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443 lines
9.0 KiB
443 lines
9.0 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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# Objects365 dataset https://www.objects365.org/ by Megvii |
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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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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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import numpy as np |
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from pathlib import Path |
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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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if split == 'train': |
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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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elif split == 'val': |
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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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# 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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