train # train images (relative to 'path') 1742289 images
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val: images/val # val images (relative to 'path') 80000 images
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test: # test images (optional)
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# Classes
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names:
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0: Person
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1: Sneakers
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2: Chair
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3: Other Shoes
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4: Hat
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5: Car
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6: Lamp
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7: Glasses
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8: Bottle
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9: Desk
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10: Cup
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11: Street Lights
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12: Cabinet/shelf
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13: Handbag/Satchel
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14: Bracelet
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15: Plate
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16: Picture/Frame
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17: Helmet
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18: Book
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19: Gloves
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20: Storage box
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21: Boat
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22: Leather Shoes
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23: Flower
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24: Bench
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25: Potted Plant
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26: Bowl/Basin
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27: Flag
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28: Pillow
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29: Boots
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30: Vase
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31: Microphone
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32: Necklace
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33: Ring
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34: SUV
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35: Wine Glass
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36: Belt
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37: Monitor/TV
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38: Backpack
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39: Umbrella
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40: Traffic Light
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41: Speaker
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42: Watch
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43: Tie
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44: Trash bin Can
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45: Slippers
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46: Bicycle
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47: Stool
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48: Barrel/bucket
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49: Van
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50: Couch
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51: Sandals
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52: Basket
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53: Drum
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54: Pen/Pencil
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55: Bus
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56: Wild Bird
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57: High Heels
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58: Motorcycle
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59: Guitar
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60: Carpet
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61: Cell Phone
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62: Bread
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63: Camera
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64: Canned
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65: Truck
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66: Traffic cone
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67: Cymbal
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68: Lifesaver
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69: Towel
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70: Stuffed Toy
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71: Candle
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72: Sailboat
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73: Laptop
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74: Awning
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75: Bed
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76: Faucet
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77: Tent
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78: Horse
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79: Mirror
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80: Power outlet
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81: Sink
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82: Apple
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83: Air Conditioner
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84: Knife
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85: Hockey Stick
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86: Paddle
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87: Pickup Truck
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88: Fork
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89: Traffic Sign
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90: Balloon
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91: Tripod
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92: Dog
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93: Spoon
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94: Clock
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95: Pot
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96: Cow
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97: Cake
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98: Dinning Table
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99: Sheep
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100: Hanger
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101: Blackboard/Whiteboard
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102: Napkin
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103: Other Fish
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104: Orange/Tangerine
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105: Toiletry
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106: Keyboard
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107: Tomato
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108: Lantern
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109: Machinery Vehicle
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110: Fan
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111: Green Vegetables
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112: Banana
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113: Baseball Glove
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114: Airplane
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115: Mouse
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116: Train
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117: Pumpkin
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118: Soccer
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119: Skiboard
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120: Luggage
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121: Nightstand
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122: Tea pot
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123: Telephone
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124: Trolley
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125: Head Phone
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126: Sports Car
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127: Stop Sign
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128: Dessert
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129: Scooter
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130: Stroller
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131: Crane
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132: Remote
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133: Refrigerator
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134: Oven
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135: Lemon
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136: Duck
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137: Baseball Bat
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138: Surveillance Camera
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139: Cat
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140: Jug
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141: Broccoli
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142: Piano
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143: Pizza
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144: Elephant
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145: Skateboard
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146: Surfboard
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147: Gun
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148: Skating and Skiing shoes
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149: Gas stove
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150: Donut
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151: Bow Tie
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152: Carrot
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153: Toilet
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154: Kite
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155: Strawberry
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156: Other Balls
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157: Shovel
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158: Pepper
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159: Computer Box
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160: Toilet Paper
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161: Cleaning Products
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162: Chopsticks
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163: Microwave
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164: Pigeon
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165: Baseball
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166: Cutting/chopping Board
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167: Coffee Table
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168: Side Table
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169: Scissors
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170: Marker
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171: Pie
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172: Ladder
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173: Snowboard
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174: Cookies
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175: Radiator
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176: Fire Hydrant
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177: Basketball
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178: Zebra
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179: Grape
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180: Giraffe
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181: Potato
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182: Sausage
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183: Tricycle
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184: Violin
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185: Egg
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186: Fire Extinguisher
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187: Candy
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188: Fire Truck
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189: Billiards
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190: Converter
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191: Bathtub
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192: Wheelchair
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193: Golf Club
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194: Briefcase
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195: Cucumber
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196: Cigar/Cigarette
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197: Paint Brush
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198: Pear
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199: Heavy Truck
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200: Hamburger
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201: Extractor
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202: Extension Cord
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203: Tong
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204: Tennis Racket
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205: Folder
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206: American Football
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207: earphone
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208: Mask
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209: Kettle
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210: Tennis
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211: Ship
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212: Swing
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213: Coffee Machine
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214: Slide
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215: Carriage
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216: Onion
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217: Green beans
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218: Projector
