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