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219: Frisbee
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220: Washing Machine/Drying Machine
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221: Chicken
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222: Printer
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223: Watermelon
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224: Saxophone
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225: Tissue
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226: Toothbrush
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227: Ice cream
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228: Hot-air balloon
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229: Cello
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230: French Fries
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231: Scale
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232: Trophy
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233: Cabbage
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234: Hot dog
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235: Blender
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236: Peach
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237: Rice
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238: Wallet/Purse
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239: Volleyball
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240: Deer
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241: Goose
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242: Tape
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243: Tablet
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244: Cosmetics
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245: Trumpet
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246: Pineapple
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247: Golf Ball
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248: Ambulance
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249: Parking meter
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250: Mango
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251: Key
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252: Hurdle
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253: Fishing Rod
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254: Medal
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255: Flute
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256: Brush
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257: Penguin
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258: Megaphone
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259: Corn
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260: Lettuce
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261: Garlic
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262: Swan
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263: Helicopter
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264: Green Onion
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265: Sandwich
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266: Nuts
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267: Speed Limit Sign
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268: Induction Cooker
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269: Broom
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270: Trombone
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271: Plum
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272: Rickshaw
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273: Goldfish
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274: Kiwi fruit
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275: Router/modem
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276: Poker Card
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277: Toaster
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278: Shrimp
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279: Sushi
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280: Cheese
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281: Notepaper
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282: Cherry
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283: Pliers
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284: CD
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285: Pasta
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286: Hammer
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287: Cue
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288: Avocado
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289: Hamimelon
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290: Flask
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291: Mushroom
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292: Screwdriver
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293: Soap
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294: Recorder
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295: Bear
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296: Eggplant
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297: Board Eraser
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298: Coconut
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299: Tape Measure/Ruler
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300: Pig
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301: Showerhead
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302: Globe
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303: Chips
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304: Steak
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305: Crosswalk Sign
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306: Stapler
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307: Camel
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308: Formula 1
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309: Pomegranate
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310: Dishwasher
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311: Crab
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312: Hoverboard
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313: Meat ball
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314: Rice Cooker
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315: Tuba
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316: Calculator
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317: Papaya
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318: Antelope
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319: Parrot
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320: Seal
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321: Butterfly
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322: Dumbbell
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323: Donkey
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324: Lion
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325: Urinal
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326: Dolphin
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327: Electric Drill
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328: Hair Dryer
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329: Egg tart
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330: Jellyfish
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331: Treadmill
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332: Lighter
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333: Grapefruit
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334: Game board
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335: Mop
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336: Radish
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337: Baozi
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338: Target
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339: French
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340: Spring Rolls
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341: Monkey
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342: Rabbit
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343: Pencil Case
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344: Yak
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345: Red Cabbage
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346: Binoculars
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347: Asparagus
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348: Barbell
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349: Scallop
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350: Noddles
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351: Comb
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352: Dumpling
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353: Oyster
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354: Table Tennis paddle
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355: Cosmetics Brush/Eyeliner Pencil
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356: Chainsaw
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357: Eraser
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358: Lobster
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359: Durian
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360: Okra
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361: Lipstick
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362: Cosmetics Mirror
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363: Curling
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364: Table Tennis
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from tqdm import tqdm
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1 year ago
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.downloads import download
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from ultralytics.utils.ops import xyxy2xywhn
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2 years ago
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import numpy as np
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from pathlib import Path
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2 years ago
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check_requirements(('pycocotools>=2.0',))
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from pycocotools.coco import COCO
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# Make Directories
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dir = Path(yaml['path']) # dataset root dir
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for p in 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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for q in 'train', 'val':
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(dir / p / q).mkdir(parents=True, exist_ok=True)
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# Train, Val Splits
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for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
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print(f"Processing {split} in {patches} patches ...")
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images, labels = dir / 'images' / split, dir / 'labels' / split
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# Download
|
||
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|
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
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||
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if split == 'train':
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||
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2 years ago
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download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir) # annotations json
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download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, threads=8)
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2 years ago
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elif split == 'val':
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2 years ago
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download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir) # annotations json
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download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, threads=8)
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download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, threads=8)
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2 years ago
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# Move
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for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
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f.rename(images / f.name) # move to /images/{split}
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# Labels
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coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
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names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
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for cid, cat in enumerate(names):
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catIds = coco.getCatIds(catNms=[cat])
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imgIds = coco.getImgIds(catIds=catIds)
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for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
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width, height = im["width"], im["height"]
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path = Path(im["file_name"]) # image filename
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try:
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with open(labels / path.with_suffix('.txt').name, 'a') as file:
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annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
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for a in coco.loadAnns(annIds):
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x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
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xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
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x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
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file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
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except Exception as e:
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print(e)
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